title: CRITICAL SUCCESS FACTORS FOR CUSTOMER RELATIONSHIP MANAGEMENT IMPLEMENTATIONS

text: The growing forces of increasing global competition, continuing customer demands, and the significant revolution in Commercial Off The Shelf (COTS) solutions, especially Customer Relationship Management (CRM) applications, have together put pressure upon many organisations to implement CRM solutions and to switch their organisational processes from being product-centric to being customer-centric. A CRM initiative is not only technology; it is a business strategy supported by technology which automates and enhances the processes associated with managing customer relationships. By the end of 2010, it is predicted that companies will be spending almost $11 billion yearly on CRM solutions. However, studies have found that 70% of CRM projects have failed. Understanding the factors that enable success of CRM is vital. There is very few existing specific research into Critical Success Factors (CSFs) of CRM implementations, and there is no comprehensive view that captures all the aspects for successful CRM implementation and their inter-relationships. Therefore, the aim of this paper is to explore the current literature base of CSFs for CRM implementations and proposes a taxonomy for them. Future research work will continue to investigate in depth these factors by exploring the complex system links between CSFs using systems thinking techniques such as causal maps to investigate the complex, systemic networks of CSFs in organisations which result in emergent effects which themselves influence the failure or success of a CRM.


title: Log-Euclidean metrics for fast and simple calculus on diffusion tensors.

text: Diffusion tensor imaging (DT-MRI or DTI) is an emerging imaging modality whose importance has been growing considerably. However, the processing of this type of data (i.e., symmetric positive-definite matrices), called "tensors" here, has proved difficult in recent years. Usual Euclidean operations on matrices suffer from many defects on tensors, which have led to the use of many ad hoc methods. Recently, affine-invariant Riemannian metrics have been proposed as a rigorous and general framework in which these defects are corrected. These metrics have excellent theoretical properties and provide powerful processing tools, but also lead in practice to complex and slow algorithms. To remedy this limitation, a new family of Riemannian metrics called Log-Euclidean is proposed in this article. They also have excellent theoretical properties and yield similar results in practice, but with much simpler and faster computations. This new approach is based on a novel vector space structure for tensors. In this framework, Riemannian computations can be converted into Euclidean ones once tensors have been transformed into their matrix logarithms. Theoretical aspects are presented and the Euclidean, affine-invariant, and Log-Euclidean frameworks are compared experimentally. The comparison is carried out on interpolation and regularization tasks on synthetic and clinical 3D DTI data.


title: Mining hidden community in heterogeneous social networks

text: Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the users' needs or preferences. However, in reality, there exist multiple, heterogeneous social networks, each representing a particular kind of relationship, and each kind of relationship may play a distinct role in a particular task. Thus mining networks by assuming only one kind of relation may miss a lot of valuable hidden community information and may not be adaptable to the diverse information needs from different users.In this paper, we systematically analyze the problem of mining hidden communities on heterogeneous social networks. Based on the observation that different relations have different importance with respect to a certain query, we propose a new method for learning an optimal linear combination of these relations which can best meet the user's expectation. With the obtained relation, better performance can be achieved for community mining. Our approach to social network analysis and community mining represents a major shift in methodology from the traditional one, a shift from single-network, user-independent analysis to multi-network, user-dependant, and query-based analysis. Experimental results on Iris data set and DBLP data set demonstrate the effectiveness of our method.


title: Impact of Halo Doping on the Subthreshold Performance of Deep-Submicrometer CMOS Devices and Circuits for Ultralow Power Analog/Mixed-Signal Applications

text: In addition to its attractiveness for ultralow power applications, analog CMOS circuits based on the subthreshold operation of the devices are known to have significantly higher gain as compared to their superthreshold counterpart. The effects of halo [both double-halo (DH) and single-halo or lateral asymmetric channel (LAC)] doping on the subthreshold analog performance of 100-nm CMOS devices are systematically investigated for the first time with extensive process and device simulations. In the subthreshold region, although the halo doping is found to improve the device performance parameters for analog applications (such as gm/Id, output resistance and intrinsic gain) in general, the improvement is significant in the LAC devices. Low angle of tilt of the halo implant is found to give the best improvement in both the LAC and DH devices. Our results show that the CMOS amplifiers made with the halo implanted devices have higher voltage gain over their conventional counterpart, and a more than 100% improvement in the voltage gain is observed when LAC doping is made on both the p- and n-channel devices of the amplifier


title: Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities

text: In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs.


title: An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information

text: Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.


title: Low-light image enhancement using variational optimization-based Retinex model

text: This paper presents a low-light image enhancement method using the variational-optimization-based Retinex algorithm. The proposed enhancement method first estimates the initial illumination and uses its gamma corrected version to constrain the illumination component. Next, the variational-based minimization is iteratively performed to separate the reflectance and illumination components. The color assignment of the estimated reflectance component is then performed to restore the color component using the input RGB color channels. Experimental results show that the proposed method can provide better enhanced result without saturation, noise amplification or color distortion.


title: Big Data Reduction Methods: A Survey

text: Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.


title: Nonsuicidal self-injury as a gateway to suicide in young adults.

text:
PURPOSE
To investigate the extent to which nonsuicidal self-injury (NSSI) contributes to later suicide thoughts and behaviors (STB) independent of shared risk factors.

METHODS
One thousand four hundred and sixty-six students at five U.S. colleges participated in a longitudinal study of the relationship between NSSI and suicide. NSSI, suicide history, and common risk/protective factors were assessed annually for three years. Analyses tested the hypotheses that the practice of NSSI prior to STB and suicide behavior (excluding ideation) reduced inhibition to later STB independent of shared risk factors. Analyses also examined factors that predicted subsequent STB among individuals with NSSI history.

RESULTS
History of NSSI did significantly predict concurrent or later STB (AOR 2.8, 95%, CI 1.9-4.1) independent of covariates common to both. Among those with prior or concurrent NSSI, risk of STB is predicted by > 20 lifetime NSSI incidents (AOR 3.8, 95% CI, 1.4-10.3) and history of mental health treatment (AOR 2.2, 95% CI, 1.9-4.6). Risk of moving from NSSI to STB is decreased by presence of meaning in life (AOR .6, 95% CI, .5-.7) and reporting parents as confidants (AOR, .3, 95% CI, .1-.9).

CONCLUSIONS
NSSI prior to suicide behavior serves as a "gateway" behavior for suicide and may reduce inhibition through habituation to self-injury. Treatments focusing on enhancing perceived meaning in life and building positive relationships with others, particularly parents, may be particularly effective in reducing suicide risk among youth with a history of NSSI.


title: An airborne experimental test platform: From theory to flight

text: This paper provides an overview of the experimental flight test platform developed by the University of Minnesota Unmanned Aerial Vehicle Research Group. Key components of the current infrastructure are highlighted, including the flight test system, high-fidelity nonlinear simulations, software-and hardware-in-the-loop simulations, and the real-time flight software. Recent flight control research and educational applications are described to showcase the advanced capabilities of the platform. A view towards future expansion of the platform is given in the context of upcoming research projects.


title: Bias and causal associations in observational research

text: Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.


title: Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

text: ÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.


title: Aggregate and Verifiably Encrypted Signatures from Bilinear Maps

text: An aggregate signature scheme is a digital signature that supports aggregation: Given n signatures on n distinct messages from n distinct users, it is possible to aggregate all these signatures into a single short signature. This single signature (and the n original messages) will convince the verifier that the n users did indeed sign the n original messages (i.e., user i signed message Mi for i = 1, . . . , n). In this paper we introduce the concept of an aggregate signature, present security models for such signatures, and give several applications for aggregate signatures. We construct an efficient aggregate signature from a recent short signature scheme based on bilinear maps due to Boneh, Lynn, and Shacham. Aggregate signatures are useful for reducing the size of certificate chains (by aggregating all signatures in the chain) and for reducing message size in secure routing protocols such as SBGP. We also show that aggregate signatures give rise to verifiably encrypted signatures. Such signatures enable the verifier to test that a given ciphertext C is the encryption of a signature on a given message M . Verifiably encrypted signatures are used in contract-signing protocols. Finally, we show that similar ideas can be used to extend the short signature scheme to give simple ring signatures.


title: Pors: proofs of retrievability for large files

text:
In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.
 A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.
 In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.
 We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.


title: The Exact Security of Digital Signatures - HOw to Sign with RSA and Rabin

text: We describe an RSA-based signing scheme which combines essentially optimal e ciency with attractive security properties. Signing takes one RSA decryption plus some hashing, veri cation takes one RSA encryption plus some hashing, and the size of the signature is the size of the modulus. Assuming the underlying hash functions are ideal, our schemes are not only provably secure, but are so in a tight way| an ability to forge signatures with a certain amount of computational resources implies the ability to invert RSA (on the same size modulus) with about the same computational e ort. Furthermore, we provide a second scheme which maintains all of the above features and in addition provides message recovery. These ideas extend to provide schemes for Rabin signatures with analogous properties; in particular their security can be tightly related to the hardness of factoring. Department of Computer Science and Engineering, Mail Code 0114, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. E-mail: mihir@cs.ucsd.edu ; Web page: http://www-cse.ucsd.edu/users/mihir y Department of Computer Science, University of California at Davis, Davis, CA 95616, USA. Email: rogaway@cs.ucdavis.edu ; Web page: http://wwwcsif.cs.ucdavis.edu/~rogaway/homepage.html


title: A public key cryptosystem and a signature scheme based on discrete logarithms

text: A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem. The security of both systems relies on the difficulty of computing discrete logarithms over finite fields.


title: Proofs of Work and Bread Pudding Protocols

text: We formalize the notion of a proof of work. In many cryptographic protocols, a prover seeks to convince a veriier that she possesses knowledge of a secret or that a certain mathematical relation holds true. By contrast, in a proof of work, a prover demonstrates that she has performed a certain amount of computational work in a speciied interval of time. Proofs of work have served as the basis of a number of security protocols in the literature, but have hitherto lacked careful characterization. We also introduce the dependent idea of a bread pudding protocol. Bread pudding is a dish that originated with the purpose of re-using bread that has gone stale 10]. In the same spirit, we deene a bread pudding protocol to be a proof of work such that the computational eeort invested in the proof may also be harvested to achieve a separate, useful, and veriiably correct computation. As an example of a bread pudding protocol, we show how the MicroMint scheme of Rivest and Shamir can be broken up into a collection of proofs of work. These proofs of work can not only serve in their own right as mechanisms for security protocols, but can also be harvested in order to shift the burden of the MicroMint minting operation onto a large group of un-trusted computational devices.


title: Techniques for Assessing Polygonal Approximations of Curves

text: Given the enormous number of available methods for finding po lygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Ins tead, we suggest assessing an algorithm’s results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the appl ication of 23 algorithms to a curve first used by Teh and Chin [37]; their ISEs are assessed relative to the o ptimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation c a be used to select an appropriate assessment criterion.


title: A Two-Handed Interface for Object Manipulation in Virtual Environments

text: A two-handed direct manipulation VE (virtual environment) interface has been developed as an intuitive manipulation metaphor for graphical objects. A new input device called ChordGloves introduces a simple technique for rapid and repeatable gesture recognition; the Chordgloves emulate a pair of 3-D mice and a keyboard. A drafting table is isomorphically mapped into the VE and provides hand support for 2-D interface techniques, as well as a reference frame for calibrating the mapping between real and virtual worlds. A cursor gravity function is used to grab vertices, edges, or faces and establish precisely aligned differential constraints between objects called anchors. The capability of subjects to translate, rotate, scale, align, and glue objects is tested with a puzzle building task. An approximation of the puzzle task is done in Adobe Illustrator to provide a performance reference. Results and informal user observations as well as topics for future work are presented.


title: Gazture: Design and Implementation of a Gaze based Gesture Control System on Tablets

text: We present Gazture, a light-weight gaze based real-time gesture control system on commercial tablets. Unlike existing approaches that require dedicated hardware (e.g., high resolution camera), high computation overhead (powerful CPU) or specific user behavior (keeping head steady), Gazture provides gesture recognition based on easy-to-control user gaze input with a small overhead. To achieve this goal, Gazture incorporates a two-layer structure: The first layer focuses on real-time gaze estimation with acceptable tracking accuracy while incurring a small overhead. The second layer implements a robust gesture recognition algorithm while compensating gaze estimation error. To address user posture change while using mobile device, we design a online transfer function based method to convert current eye features into corresponding eye features in reference posture, which then facilitates efficient gaze position estimation. We implement Gazture on Lenovo Tab3 8 Plus tablet with Android 6.0.1, and evaluate its performance in different scenarios. The evaluation results show that Gazture can achieve a high accuracy in gesture recognition while incurring a low overhead.


title: Optimizing Factorization Machines for Top-N Context-Aware Recommendations

text: Context-aware Collaborative Filtering (CF) techniques such as Factorization Machines (FM) have been proven to yield high precision for rating prediction. However, the goal of recommender systems is often referred to as a top-N item recommendation task, and item ranking is a better formulation for the recommendation problem. In this paper, we present two collaborative rankers, namely, Ranking Factorization Machines (RankingFM) and Lambda Factorization Machines (LambdaFM), which optimize the FM model for the item recommendation task. Specifically, instead of fitting the preference of individual items, we first propose a RankingFM algorithm that applies the cross-entropy loss function to the FM model to estimate the pairwise preference between individual item pairs. Second, by considering the ranking bias in the item recommendation task, we design two effective lambda-motivated learning schemes for RankingFM to optimize desired ranking metrics, referred to as LambdaFM. The two models we propose can work with any types of context, and are capable of estimating latent interactions between the context features under sparsity. Experimental results show its superiority over several state-of-the-art methods on three public CF datasets in terms of two standard ranking metrics.


title: A Review on the Meaning of Cognitive Cities

text: Over the last years, the cognitive city paradigm has emerged as a promising solution to the challenges that megacities of the future will have to face. In this article, we provide a thorough review of the literature on Cognitive Cities. We put in place a clear and strict methodology that allows as to present a carefully curated set of articles that represent the foundations of the current understanding of the concept of cognitive city. Hence, this article is intended to serve as a reference for future studies in the field. We emphasise the ambiguities and overlapping meanings of the cognitive city term depending on the domain of study and the underlying philosophy. Also, we discuss some of the implications that cognitive cities might have on society pillars such as the healthcare sector, and we point out some of the main challenges for the adoption of the concept. Last but not least, we suggest some possible research lines that are to be pursued in the years to come.


title: TANDEM-bottleneck feature combination using hierarchical Deep Neural Networks

text: To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone recordings of the Cantonese IARPA Babel corpus using both standard MFCCs and Gabor as input features.


title: Parking lot guidance software based on MQTT Protocol

text: To reduce the amount of CO2 produced from the personal cars in the world, the parking lot guidance systems are considered to be the solution in shopping malls or department stores in many countries. However, most of the current parking lot systems are located in the parking area of each shopping mall and they cannot show the parking lots information for the driver that are driving on the road. So, The drivers can see the parking lot information if and only if they arrive at the shopping mall area. This is the fact that the CO2 are still produced to the world although we use the parking lot guidance system. From the reason as mentioned, we propose the parking lots guidance software to share a parking lot information to a large number of clients in real-time based on MQTT Protocol. The proposed software can share the real-time parking lot information to the mobile devices of the drivers in any location or when they are driving on the road. The results show that the proposed software can share the parking lot information in real-time for at least 1,000 sessions simultaneously with the high average score of usability, design and the benefits.


title: Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

text: The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.


title: Design of a haptic arm exoskeleton for training and rehabilitation

text: A high-quality haptic interface is typically characterized by low apparent inertia and damping, high structural stiffness, minimal backlash, and absence of mechanical singularities in the workspace. In addition to these specifications, exoskeleton haptic interface design involves consideration of space and weight limitations, workspace requirements, and the kinematic constraints placed on the device by the human arm. These constraints impose conflicting design requirements on the engineer attempting to design an arm exoskeleton. In this paper, the authors present a detailed review of the requirements and constraints that are involved in the design of a high-quality haptic arm exoskeleton. In this context, the design of a five-degree-of-freedom haptic arm exoskeleton for training and rehabilitation in virtual environments is presented. The device is capable of providing kinesthetic feedback to the joints of the lower arm and wrist of the operator, and will be used in future work for robot-assisted rehabilitation and training. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. As a training tool, the device provides a means to implement flexible, repeatable, and safe training methodologies.


title: Active learning: theory and applications to automatic speech recognition

text: We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision for training acoustic and language models and to maximize the performance given the transcribed and untranscribed data. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. In this paper we describe how to estimate the confidence score for each utterance through an on-line algorithm using the lattice output of a speech recognizer. The utterance scores are filtered through the informativeness function and an optimal subset of training samples is selected. The active learning algorithm has been applied to both batch and on-line learning scheme and we have experimented with different selective sampling algorithms. Our experiments show that by using active learning the amount of labeled data needed for a given word accuracy can be reduced by more than 60% with respect to random sampling.


title: Seeing Stars of Valence and Arousal in Blog Posts

text: Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.


title: Uniport: A Uniform Programming Support Framework for Mobile Cloud Computing

text: Personal mobile devices (PMDs) have become the most used computing devices for many people. With the introduction of mobile cloud computing, we can augment the storage and computing capabilities of PMDs via cloud support. However, there are many challenges in developing mobile cloud applications (MCAs) that incorporate cloud computing efficiently, especially for developers targeting multiple mobile platforms. This paper presents Uniport, a uniform framework for developing MCAs. We introduce a uniform architecture for MCAs based on the Model-View-Controller (MVC) pattern and a set of programming primitives and runtime libraries. Not only can Uniport support the creation of new MCAs, it can also help transform existing mobile applications to MCAs efficiently. We demonstrate the applicability and flexibility of Uniport in a case study to transform three existing mobile applications on iOS, Android and Windows Phone, to their mobile cloud versions respectively. Evaluation results show that, with very few modifications, we can easily transform mobile applications to MCAs that can exploit the cloud support to improve performance by 3 - 7x and save more than half of their energy consumption.


title: StreamOp: An Innovative Middleware for Supporting Data Management and Query Functionalities over Sensor Network Streams Efficiently

text: During the last decade, a lot of progress has happened in the field of autonomous embedded devices (also known as motes) and wireless sensor networks. In this context, we envision next-generation systems to have autonomous database management functionalities on each mote, for supporting easy management of data on flash disks, adequate querying capabilities, and ubiquitous computing with no-effort. Following this major vision, in this paper we describe application scenarios, middleware approach and data management algorithms of a novel system, called Stream Operation (StreamOp), which effectively and efficiently realizes the depicted challenges. In particular, StreamOp supports heterogeneity, i.e. It works on top of different platforms, efficiency, i.e. It returns responses quickly, and autonomy, i.e. It saves battery power. We show how StreamOp provides these features, along with some experimental results that clearly validate our main assumptions.


title: Pulse width modulation with resonant DC link converters

text: A technique for realizing pulse-width modulation capability in resonant DC link converters is presented. The technique eliminates the subharmonics present in the conventional discrete pulse modulated converters without requiring additional switching devices. Detailed design-oriented analyses enumerating the tradeoffs involved are presented. Control techniques for simultaneously accomplishing link control and output control are considered. Simulation results verifying the principle of operation are presented.<<ETX>>


title: From Modeling to Implementation of Virtual Sensors in Body Sensor Networks

text: Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.


title: Change in perceived psychosocial status following a 12-week Tai Chi exercise programme.

text:
AIM
This paper reports a study to examine change in psychosocial status following a 12-week Tai Chi exercise intervention among ethnic Chinese people with cardiovascular disease risk factors living in the United States of America.

BACKGROUND
Regular participation in physical activity is associated with protection against cardioavascular disease, and improvements in physical and psychological health. Increasing amounts of scientific evidence suggests that mind-body exercise, such as Tai Chi, are related to improvements in mental health, emotional well-being, and stress reduction. No prior study has examined the effect of a Tai Chi exercise intervention on psychosocial status among people with cardiovascular disease risk factors.

METHODS
This was a quasi-experimental study. Participants attended a 60-minute Tai Chi exercise class three times per week for 12 weeks. Data were collected at baseline, 6 and 12 weeks following the intervention. Psychosocial status was assessed using Chinese versions of Cohen's Perceived Stress Scale, Profile of Mood States, Multidimensional Scale of Perceived Social Support, and Tai Chi exercise self-efficacy.

RESULTS
A total of 39 participants, on average 66-year-old (+/-8.3), married (85%), Cantonese-speaking (97%), immigrants participated. The majority were women (69%), with < or =12 years education (87%). Statistically significant improvements in all measures of psychosocial status were found (P < or = 0.05) following the intervention. Improvement in mood state (eta2 = 0.12), and reduction in perceived stress (eta2 = 0.13) were found. In addition, Tai Chi exercise statistically significantly increased self-efficacy to overcome barriers to Tai Chi (eta2 = 0.19), confidence to perform Tai Chi (eta2 = 0.27), and perceived social support (eta2 = 0.12).

CONCLUSIONS
Tai Chi was a culturally appropriate mind-body exercise for these older adults, with statistically significant psychosocial benefits observed over 12-weeks. Further research examining Tai Chi exercise using a randomized clinical trial design with an attention-control group may reduce potential confounding effects, while exploring potential mechanisms underlying the relaxation response associated with mind-body exercise. In addition, future studies with people with other chronic illnesses in all ethnic groups are recommended to determine if similar benefits can be achieved.


title: Resolver Models for Manufacturing

text: This paper develops a new mathematical model, for pancake resolvers, that is dependent on a set of variables controlled by a resolver manufacturer-the winding parameters. This model allows a resolver manufacturer to manipulate certain in-process controllable variables in order to readjust already assembled resolvers that, without any action, would be scrapped for the production line. The developed model follows a two-step strategy where, on a first step, a traditional transformer's model computes the resolver nominal conditions and, on a second step, a linear model computes the corrections on the controllable variables, in order to compensate small deviations in design assumptions, caused by the variability of the manufacturing process. An experimental methodology for parameter identification is presented. The linearized model develops a complete new approach to simulate the product characteristics of a pancake resolver from the knowledge of manufacturer controllable variables (winding parameters). This model had been simulated and experimentally tested in a resolver manufacturing plant. The performed tests prove the efficiency of the developed model, stabilizing the product specifications in a dynamic environment with high variability of the production processes. All experiments had been led at the resolver manufacturer Tyco Electronics-Évora plant.


title: On Type-Aware Entity Retrieval

text: Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.


title: Skintillates: Designing and Creating Epidermal Interactions

text: Skintillates is a wearable technology that mimics tattoos - the oldest and most commonly used on-skin displays in human culture. We demonstrate that by fabricating electrical traces and thin electronics on temporary tattoo paper, a wide array of displays and sensors can be created. Just like the traditional temporary tattoos often worn by children and adults alike, Skintillates flex naturally with the user's skin. Our simple fabrication technique also enables users to freely design and print with a full range of colors to create application-specific customized designs. We demonstrate the technical capabilities of Skintillates as sensors and as expressive personal and private displays through a series of application examples. Finally, we detail the results of a set of user studies that highlight the user experience, comfort, durability, acceptability, and application potential for Skintillates.


title: An Efficient and Trustworthy Resource Sharing Platform for Collaborative Cloud Computing

text: Advancements in cloud computing are leading to a promising future for collaborative cloud computing (CCC), where globally-scattered distributed cloud resources belonging to different organizations or individuals (i.e., entities) are collectively used in a cooperative manner to provide services. Due to the autonomous features of entities in CCC, the issues of resource management and reputation management must be jointly addressed in order to ensure the successful deployment of CCC. However, these two issues have typically been addressed separately in previous research efforts, and simply combining the two systems generates double overhead. Also, previous resource and reputation management methods are not sufficiently efficient or effective. By providing a single reputation value for each node, the methods cannot reflect the reputation of a node in providing individual types of resources. By always selecting the highest-reputed nodes, the methods fail to exploit node reputation in resource selection to fully and fairly utilize resources in the system and to meet users' diverse QoS demands. We propose a CCC platform, called Harmony, which integrates resource management and reputation management in a harmonious manner. Harmony incorporates three key innovations: integrated multi-faceted resource/reputation management, multi-QoS-oriented resource selection, and price-assisted resource/reputation control. The trace data we collected from an online trading platform implies the importance of multi-faceted reputation and the drawbacks of highest-reputed node selection. Simulations and trace-driven experiments on the real-world PlanetLab testbed show that Harmony outperforms existing resource management and reputation management systems in terms of QoS, efficiency and effectiveness.


title: Recurrent Neural Word Segmentation with Tag Inference

text: In this paper, we present a Long Short-TermMemory (LSTM) based model for the task of Chinese Weibo word segmentation. The model adopts a LSTM layer to capture long-range dependencies in sentence and learn the underlying patterns. In order to infer the optimal tag path, we introduce a transition score matrix for jumping between tags of successive characters. Integrated with some unsupervised features, the performance of the model is further improved. Finally, our model achieves a weighted F1-score of 0.8044 on close track, 0.8298 on the semi-open track.


title: From Pose to Activity: Surveying Datasets and Introducing CONVERSE

text: We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling. We categorize datasets regarding several key properties for usage as a benchmark dataset; including the number of class labels, ground truths provided, and application domain they occupy. We also consider the level of abstraction of each dataset; grouping those that present actions, interactions and higher level semantic activities. The survey identifies key appearance and pose based datasets, noting a tendency for simplistic, emphasized, or scripted action classes that are often readily definable by a stable collection of subaction gestures. There is a clear lack of datasets that provide closely related actions, those that are not implicitly identified via a series of poses and gestures, but rather a dynamic set of interactions. We therefore propose a novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. The intention is to provide events that are constructed from numerous primitive actions, interactions and motions, over a period of time; providing a set of subtle action classes that are more representative of the real world, and a challenge to currently developed recognition methodologies. We believe this is among one of the first datasets devoted to conversational interaction classification using 3D pose Preprint submitted to Elsevier October 27, 2015 features and the attributed papers show this task is indeed possible. The full dataset is made publicly available to the research community at [1].


title: Security in Container-Based Virtualization through vTPM

text:
Cloud computing is a wide-spread technology that enables the enterprises to provide services to their customers with a lower cost, higher performance, better availability and scalability. However, privacy and security in cloud computing has always been a major challenge to service providers and a concern to its users. Trusted computing has led its way in securing the cloud computing and virtualized environment, during the past decades.
 In this paper, first we study virtualized trusted platform modules and integration of vTPM in hypervisor-based virtualization. Then we propose two architectural solutions for integrating the vTPM in container-based virtualization model.


title: Facebook Use Predicts Declines in Subjective Well-Being in Young Adults

text: Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people "directly" did not predict these negative outcomes. They were also not moderated by the size of people's Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.


title: Relay Attacks on Passive Keyless Entry and Start Systems in Modern Cars

text: We demonstrate relay attacks on Passive Keyless Entry and Start (PKES) systems used in modern cars. We build two efficient and inexpensive attack realizations, wired and wireless physical-layer relays, that allow the attacker to enter and start a car by relaying messages between the car and the smart key. Our relays are completely independent of the modulation, protocol, or presence of strong authentication and encryption. We perform an extensive evaluation on 10 car models from 8 manufacturers. Our results show that relaying the signal in one direction only (from the car to the key) is sufficient to perform the attack while the true distance between the key and car remains large (tested up to 50 meters, non line-of-sight). We also show that, with our setup, the smart key can be excited from up to 8 meters. This removes the need for the attacker to get close to the key in order to establish the relay. We further analyze and discuss critical system characteristics. Given the generality of the relay attack and the number of evaluated systems, it is likely that all PKES systems based on similar designs are also vulnerable to the same attack. Finally, we propose immediate mitigation measures that minimize the risk of relay attacks as well as recent solutions that may prevent relay attacks while preserving the convenience of use, for which PKES systems were initially introduced.


title: Two-factor authentication: too little, too late

text: T wo-factor authentication isn't our savior. It won't defend against phishing. It's not going to prevent identity theft. It's not going to secure online accounts from fraudulent transactions. It solves the security problems we had 10 years ago, not the security problems we have today. The problem with passwords is that it is too easy to lose control of them. People give their passwords to other people. People write them down, and other people read them. People send them in email, and that email is intercepted. People use them to log into remote servers, and their communications are eavesdropped on. Passwords are also easy to guess. And once any of that happens, the password no longer works as an authentication token because you can never be sure who is typing in that password. Two-factor authentication mitigates this problem. If your password includes a number that changes every minute, or a unique reply to a random challenge, then it's difficult for someone else to intercept. You can't write down the ever-changing part. An intercepted password won't be usable the next time it's needed. And a two-factor password is more difficult to guess. Sure, someone can always give his password and token to his secretary, but no solution is foolproof. These tokens have been around for at least two decades, but it's only recently that they have received mass-market attention. AOL is rolling them out. Some banks are issuing them to customers , and even more are talking about doing it. It seems that corporations are finally recognizing the fact that passwords don't provide adequate security , and are hoping that two-factor authentication will fix their problems. Unfortunately, the nature of attacks has changed over those two decades. Back then, the threats were all passive: eavesdropping and offline password guessing. Today, the threats are more active: phishing and Trojan horses. Two new active attacks we're starting to see include: Man-in-the-Middle Attack. An attacker puts up a fake bank Web site and entices a user to that Web site. The user types in his password, and the attacker in turn uses it to access the bank's real Web site. Done correctly, the user will never realize that he isn't at the bank's Web site. Then the attacker either disconnects the user and makes any fraudulent transactions he wants, or passes along the user's banking transactions while making his own transactions at the same …


title: The Secure Remote Password Protocol

text: This paper presents a new password authentication and key-exchange protocol suitable for authenticating users and exchanging keys over an untrusted network. The new protocol resists dictionary attacks mounted by either passive or active network intruders, allowing, in principle, even weak passphrases to be used safely. It also o ers perfect forward secrecy, which protects past sessions and passwords against future compromises. Finally, user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the host. This new protocol combines techniques of zero-knowledge proofs with asymmetric key exchange protocols and o ers signi cantly improved performance over comparably strong extended methods that resist stolen-veri er attacks such as Augmented EKE or B-SPEKE.


title: Security and Privacy Challenges in the Smart Grid

text: Global electrical grids are verging on the largest technological transformation since the introduction of electricity into the home. The antiquated infrastructure that delivers power to our homes and businesses is being replaced with a collection of digital systems called the smart grid. This grid is the modernization of the existing electrical system that enhances customers' and utilities' ability to monitor, control, and predict energy use.


title: Real-time techniques for 3D flow visualization

text: Visualization of three-dimensional steady flow has to overcome a lot of problems to be effective. Among them are occlusion of distant details, lack of directional and depth hints and occlusion. We present methods which address these problems for real-time graphic representations applicable in virtual environments. We use dashtubes, i.e., animated, opacity-mapped streamlines, as a visualization icon for 3D-flow visualization. We present a texture mapping technique to keep the level of texture detail along a streamline nearly constant even when the velocity of the flow varies considerably. An algorithm is described which distributes the dashtubes evenly in space. We apply magic lenses and magic boxes as interaction techniques for investigating densely filled areas without overwhelming the observer with visual detail. Implementation details of these methods and their integration in our virtual environment conclude the paper.


title: SQL Database Primitives for Decision Tree Classifiers

text: Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.


title: Human Body as Antenna and Its Effect on Human Body Communications

text: Human body communication (HBC) is a promising wireless technology that uses the human body as part of the communication channel. HBC operates in the near-field of the high frequency (HF) band and in the lower frequencies of the very high frequency (VHF) band, where the electromagnetic field has the tendency to be confined inside the human body. Electromagnetic interference poses a serious reliability issue in HBC; consequently, it has been given increasing attention in regard to adapting techniques to curtail its degrading effect. Nevertheless, there is a gap in knowledge on the mechanism of HBC interference that is prompted when the human body is exposed to electromagnetic fields as well as the effect of the human body as an antenna on HBC. This paper narrows the gap by introducing the mechanisms of HBC interference caused by electromagnetic field exposure of human body. We derived analytic expressions for induced total axial current in the body and associated fields in the vicinity of the body when an imperfectly conducting cylindrical antenna model of the human body is illuminated by a vertically polarized plane wave within the 1–200 MHz frequency range. Also, fields in the vicinity of the human body model from an on-body HBC transmitter are calculated. Furthermore, conducted electromagnetic interference on externally embedded HBC receivers is also addressed. The results show that the maximum HBC gain near 50 MHz is due to whole-body resonance, and the maximum at 80 MHz is due to the resonance of the arm. Similarly, the results also suggest that the magnitude of induced axial current in the body due to electromagnetic field exposure of human body is higher near 50MHz.


title: High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons

text: Understanding the transmission of sensory information at individual synaptic connections requires knowledge of the properties of presynaptic terminals and their patterns of firing evoked by sensory stimuli. Such information has been difficult to obtain because of the small size and inaccessibility of nerve terminals in the central nervous system. Here we show, by making direct patch-clamp recordings in vivo from cerebellar mossy fibre boutons—the primary source of synaptic input to the cerebellar cortex—that sensory stimulation can produce bursts of spikes in single boutons at very high instantaneous firing frequencies (more than 700 Hz). We show that the mossy fibre–granule cell synapse exhibits high-fidelity transmission at these frequencies, indicating that the rapid burst of excitatory postsynaptic currents underlying the sensory-evoked response of granule cells can be driven by such a presynaptic spike burst. We also demonstrate that a single mossy fibre can trigger action potential bursts in granule cells in vitro when driven with in vivo firing patterns. These findings suggest that the relay from mossy fibre to granule cell can act in a ‘detonator’ fashion, such that a single presynaptic afferent may be sufficient to transmit the sensory message. This endows the cerebellar mossy fibre system with remarkable sensitivity and high fidelity in the transmission of sensory information.


title: Spectrum Sensing in Cognitive Radio Networks

text: The rising number and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. In this approach, the major challenge in realizing the full potential of CR systems is to identify the spectrum opportunities in the wide band regime reliably and optimally. In the spectrum underlay approach, CR systems enable dynamic spectrum access by co-existing and transmitting simultaneously with licensed primary users without creating harmful interference to them. In this case, the challenge is to transmit with low power so as not to exceed the tolerable interference level to the primary users. Spectrum sensing and estimation is an integral part of the CR system, which is used to identify the spectrum opportunities in spectrum overlay and to identify the interference power to primary users in spectrum underlay approach. In this chapter, the authors present a comprehensive study of signal detection techniques for spectrum sensing proposed for CR systems. Specifically, they outline the state of the art research results, challenges, and future perspectives of spectrum sensing in CR systems, and also present a comparison of different methods. With this chapter, readers can have a comprehensive insight of signal processing methods of spectrum sensing for cognitive radio networks and the ongoing research and development in this area.


title: Noncontact Monitoring of Blood Oxygen Saturation Using Camera and Dual-Wavelength Imaging System

text: We present a noncontact method to monitor blood oxygen saturation (SpO2). The method uses a CMOS camera with a trigger control to allow recording of photoplethysmography (PPG) signals alternatively at two particular wavelengths, and determines the SpO2 from the measured ratios of the pulsatile to the nonpulsatile components of the PPG signals at these wavelengths. The signal-to-noise ratio (SNR) of the SpO2 value depends on the choice of the wavelengths. We found that the combination of orange (λ = 611 nm) and near infrared (λ = 880 nm) provides the best SNR for the noncontact video-based detection method. This combination is different from that used in traditional contact-based SpO2 measurement since the PPG signal strengths and camera quantum efficiencies at these wavelengths are more amenable to SpO2 measurement using a noncontact method. We also conducted a small pilot study to validate the noncontact method over an SpO2 range of 83%-98%. This study results are consistent with those measured using a reference contact SpO2 device (r = 0.936, p <; 0.001). The presented method is particularly suitable for tracking one's health and wellness at home under free-living conditions, and for those who cannot use traditional contact-based PPG devices.


title: Autonomous land vehicle project at CMU

text: 1 . Introduction This paper provides an overview of the Autonomous Land Vehicle (ALV) Project at CMU. The goal of the CMU ALV Project is to build vision and intelligence for a mobile robot capable of operating in the real world outdoors. We are attacking this on a number of fronts: building appropriate research vehicles, exploiting high. speed experimental computers, and building software for reasoning about the perceived world. Research topics includes: • Construction of research vehicles • Perception systems to perceive the natural outdoor scenes by means of multiple sensors including cameras (color, stereo, and motion), sonar sensors, and a 3D range finder • Path planning for obstacle avoidance Use of topological and terrain map • System architecture to facilitate the system integration • Utilization of parallel computer architectures Our current research vehicle is the Terregator built at CMU which is equipped with a sonar ring, a color camera, and the ERIM laser range finder. Its initial task is to follow roads and sidewalks in the park and on the campus, and avoid obstacles such as trees, humans, and traffic cones. 2. Vehicle, Sensors, and Host Computers The primary vehicle of the AMU ALV Project has been the Terregator, designed and built at CMU. The Terregator, short for Terrestrial Navigator, is designed to provide a clean separation between the vehicle itself and its sensor payload. As shown in


title: Robust real-time lane and road detection in critical shadow conditions

text: This paper presents the vision-based road detection system currently installed onto the MOB-LAB land vehicle. Based on a geometrical transform and on a fast morphological processing, the system is capable to detect road markings even in extremely severe shadow conditions on at and structured roads. The use of a special-purpose massively architecture (PAPRICA) allows to achieve a processing rate of about 17 Hz.


title: E-LAMP: integration of innovative ideas for multimedia event detection

text: Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED’11 and MED’12 demonstrate the excellent performance of the integration of these ideas.


title: Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers

text: Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.


title: On the Latency and Energy Efficiency of Distributed Storage Systems

text: The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient distributed storage systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq1-2459711.gif"/></alternatives></inline-formula> storage servers (disks) store the data of <inline-formula><tex-math notation="LaTeX">$R$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq2-2459711.gif"/></alternatives></inline-formula> distinct classes. Data of class <inline-formula><tex-math notation="LaTeX">$i$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq3-2459711.gif"/></alternatives></inline-formula> is encoded using a <inline-formula><tex-math notation="LaTeX">$(n,k_{i})$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq4-2459711.gif"/></alternatives></inline-formula> erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS.


title: A 1.5 nW, 32.768 kHz XTAL Oscillator Operational From a 0.3 V Supply

text: This paper presents an ultra-low power crystal (XTAL) oscillator circuit for generating a 32.768 kHz clock source for real-time clock generation. An inverting amplifier operational from 0.3 V VDD oscillates the XTAL resonator and achieves a power consumption of 2.1 nW. A duty-cycling technique powers down the XTAL amplifier without losing the oscillation and reduces the power consumption to 1.5 nW. The proposed circuit is implemented in 130 nm CMOS with an area of 0.0625 mm2 and achieves a temperature stability of 1.85 ppm/°C.


title: A multivariate time series clustering approach for crime trends prediction

text: In recent past, there is an increased interest in time series clustering research, particularly for finding useful similar trends in multivariate time series in various applied areas such as environmental research, finance, and crime. Clustering multivariate time series has potential for analyzing large volume of crime data at different time points as law enforcement agencies are interested in finding crime trends of various police administration units such as states, districts and police stations so that future occurrences of similar incidents can be overcome. Most of the traditional time series clustering algorithms deals with only univariate time series data and for clustering high dimensional data, it has to be transformed into single dimension using a dimension reduction technique. The conventional time series clustering techniques do not provide desired results for crime data set, since crime data is high dimensional and consists of various crime types with different weight age. In this paper, a novel approach based on dynamic time wrapping and parametric Minkowski model has been proposed to find similar crime trends among various crime sequences of different crime locations and subsequently use this information for future crime trends prediction. Analysis on Indian crime records show that the proposed technique generally outperforms the existing techniques in clustering of such multivariate time series data.


title: Mechanisms linking obesity to insulin resistance and type 2 diabetes

text: Obesity is associated with an increased risk of developing insulin resistance and type 2 diabetes. In obese individuals, adipose tissue releases increased amounts of non-esterified fatty acids, glycerol, hormones, pro-inflammatory cytokines and other factors that are involved in the development of insulin resistance. When insulin resistance is accompanied by dysfunction of pancreatic islet β-cells — the cells that release insulin — failure to control blood glucose levels results. Abnormalities in β-cell function are therefore critical in defining the risk and development of type 2 diabetes. This knowledge is fostering exploration of the molecular and genetic basis of the disease and new approaches to its treatment and prevention.


title: Company event popularity for financial markets using Twitter and sentiment analysis

text: The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies. © 2016 Elsevier Ltd. All rights reserved.


title: Detecting and Estimating Signals in Noisy Cable Structures, I: Neuronal Noise Sources

text: In recent theoretical approaches addressing the problem of neural coding, tools from statistical estimation and information theory have been applied to quantify the ability of neurons to transmit information through their spike outputs. These techniques, though fairly general, ignore the specific nature of neuronal processing in terms of its known biophysical properties. However, a systematic study of processing at various stages in a biophysically faithful model of a single neuron can identify the role of each stage in information transfer. Toward this end, we carry out a theoretical analysis of the information loss of a synaptic signal propagating along a linear, one-dimensional, weakly active cable due to neuronal noise sources along the way, using both a signal reconstruction and a signal detection paradigm. Here we begin such an analysis by quantitatively characterizing three sources of membrane noise: (1) thermal noise due to the passive membrane resistance, (2) noise due to stochastic openings and closings of voltage-gated membrane channels (Na+ and K+), and (3) noise due to random, background synaptic activity. Using analytical expressions for the power spectral densities of these noise sources, we compare their magnitudes in the case of a patch of membrane from a cortical pyramidal cell and explore their dependence on different biophysical parameters.


title: Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects' Information

text: Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to "shoulder-surfing" attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user's actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set's input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user's actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack.


title: The Evolution of the Peer-to-Peer File Sharing Industry and the Security Risks for Users

text: Peer-to-peer file sharing is a growing security risk for firms and individuals. Users who participate in these networks to share music, pictures, and video are subject to many security risks including inadvertent publishing of private information, exposure to viruses and worms, and the consequences of spyware. In this paper, we examine the peer-to-peer file sharing phenomena, including an overview of the industry, its business models, and evolution. We describe the information security risks users' face including personal identification disclosure and leakage of proprietary business information. We illustrate those risks through honey-pot experiments and discuss how peer-to-peer industry dynamics are contributing to the security problem.


title: Wide-band CMOS low-noise amplifier exploiting thermal noise canceling

text: Known elementary wide-band amplifiers suffer from a fundamental tradeoff between noise figure (NF) and source impedance matching, which limits the NF to values typically above 3 dB. Global negative feedback can be used to break this tradeoff, however, at the price of potential instability. In contrast, this paper presents a feedforward noise-canceling technique, which allows for simultaneous noise and impedance matching, while canceling the noise and distortion contributions of the matching device. This allows for designing wide-band impedance-matching amplifiers with NF well below 3 dB, without suffering from instability issues. An amplifier realized in 0.25-/spl mu/m standard CMOS shows NF values below 2.4 dB over more than one decade of bandwidth (i.e., 150-2000 MHz) and below 2 dB over more than two octaves (i.e., 250-1100 MHz). Furthermore, the total voltage gain is 13.7 dB, the -3-dB bandwidth is from 2 MHz to 1.6 GHz, the IIP2 is +12 dBm, and the IIP3 is 0 dBm. The LNA drains 14 mA from a 2.5-V supply and the die area is 0.3/spl times/0.25 mm/sup 2/.


title: A robust adaptive stochastic gradient method for deep learning

text: Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose an adaptive learning rate algorithm, which utilizes stochastic curvature information of the loss function for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient algorithms.1


title: Multiresolution genetic clustering algorithm for texture segmentation

text: This work plans to approach the texture segmentation problem by incorporating genetic algorithm and K-means clustering method within a multiresolution structure. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class–position uncertainty and to improve the segmentation accuracy. The procedure is described as follows. In the first step, a quad-tree structure of multiple resolutions is constructed. Sampling windows of different sizes are utilized to partition the underlying image into blocks at different resolution levels and texture features are extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. While the select and mutate operators of the traditional genetic algorithm are adopted in this work, the crossover operator is replaced with K-means clustering method. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively. q 2003 Elsevier B.V. All rights reserved.


title: Mathematical modeling of photovoltaic module with Simulink

text: This paper presents a unique step-by-step procedure for the simulation of photovoltaic modules with Matlab/ Simulink. One-diode equivalent circuit is employed in order to investigate I-V and P-V characteristics of a typical 36 W solar module. The proposed model is designed with a user-friendly icons and a dialog box like Simulink block libraries.


title: Audio Fingerprinting: Concepts And Applications

text: An audio fingerprint is a compact digest derived from perceptually relevant aspects of a recording. Fingerprinting technologies allow the monitoring of audio content without the need of meta-data or watermark embedding. However, additional uses exist for audio fingerprinting and some are reviewed in this article.


title: Advantage of CNTFET characteristics over MOSFET to reduce leakage power

text: In this paper we compare and justify the advantage of CNTFET devices over MOSFET devices in nanometer regime. Thereafter we have analyzed the effect of chiral vector, and temperature on threshold voltage of CNTFET device. After simulation on HSPICE tool we observed that the high threshold voltage can be achieved at low chiral vector pair. It is also observed that the effect of temperature on threshold voltage of CNTFET is negligibly small. After analysis of channel length variation and their impact on threshold voltage of CNTFET as well as MOSFET devices, we found an anomalous result that the threshold voltage increases with decreasing channel length in CNTFET devices, this is quite contrary to the well known short channel effect. It is observed that at below 10 nm channel length the threshold voltage is increased rapidly in case of CNTFET device whereas in case of MOSFET device the threshold voltage decreases drastically below 10 nm channel length.


title: Advanced obfuscation techniques for Java bytecode

text: There exist several obfuscation tools for preventing Java bytecode from being decompiled. Most of these tools simply scramble the names of the identifiers stored in a bytecode by substituting the identifiers with meaningless names. However, the scrambling technique cannot deter a determined cracker very long. We propose several advanced obfuscation techniques that make Java bytecode impossible to recompile or make the decompiled program difficult to understand and to recompile. The crux of our approach is to over use an identifier. That is, an identifier can denote several entities, such as types, fields, and methods, simultaneously. An additional benefit is that the size of the bytecode is reduced because fewer and shorter identifier names are used. Furthermore, we also propose several techniques to intentionally introduce syntactic and semantic errors into the decompiled program while preserving the original behaviors of the bytecode. Thus, the decompiled program would have to be debugged manually. Although our basic approach is to scramble the identifiers in Java bytecode, the scrambled bytecode produced with our techniques is much harder to crack than that produced with other identifier scrambling techniques. Furthermore, the run-time efficiency of the obfuscated bytecode is also improved because the size of the bytecode becomes smaller after obfuscation. 2002 Elsevier Inc. All rights reserved.


title: Histograms of oriented gradients for human detection

text: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.


title: Measuring Invariances in Deep Networks

text: For many pattern recognition tasks, the ideal input feature would be invariant to multiple confounding properties (such as illumination and viewing angle, in computer vision applications). Recently, deep architectures trained in an unsupervised manner have been proposed as an automatic method for extracting useful features. However, it is difficult to evaluate the learned features by any means other than using them in a classifier. In this paper, we propose a number of empirical tests that directly measure the degree to which these learned features are invariant to different input transformations. We find that stacked autoencoders learn modestly increasingly invariant features with depth when trained on natural images. We find that convolutional deep belief networks learn substantially more invariant features in each layer. These results further justify the use of “deep” vs. “shallower” representations, but suggest that mechanisms beyond merely stacking one autoencoder on top of another may be important for achieving invariance. Our evaluation metrics can also be used to evaluate future work in deep learning, and thus help the development of future algorithms.


title: 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

text: With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.


title: Decoding by linear programming

text: This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to recover f exactly from the data y? We prove that under suitable conditions on the coding matrix A, the input f is the unique solution to the /spl lscr//sub 1/-minimization problem (/spl par/x/spl par//sub /spl lscr/1/:=/spl Sigma//sub i/|x/sub i/|) min(g/spl isin/R/sup n/) /spl par/y - Ag/spl par//sub /spl lscr/1/ provided that the support of the vector of errors is not too large, /spl par/e/spl par//sub /spl lscr/0/:=|{i:e/sub i/ /spl ne/ 0}|/spl les//spl rho//spl middot/m for some /spl rho/>0. In short, f can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program). In addition, numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant fraction of the output is corrupted. This work is related to the problem of finding sparse solutions to vastly underdetermined systems of linear equations. There are also significant connections with the problem of recovering signals from highly incomplete measurements. In fact, the results introduced in this paper improve on our earlier work. Finally, underlying the success of /spl lscr//sub 1/ is a crucial property we call the uniform uncertainty principle that we shall describe in detail.


title: Credit card fraud detection using Naïve Bayes model based and KNN classifier

text: Machine Learning is the technology, in which algorithms which are capable of learning from previous cases and past experiences are designed. It is implemented using various algorithms which reiterate over the same data repeatedly to analyze the pattern of data. The techniques of data mining are no far behind and are widely used to extract data from large databases to discover some patterns making decisions. This paper presents the Naïve Bayes improved K-Nearest Neighbor method (NBKNN) for Fraud Detection of Credit Card. Experimental results illustrate that both classifiers work differently for the same dataset. The purpose is to enhance the accuracy and enhance the flexibility of the algorithm.


title: A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets

text: Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.


title: Strip-type Grid Array Antenna with a Two-Layer Rear-Space Structure

text: A strip-type grid array antenna is analyzed using the finite-difference time-domain method. The space between the grid array and the ground plane, designated as the rear space, is filled with a dielectric layer and an air layer (two-layer rear-space structure). A VSWR bandwidth of approximately 13% is obtained with a maximum directivity of approximately 18 dB. The grid array exhibits a narrow beam with low side lobes. The cross-polarization component is small (-25 dB), as desired.


title: Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran

text: The SLEUTH model, based on the Cellular Automata (CA), can be applied to city development simulation in metropolitan areas. In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran. The fundamental data were five Landsat TM and ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to simulate the spatial pattern. The first scenario assumed historical urbanization mode would persist and the only limitations for development were height and slope. The second one was a compact scenario which makes the growth mostly internal and limited the expansion of suburban areas. The last scenario proposed a polycentric urban structure which let the little patches * Corresponding author. Tel.: +98 912 3572913 E-mail address: shaghayegh.kargozar@yahoo.com


title: Confidence Measures for Neural Network Classifiers

text: Neural Networks are commonly used in classification and decision tasks. In this paper, we focus on the problem of the local confidence of their results. We review some notions from statistical decision theory that offer an insight on the determination and use of confidence measures for classification with Neural Networks. We then present an overview of the existing confidence measures and finally propose a simple measure which combines the benefits of the probabi-listic interpretation of network outputs and the estimation of the quality of the model by bootstrap error estimation. We discuss empirical results on a real-world application and an artificial problem and show that the simplest measure behaves often better than more sophisticated ones, but may be dangerous under certain situations.


title: Using parallel stiffness to achieve improved locomotive efficiency with the Sandia STEPPR robot

text: In this paper we introduce STEPPR (Sandia Transmission-Efficient Prototype Promoting Research), a bipedal robot designed to explore efficient bipedal walking. The initial iteration of this robot achieves efficient motions through powerful electromagnetic actuators and highly back-drivable synthetic rope transmissions. We show how the addition of parallel elastic elements at select joints is predicted to provide substantial energetic benefits: reducing cost of transport by 30 to 50 percent. Two joints in particular, hip roll and ankle pitch, reduce dissipated power over three very different gait types: human walking, human-like robot walking, and crouched robot walking. Joint springs based on this analysis are tested and validated experimentally. Finally, this paper concludes with the design of two unique parallel spring mechanisms to be added to the current STEPPR robot in order to provide improved locomotive efficiency.


title: The Theory Behind Mp3

text: Since the MPEG-1 Layer III encoding technology is nowadays widely used it might be interesting to gain knowledge of how this powerful compression/decompression scheme actually functions. How come the MPEG-1 Layer III is capable of reduc ing the bit rate with a factor of 12 without almost any audible degradation? Would it be fairly easy to implement this encoding algorithm? This paper will answer these questions and give further additional detailed information.


title: Chatbot for IT Security Training: Using Motivational Interviewing to Improve Security Behaviour

text: We conduct a pre-study with 25 participants on Mechanical Turk to find out which security behavioural problems are most important for online users. These questions are based on motivational interviewing (MI), an evidence-based treatment methodology that enables to train people about different kinds of behavioural changes. Based on that the chatbot is developed using Artificial Intelligence Markup Language (AIML). The chatbot is trained to speak about three topics: passwords, privacy and secure browsing. These three topics were ’most-wanted’ by the users of the pre-study. With the chatbot three training sessions with people are conducted.