title: Classical grasp quality evaluation: New algorithms and theory

text: This paper investigates theoretical properties of a well-known L1 grasp quality measure Q whose approximation Q-l is commonly used for the evaluation of grasps and where the precision of Q-l depends on an approximation of a cone by a convex polyhedral cone with l edges. We prove the Lipschitz continuity of Q and provide an explicit Lipschitz bound that can be used to infer the stability of grasps lying in a neighbourhood of a known grasp. We think of Q-l as a lower bound estimate to Q and describe an algorithm for computing an upper bound Q+. We provide worst-case error bounds relating Q and Q-l. Furthermore, we develop a novel grasp hypothesis rejection algorithm which can exclude unstable grasps much faster than current implementations. Our algorithm is based on a formulation of the grasp quality evaluation problem as an optimization problem, and we show how our algorithm can be used to improve the efficiency of sampling based grasp hypotheses generation methods.


title: A high frequency high voltage power supply

text: Novel compact high frequency high voltage (HV) power supply is introduced for small volume, lightweight, ultra fast high voltage output application. The two key factors high frequency HV transformer and voltage multiplier diodes are evaluated in this paper. The HV resonant tank size is only 1L for 400kHz∼500kHz 35kV 2kW output, the rise time for output HV is around 100us by lab prototype experiment results.


title: Data-Driven Intelligent Transportation Systems: A Survey

text: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.


title: Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer

text: This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.


title: Designing Sketches for Similarity Filtering

text: The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique. Further, we focus on the length of sketches by studying mutual influence of sketch properties such as correlation of their bits and the intrinsic dimensionality of a set of sketches. The outcome is an equation that allows us to estimate a suitable length of sketches for an arbitrary given dataset. Finally, we empirically verify proposed approach on two real-life datasets.


title: Are emoticons good enough to train emotion classifiers of Arabic tweets?

text: Nowadays, the automatic detection of emotions is employed by many applications across different fields like security informatics, e-learning, humor detection, targeted advertising, etc. Many of these applications focus on social media. In this study, we address the problem of emotion detection in Arabic tweets. We focus on the supervised approach for this problem where a classifier is trained on an already labeled dataset. Typically, such a training set is manually annotated, which is expensive and time consuming. We propose to use an automatic approach to annotate the training data based on using emojis, which are a new generation of emoticons. We show that such an approach produces classifiers that are more accurate than the ones trained on a manually annotated dataset. To achieve our goal, a dataset of emotional Arabic tweets is constructed, where the emotion classes under consideration are: anger, disgust, joy and sadness. Moreover, we consider two classifiers: Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). The results of the tests show that the automatic labeling approaches using SVM and MNB outperform manual labeling approaches.


title: X-Ray PoseNet: 6 DoF Pose Estimation for Mobile X-Ray Devices

text: Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. However, when dealing with mobile X-ray devices such calibration parameters are unknown. Joint estimation of the systems calibration parameters and 3d reconstruction is a heavily unconstrained problem, especially when the projections are arbitrary. In industrial applications, that we target here, nominal CAD models of the object to be reconstructed are usually available. We rely on this prior information and employ Deep Learning to learn the mapping between simulated X-ray projections and its pose. Moreover, we introduce the reconstruction loss in addition to the pose loss to further improve the reconstruction quality. Finally, we demonstrate the generalization capabilities of our method in case where poses can be learned on instances of the objects belonging to the same class, allowing pose estimation of unseen objects from the same category, thus eliminating the need for the actual CAD model. We performed exhaustive evaluation demonstrating the quality of our results on both synthetic and real data.


title: A low-cost smart sensor for non intrusive load monitoring applications

text: Next generation Smart Cities have the potential to enhance the citizens quality of life and to reduce the overall energy expenditure. In particular, emerging smart metering technologies promise to greatly increase energy efficiency in the residential and industrial areas. In this context, new power metering methods such as Non-Intrusive Load Monitoring (NILM) can provide important real-time information about the contribution of any single appliance. In this paper, we present a complete hardware-software design that concentrates locally an efficient event-based supervised NILM algorithm for load disaggregation. This new kind of power analysis, which usually requires high computing capability, is executed real-time on a low-cost and easy-to-install smart meter ready for the Internet of Things (IoT).


title: Linearity and efficiency enhancement strategies for 4G wireless power amplifier designs

text: Next generation wireless transmitters will rely on highly integrated silicon-based solutions to realize the cost and performance goals of the 4G market. This will require increased use of digital compensation techniques and innovative circuit approaches to maximize power and efficiency and minimize linearity degradation. This paper summarizes the circuit and system strategies being developed to meet these aggressive performance goals.


title: Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

text: Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.


title: Deep Network Embedding with Aggregated Proximity Preserving

text: Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.


title: Counterfactual Fairness

text: Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.


title: A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication

text: The strength with which a statement is made can have a significant impact on the audience. For example, international relations can be strained by how the media in one country describes an event in another; and papers can be rejected because they overstate or understate their findings. It is thus important to understand the effects of statement strength. A first step is to be able to distinguish between strong and weak statements. However, even this problem is understudied, partly due to a lack of data. Since strength is inherently relative, revisions of texts that make claims are a natural source of data on strength differences. In this paper, we introduce a corpus of sentence-level revisions from academic writing. We also describe insights gained from our annotation efforts for this task.


title: CVSS Attack Graphs

text: Attack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs.


title: Better Summarization Evaluation with Word Embeddings for ROUGE

text: ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank co-


title: QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns

text: Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.


title: Greedy Attribute Selection

text: Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain. We show that ID3/C4.5 generalizes poorly on these tasks if allowed to use all available attributes. We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance. We present a caching scheme that makes attribute hillclimbing more practical computationally. We also compare the results of hillclimbing in attribute space with FOCUS and RELIEF on the two tasks.


title: RCS Reduction of Patch Array Antenna by Electromagnetic Band-Gap Structure

text: In this letter, electromagnetic band-gap (EBG) structure is used to reduce the radar cross section (RCS) of the patch array antenna. The proposition of this method is based on the high impedance characteristic of the mushroom-like EBG structure. The basic patch array antenna is designed with a central frequency of 5.0 GHz while replacing the substrate of the array with the mushroom-like EBG structure. The frequency band in which RCS of the patch array antenna reduced significantly can be adjusted by parameters of the EBG. The backward RCS of the patch array antenna with EBG can be reduced as much as 10 dB compared to that of the conventional array antenna, and the degradation of the antenna performance is not significant.


title: A Frequency Selective Radome With Wideband Absorbing Properties

text: A frequency selective radome is presented, acting as a pass band filter at a given frequency band, while behaving as an absorber above the transmission band. The pass band behavior is obtained by a metallic FSS realized through a compact interdigitated Jerusalem cross element characterized by a very large rejection band. The metallic FSS is used as the ground plane of a thin wideband absorber based on resistive high-impedance surfaces within the total reflection band. The outer absorber reduces the signature of the antenna system when the radome is illuminated by out of band signals. The resistive FSS which comprises the absorber is designed so to minimize losses within the transmitting band of the radome. The composite structure is thoroughly analyzed by an efficient equivalent circuit approach and by full-wave numerical simulations.


title: Design of Polarization Reconfigurable Antenna Using Metasurface

text: A planar polarization-reconfigurable metasurfaced antenna (PRMS) designed using metasurface (MS) is proposed. The PRMS antenna consists of a planar MS placed atop of and in direct contact with a planar slot antenna, both having a circular shape with a diameter of 78 mm (0.9 λ0), making it compact and low profile. By rotating the MS around the center with respect to the slot antenna, the PRMS antenna can be reconfigured to linear polarization, left-hand and right-hand circular polarizations. An equivalent circuit is used to explain the reconfigurability of the antenna. The PRMS antenna is studied and designed to operate at around 3.5 GHz using computer simulation. For verification of simulation results, the PRMS antenna is fabricated and measured. The antenna performance, in terms of polarization reconfigurability, axial-ratio bandwidth, impedance bandwidth, realized boresight gain and radiation pattern, is presented. Results show that the PRMS antenna in circular polarizations achieves an operating bandwidth of 3.3-3.7 GHz (i.e., fractional bandwidth 11.4%), a boresight gain of above 5 dBi and high-polarization isolation of larger than 15 dB. While the PRMS antenna in linear polarization achieves a gain of above 7.5 dBi with cross-polarization isolation larger than 50 dB.


title: Understanding of DC leakage and faults in floating photovoltaic systems: Trojan horse to undermine metallic infrastructure and safety

text: This article elaborates on a recently published issue that calls the entire PV community's and utility management attention. It specifically provides further analysis on DC leakage and fault detection blind spots associated with the operation of floating PV Systems. Floating systems are almost a standard practice in Europe when it comes to the operation of large-scale PV parks. A floating PV system has neither the positive nor the negative DC current-carrying conductors connected to earth. The system nevertheless benefits from other earthing connections, including the bonding to earth of exposed metals (e.g. metal frames of the PV modules, supporting infrastructure, combiner boxes) that could become energised in a fault situation or even under normal operation due to DC coupling mechanisms. These may entail safety as well as accelerated DC corrosion concerns.


title: Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion

text: The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.


title: X-Ku Wide-Bandwidth GaN HEMT MMIC Amplifier with Small Deviation of Output Power and PAE

text: A new design methodology was proposed to obtain wide-bandwidth and flat-group-delay reactive-matching GaN HEMT MMIC amplifiers. Frequency dependence of the optimal source and load impedance of a GaN HEMT are derived as polynomial equations and matching circuits are designed by small signal simulation without the use of large-signal transistor model and large-signal simulation. Fabricated GaN HEMT MMIC amplifiers, which show a small deviation of Pout and PAE in the range of 8-18 GHz, prove that our methodology is suitable for the design of a wide-bandwidth MMIC amplifier.


title: Capacity Analysis of Cooperative Relaying Systems Using Non-Orthogonal Multiple Access

text: In this letter, we propose the cooperative relaying system using non-orthogonal multiple access (NOMA) to improve the spectral efficiency. The achievable average rate of the proposed system is analyzed for independent Rayleigh fading channels, and also its asymptotic expression is provided. In addition, a suboptimal power allocation scheme for NOMA used at the source is proposed.


title: Tensor Decompositions, State of the Art and Applications

text: In this paper, we present a partial survey of the tools borrowed from tensor algebra, which have been utilized recently in Statistics and Signal Processing. It is shown why the decompositions well known in linear algebra can hardly be extended to tensors. The concept of rank is itself difficult to define, and its calculation raises difficulties. Numerical algorithms have nevertheless been developed, and some are reported here, but their limitations are emphasized. These reports hopefully open research perspectives for enterprising readers. in Mathematics in Signal Processing V, J. G. McWhirter and I. K. Proudler Eds., Oxford University Press, Oxford, UK, 2001


title: Twin Clouds: An Architecture for Secure Cloud Computing (Extended Abstract)

text: Cloud computing promises a more cost effective enabling technology to outsource storage and computations. Existing approaches for secure outsourcing of data and arbitrary computations are either based on a single tamper-proof hardware, or based on recently proposed fully homomorphic encryption. The hardware based solutions are not scaleable, and fully homomorphic encryption is currently only of theoretical interest and very inefficient. In this paper we propose an architecture for secure outsourcing of data and arbitrary computations to an untrusted commodity cloud. In our approach, the user communicates with a trusted cloud (either a private cloud or built from multiple secure hardware modules) which encrypts and verifies the data stored and operations performed in the untrusted commodity cloud. We split the computations such that the trusted cloud is mostly used for security-critical operations in the less time-critical setup phase, whereas queries to the outsourced data are processed in parallel by the fast commodity cloud on encrypted data.


title: Etiology of the Psychopathic Serial Killer : An Analysis of Antisocial Personality Disorder , Psychopathy , and Serial Killer Personality and Crime Scene Characteristics

text: The purpose of this article is tomake the distinction between antisocial personality disorder and psychopathy, discuss possible etiologies of psychopathy, and analyze the crimes, personality characteristics, and historical aspects of psychopathic serial killers. The research indicates that both environmental and biological factors affect the development of psychopathy. Several different serial killers were compared to assess the similarities and differences between their histories, crimes, and personalities. Though there were marked differences between their crimes, startling historical and personality similarities were clearly identified. Based on these findings, the validity and reliability of offender profiling is also discussed. [Brief Treatment and Crisis Intervention 7:151–160 (2007)]


title: May I help you? - Design of Human-like Polite Approaching Behavior-

text: When should service staff initiate interaction with a visitor? Neither simply-proactive (e.g. talk to everyone in a sight) nor passive (e.g. wait until being talked to) strategies are desired. This paper reports our modeling of polite approaching behavior. In a shopping mall, there are service staff members who politely approach visitors who need help. Our analysis revealed that staff members are sensitive to "intentions" of nearby visitors. That is, when a visitor intends to talk to a staff member and starts to approach, the staff member also walks a few steps toward the visitors in advance to being talked. Further, even when not being approached, staff members exhibit "availability" behavior in the case that a visitor's intention seems uncertain. We modeled these behaviors that are adaptive to pedestrians' intentions, occurred prior to initiation of conversation. The model was implemented into a robot and tested in a real shopping mall. The experiment confirmed that the proposed method is less intrusive to pedestrians, and that our robot successfully initiated interaction with pedestrians.


title: A Review on Deep Learning Techniques Applied to Semantic Segmentation

text: Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.


title: A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

text: Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.


title: Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

text: Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.


title: Alternative Splicing: New Insights from Global Analyses

text: Recent analyses of sequence and microarray data have suggested that alternative splicing plays a major role in the generation of proteomic and functional diversity in metazoan organisms. Efforts are now being directed at establishing the full repertoire of functionally relevant transcript variants generated by alternative splicing, the specific roles of such variants in normal and disease physiology, and how alternative splicing is coordinated on a global level to achieve cell- and tissue-specific functions. Recent progress in these areas is summarized in this review.


Theorizing Language Teacher Identity : Three Perspectives and Beyond


title: Survey of STT-MRAM Cell Design Strategies: Taxonomy and Sense Amplifier Tradeoffs for Resiliency

text: Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed in this article. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, sense margin, and energy or power consumption costs versus resiliency benefits. Solutions to the reliability issues identified are addressed within a taxonomy created to categorize the current and future approaches to reliable STT-MRAM designs. A variety of destructive and non-destructive sensing schemes are assessed for process variation tolerance, read disturbance reduction, sense margin, and write polarization asymmetry compensation. The highest resiliency strategies deliver a sensing margin above 300mV while incurring low power and energy consumption on the order of picojoules and microwatts, respectively, and attaining read sense latency of a few nanoseconds down to hundreds of picoseconds for non-destructive and destructive sensing schemes, respectively.


title: Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social

text: The traditional humanism of the twentieth century, inspired by the culture of the book, systematically distanced itself from the new society of digital information; the Internet and tools of information processing revolutionized the world, society during this period developed certain adaptive characteristics based on coexistence (Human Machine), this transformation sets based on the impact of three technology segments: devices, applications and infrastructure of social communication, which are involved in various physical, behavioural and cognitive changes of the human being; and the emergence of new models of influence and social control through the new ubiquitous communication; however in this new process of conviviality new models like the "collaborative thinking" and "InfoSharing" develop; managing social information under three Human ontological dimensions (h) Information (i) Machine (m), which is the basis of a new physical-cyber ecosystem, where they coexist and develop new social units called "virtual communities ". This new communication infrastructure and social management of information given discovered areas of vulnerability "social perspective of risk", impacting all social units through massive impact vector (i); The virtual environment "H + i + M"; and its components, as well as the life cycle management of social information allows us to understand the path of integration "Techno Social" and setting a new contribution to cybernetics, within the convergence of technology with society and the new challenges of coexistence, aimed at a new holistic and not pragmatic vision, as the human component (h) in the virtual environment is the precursor of the future and needs to be studied not as an application, but as the hub of a new society.


title: Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis

text: We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees.


title: Positive academic emotions moderate the relationship between self-regulation and academic achievement.

text:
BACKGROUND
Research has shown how academic emotions are related to achievement and to cognitive/motivational variables that promote achievement. Mediated models have been proposed to account for the relationships among academic emotions, cognitive/motivational variables, and achievement, and research has supported such mediated models, particularly with negative emotions.

AIMS
The study tested the hypotheses: (1) self-regulation and the positive academic emotions of enjoyment and pride are positive predictors of achievement; and (2) enjoyment and pride both moderate the relationship between self-regulation and achievement.

SAMPLE
Participants were 1,345 students enrolled in various trigonometry classes in one university.

METHODS
Participants answered the Academic Emotions Questionnaire-Math (Pekrun, Goetz, & Frenzel, 2005) and a self-regulation scale (Pintrich, Smith, Garcia, & McKeachie, 1991) halfway through their trigonometry class. The students' final grades in the course were regressed to self-regulation, positive emotions, and the interaction terms to test the moderation effects.

RESULTS AND CONCLUSIONS
Enjoyment and pride were both positive predictors of grades; more importantly, both moderated the relationship between self-regulation and grades. For students who report higher levels of both positive emotions, self-regulation was positively associated with grades. However, for those who report lower levels of pride, self-regulation was not related to grades; and, for those who reported lower levels of enjoyment, self-regulation was negatively related to grades. The results are discussed in terms of how positive emotions indicate positive appraisals of task/outcome value, and thus enhance the positive links between cognitive/motivational variables and learning.


title: Adaptive systems for foreign exchange trading

text: Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University’s Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank’s proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.


title: Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

text: Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( ~ 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.


title: A convolutional neural network approach for face verification

text: In this paper, we present a convolutional neural network (CNN) approach for the face verification task. We propose a “Siamese” architecture of two CNNs, with each CNN reduced to only four layers by fusing convolutional and subsampling layers. Network training is performed using the stochastic gradient descent algorithm with annealed global learning rate. Generalization ability of network is investigated via unique pairing of face images, and testing is done on AT&T face database. Experimental work shows that the proposed CNN system can classify a pair of 46×46 pixel face images in 0.6 milliseconds, which is significantly faster compared to equivalent network architecture with cascade of convolutional and subsampling layers. The verification accuracy achieved is 3.33% EER (equal error rate). Learning converges within 20 epochs, and the proposed technique can verify a test subject unseen in training. This work shows the viability of the “Siamese” CNN for face verification applications, and further improvements to the architecture are under construction to enhance its performance.


title: Face Detection Using Convolutional Neural Networks and Gabor Filters

text: This paper proposes a method for detecting facial regions by combining a Gabor filter and a convolutional neural network. The first stage uses the Gabor filter which extracts intrinsic facial features. As a result of this transformation we obtain four subimages. The second stage of the method concerns the application of the convolutional neural network to these four images. The approach presented in this paper yields better classification performance in comparison to the results obtained by the convolutional neural network alone.


title: Multiclass Posterior Probability Support Vector Machines

text: Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.


title: A Generalized Representer Theorem

text: Wahba’s classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.


title: An extensive comparative study of cluster validity indices

text: The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices. & 2012 Elsevier Ltd. All rights reserved.


title: Joint precision optimization and high level synthesis for approximate computing

text: Approximate computing has been recognized as an effective low power technique for applications with intrinsic error tolerance, such as image processing and machine learning. Existing efforts on this front are mostly focused on approximate circuit design, approximate logic synthesis or processor architecture approximation techniques. This work aims at how to make good use of approximate circuits at system and block level. In particular, approximation aware scheduling, functional unit allocation and binding algorithms are developed for data intensive applications. Simple yet credible error models, which are essential for precision control in the optimizations, are investigated. The algorithms are further extended to include bitwidth optimization in fixed point computations. Experimental results, including those from Verilog simulations, indicate that the proposed techniques facilitate desired energy savings under latency and accuracy constraints.


title: On the impact of knowledge discovery and data mining

text: Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these technologies is presented, followed by a detailed discussion of each issue. The paper's intention is to primarily illustrate the cultural context of each issue and, secondly, to describe the impact of KD and DM in each case. Existing solutions specific to each issue are identified and examined for feasibility and effectiveness, and a solution that provides a suitably contextually sensitive means for gathering and analysing sensitive data is proposed and briefly outlined. The paper concludes with a discussion of topics for further consideration.


title: Analysis of Various Sentiment Classification Techniques

text: Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.


title: Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining

text: Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.


title: A real-coded genetic algorithm for training recurrent neural networks

text: The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.


title: Behavioral inhibition , behavioral activation , and affective responses to impending reward and punishment : The BIS / BAS Scales

text: Gray (1981, 1982) holds that 2 general motivational systems underlie behavior and affect: a behavioral inhibition system (BIS) and a behavioral activation system (BAS). Self-report scales to assess dispositional BIS and BAS sensitivities were created. Scale development (Study 1) and convergent and discriminant validity in the form of correlations with alternative measures are reported (Study 2). In Study 3, a situation in which Ss anticipated a punishment was created. Controlling for initial nervousness, Ss high in BIS sensitivity (assessed earlier) were more nervous than those low. In Study 4, a situation in which Ss anticipated a reward was created. Controlling for initial happiness, Ss high in BAS sensitivity (Reward Responsiveness and Drive scales) were happier than those low. In each case the new scales predicted better than an alternative measure. Discussion is focused on conceptual implications.


title: Workshop on novelty and diversity in recommender systems - DiveRS 2011

text: Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.


title: Autonomous Indoor Robot Navigation Using Sketched Maps and Routes

text: Hand drawn sketches are natural means by which a high level description of an environment can be provided. They can be exploited to impart coarse prior information about the scene to a robot, thereby enabling it to perform autonomous navigation and exploration when a full metrical description of the scene is not available beforehand. In this paper, we present a navigation system supplemented by a tablet interface that allows a user to sketch a rough map of an indoor environment and a desired trajectory for the robot to follow. We propose a novel theoretical framework for sketch interpretation based upon the manifold formalism in which associations between the sketch and the real world are modeled as local deformation of a suitable metric manifold. We also present empirical results from experimental evaluations of our approach in real world scenarios both from the perspective of the navigation capability and the usability of the interface.


title: Analysis and Defensive Tools for Social-Engineering Attacks on Computer Systems

text: The weakest link in an information-security chain is often the user because people can be manipulated. Attacking computer systems with information gained from social interactions is one form of social engineering (K. Mitnick, et al. 2002). It can be much easier to do than targeting the complex technological protections of systems (J. McDermott, Social engineering - the weakest link in information security). In an effort to formalize social engineering for cyberspace, we are building models of trust and attack. Models help in understanding the bewildering number of different tactics that can be employed. Social engineering attacks can be complex with multiple ploys and targets; our models function as subroutines that are called multiple times to accomplish attack goals in a coordinated plan. Models enable us to infer good countermeasures to social engineering


title: Conducted EMI noise mitigation in DC-DC converters using active filtering method

text: Electromagnetic interference (EMI) noise mitigation is an important issue that should be addressed and emphasized when designing DC/DC converters. These later, are known to be the primary culprit of the EMI noise generation in most of the electronic systems, mainly due to the switching action of the MOSFET circuitries. Passive input EMI LC filters have been the intuitive solution for EMI noise mitigation; hence they have been integrated in almost every DC/DC converters. However, their size, weight and cost can cause a significant constraint in some applications. To overcome these constraints, an input active EMI filter is proposed. The active filter is based on the noise current phase shift and the injection of this noise current back to the DC input bus. However, the combination of the input active and the passive filters shows a substantial attenuation of the conducted emissions as compared to the passive filter only, which in turn contributes to the reduction of the size and weight of the input passive EMI filter. The proposed combination provides a design solution for compliance engineers where the PCB real-estate is an issue. Experimental results to demonstrate the performance and the effectiveness of the input active EMI filter in DC/DC converters are presented.


title: ITU-T G.729.1: AN 8-32 Kbit/S Scalable Coder Interoperable with G.729 for Wideband Telephony and Voice Over IP

text: This paper describes the scalable coder - G.729.1 - which has been recently standardized by ITU-T for wideband telephony and voice over IP (VoIP) applications. G.729.1 can operate at 12 different bit rates from 32 down to 8 kbit/s with wideband quality starting at 14 kbit/s. This coder is a bitstream interoperable extension of ITU-T G.729 based on three embedded stages: narrowband cascaded CELP coding at 8 and 12 kbit/s, time-domain bandwidth extension (TDBWE) at 14 kbit/s, and split-band MDCT coding with spherical vector quantization (VQ) and pre-echo reduction from 16 to 32 kbit/s. Side information - consisting of signal class, phase, and energy - is transmitted at 12, 14 and 16 kbit/s to improve the resilience and recovery of the decoder in case of frame erasures. The quality, delay, and complexity of G.729.1 are summarized based on ITU-T results.


title: MultiSE: multi-path symbolic execution using value summaries

text: Dynamic symbolic execution (DSE) has been proposed to effectively generate test inputs for real-world programs. Unfortunately, DSE techniques do not scale well for large realistic programs, because often the number of feasible execution paths of a program increases exponentially with the increase in the length of an execution path. In this paper, we propose MultiSE, a new technique for merging states incrementally during symbolic execution, without using auxiliary variables. The key idea of MultiSE is based on an alternative representation of the state, where we map each variable, including the program counter, to a set of guarded symbolic expressions called a value summary. MultiSE has several advantages over conventional DSE and conventional state merging techniques: value summaries enable sharing of symbolic expressions and path constraints along multiple paths and thus avoid redundant execution. MultiSE does not introduce auxiliary symbolic variables, which enables it to 1) make progress even when merging values not supported by the constraint solver, 2) avoid expensive constraint solver calls when resolving function calls and jumps, and 3) carry out most operations concretely. Moreover, MultiSE updates value summaries incrementally at every assignment instruction, which makes it unnecessary to identify the join points and to keep track of variables to merge at join points. We have implemented MultiSE for JavaScript programs in a publicly available open-source tool. Our evaluation of MultiSE on several programs shows that 1) value summaries are an eective technique to take advantage of the sharing of value along multiple execution path, that 2) MultiSE can run significantly faster than traditional dynamic symbolic execution and, 3) MultiSE saves a substantial number of state merges compared to conventional state-merging techniques.


title: Variational Bayes with synthetic likelihood

text: Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Monte Carlo simulation from the model. In this article, we develop alternatives to Markov chain Monte Carlo implementations of Bayesian synthetic likelihoods with reduced computational overheads. Our approach uses stochastic gradient variational inference methods for posterior approximation in the synthetic likelihood context, employing unbiased estimates of the log likelihood. We compare the new method with a related likelihood free variational inference technique in the literature, while at the same time improving the implementation of that approach in a number of ways. These new algorithms are feasible to implement in situations which are challenging for conventional approximate Bayesian computation (ABC) methods, in terms of the dimensionality of the parameter and summary statistic.


title: Head Pose and Expression Transfer Using Facial Status Score

text: We propose a method to transfer both head poseand facial expression of a source person in a video to the faceof a target person in an output video. Our method models theentire 2D frame instead of the 3D face, and it generates outputresults using a status score, which includes the relative facialstatus about the head pose and expression in a frame. From thetarget video, the learning process obtains frame features neededfor moving to each frame from the neutral frame for all frames,and generates the basis of these features via principal componentanalysis (PCA). Then, it learns to generate these features from agiven status score sequentially. In the transfer process, it obtainsa status score from a source frame of the video and generatesthe features from the given status score. Then, it generates theoutput frame using the reconstructed features. An output videois generated by repeating these steps for each source frame.Our method generates output results on the trajectory of thetarget video by using the advantage of PCA. Therefore, in theoutput results generated by our methods, both head pose andexpression are transferred correctly while the non-face regionsof the frames are supported. Finally, we experimentally comparethe effectiveness of our method and conventional methods.


title: Photovoltaic Power Conditioning System With Line Connection

text: A photovoltaic (PV) power conditioning system (PCS) with line connection is proposed. Using the power slope versus voltage of the PV array, the maximum power point tracking (MPPT) controller that produces a smooth transition to the maximum power point is proposed. The dc current of the PV array is estimated without using a dc current sensor. A current controller is suggested to provide power to the line with an almost-unity power factor that is derived using the feedback linearization concept. The disturbance of the line voltage is detected using a fast sensing technique. All control functions are implemented in software with a single-chip microcontroller. Experimental results obtained on a 2-kW prototype show high performance such as an almost-unity power factor, a power efficiency of 94%, and a total harmonic distortion (THD) of 3.6%


title: Predicting Movie Revenue from IMDb Data

text: Given the information known about a movie in the we ek of its release, can we predict the total gross revenue for that movie? Such information would be u s f l to marketers, theater operators, and others i n the movie industry, but it is a hard problem, even for human beings. We found that, given a set of numeric , textbased, and sentiment features from IMDb, linear reg ession outperforms class-based logistic regression at predicting gross revenue. However, neither gives su fficiently precise results to be used in practice.


title: Semi-productive Polysemy and Sense Extension

text: In this paper we discuss various aspects of systematic or conventional polysemy and their formal treatment within an implemented constraint based approach to linguistic representation. We distinguish between two classes of systematic polysemy: constructional polysemy, where a single sense assigned to a lexical entry is contextually specialised, and sense extension, which predictably relates two or more senses. Formally the first case is treated as instantiation of an underspecified lexical entry and the second by use of lexical rules. The problems of distinguishing between these two classes are discussed in detail. We illustrate how lexical rules can be used both to relate fully conventionalised senses and also applied productively to recognise novel usages and how this process can be controlled to account for semi-productivity by utilising probabilities.


title: Type shifting in construction grammar : An integrated approach to aspectual coercion *

text: Implicit type shifting, or coercion, appears to indicate a modular grammatical architecture, in which the process of semantic composition may add meanings absent from the syntax in order to ensure that certain operators, e.g., the progressive, receive suitable arguments (Jackendo¤ 1997; De Swart 1998). I will argue that coercion phenomena actually provide strong support for a sign-based model of grammar, in which rules of morphosyntactic combination can shift the designations of content words with which they combine. On this account, enriched composition is a by-product of the ordinary referring behavior of constructions. Thus, for example, the constraint which requires semantic concord between the syntactic sisters in the string a bottle is also what underlies the coerced interpretation found in a beer. If this concord constraint is stated for a rule of morphosyntactic combination, we capture an important generalization: a single combinatory mechanism, the construction, is responsible for both coerced and compositional meanings. Since both type-selecting constructions (e.g., the French Imparfait) and type-shifting constructions (e.g., English progressive aspect) require semantic concord between syntactic sisters, we account for the fact that constructions of both types perform coercion. Coercion data suggest that aspectual sensitivity is not merely a property of formally di¤erentiated past tenses, as in French and Latin, but a general property of tense constructions, including the English present and past tenses.


title: Grammatical Constructions and Linguistic Generalizations : the What ' s X doing Y ? Construction 1

text: To adopt a constructional approach is to undertake a commitment in principle to account for the entirety of each language. This means that the relatively general patterns of the language, such as the one licensing the ordering of a finite auxiliary verb before its subject in English as illustrated in (1), and the more idiomatic patterns, such as those exemplified in (2), stand on an equal footing as data for which the grammar must provide an account.


title: On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies.

text: Even at young ages, children self-report experiencing math anxiety, which negatively relates to their math achievement. Leveraging a large dataset of first and second grade students' math achievement scores, math problem solving strategies, and math attitudes, we explored the possibility that children's math anxiety (i.e., a fear or apprehension about math) negatively relates to their use of more advanced problem solving strategies, which in turn relates to their math achievement. Our results confirm our hypothesis and, moreover, demonstrate that the relation between math anxiety and math problem solving strategies is strongest in children with the highest working memory capacity. Ironically, children who have the highest cognitive capacity avoid using advanced problem solving strategies when they are high in math anxiety and, as a result, underperform in math compared with their lower working memory peers.


title: Selective harmonic elimination modulation technique applied on four-leg NPC

text: In this paper the selective harmonic elimination pulse width modulation (SHE-PWM) technique is proposed to control the three-level four-leg neutral point clamped (NPC) inverter in order to have both advantages of low switching frequency of SHE and neutral point of fourth leg in four wire systems. In this proposed method, the obtained switching angles of inverter phase legs are used to eliminate the non-triplen, 5th to 23th harmonics orders from the output voltage. As well, switching angles calculated for the fourth leg are considered to eliminate the triplen harmonics containing 3th, 9th, 15th, 21th and 27th orders from phase voltage. The efficiency of the proposed modulation technique is verified by simulations of a four leg NPC inverter as an UPS feeding different types of dynamic and unbalanced loads.


title: Evaluation of direct attacks to fingerprint verification systems

text: The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.


title: Conjunctive representation of position, direction, and velocity in entorhinal cortex.

text: Grid cells in the medial entorhinal cortex (MEC) are part of an environment-independent spatial coordinate system. To determine how information about location, direction, and distance is integrated in the grid-cell network, we recorded from each principal cell layer of MEC in rats that explored two-dimensional environments. Whereas layer II was predominated by grid cells, grid cells colocalized with head-direction cells and conjunctive grid x head-direction cells in the deeper layers. All cell types were modulated by running speed. The conjunction of positional, directional, and translational information in a single MEC cell type may enable grid coordinates to be updated during self-motion-based navigation.


title: Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model

text: The General Data Protection Regulations (GDPR) imposes greater restrictions on obtaining valid user consents involving the use of personal data. A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming a semantic model of consent will satisfy the GDPR requirements of specificity and unambiguity and is an important step towards ensuring compliance. In this paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes the open vocabulary of consent and incorporates the change of context into the data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across different information systems specifically adhering to the GDPR and helping controllers to demonstrate compliance.


title: Risk and parameter convergence of logistic regression

text: The logistic loss is strictly convex and does not attain its infimum; consequently the solutions of logistic regression are in general off at infinity. This work provides a convergence analysis of stochastic and batch gradient descent for logistic regression. Firstly, under the assumption of separability, stochastic gradient descent minimizes the population risk at rate O(ln(t)/t) with high probability. Secondly, with or without separability, batch gradient descent minimizes the empirical risk at rate O(ln(t)/t). Furthermore, parameter convergence can be characterized along a unique pair of complementary subspaces defined by the problem instance: one subspace along which strong convexity induces parameters to converge at rate O(ln(t)/ √ t), and its orthogonal complement along which separability induces parameters to converge in direction at rate O(ln ln(t)/ ln(t)). 1 Overview Logistic regression is the task of finding a vector w ∈ R which approximately minimizes the population or empirical logistic risk, namely Rlog(w) := E [ `log(〈w,−yx〉) ] or R̂log(w) := 1 n n ∑ i=1 `log(〈w,−yixi〉), where `log(r) := ln ( 1 + exp(r) ) is the logistic loss. A traditional way to minimize Rlog or R̂log is to pick an arbitrary w0, and from there recursively construct stochastic gradient descent (SGD) or gradient descent (GD) iterates (wj)j≥0 via wj+1 := wj − ηjgj , where (ηj)j≥0 are step sizes, and gj := ` ′ log (〈 wj ,−yjxj 〉) (−yjxj) for SGD or gj := ∇R̂`log(wj) for GD. A common assumption is that the distribution on (x, y) is separable (Novikoff, 1962; Soudry et al., 2017). Under this assumption, the first result here is that SGD with constant step sizes minimizes the population risk Rlog at rate Õ(1/t) with high probability. Theorem 1.1 (Simplification of Theorem 2.1). Suppose there exists a unit vector ū with 〈ū, yx〉 ≥ γ, and |yx| ≤ 1 almost surely. Consider SGD iterates (wj)j≥0 as above, with w0 = 0 and ηj = 1. Then for any t ≥ 1, |wt| ≤ O(ln(t)), and with probability at least 1− δ, the average iterate ŵt := t−1 ∑ j<t wj satisfies Rlog(ŵt) ≤ O ( ln(t) + ln(t) ln(1/δ) t ) . This result improves upon prior work in the following ways. 1. Since `log exhibits an exponential tail and Lipschitz continuity, standard analyses give a Õ(1/ √ t) rate of SGD with step size ηj = 1/ √ j + 1, but only in expectation (Nesterov, 2004; Bubeck, 2015). With the O(ln(t)) bound on |wt| given by Theorem 1.1, Azuma-Hoeffding’s inequality gives a high-probability Õ(1/ √ t) rate. However, since standard analyses do not ensure a vanishing risk with constant step sizes, they do not give a Õ(1/t) rate. 1 ar X iv :1 80 3. 07 30 0v 2 [ cs .L G ] 1 J un 2 01 8 2. The only other prior high-probability Õ(1/t) rate is under strong convexity (e.g., (Rakhlin et al., 2012)): |wt − w̄| ≤ Õ(1/t) with high probability, where w̄ is the global optimum. However, `log is not strongly convex on R, and even in trivial cases the optimum of logistic regression may be off at infinity: suppose examples (xi) n i=1 lie on the positive real line, and labels (yi) n i=1 are all +1; then infw∈R R̂log(w) = 0, but R̂log(w) > 0 for every w ∈ R. Furthermore, not only do the guarantees here avoid strong convexity; the proofs are different. The proof of Theorem 1.1 is based on the perceptron proof, and might be of independent interest. Now consider batch gradient descent on empirical risk. In this case, a stronger result is possible: no assumption is made (separability is dropped), and the parameters may be precisely characterized. Theorem 1.2 (Simplification of Theorems 3.1, 3.2 and 3.6). Let examples ((xi, yi)) n i=1 be given with |xiyi| ≤ 1, along with a loss ` ∈ {`log, exp}, with corresponding empirical risk R̂ as above. Consider GD iterates (wj)j≥0 as above, with w0 = 0. 1. (Convergence in risk.) For any step sizes ηj ≤ 1 and any t ≥ 1, R̂(wt)− inf w∈Rd R̂(w) = O ( 1 t + ln(t) ∑ j<t ηj ) = { O(ln(t)/t) ηj = Ω(1), O(ln(t)/ √ t) ηj = Ω(1/ √ j + 1). 2. (Convergence in parameters.) There exists a unique subspace S, unique unit vector ū ∈ S⊥, and unique vector v̄ ∈ S which characterize convergence as follows. Fixing step size ηj := 1/ √ j + 1, letting ΠS denote orthogonal projection onto S, and defining optimal iterates w̄t := inf { R̂(w) : |w| ≤ |wt| } , if t = Ω(n ln(t)), then |ΠSwt| = Θ(1) and max { |ΠSwt − v̄|, |ΠSw̄t − v̄| } = O ( ln(t) √ t ) , |ΠS⊥wt| = Θ(ln(t)) and max ∣∣∣∣ wt |wt| − ū ∣∣∣∣2 , ∣∣∣∣ w̄t |w̄t| − ū ∣∣∣∣2 } = O ( ln ln t ln t ) . In particular, wt/|wt|→ ū and ΠSwt → v̄. To make sense of this, recall firstly the earlier discussion that the solutions are off at infinity in general. As will be made precise in Section 3.1, if examples are separable, then S = {0}, v̄ = 0, and ū is the unique maximum margin separator. The GD iterates not only keep driving R̂ to its infimum 0, but also closely track ū in direction, meaning wt/|wt|→ ū. Note that this is a strong mode of convergence: R̂(wt) = Õ(1/t) and |wt| = Θ(ln(t)) do not exclude the possibility that there is always a large angle between wt/|wt| and ū. The full part 2 of Theorem 1.2 gives a characterization of parameter convergence in the general setting: the parameter space can be split into a subspace S along which optimization is strongly convex, and its complement S⊥ along which the problem behaves as though separable. While there does exist recent prior work on parameter convergence of gradient descent for logistic regression (Soudry et al., 2017; Gunasekar et al., 2018; Nacson et al., 2018), it assumes separability. The analysis essentially for free gives convergence not only to (v̄, ū), but also to the optimal iterates w̄t. Consequently, logistic regression is implicitly regularized in a strong sense: it produces a sequence of iterates which closely track the outputs of a sequence of constraint optimization problems. This paper is organized as follows. SGD population risk convergence (Section 2). An online guarantee is given by a perceptron-style analysis, and transformed into a high-probability bound via a martingale Bernstein bound. Empirical samples structure (Section 3.1). The first step of the analysis is to derive the unique complementary subspaces (S, S⊥), along with unique optimal direction ū ∈ S⊥ and unique vector v̄ ∈ S. (S, S⊥) and ū are unique given the data ((xi, yi)) n i=1 and are not affected by the choice of ` or optimization method; v̄ depends on ` but not on the optimization method.


title: Enabling ECN in Multi-Service Multi-Queue Data Centers

text: Recent proposals have leveraged Explicit Congestion Notification (ECN) to achieve high throughput low latency data center network (DCN) transport. However, most of them implicitly assume each switch port has one queue, making the ECN schemes they designed inapplicable to production DCNs where multiple service queues per port are employed to isolate different traffic classes through weighted fair sharing. In this paper, we reveal this problem by leveraging extensive testbed experiments to explore the intrinsic tradeoffs between throughput, latency, and weighted fair sharing in multi-queue scenarios. Using the guideline learned from the exploration, we design MQ-ECN, a simple yet effective solution to enable ECN for multi-service multiqueue production DCNs. Through a series of testbed experiments and large-scale simulations, we show that MQ-ECN breaks the tradeoffs by delivering both high throughput and low latency simultaneously, while still preserving weighted fair sharing.


title: Quantization Mimic: Towards Very Tiny CNN for Object Detection

text: In this paper, we propose a simple and general framework for training very tiny CNNs (e.g. VGG with the number of channels reduced to 1 32 ) for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.


title: Data Approximation Using Biarcs

text: . An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bézier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.


title: Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs

text: SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.


title: Hypervisor Memory Forensics

text: Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques (both as part of the cloud and in normal desktop environments), we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin allowing analysts to reuse their code for the analysis of virtual environments.


title: A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure

text: The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.


title: Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

text: Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.


title: Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations

text: Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various realworld short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.


title: Empirical research on consumers' initial trust and gender differences in B2C e-business

text: Based on the Technology Acceptance Model and related literatures, this paper proposes an integrated conceptual model of consumers' initial trust in B2C e-business. Empirical research results show that, perceived usefulness, perceived safety, perceived website reputation and propensity to trust significantly positively affect consumers' initial trust in e-vendor, which further positively influence consumers' intention to purchase online, by the complete mediation of attitude; however, the effect of perceived use of use on initial trust doesn't show the statistic significance. Furthermore, there are significant gender differences in the effects of perceived ease of use, perceived safety, perceived website reputation on consumers' initial trust.


title: Digital array radar: Technology and trends

text: This paper presents the architecture of digital array radar and analysis the key technologies, digital T/R modules, waveform generation and amplitude-phase control module based on DDS, frequency up/down convert, high efficiency power amplifier, hybrid digital/microwave multilayer circuit and high performance computing are described as the main technologies. The correlation between the microsystems technologies and digital array architectures trends is also discussed.


title: ACCIDENT AVOIDANCE AND DETECTION ON HIGHWAYS

text: Technological approaches for detecting and monitoring fatigue levels of driver fatigue continue to emerge and many are now in the development, validation testing, or early implementation stages. Previous studies have reviewed available fatigue detection and prediction technologies and methodologies. As the name indicates this project is about advanced technologies in cars for making it more intelligent and interactive for avoiding accidents on roads. By using ARM7 this system becomes more efficient, reliable & effective. There are very less number of systems implemented on human behaviour detection in or with cars. In this paper, we describe a real-time online safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. The main components of the system consist of number of real time sensors like gas, eye blink, alcohol, fuel, impact sensors and a software interface with GPS and Google Maps APIs for location.


title: A cryptosystem based on Vigenère cipher with varying key

text: — Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a new way of implementing Vigenère cipher encryption algorithm by automatically changing the cipher key after each encryption step. The new method will use successive keys that will be dependent on the initial key value during the encryption process. The algorithm ultimately makes it possible for encryption and decryption of the text. The ciphertext will have different encryption key pattern and the Vigenère cryptosystem will be more difficult to decipher using frequency attack. The implementation will be done using java programming.


title: Learning to recommend with explicit and implicit social relations

text: Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our trusted friends for recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework which naturally fuses the users' tastes and their trusted friends' favors together. The proposed framework is quite general, and it can also be applied to pure user-item rating matrix even if we do not have explicit social trust information among users. In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method outperforms state-of-the-art approaches.


title: Activity Monitoring: Noticing Interesting Changes in Behavior

text: We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.


title: LOCI: Fast Outlier Detection Using the Local Correlation Integral

text: Outlier detection is an integral part of data mining and has attracted much attention recently [BKNS00, JTH01, KNT00]. In this paper, we propose a new method for evaluating outlier-ness, which we call theLocal Correlation Integral(LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers ( a.k.a.micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cut-off to determine whether a point is an outlier—in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score.(c) Our LOCI method can be computed as quickly as the best previous methods. (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI ), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.


title: A Fast Greedy Algorithm for Outlier Mining

text: The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.


title: Outlier detection by active learning

text: Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.


title: Transfer metric learning for action similarity using high-level semantics

text: The goal of transfer learning is to exploit previous experiences and knowledge in order to improve learning in a novel domain. This is especially beneficial for the challenging task of learning classifiers that generalize well when only few training examples are available. In such a case, knowledge transfer methods can help to compensate for the lack of data. The performance and robustness against negative transfer of these approaches is influenced by the interdependence between knowledge representation and transfer type. However, this important point is usually neglected in the literature; instead the focus lies on either of the two aspects. In contrast, we study in this work the effect of various high-level semantic knowledge representations on different transfer types in a novel generic transfer metric learning framework. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the semantic attribute space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach compared to state-of-the-art. c © 2015 Elsevier Ltd. All rights reserved.


title: Songle: A Web Service for Active Music Listening Improved by User Contributions

text: This paper describes a public web service for active music listening,Songle, that enriches music listening experiences by using music-understanding technologies based on signal processing. Although various research-level interfaces and technologies have been developed, it has not been easy to get people to use them in everyday life. Songle serves as a showcase to demonstrate how people can benefit from music-understanding technologies by enabling people to experience active music listening interfaces on the web. Songle facilitates deeper understanding of music by visualizing music scene descriptions estimated automatically, such as music structure, hierarchical beat structure, melody line, and chords. When using music-understanding technologies, however, estimation errors are inevitable. Songle therefore features an efficient error correction interface that encourages people to contribute by correcting those errors to improve the web service. We also propose a mechanism of collaborative training for music-understanding technologies, in which corrected errors will be used to improve the music-understanding performance through machine learning techniques. We hope Songle will serve as a research platform where other researchers can exhibit results of their music-understanding technologies to jointly promote the popularization of the field of music information research.


title: Combining Data Mining and Machine Learning for Effective Fraud Detection *

text: This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specifically, we use a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls. These indicators are used to create profilers, which then serve as features to a system that combines evidence from multiple profilers to generate high-confidence alarms. Experiments indicate that this automatic approach performs nearly as well as the best hand-tuned methods for detecting fraud.