title: Correlation for user confidence in predictive decision making

text: Despite the recognized value of Machine Learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to the widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes), as well as confidence (in decision making) by users. This paper investigates the effects of correlation between features and target values on user confidence in data analytics-driven decision making. Our user study found that revealing the correlation between features and target variables affected user confidence in decision making significantly. Moreover, users felt more confident in decision making when correlation shared the same trend with the prediction model performance. These findings would help design intelligent user interfaces and evaluate the effectiveness of machine learning models in applications.


title: Databases, features and classifiers for speech emotion recognition: a review

text: Abstract Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person’s speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.


title: A WL-SPPIM Semantic Model for Document Classification

text: In this paper, we explore SPPIM-based text classification method, and the experiment reveals that the SPPIM method is equal to or even superior than SGNS method in text classification task on three international and standard text datasets, namely 20newsgroups, Reuters52 and WebKB. Comparing to SGNS, although SPPMI provides a better solution, it is not necessarily better than SGNS in text classification tasks.. Based on our analysis, SGNS takes into the consideration of weight calculation during decomposition process, so it has better performance than SPPIM in some standard datasets. Inspired by this, we propose a WL-SPPIM semantic model based on SPPIM model, and experiment shows that WL-SPPIM approach has better classification and higher scalability in the text classification task compared with LDA, SGNS and SPPIM approaches.


title: Estimation of human body shape and cloth field in front of a kinect

text: This paper describes an easy-to-use system to estimate the shape of a human body and his/her clothes. The system uses a Kinect to capture the human's RGB and depth information from different views. Using the depth data, a non-rigid deformation method is devised to compensate motions between different views, thus to align and complete the dressed shape. Given the reconstructed dressed shape, the skin regions are recognized by a skin classifier from the RGB images, and these skin regions are taken as a tight constraints for the body estimation. Subsequently, the body shape is estimated from the skin regions of the dressed shape by leveraging a statistical model of human body. After the body estimation, the body shape is non-rigidly deformed to fit the dressed shape, so as to extract the cloth field of the dressed shape. We demonstrate our system and the therein algorithms by several experiments. The results show the effectiveness of the proposed method. & 2014 Elsevier B.V. All rights reserved.


title: Identifying and Eliminating Mislabeled Training Instances

text: This paper presents a new approach to identifying and eliminating mislabeled training instances. The goal of this technique is to improve classiication accuracies produced by learning algorithms by improving the quality of the training data. The approach employs an ensemble of clas-siiers that serve as a lter for the training data. Using an n-fold cross validation, the training data is passed through the lter. Only instances that the lter classiies correctly are passed to the-nal learning algorithm. We present an empirical evaluation of the approach for the task of automated land cover mapping from remotely sensed data. Labeling error arises in these data from a multitude of sources including lack of consistency in the vegetation classiication used, variable measurement techniques, and variation in the spatial sampling resolution. Our evaluation shows that for noise levels of less than 40%, lter-ing results in higher predictive accuracy than not ltering, and for levels of class noise less than or equal to 20% ltering allows the base-line accuracy to be retained. Our empirical results suggest that the ensemble lter approach is an eeective method for identifying labeling errors, and further , that the approach will signiicantly beneet ongoing research to develop accurate and robust remote sensing-based methods to map land cover at global scales.


title: Low-power energy harvester for wiegand transducers

text: This paper discusses the use of Wiegand magnetic sensors as energy harvesters for powering low-power electronic equipments. A Wiegand device typically releases a ~10 μs voltage pulse several volts wide when subject to an external, time-varying magnetic field. Due to the sharpness of the magnetic transition, pulse generation occurs regardless of how slow the magnetic field variation is, an attractive feature which enables its use in energy harvesting scenarios even when low-frequency sources are considered. The paper first identifies the theoretical conditions for maximum energy extraction. An efficient energy harvesting circuit is then proposed which interfaces the Wiegand source to a fixed DC voltage provided, for instance, by a rechargeable Lithium-Ion battery. Simulations and experimental results are provided supporting the developed theoretical framework and the effectiveness of the proposed implementation.


title: Improving Estimation Accuracy using Better Similarity Distance in Analogy-based Software Cost Estimation

text: Software cost estimation nowadays plays a more and more important role in practical projects since modern software projects become more and more complex as well as diverse. To help estimate software development cost accurately, this research does a systematic analysis of the similarity distances in analogy-based software cost estimation and based on this, a new non-orthogonal space distance (NoSD) is proposed as a measure of the similarities between real software projects. Different from currently adopted measures like the Euclidean distance and so on, this non-orthogonal space distance not only considers the different features to have different importance for cost estimation, but also assumes project features to have a non-orthogonal dependent relationship which is considered independent to each other in Euclidean distance. Based on such assumptions, NoSD method describes the non-orthogonal angles between feature axes using feature redundancy and it represents the feature weights using feature relevance, where both redundancy and relevance are defined in terms of mutual information. It can better reveal the real dependency relationships between real life software projects based on this non-orthogonal space distance. Also experiments show that it brings a greatest of 13.1% decrease of MMRE and a 12.5% increase of PRED(0.25) on ISBSG R8 dataset, and 7.5% and 20.5% respectively on the Desharnais dataset. Furthermore, to make it better fit the complex data distribution of real life software projects data, this research leverages the particle swarm optimization algorithm for an optimization of the proposed non-orthogonal space distance and proposes a PSO optimized non-orthogonal space distance (PsoNoSD). It brings further improvement in the estimation accuracy. As shown in experiments, compared with the normally used Euclidean distance, PsoNoSD improves the estimation accuracy by 38.73% and 11.59% in terms of MMRE and PRED(0.25) on ISBSG R8 dataset. On the Desharnais dataset, the improvements are 23.38% and 24.94% respectively. In summary, the new methods proposed in this research, which are based on theoretical study as well as systematic experiments, have solved some problems of currently used techniques and they show a great ability of notably improving the software cost estimation accuracy.


title: A Counterexample to Theorems of Cox and Fine

text: Cox s well known theorem justifying the use of probability is shown not to hold in nite domains The counterexample also suggests that Cox s assumptions are insu cient to prove the result even in in nite domains The same counterexample is used to disprove a result of Fine on comparative conditional probability


title: Location Assisted Coding (LAC): Embracing Interference in Free Space Optical Communications

text: As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.


title: Innovative enhancement of the Caesar cipher algorithm for cryptography

text: The Caesar Cipher algorithm for cryptography is one of the oldest algorithms. Now much newer algorithms have arrived that are much more secure, however in terms of speed of execution Caesar cipher algorithm is still the fastest owing to its simplicity. However the algorithm is extremely easy to crack. This is because in this algorithm each character of a message is always replaced by the same fixed character that has been predetermined. To improve the algorithm and enhance it's security feature, a few changes can be added. This paper proposes an enhancement to the existing algorithm by making use first of a simple Diffie-Hellman key exchange scenario to obtain a secret key and later using simple mathematics to ensure the encryption of data is much more safer. Once a private shared key is obtained by making use of the Diffie-Hellman method, the key is subject to the mod operation with 26 to obtain a value less than or equal to 26, then the current character is taken and to this the key value obtained is added to obtain a new character. For any character in the `x' position the key is simply first multiplied with `x' and then mod is done to obtain the encrypted character. So 2nd character of the message is multiplied with 2, third character with 3 and so on. This enhances the security and also does not increase the time of execution by a large margin.


title: Turret: A Platform for Automated Attack Finding in Unmodified Distributed System Implementations

text: Security and performance are critical goals for distributed systems. The increased design complexity, incomplete expertise of developers, and limited functionality of existing testing tools often result in bugs and vulnerabilities that prevent implementations from achieving their design goals in practice. Many of these bugs, vulnerabilities, and misconfigurations manifest after the code has already been deployed making the debugging process difficult and costly. In this paper, we present Turret, a platform for automatically finding performance attacks in unmodified implementations of distributed systems. Turret does not require the user to provide any information about vulnerabilities and runs the implementation in the same operating system setup as the deployment, with an emulated network. Turret uses a new attack finding algorithm and several optimizations that allow it to find attacks in a matter of minutes. We ran Turret on 5 different distributed system implementations specifically designed to tolerate insider attacks, and found 30 performance attacks, 24 of which were not previously reported to the best of our knowledge.


title: A Relational Approach to Monitoring Complex Systems

text: Monitoring is an essential part of many program development tools, and plays a central role in debugging, optimization, status reporting, and reconfiguration. Traditional monitoring techniques are inadequate when monitoring complex systems such as multiprocessors or distributed systems. A new approach is described in which a historical database forms the conceptual basis for the information processed by the monitor. This approach permits advances in specifying the low-level data collection, specifying the analysis of the collected data, performing the analysis, and displaying the results. Two prototype implementations demonstrate the feasibility of the approach.


title: Mapping and Localization in 3D Environments Using a 2D Laser Scanner and a Stereo Camera

text: 2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping.


title: Architecting the next generation of service-based SCADA/DCS system of systems

text: SCADA and DCS systems are in the heart of the modern industrial infrastructure. The rapid changes in the networked embedded systems and the way industrial applications are designed and implemented, call for a shift in the architectural paradigm. Next generation SCADA and DCS systems will be able to foster cross-layer collaboration with the shop-floor devices as well as in-network and enterprise applications. Ecosystems driven by (web) service based interactions will enable stronger coupling of real-world and the business side, leading to a new generation of monitoring and control applications and services witnessed as the integration of large-scale systems of systems that are constantly evolving to address new user needs.


title: Information Processing in Medical Imaging

text: In this paper, we present novel algorithms to compute robust statistics from manifold-valued data. Specifically, we present algorithms for estimating the robust Fréchet Mean (FM) and performing a robust exact-principal geodesic analysis (ePGA) for data lying on known Riemannian manifolds. We formulate the minimization problems involved in both these problems using the minimum distance estimator called the L2E. This leads to a nonlinear optimization which is solved efficiently using a Riemannian accelerated gradient descent technique. We present competitive performance results of our algorithms applied to synthetic data with outliers, the corpus callosum shapes extracted from OASIS MRI database, and diffusion MRI scans from movement disorder patients respectively.


title: Complex Embeddings for Simple Link Prediction

text: In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.1


title: SQuAD: 100, 000+ Questions for Machine Comprehension of Text

text: We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.


title: Freebase: A Shared Database of Structured General Human Knowledge

text: Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.


title: TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

text: We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a featurebased classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.1


title: Emotion recognition with multimodal features and temporal models

text: This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.


title: Recommendation to Groups

text: Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.


title: The cyber threat landscape: Challenges and future research directions

text: Cyber threats are becoming more sophisticated with the blending of once distinct types of attack into more damaging forms. Increased variety and volume of attacks is inevitable given the desire of financially and criminally-motivated actors to obtain personal and confidential information, as highlighted in this paper. We describe how the Routine Activity Theory can be applied to mitigate these risks by reducing the opportunities for cyber crime to occur, making cyber crime more difficult to commit and by increasing the risks of detection and punishment associated with committing cyber crime. Potential research questions are also identified. a 2011 Elsevier Ltd. All rights reserved.


title: Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study

text: The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical non-evolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.


title: A connectionist approach to dynamic resource management for virtualised network functions

text: Network Functions Virtualisation (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating Network Functions (NFs) from traditional middleboxes, NFV is expected to lead to reduced CAPEX and OPEX, and to more agile services. However, one of the main challenges to achieving these objectives is on how physical resources can be efficiently, autonomously, and dynamically allocated to Virtualised Network Functions (VNFs) whose resource requirements ebb and flow. In this paper, we propose a Graph Neural Network (GNN)-based algorithm which exploits Virtual Network Function Forwarding Graph (VNF-FG) topology information to predict future resource requirements for each Virtual Network Function Component (VNFC). The topology information of each VNFC is derived from combining its past resource utilisation as well as the modelled effect on the same from VNFCs in its neighbourhood. Our proposal has been evaluated using a deployment of a virtualised IP Multimedia Subsystem (IMS), and real VoIP traffic traces, with results showing an average prediction accuracy of 90%. Moreover, compared to a scenario where resources are allocated manually and/or statically, our proposal reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.


title: A Chopper Current-Feedback Instrumentation Amplifier With a 1 mHz $1/f$  Noise Corner and an AC-Coupled Ripple Reduction Loop

text: This paper presents a chopper instrumentation amplifier for interfacing precision thermistor bridges. For high CMRR and DC gain, the amplifier employs a three-stage current-feedback topology with nested-Miller compensation. By chopping both the input and intermediate stages of the amplifier, a 1 mHz 1/f noise corner was achieved at an input-referred noise power spectral density (PSD) of 15 nV/¿Hz. To reduce chopper ripple, the amplifier employs a continuous-time AC-coupled ripple reduction loop. Due to its continuous-time nature, the loop causes no noise folding to DC and hence offers improved noise performance over auto-zeroed amplifiers. The loop reduces chopper ripple by more than 60 dB, to levels below the amplifier's own input-referred noise. Furthermore, a maximum input referred offset of 5 ¿ V and a CMRR greater than 120 dB were measured at a supply current of 230 ¿A at 5 V.


title: Digitalism: The New Realism?

text: Today’s society is increasingly digitalised, with mobile smartphones being routinely carried and used by a significant percentage of the population. This provides an augmented experience for the individual that does not depend on their geographical separation with respect to their community of friends and other contacts. This changes the nature of relationships between people. Individuals may live in a “digital bubble”, close to others physically, but far away from them in their digital world. More specifically, digital images can be generated and shared with ever greater ease. Sometimes the digital image takes on an important part of the individual’s experience of reality. This paper explores examples of the phenomenon, within the context of the arts in particular and culture in general. We also consider the assortment of terms used in a variety of ways by researchers in different fields with regard to our ever more digital society, such as digitalism, digitality, digitalisation, digital culture, digital philosophy, etc. We survey these terms, exploring them from alternative viewpoints, including sociological and philosophical aspects, and attempt to pinpoint some of these terms more precisely, especially in a cultural and artistic context.


title: Strategic Business / IT Alignment using Goal Models

text: Since few years, enterprise information technologies (IT) are no more seen as a simple technological support for business strategies in the enterprise. Moreover, standalone IT departments are created in order to support evolution and growth of IT in the enterprise. Often, IT department defines a specific strategy describing vision, goals and objectives of IT development in the organization. However, to remain competitive, IT strategy and IT investment should be coherent with global enterprise strategies. The continuous process of preserving coherence between Business/IT strategies is widely known as strategic Business/IT alignment. Our work aims at discussing the relation between interview based Business IT alignement discovery and the Strategic Alignement Model proposed by Venkatraman. The paper proposes also modeling tools and engineering methodologies to support this alignment process.


title: On the use of X-vectors for Robust Speaker Recognition

text: Text-independent speaker verification (SV) is currently in the process of embracing DNN modeling in every stage of SV system. Slowly, the DNN-based approaches such as end-to-end modelling and systems based on DNN embeddings start to be competitive even in challenging and diverse channel conditions of recent NIST SREs. Domain adaptation and the need for a large amount of training data are still a challenge for current discriminative systems and (unlike with generative models), we see significant gains from data augmentation, simulation and other techniques designed to overcome lack of training data. We present an analysis of a SV system based on DNN embeddings (x-vectors) and focus on robustness across diverse data domains such as standard telephone and microphone conversations, both in clean, noisy and reverberant environments. We also evaluate the system on challenging far-field data created by re-transmitting a subset of NIST SRE 2008 and 2010 microphone interviews. We compare our results with the stateof-the-art i-vector system. In general, we were able to achieve better performance with the DNN-based systems, but most importantly, we have confirmed the robustness of such systems across multiple data domains.


title: User experiences and expectations of vibrotactile, thermal and squeeze feedback in interpersonal communication

text: Katja Suhonen Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland katja.suhonen@tut.fi Kaisa Väänänen-Vainio-Mattila Tampere University of Technology, Human-Centered Technology, P.O.Box 589, 33101 Tampere, Finland kaisa.vaananen-vainiomattila@tut.fi Kalle Mäkelä Tampere University of Technology, Department of Electronics, P.O.Box 692, 33101 Tampere, Finland kalle.makela@tut.fi


title: Differences in kinematics and electromyographic activity between men and women during the single-legged squat.

text:
BACKGROUND
Numerous factors have been identified as potentially increasing the risk of anterior cruciate ligament injury in the female athlete. However, differences between the sexes in lower extremity coordination, particularly hip control, are only minimally understood.

HYPOTHESIS
There is no difference in kinematic or electromyographic data during the single-legged squat between men and women.

STUDY DESIGN
Descriptive comparison study.

METHODS
We kinematically and electromyographically analyzed the single-legged squat in 18 intercollegiate athletes (9 male, 9 female). Subjects performed five single-legged squats on their dominant leg, lowering themselves as far as possible and then returning to a standing position without losing balance.

RESULTS
Women demonstrated significantly more ankle dorsiflexion, ankle pronation, hip adduction, hip flexion, hip external rotation, and less trunk lateral flexion than men. These factors were associated with a decreased ability of the women to maintain a varus knee position during the squat as compared with the men. Analysis of all eight tested muscles demonstrated that women had greater muscle activation compared with men. When each muscle was analyzed separately, the rectus femoris muscle activation was found to be statistically greater in women in both the area under the linear envelope and maximal activation data.

CONCLUSIONS
Under a physiologic load in a position commonly assumed in sports, women tend to position their entire lower extremity and activate muscles in a manner that could increase strain on the anterior cruciate ligament.


title: Procedural Content Generation: Goals, Challenges and Actionable Steps

text: This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multi-content PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately. 1998 ACM Subject Classification I.2.1 Applications and Expert Systems: Games


title: Time-to-Digital Converter Using a Tuned-Delay Line Evaluated in 28-, 40-, and 45-nm FPGAs

text: This paper proposes a bin-width tuning method for a field-programmable gate array (FPGA)-based delay line for a time-to-digital converter (TDC). Changing the hit transitions and sampling patterns of the carry chain considering delays of the sum and carry-out bins can improve the bin-width uniformity and thus measurement precision. The proposed sampling method was evaluated and compared with the ordinary tapped-delay-line (TDL) method in three different types of FPGAs: Kintex-7, Virtex-6, and Spartan-6. The linearity, equivalent bin width, and measurement precision improved for all the evaluated FPGAs by adopting the proposed method. The measurement precision obtained using the simple TDL architecture is comparable with other complex TDC architectures. In addition, the proposed method improves bin-width uniformity and measurement precision while maintaining the advantages of TDL TDCs, that is, fast conversion rate and small resource usage. Furthermore, the enhanced linearity of the delay line can also improve other carry-chain-based FPGA-TDCs.


title: Control structure design for complete chemical plants

text: Control structure design deals with the structural decisions of the control system, including what to control and how to pair the variables to form control loops. Although these are very important issues, these decisions are in most cases made in an ad hoc fashion, based on experience and engineering insight, without considering the details of each problem. In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented. It starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them. Other issues, discussed in the paper, include inventory and production rate control, decentralized versus multivariable control, loss in performance by bottom-up design, and a definition of a the ‘‘complexity number’’ for the control system. # 2003 Elsevier Ltd. All rights reserved.


title: Reducing Uncertainty of Low-Sampling-Rate Trajectories

text: The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.


title: Towards reliable traffic sign recognition

text: The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS [1]. In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.


title: 3-Dimensional Analysis on the GIDL Current of Body-tied Triple Gate FinFET

text: Triple gate FinFET is emerging as a promising candidate for the future CMOS device structures because of its immunity to short-channel effect. However, the suppression of GIDL is a significant challenge for its application. In this paper, we discuss the characteristics of GIDL on FinFET and extensively analyze the influence of the device technology on GIDL. The analysis is expected to give guidelines to the future development of triple gate FinFET


title: Perspectives on the Productivity Dilemma

text: Editor’s note The authors of this paper presented an All-academy session at the 2008 Academy of Management annual meeting in Anaheim, California. We were excited by the dynamic nature of the debate and felt that it related closely to critical issues in the areas of operations management, strategy, product development and international business. We thus invited the authors to write an article offering their individual and joint views on the productivity dilemma. We trust you will find it to be stimulating and thought provoking. We invite you to add your voice to the discussion by commenting on this article at the Operations and Supply Chain (OSM) Forum at http://www.journaloperationsmanagement.org/OSM.asp. – Kenneth K. Boyer and Morgan L. Swink


title: Applying the Waek Learning Framework to Understand and Improve C4.5

text: There has long been a chasm between theoretical mod els of machine learning and practical machine learn ing algorithms For instance empirically successful algorithms such as C and backpropagation have not met the criteria of the PAC model and its vari ants Conversely the algorithms suggested by com putational learning theory are usually too limited in various ways to nd wide application The theoreti cal status of decision tree learning algorithms is a case in point while it has been proven that C and all reasonable variants of it fails to meet the PAC model criteria other recently proposed decision tree algo rithms that do have non trivial performance guaran tees unfortunately require membership queries Two recent developments have narrowed this gap between theory and practice not for the PAC model but for the related model known as weak learning or boosting First an algorithm called Adaboost was proposed that meets the formal criteria of the boosting model and is also competitive in practice Second the basic algorithms underlying the popular C and CART programs have also very recently been shown to meet the formal criteria of the boosting model Thus it seems plausible that the weak learning frame work may provide a setting for interaction between formal analysis and machine learning practice that is lacking in other theoretical models Our aim in this paper is to push this interaction further in light of these recent developments In par ticular we perform experiments suggested by the for mal results for Adaboost and C within the weak learning framework We concentrate on two particu larly intriguing issues First the theoretical boosting results for top down decision tree algorithms such as C suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual information gain We con rm this suggestion experimentally Second a super cial interpretation of the theo retical results suggests that Adaboost should vastly outperform C This is not the case in practice and we argue through experimental results that the theory must be understood in terms of a measure of a boosting algorithm s behavior called its advantage sequence We compare the advantage sequences for C and Adaboost in a number of experiments We nd that these sequences have qualitatively dif ferent behavior that explains in large part the dis crepancies between empirical performance and the theoretical results Brie y we nd that although C and Adaboost are both boosting algorithms Adaboost creates successively harder ltered dis tributions while C creates successively easier ones in a sense that will be made precise


title: Sex similarities and differences in preferences for short-term mates: what, whether, and why.

text: Are there sex differences in criteria for sexual relationships? The answer depends on what question a researcher asks. Data suggest that, whereas the sexes differ in whether they will enter short-term sexual relationships, they are more similar in what they prioritize in partners for such relationships. However, additional data and context of other findings and theory suggest different underlying reasons. In Studies 1 and 2, men and women were given varying "mate budgets" to design short-term mates and were asked whether they would actually mate with constructed partners. Study 3 used a mate-screening paradigm. Whereas women have been found to prioritize status in long-term mates, they instead (like men) prioritize physical attractiveness much like an economic necessity in short-term mates. Both sexes also show evidence of favoring well-rounded long- and short-term mates when given the chance. In Studies 4 and 5, participants report reasons for having casual sex and what they find physically attractive. For women, results generally support a good genes account of short-term mating, as per strategic pluralism theory (S. W. Gangestad & J. A. Simpson, 2000). Discussion addresses broader theoretical implications for mate preference, and the link between method and theory in examining social decision processes.


title: Interpersonal connectedness: conceptualization and directions for a measurement instrument

text: Interpersonal connectedness is the sense of belonging based on the appraisal of having sufficient close social contacts. This feeling is regarded as one of the major outcomes of successful (mediated) social interaction and as such an important construct for HCI. However, the exact nature of this feeling, how to achieve it, and how to assess it remain unexplored to date. In the current paper we start the theoretical conceptualization of this phenomenon by exploring its basic origins in psychological literature and simultaneously formulate requirements for a measurement instrument to be developed in the service of exploring and testing CMC applications, in particular awareness technologies.


title: Security challenges in software defined network and their solutions

text: The main purpose of Software Defined Networking (SDN) is to allow network engineers to respond quickly to changing network industrial requirements. This network technology focuses on making network as adaptable and active as virtual server. SDN is physical separation of Control plane from Data plane and control plane is centralized to manage underlying infrastructure. Hence, the SDN permit network administrator to adjust wide traffic flow from centralized control console without having to touch Switches and Routers, and can provide the services to wherever they are needed in the network. As in SDN the control plane is disassociated from underlying forwarding plane, however, susceptible to many security challenges like Denial of Service (DoS) attack, Distributed DoS (DDoS) attack, Volumetric attack. In this paper, we highlight some security challenges and evaluate some security solutions.


title: The Seventh PASCAL Recognizing Textual Entailment Challenge

text: This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7.


title: Mobile applications for weight management: theory-based content analysis.

text:
BACKGROUND
The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.

PURPOSE
The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.

METHODS
A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012.

RESULTS
Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.

CONCLUSIONS
All apps received low overall scores for inclusion of behavioral theory-based strategies.


title: Constructions from Dots and Lines

text: Marko A. Rodriguez is graph systems architect at AT&T Interactive. He can be reached at marko<at>markorodriguez.com. Peter Neubauer is chief operating officer of Neo Technology. He can be reached at peter.neubauer<at>neotechnology.com A graph is a data structure composed of dots (i.e., vertices) and lines (i.e., edges). The dots and lines of a graph can be organized into intricate arrangements. A graph’s ability to denote objects and their relationships to one another allows for a surprisingly large number of things to be modeled as graphs. From the dependencies that link software packages to the wood beams that provide the framing to a house, most anything has a corresponding graph representation. However, just because it is possible to represent something as a graph does not necessarily mean that its graph representation will be useful. If a modeler can leverage the plethora of tools and algorithms that store and process graphs, then such a mapping is worthwhile. This article explores the world of graphs in computing and exposes situations in which graphical models are beneficial.


title: Hilbert Space Embeddings of Hidden Markov Models

text: Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a localminimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.


title: Closing the learning-planning loop with predictive state representations

text: A central problem in artificial intelligence is that of planning to maximize future reward under uncertainty in a partially observable environment. In this paper we propose and demonstrate a novel algorithm which accurately learns a model of such an environment directly from sequences of action-observation pairs. We then close the loop from observations to actions by planning in the learned model and recovering a policy which is near-optimal in the original environment. Specifically, we present an efficient and statistically consistent spectral algorithm for learning the parameters of a Predictive State Representation (PSR). We demonstrate the algorithm by learning a model of a simulated high-dimensional, vision-based mobile robot planning task, and then perform approximate point-based planning in the learned PSR. Analysis of our results shows that the algorithm learns a state space which efficiently captures the essential features of the environment. This representation allows accurate prediction with a small number of parameters, and enables successful and efficient planning.


title: A Spectral Algorithm for Learning Hidden Markov Models

text: Hidden Markov Models (HMMs) are one of the most fundamental a nd widely used statistical tools for modeling discrete time series. Typically, they are learned using sea rch heuristics (such as the Baum-Welch / EM algorithm), which suffer from the usual local optima issues. While in gen eral these models are known to be hard to learn with samples from the underlying distribution, we provide t h first provably efficient algorithm (in terms of sample and computational complexity) for learning HMMs under a nat ur l separation condition. This condition is roughly analogous to the separation conditions considered for lear ning mixture distributions (where, similarly, these model s are hard to learn in general). Furthermore, our sample compl exity results do not explicitly depend on the number of distinct (discrete) observations — they implicitly depend on this number through spectral properties of the underlyin g HMM. This makes the algorithm particularly applicable to se ttings with a large number of observations, such as those in natural language processing where the space of observati on is sometimes the words in a language. Finally, the algorithm is particularly simple, relying only on a singula r value decomposition and matrix multiplications.


title: Hilbert space embeddings of conditional distributions with applications to dynamical systems

text: In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.


title: Knowledge tracing: Modeling the acquisition of procedural knowledge

text: This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.


title: Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection

text: In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. Experimental results indicate that the proposed algorithm shows its effectiveness at cooperative work with multiple strategies. It can obtain a good balance between exploration and exploitation. The proposed algorithm can guide the search for a global optimal solution with quick convergence compared with other improved DE variants.


title: Managing the knowledge lifecycle: A integrated knowledge management process model

text: The main purpose of this study is to propose an integrated conceptual model for exploring knowledge management process. Reviewing literature on knowledge management and tracing its historical background, definitions and dominant paradigms and analysis 32 frameworks of KM, a relatively integrated model of KM was presented in this paper. The study found many similarities of models and its presents a model which combine all the best features of the models analyzed. A 10-stage integrated model was proposed. These stages include some activities such as knowledge goal setting, identification, acquisition, creation, organization, sharing, evaluation, preservation, retention and updating and KM effectiveness evaluation. The findings can be used by managers in all organizational environments to implementation and auditing of KM practices.


title: An Augmented Reality museum guide

text: Recent years have seen advances in many enabling augmented reality technologies. Furthermore, much research has been carried out on how augmented reality can be used to enhance existing applications. This paper describes our experiences with an AR-museum guide that combines some of the latest technologies. Amongst other technologies, markerless tracking, hybrid tracking, and an ultra-mobile-PC were used. Like existing audio guides, the AR-guide can be used by any museum visitor, during a six-month exhibition on Islamic art. We provide a detailed description of the museumpsilas motivation for using AR, of our experiences in developing the system, and the initial results of user surveys. Taking this information into account, we can derive possible system improvements.


title: Security in Cloud Computing Using Cryptographic Algorithms Miss .

text: Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. To secure the Cloud means secure the treatments (calculations) and storage (databases hosted by the Cloud provider). In this paper we analyses different security issues to cloud and different cryptographic algorithms adoptable to better security for the cloud. Keywords— Cloud Computing, Cryptographic Algorithm, Internet, Security Algorithms, Security Attacks, Security Issue


title: CEFAM: Comprehensive Evaluation Framework for Agile Methodologies

text: Agile software development is regarded as an effective and efficient approach, mainly due to its ability to accommodate rapidly changing requirements, and to cope with modern software development challenges. There is therefore a strong tendency to use agile software development methodologies where applicable; however, the sheer number of existing agile methodologies and their variants hinders the selection of an appropriate agile methodology or method chunk. Methodology evaluation tools address this problem through providing detailed evaluations, yet no comprehensive evaluation framework is available for agile methodologies. We introduce the comprehensive evaluation framework for agile methodologies (CEFAM) as an evaluation tool for project managers and method engineers. The hierarchical (and mostly quantitative) evaluation criterion set introduced in this evaluation framework enhances the usability of the framework and provides results that are precise enough to be useful for the selection, adaptation and construction of agile methodologies.


title: Design and operation of Wi-Fi agribot integrated system

text: Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.


title: A practical VEP-based brain-computer interface

text: This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.


title: Projector Calibration by "Inverse Camera Calibration"

text: The accuracy of 3-D reconstructions depends substantially on the accuracy of active vision system calibration. In this work, the problem of video projector calibration is solved by inverting the standard camera calibration work flow. The calibration procedure requires a single camera, which does not need to be calibrated and which is used as the sensor whether projected dots and calibration pattern landmarks, such as the checkerboard corners, coincide. The method iteratively adjusts the projected dots to coincide with the landmarks and the final coordinates are used as inputs to a camera calibration method. The otherwise slow iterative adjustment is accelerated by estimating a plane homography between the detected landmarks and the projected dots, which makes the calibration method fast.


title: FEEL: Featured Event Embedding Learning

text: Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.


title: Attribute-based encryption with non-monotonic access structures

text: We construct an Attribute-Based Encryption (ABE) scheme that allows a user's private key to be expressed in terms of any access formula over attributes. Previous ABE schemes were limited to expressing only monotonic access structures. We provide a proof of security for our scheme based on the Decisional Bilinear Diffie-Hellman (BDH) assumption. Furthermore, the performance of our new scheme compares favorably with existing, less-expressive schemes.


title: Browser Fingerprinting as user tracking technology

text: The Web has become an indispensable part of our society and is currently most commonly used mode of information delivery. Millions of users access the free services provided by the websites on daily basis and while providing these free services websites track and profile their web users. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. The objective of this paper is to study about the increasingly common yet hardly discussed technique of identifying individual Web users and tracking them across multiple websites known as “Browser Fingerprinting”. A unique browser fingerprint is derived by the unique pattern of information visible whenever a computer visits a website. The permutations thus collected are sufficiently distinct that they can be used as a tool for tracking. Unlike cookies, Fingerprints are generated on server side and are difficult for a user to influence. The main objective of this research is study about how the fingerprinting was evolved, its positives and negatives, what threat it poses to users' online privacy and what countermeasures could be used to prevent it. This paper will also analyse which different properties the browsers send to the server, allowing a unique fingerprint of those browsers to be created.


title: Zone Divisional Hierarchical Routing Protocol for Wireless Sensor Network

text: Clustering prolongs energy resources, improves scalability and preserves communication bandwidth. Clustering is either classified as static and dynamic or as equal and unequal. In cluster based routing protocols that employ multi-hop communication, imbalanced energy consumption among the nodes results in hot-spots. Unequal clustering overcomes hot-spots but requires a high overhead and is prone to connectivity issues. To offer guaranteed connectivity and alleviate the hot-spot problem, a zone divisional hierarchical routing protocol has been proposed in this paper. The network is divided into equal sized static rectangular clusters which are assigned to two zones namely near zone and far zone. The zone facing the base station is known as the near zone and rest of the network space makes up the far zone which is further divided into sub-zones. Dual cluster heads approach for sharing the reception, aggregation and forwarding tasks is proposed. The performance evaluation of the proposed protocol against the existing protocols reveals that the method offers energy-efficient multi-hop communication support, uses negligible overhead, prevents creation of hot-spots, avoids early death of nodes, uses balanced energy consumption across the network and maximizes the network lifetime.


title: MOBA: a New Arena for Game AI

text: Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.


title: A 20-GHz phase-locked loop for 40-gb/s serializing transmitter in 0.13-/spl mu/m CMOS

text: A 20-GHz phase-locked loop with 4.9 ps/sub pp//0.65 ps/sub rms/ jitter and -113.5 dBc/Hz phase noise at 10-MHz offset is presented. A half-duty sampled-feedforward loop filter that simply replaces the resistor with a switch and an inverter suppresses the reference spur down to -44.0 dBc. A design iteration procedure is outlined that minimizes the phase noise of a negative-g/sub m/ oscillator with a coupled microstrip resonator. Static frequency dividers made of pulsed latches operate faster than those made of flip-flops and achieve near 2:1 frequency range. The phase-locked loop fabricated in a 0.13-/spl mu/m CMOS operates from 17.6 to 19.4GHz and dissipates 480mW.


title: Color Balance and Fusion for Underwater Image Enhancement

text: We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.


title: Rationale-Augmented Convolutional Neural Networks for Text Classification

text: We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.


title: Informing Pedagogical Action: Aligning Learning Analytics With Learning Design

text: This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.


title: Maternal depression and early childhood growth in developing countries: systematic review and meta-analysis.

text:
OBJECTIVE
To investigate the relationship between maternal depression and child growth in developing countries through a systematic literature review and meta-analysis.

METHODS
Six databases were searched for studies from developing countries on maternal depression and child growth published up until 2010. Standard meta-analytical methods were followed and pooled odds ratios (ORs) for underweight and stunting in the children of depressed mothers were calculated using random effects models for all studies and for subsets of studies that met strict criteria on study design, exposure to maternal depression and outcome variables. The population attributable risk (PAR) was estimated for selected studies.

FINDINGS
Seventeen studies including a total of 13,923 mother and child pairs from 11 countries met inclusion criteria. The children of mothers with depression or depressive symptoms were more likely to be underweight (OR: 1.5; 95% confidence interval, CI: 1.2-1.8) or stunted (OR: 1.4; 95% CI: 1.2-1.7). Subanalysis of three longitudinal studies showed a stronger effect: the OR for underweight was 2.2 (95% CI: 1.5-3.2) and for stunting, 2.0 (95% CI: 1.0-3.9). The PAR for selected studies indicated that if the infant population were entirely unexposed to maternal depressive symptoms 23% to 29% fewer children would be underweight or stunted.

CONCLUSION
Maternal depression was associated with early childhood underweight and stunting. Rigorous prospective studies are needed to identify mechanisms and causes. Early identification, treatment and prevention of maternal depression may help reduce child stunting and underweight in developing countries.


title: Application of Artificial Neural Networks to Multiple Criteria Inventory Classification

text: Original scientific paper Inventory classification is a very important part of inventory control which represents the technique of operational research discipline. A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. The paper describes the results obtained by investigating the application of neural networks in multiple criteria inventory classification. Various structures of a back-propagation neural network have been analysed and the optimal one with the minimum Root Mean Square error selected. The predicted results are compared to those obtained by the multiple criteria classification using the analytical hierarchy process.


Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Review Methods for Pretreatment of Lignocellulosic Biomass for Efficient Hydrolysis and Biofuel Production Parveen Kumar, Diane M. Barrett, Michael J. Delwiche, and Pieter Stroeve Ind. Eng. Chem. Res., Article ASAP • DOI: 10.1021/ie801542g • Publication Date (Web): 20 March 2009 Downloaded from http://pubs.acs.org on March 26, 2009


title: Automatic Game Design via Mechanic Generation

text: Game designs often center on the game mechanics— rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.


title: Central Retinal Artery Occlusion and Partial Ophthalmoplegia Caused by Hyaluronic Acid Injection Filler Cosmetic

text: Central retinal artery occlusion is a rare ocular emergency condition that can lead to a total blindness. Hereby we report a 30-year-old woman with a sudden visual loss of the left eye after being injected by hyaluronic acid filler at the dorsum nasal area for cosmetic purpose. Visual acuity was light perception while anterior segment and intraocular pressure were within normal limit. Partial ophthalmoplegia include restriction to the nasal gaze were found. Funduscopic examination revealed total retinal edema with a cherry red spot at the fovea. Ocular massage was performed as initial management followed by humor aqueous paracentesis. All procedures were done within 90 minutes. The patients discharge with the visual improvement to hand motion and the Partial ophthalmophlegia has been improved after being evaluated less than a month.


title: Critiquing-based recommenders: survey and emerging trends

text: Critiquing-based recommender systems elicit users’ feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users’ profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.


title: Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

text: This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.


title: Design Issues and Challenges in Wireless Sensor Networks

text: Wireless Sensor Networks (WSNs) are composed self-organized wireless ad hoc networks which comprise of a large number of resource constrained sensor nodes. The major areas of research in WSN is going on hardware, and operating system of WSN, deployment, architecture, localization, synchronization, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. This paper study highlights ongoing research activities and issues that affect the design and performance of Wireless Sensor Network.


title: Deep Learning

text: Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. Increasingly, these applications make use of a class of techniques called deep learning. Conventional machine-learning techniques were limited in their ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. Deep-learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level. With the composition of enough such transformations, very complex functions can be learned. For classification tasks, higher layers of representation amplify aspects of the input that are important for discrimination and suppress irrelevant variations. An image, for example, comes in the form of an array of pixel values, and the learned features in the first layer of representation typically represent the presence or absence of edges at particular orientations and locations in the image. The second layer typically detects motifs by spotting particular arrangements of edges, regardless of small variations in the edge positions. The third layer may assemble motifs into larger combinations that correspond to parts of familiar objects, and subsequent layers would detect objects as combinations of these parts. The key aspect of deep learning is that these layers of features are not designed by human engineers: they are learned from data using a general-purpose learning procedure. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition to beating records in image recognition and speech recognition, it has beaten other machine-learning techniques at predicting the activity of potential drug molecules, analysing particle accelerator data, reconstructing brain circuits, and predicting the effects of mutations in non-coding DNA on gene expression and disease. Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding, particularly topic classification, sentiment analysis, question answering and language translation. We think that deep learning will have many more successes in the near future because it requires very little engineering by hand, so it can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are currently being developed for deep neural networks will only accelerate this progress.


title: A Persona-Based Neural Conversation Model

text: We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.


title: Intelligent Tutoring Systems with Conversational Dialogue

text: been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students’ attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixedinitiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.


title: Deep Reinforcement Learning with Double Q-learning

text: The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games. The goal of reinforcement learning (Sutton and Barto 1998) is to learn good policies for sequential decision problems, by optimizing a cumulative future reward signal. Q-learning (Watkins 1989) is one of the most popular reinforcement learning algorithms, but it is known to sometimes learn unrealistically high action values because it includes a maximization step over estimated action values, which tends to prefer overestimated to underestimated values. In previous work, overestimations have been attributed to insufficiently flexible function approximation (Thrun and Schwartz 1993) and noise (van Hasselt 2010, 2011). In this paper, we unify these views and show overestimations can occur when the action values are inaccurate, irrespective of the source of approximation error. Of course, imprecise value estimates are the norm during learning, which indicates that overestimations may be much more common than previously appreciated. It is an open question whether, if the overestimations do occur, this negatively affects performance in practice. Overoptimistic value estimates are not necessarily a problem in and of themselves. If all values would be uniformly higher then the relative action preferences are preserved and we would not expect the resulting policy to be any worse. Furthermore, it is known that sometimes it is good to be optimistic: optimism in the face of uncertainty is a well-known Copyright c © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. exploration technique (Kaelbling et al. 1996). If, however, the overestimations are not uniform and not concentrated at states about which we wish to learn more, then they might negatively affect the quality of the resulting policy. Thrun and Schwartz (1993) give specific examples in which this leads to suboptimal policies, even asymptotically. To test whether overestimations occur in practice and at scale, we investigate the performance of the recent DQN algorithm (Mnih et al. 2015). DQN combines Q-learning with a flexible deep neural network and was tested on a varied and large set of deterministic Atari 2600 games, reaching human-level performance on many games. In some ways, this setting is a best-case scenario for Q-learning, because the deep neural network provides flexible function approximation with the potential for a low asymptotic approximation error, and the determinism of the environments prevents the harmful effects of noise. Perhaps surprisingly, we show that even in this comparatively favorable setting DQN sometimes substantially overestimates the values of the actions. We show that the Double Q-learning algorithm (van Hasselt 2010), which was first proposed in a tabular setting, can be generalized to arbitrary function approximation, including deep neural networks. We use this to construct a new algorithm called Double DQN. This algorithm not only yields more accurate value estimates, but leads to much higher scores on several games. This demonstrates that the overestimations of DQN indeed lead to poorer policies and that it is beneficial to reduce them. In addition, by improving upon DQN we obtain state-of-the-art results on the Atari domain.


title: Attention with Intention for a Neural Network Conversation Model

text: In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network that produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-toend without labeling data. Experiments show that this model generates natural responses to user inputs.


title: Low cost power failure protection for MLC NAND flash storage systems with PRAM/DRAM hybrid buffer

text: In the latest PRAM/DRAM hybrid MLC NAND flash storage systems (NFSS), DRAM is used to temporarily store file system data for system response time reduction. To ensure data integrity, super-capacitors are deployed to supply the backup power for moving the data from DRAM to NAND flash during power failures. However, the capacitance degradation of super-capacitor severely impairs system robustness. In this work, we proposed a low cost power failure protection scheme to reduce the energy consumption of power failure protection and increase the robustness of the NFSS with PRAM/DRAM hybrid buffer. Our scheme enables the adoption of the more reliable regular capacitor to replace the super capacitor as the backup power. The experimental result shows that our scheme can substantially reduce the capacitance budget of power failure protection circuitry by 75.1% with very marginal performance and energy overheads.


title: Latent Variable Analysis Growth Mixture Modeling and Related Techniques for Longitudinal Data

text: This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeling in the form of random effects that capture individual variation in development over time. The use of categorical latent variables in growth modeling is, in contrast, perhaps less familiar, and new techniques have recently emerged. The aim of this chapter is to show the usefulness of growth model extensions using categorical latent variables. The discussion also has implications for latent variable analysis of cross-sectional data. The chapter begins with two major parts corresponding to continuous outcomes versus categorical outcomes. Within each part, conventional modeling using continuous latent variables will be described


title: Code quality analysis in open source software development

text: Proponents of open source style software development claim that better software is produced using this model compared with the traditional closed model. However, there is little empirical evidence in support of these claims. In this paper, we present the results of a pilot case study aiming: (a) to understand the implications of structural quality; and (b) to figure out the benefits of structural quality analysis of the code delivered by open source style development. To this end, we have measured quality characteristics of 100 applications written for Linux, using a software measurement tool, and compared the results with the industrial standard that is proposed by the tool. Another target of this case study was to investigate the issue of modularity in open source as this characteristic is being considered crucial by the proponents of open source for this type of software development. We have empirically assessed the relationship between the size of the application components and the delivered quality measured through user satisfaction. We have determined that, up to a certain extent, the average component size of an application is negatively related to the user satisfaction for this application.


title: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation.

text: Image segmentation for detection of vessel walls is necessary for quantitative assessment of vessel diseases by intravascular ultrasound. A new segmentation method based on gradient vector flow (GVF) snake model is proposed in this paper. The main characteristics of the proposed method include two aspects: one is that nonlinear filtering is performed on GVF field to reduce the critical points, change the morphological structure of the parallel curves and extend the capture range; the other is that balloon snake is combined with the model. Thus, the improved GVF and balloon snake can be automatically initialized and overcome the problem caused by local energy minima. Results of 20 in vivo cases validated the accuracy and stability of the segmentation method for intravascular ultrasound images.


title: Basket-Sensitive Personalized Item Recommendation

text: Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket. Towards this goal, we propose two approaches. First, we explore a factorizationbased model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket. Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints. Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.


title: A Review of Classic Edge Detectors

text: In this paper some of the classic alternatives for edge detection in digital images are studied. The main idea of edge detection algorithms is to find where abrupt changes in the intensity of an image have occurred. The first family of algorithms reviewed in this work uses the first derivative to find the changes of intensity, such as Sobel, Prewitt and Roberts. In the second reviewed family, second derivatives are used, for example in algorithms like Marr-Hildreth and Haralick. The obtained results are analyzed from a qualitative point of view (perceptual) and from a quantitative point of view (number of operations, execution time), considering different ways to convolve an image with a kernel (step required in some of the algorithms). Source Code For all the reviewed algorithms, an open source C implementation is provided which can be downloaded from the IPOL web page of this article1. An online demonstration is also available, where the user can test and reproduce our results.


title: An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications

text: This study investigated the impact of Bluetooth Low Energy devices in advertising/beaconing mode on fingerprint-based indoor positioning schemes. Early experimentation demonstrated that the low bandwidth of BLE signals compared to WiFi is the cause of significant measurement error when coupled with the use of three BLE advertising channels. The physics underlying this behaviour is verified in simulation. A multipath mitigation scheme is proposed and tested. It is determined that the optimal positioning performance is provided by 10Hz beaconing and a 1 second multipath mitigation processing window size. It is determined that a steady increase in positioning performance with fingerprint size occurs up to 7 ±1, above this there is no clear benefit to extra beacon coverage.