title: Neither Global Nor Local: Regularized Patch-Based Representation for Single Sample Per Person Face Recognition

abstract: This paper presents a regularized patch-based representation for single sample per person face recognition. We represent each image by a collection of patches and seek their sparse representations under the gallery images patches and intra-class variance dictionaries at the same time. For the reconstruction coefficients of all the patches from the same image, by imposing a group sparsity constraint on the reconstruction coefficients corresponding to the patches from the gallery images, and by imposing a sparsity constraint on the reconstruction coefficients corresponding to the intra-class variance dictionaries, our formulation harvests the advantages of both patch-based image representation and global image representation, i.e. our method overcomes the side effect of those patches which are severely corrupted by the variances in face recognition, while enforcing those less discriminative patches to be constructed by the gallery patches from the right person. Moreover, instead of using the manually designed intra-class variance dictionaries, we propose to learn the intra-class variance dictionaries which not only greatly accelerate the prediction of the probe images but also improve the face recognition accuracy in the single sample per person scenario. Experimental results on the AR, Extended Yale B, CMU-PIE, and LFW datasets show that our method outperforms sparse coding related face recognition methods as well as some other specially designed single sample per person face representation methods, and achieves the best performance. These encouraging results demonstrate the effectiveness of regularized patch-based face representation for single sample per person face recognition.


title: Aggregated Temporal Tensor Factorization Model for Point-of-Interest Recommendation

abstract: Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which mines user check-in sequences to suggest interesting locations for users. Because user check-in behavior exhibits strong temporal patterns—for instance, users would like to check-in at restaurants at noon and visit bars at night. Hence, capturing the temporal influence is necessary to ensure the high performance in a POI recommendation system. Previous studies observe that the temporal characteristics of user mobility in LBSNs can be summarized in three aspects: periodicity, consecutiveness, and non-uniformness. However, previous work does not model the three characteristics together. More importantly, we observe that the temporal characteristics exist at different time scales, which cannot be modeled in prior work. In this paper, we propose an Aggregated Temporal Tensor Factorization (ATTF) model for POI recommendation to capture the three temporal features together, as well as at different time scales. Specifically, we employ a temporal tensor factorization method to model the check-in activity, subsuming the three temporal features together. Next, we exploit a linear combination operator to aggregate temporal latent features’ contributions at different time scales. Experiments on two real-world data sets show that the ATTF model achieves better performance than the state-of-the-art temporal models for POI recommendation.


title: Data security and privacy in E-health Cloud: Comparative study

abstract: The use of cloud computing in the medical sector has many advantages such as: reducing costs, sharing and exchanging information between medical institutions, data availability ... However, the centralization of sensitive health data in the Cloud Computing raises numerous problems concerning security and data privacy. Since the importance and sensitive nature of health data, medical institutions are reluctant to move those data's patients in the Cloud. This paper analysis the issues of security and privacy raised by the use of cloud in e-health. We will describe the architectures of the eHealth cloud, the problems of security and privacy related to these architectures and some of the different approaches proposed to address these issues.


title: IoT based solar powered Agribot for irrigation and farm monitoring: Agribot for irrigation and farm monitoring

abstract: Agriculture contributes to a major portion of India's GDP. Two major issues in modern agriculture are water scarcity and high labor costs. These issues can be resolved using agriculture task automation, which encourages precision agriculture. Considering abundance of sunlight in India, this paper discusses the design and development of an IoT based solar powered Agribot that automates irrigation task and enables remote farm monitoring. The Agribot is developed using an Arduino microcontroller. It harvests solar power when not performing irrigation. While executing the task of irrigation, it moves along a pre-determined path of a given farm, and senses soil moisture content and temperature at regular points. At each sensing point, data acquired from multiple sensors is processed locally to decide the necessity of irrigation and accordingly farm is watered. Further, Agribot acts as an IoT device and transmits the data collected from multiple sensors to a remote server using Wi-Fi link. At the remote server, raw data is processed using signal processing operations such as filtering, compression and prediction. Accordingly, the analyzed data statistics are displayed using an interactive interface, as per user request.


title: Graption: A graph-based P2P traffic classification framework for the internet backbone

abstract: Monitoring network traffic and classifying applications are essential functions for network administrators. Current traffic classification methods can be grouped in three categories: (a) flow-based (e.g., packet sizing/timing features), (b) payload-based, and (c) host-based. Methods from all three categories have limitations, especially when it comes to detecting new applications, and classifying traffic at the backbone. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) to remedy these limitations. Given a set of flows, a TDG is a graph with an edge between any two IP addresses that communicate; thus TDGs capture network-wide interactions. Using TDGs, we develop an application classification framework dubbed Graption (Graph-based classification). Our framework provides a systematic way to classify traffic by using information from the network-wide behavior and flow-level characteristics of Internet applications. As a proof of concept, we instantiate our framework to detect P2P traffic, and show that it can identify 90% of P2P flows with 95% accuracy in backbone traces, which are particularly challenging for other methods.


title: A dynamic bayesian network click model for web search ranking

abstract: As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.


title: Exploiting neighborhood knowledge for single document summarization and keyphrase extraction

abstract: Document summarization and keyphrase extraction are two related tasks in the IR and NLP fields, and both of them aim at extracting condensed representations from a single text document. Existing methods for single document summarization and keyphrase extraction usually make use of only the information contained in the specified document. This article proposes using a small number of nearest neighbor documents to improve document summarization and keyphrase extraction for the specified document, under the assumption that the neighbor documents could provide additional knowledge and more clues. The specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results on the Document Understanding Conference (DUC) benchmark datasets demonstrate the effectiveness and robustness of our proposed approaches. The cross-document sentence relationships in the expanded document set are validated to be beneficial to single document summarization, and the word cooccurrence relationships in the neighbor documents are validated to be very helpful to single document keyphrase extraction.


title: Analysis of multi-phase LLC resonant converters

abstract: In this paper a topology for multi-phase interleaved LLC resonant converter is presented. The proposed solution, based on three LLC modules with transformer primary windings star connection allows to drastically reduce the output current ripple and consequently to minimize the output filter capacitor size. Differently from other multi-phase solutions, that are greatly susceptible to resonant component mismatch and consequently can be affected by a considerable current imbalance among modules, the proposed topology exhibits an inherent balancing capability. Small-signal analysis is presented and the possibility to turn-off one or two modules (phase shedding) at reduced output current levels is discussed, highlighting the trade-off between converter efficiency and output capacitor current ripple reduction. Measurements on a prototype will be included in the paper as validation of assertions and proposals.


title: Temporal Influence Blocking: Minimizing the Effect of Misinformation in Social Networks

abstract: The diffusion of rumors is a major concern for web users. Limiting the spread of rumor on social networks has become an important task. One approach is to identify nodes to start a truth campaign such that when users are aware of the truth, they would not believe or propagate the rumor. However, existing works do not take into account the delays of information diffusion or the time point beyond which propagation of misinformation is no longer critical. In this paper, we consider a more realistic situation where information is propagated with delays and the goal is to reduce the number of rumor-infected users before a deadline. We call this the Temporal Influence Blocking (TIB) problem. We propose a two-phase solution called TIB-Solver to select k nodes to start a truth campaign such that the number of users reached by a rumor is minimized. Experiments show that the proposed TIBSolver outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.


title: Music therapy in dementia: a narrative synthesis systematic review

abstract: Objective ::: Recent reviews on music therapy for people with dementia have been limited to attempting to evaluate whether it is effective, but there is a need for a critical assessment of the literature to provide insight into the possible mechanisms of actions of music therapy. This systematic review uses a narrative synthesis format to determine evidence for effectiveness and provide insight into a model of action. ::: ::: Method ::: The narrative synthesis framework consists of four elements: (i) theory development; (ii) preliminary synthesis of findings; (iii) exploration of relationships between studies; and (iv) assessment of the robustness of the synthesis. ::: ::: Results ::: Electronic and hand searches identified 263 potentially relevant studies. Eighteen studies met the full inclusion criteria. Three distinctive strands of investigations emerged: eight studies explored behavioural and psychological aspects, five studies investigated hormonal and physiological changes, and five studies focused on social and relational aspects of music therapy. The musical interventions in the studies were diverse, but singing featured as an important medium for change. ::: ::: Conclusions ::: Evidence for short-term improvement in mood and reduction in behavioural disturbance was consistent, but there were no high-quality longitudinal studies that demonstrated long-term benefits of music therapy. Future music therapy studies need to define a theoretical model, include better-focused outcome measures, and discuss how the findings may improve the well-being of people with dementia. Copyright © 2012 John Wiley & Sons, Ltd.


title: An efficient density based clustering algorithm for large databases

abstract: Clustering in data mining is used for identifying useful patterns and interesting distributions in the underlying data. Several algorithms for clustering large data sets have been proposed in the literature using different techniques. Density-based method is one of these methodologies which can detect arbitrary shaped clusters where clusters are defined as dense regions separated by low density regions. We present a new clustering algorithm to enhance the density-based algorithm DBSCAN. Synthetic datasets are used for experimental evaluation which shows that the new clustering algorithm is faster and more scalable than the original DBSCAN.


title: Sentiment Lexicon Interpolation and Polarity Estimation of Objective and Out-Of-Vocabulary Words to Improve Sentiment Classification on Microblogging

abstract: Sentiment analysis has become an important classification task because a large amount of user-generated content is published over the Internet. Sentiment lexicons have been used successfully to classify the sentiment of user review datasets. More recently, microblogging services such as Twitter have become a popular data source in the domain of sentiment analysis. However, analyzing sentiments on tweets is still difficult because tweets are very short and contain slang, informal expressions, emoticons, mistyping and many words not found in a dictionary. In addition, more than 90 percent of the words in public sentiment lexicons, such as SentiWordNet, are objective words, which are often considered less important in a classification module. In this paper, we introduce a hybrid approach that incorporates sentiment lexicons into a machine learning approach to improve sentiment classification in tweets. We automatically construct an Add-on lexicon that compiles the polarity scores of objective words and out-ofvocabulary (OOV) words from tweet corpora. We also introduce a novel feature weighting method by interpolating sentiment lexicon score into uni-gram vectors in the Support Vector Machine (SVM). Results of our experiment show that our method is effective and significantly improves the sentiment classification accuracy compared to a baseline unigram model.


title: Performance bounds for RF positioning of endoscopy camera capsules

abstract: In this paper, we evaluate the factors affecting the accuracy achievable in localization of an endoscopic wireless capsule as it passes through the digestive system of the human body. Using a three-dimension full electromagnetic wave simulation model, we obtain bounds on the capsule-location estimation errors when the capsule is in each of three individual organs: stomach, small intestine and large intestine. The simulations assume two different external sensor arrays topologies. We compare these performance bounds and draw the conclusion that location-estimation errors are different for different organs and for various topologies of the external sensor arrays.


title: Co-designing with children: a comparison of embodied and disembodied sketching techniques in the design of child age communication devices

abstract: In this paper, we present a qualitative comparison of different sketching techniques, assessing their suitability for co-designing interaction design with children. It presents a study conducted in an experimental field research, in which children aged 6-12 were engaged in a co-design process, aimed to the creation of novel communication devices or services that fit their particular needs. The study compared embodied, physical sketching (body storming that was documented as photo stories) with disembodied, drawn sketching (comics), as for their creative results, and how the children, reportedly, felt during the creation process. The results indicate that embodied sketching techniques were more suitable for the children, both as for the quality of the results, and for the subjective experience of the children while designing.


title: Securing pervasive systems against adversarial machine learning

abstract: Applications and middleware in pervasive systems frequently rely on machine learning to provide adaptivity and customization that results in a seamless user experience despite operating in a dynamic environment. Machine learning algorithms have been shown to be vulnerable to covert, strategic attacks through the manipulation of training data. Machine learning algorithms in pervasive systems frequently train on data that could be manipulated by a malicious 3rd party. In this paper, we present our ongoing work to develop a security mechanism that is designed to work in the dynamic environments of pervasive computing as opposed to traditional security mechanisms that are designed for static environments. Furthermore, we present our modular testing framework that will be used to rapidly compare our work with other security mechanisms, applications and adversarial models.


title: Osseointegration in skeletal reconstruction and rehabilitation: A review

abstract: The loosening of implants from bone tissues has been a cause of problems in reconstructive surgery and joint replacement. The thought for decades has been that the layer of fibrous tissue that develops around the implant diminishes the integrity and mechanical stability of the implant/bone interface (1). During the 1950s it had been shown by Branemark that chambers made of the metal titanium could become permanently incorporated with bone. That is, the living bone could become so fused with the titanium oxide layer of the implant that the two could not be separated without fracture (2). Branemark introduced the term “osseointegration” to describe this modality for stable fixation of titanium to bone tissue.


title: Medical Document Classification Based on MeSH

abstract: One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. I am developing a classification algorithm that classifies a medical document by analyzing its content and categorizing it under predefined topics from the Medical Subject Headings (MeSH). I collected a corpus of 50 full-text journal articles (N=50) from MEDLINE, which were already indexed by experts based on MeSH. Using natural language processing (NLP), my algorithm classifies the collected articles under MeSH subject headings. I evaluated the algorithm's outcome by measuring its precision and recall of resulting subject headings from the algorithm, comparing results to the actual documents' subject headings. The algorithm classified the articles correctly under 45% to 60% of the actual subject headings and got 40% to 53% of the total subject headings correct. This holds promising solutions for the global health arena to index and classify medical documents expeditiously.


title: Similarity Preserving Hashing: Eligible Properties and a new Algorithm MRSH-v2

abstract: Hash functions are a widespread class of functions in computer science and used in several applications, e.g. in computer forensics to identify known files. One basic property of cryptographic Hash Functions is the avalanche effect that causes a significantly different output if an input is changed slightly. As some applications also need to identify similar files (e.g. spam/virus detection) this raised the need for Similarity Preserving Hashing. In recent years, several approaches came up, all with different namings, properties, strengths and weaknesses which is due to a missing definition.


title: Is The Uncanny Valley An Uncanny Cliff?

abstract: The uncanny valley theory proposed by Mori in 1970 has been a hot topic in human robot interaction research, in particular since the development of increasingly human-like androids and computer graphics. In this paper we describe an empirical study that attempts to plot Mori's hypothesized curve. In addition, the influence of framing on the users' perception of the stimuli was investigated. Framing had no significant influence on the measurements. The pictures of robots and humans were rated independently of whether the participants knew a particular picture showed a robot or human. Anthropomorphism had a significant influence on the measurements, but not even pictures of real humans were rated as likeable as the pictures of humanoids or toy robots. As a result we suggest the existence of an uncanny cliff model as an alternative to the uncanny valley model. However, this study focused on the perception of pictures of robots and the results, including the suggested model, may be different for the perception of movies of moving robots or the perception of standing right in front of a moving robot.


title: Effective fractal dimension: foundations and applications

abstract: Lutz characterized Hausdorff dimension using gales, gambling functions which generalize martingales. Imposing computability and complexity constraints on the gales in this characterization gives the constructive and resource-bounded dimensions. These effective dimensions are useful measures of complexity for theoretical computer science and are closely related to other measures of complexity including Kolmogorov complexity, Boolean circuit-size complexity, and predictability. ::: This thesis makes several foundational contributions to effective fractal dimension. We show that packing dimension, despite the greater complexity of its definition, can also be effectivized using gales. This is used to introduce constructive strong dimension and resource-bounded strong dimension. The theory of effective strong dimension is presented alongside that of effective dimension, emphasizing the duality between the two types of dimension. We show that gales and supergales are equivalent for defining the constructive dimensions, and that the constructive dimensions can be characterized using constructive entropy rates. The resource-bounded dimensions are characterized by log-loss unpredictability in a standard model of prediction. Characterizations of the computability and polynomial-space dimensions involving resource-bounded Kolmogorov complexity and entropy rates are given. ::: Relationships between constructive dimension and the arithmetical hierarchy are investigated. We identify the levels of the arithmetical hierarchy in which the Hausdorff and constructive dimensions of a set are guaranteed to be equal. The constructive dimension classes are precisely located in the arithmetical hierarchy. ::: We use resource-bounded dimension to investigate complexity classes involving polynomial-time reductions. Resource-bounded scaled dimension is applied to extend the small span theorem of resource-bounded measure. We show that degrees of arbitrary dimension and strong dimension exist within exponential time. The dimensions of classes that are reducible to nondense languages are investigated.


title: A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm

abstract: In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.


title: A Reduced-Order Electrochemical Model of Li-Ion Batteries for Control and Estimation Applications

abstract: In this paper, a reduced-order electrochemical model of lithium-ion batteries is developed for control and estimation applications through analytical model order reduction based on a Galerkin projection method. The governing diffusion partial differential equations in the liquid and solid phases are approximated into low-order systems of ordinary differential equations while the physical meaning of all model parameters is preserved, allowing one to perform state and parameter estimation. The selection of basis functions for the Galerkin projection method and model order truncation is carefully determined based on analysis both in the frequency and time domains. With the reduced-order diffusion models in the liquid and solid phases, an extended single particle model incorporating the electrolyte dynamics is developed. The model is then validated against the experimental data gathered from two batteries with different chemistries (lithium nickel manganese cobalt oxide/graphite and lithium iron phosphate oxide/graphite) at different input conditions. Results show that the reduced-order model agrees very well with experimental data at various conditions. Meanwhile, it can be simulated thousands of times faster than the real time, making it suitable for long-term-life simulation, control, and estimation applications.


title: Teens, Gender, and Self-Presentation in Social Media

abstract: This article is concerned with how teenage boys and girls present themselves through online social media such as Facebook, Twitter, blogs, and chat forums. Based on research conducted mostly in the United States, it describes and considers the implications of social media use, profile construction, visual and textual self-presentation, profile visibility, truthfulness, and other facets of teens' self-presentation in relation to their gender.


title: Now Playing: Continuous low-power music recognition

abstract: Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entirely on a mobile device and automatically recognizes music without user interaction. To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present. Once woken, the recognizer on the application processor is provided with a few seconds of audio which is fingerprinted and compared to the stored fingerprints in the on-device fingerprint database of tens of thousands of songs. Our presented system, Now Playing, has a daily battery usage of less than 1% on average, respects user privacy by running entirely on-device and can passively recognize a wide range of music.


title: ON THE ABILITY OF A CABLE-DRIVEN ROBOT TO GENERATE A PRESCRIBED SET OF WRENCHES

abstract: This paper presents a new geometry-based method to determine if a cable-driven robot operating in a d-degree-of-freedom workspace (2≤d≤6) with n≥d cables can generate a given set of wrenches in a given pose, considering acceptable minimum and maximum tensions in the cables. To this end, the fundamental nature of the available wrench set is studied. The latter concept, defined here, is closely related to similar sets introduced by Ebert-Uphoff and co-workers (2004, “Force-Feasible Workspace Analysis for Underconstrained, Point-Mass Cable Robots,” IEEE Trans. Rob. Autom., 5, pp. 4956–4962; 2007, “Workspace Optimization of a Very Large Cable-Driven Parallel Mechanism for a Radiotelescope Application,” Proceedings of the ASME IDETC/CIE Mechanics and Robotics Conference, Las Vegas, NV). It is shown that the available wrench set can be represented mathematically by a zonotope, a special class of convex polytopes. Using the properties of zonotopes, two methods to construct the available wrench set are discussed. From the representation of the available wrench set, computationally efficient and noniterative tests are presented to verify if this set includes the task wrench set, the set of wrenches needed for a given task.


title: Function of the pancreatic B-cells in hamsters bearing a transplantable islet cell tumor

abstract: Abstract Hyperinsulinism produced in the Syrian hamster by a transplantable islet cell tumor caused a decrease in the amount of insulin extractable from the pancreas and in the amount of insulin released by the B-cell in vivo as well as in vitro. A comparable pancreatic suppression was obtained by daily injections of Lente insulin ( 1.5 U 100 Gm. ), but not by daily injections of phlorizin ( 20 mg. 100 Gm. ). These results suggest that insulin itself plays a mamor role in the islet cells inhibition of tumor-bearing animals.


title: Generating Context-aware Recommendations using Banking Data in a Mobile Recommender System

abstract: The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data.


The Use of Perceptual Data in the Assessment of Administrative Offices. AIR 1991 Annual Forum Paper.


title: Evaluating the efficiency of physical visualizations

abstract: Data sculptures are an increasingly popular form of physical visualization whose purposes are essentially artistic, communicative or educational. But can physical visualizations help carry out actual information visualization tasks? We present the first infovis study comparing physical to on-screen visualizations. We focus on 3D visualizations, as these are common among physical visualizations but known to be problematic on computers. Taking 3D bar charts as an example, we show that moving visualizations to the physical world can improve users' efficiency at information retrieval tasks. In contrast, augmenting on-screen visualizations with stereoscopic rendering alone or with prop-based manipulation was of limited help. The efficiency of physical visualizations seems to stem from features that are unique to physical objects, such as their ability to be touched and their perfect visual realism. These findings provide empirical motivation for current research on fast digital fabrication and self-reconfiguring interfaces.


title: Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants.

abstract: Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today's voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.


title: Soft-tissue facial anthropometry in three dimensions: from anatomical landmarks to digital morphology in research, clinics and forensic anthropology

abstract: Summary – The quantitative assessment of the dimensions of human facial soft-tissue structures (eyes, nose, mouth and lips, chin, ears), their reciprocal spatial positions and relative proportions, has an interdisciplinary perspective: anatomical and anthropometric descriptions, medical evaluations (clinical genetics, orthodontics, maxillo-facial and plastic surgery), forensic medicine, they all need reference three-dimensional data collected on healthy, normal individuals selected for sex, age, ethnic group, to be compared to those obtained on the single individual. The data collection technique should be non-invasive, fast, as simple as possible, performed directly on the subjects using low-cost instruments. Data should be collected in digital format, so to allow the creation of computerized data bases, and the use of the computerized techniques of visualization and simulation of treatment. Independent of classic direct anthropometry, various three-dimensional image analyzers are increasingly being used in clinical investigations and research. The instruments can be divided into two main categories: optical, non contact digitizers, and contact instruments. The first kind of instruments (mainly, laser scanners and stereophotogrammetric devices) perform a fast digitization of the face, providing a detailed analysis of the soft-tissue surface. Contact instruments (electromagnetic and electromechanic digitizers) use a landmark representation of the soft-tissue facial surface. Landmark coordinates are coupled to a mathematical and geometric model of the face, and angles, distances and ratios similar to those measured in conventional anthropometry can be obtained. Additionally, multivariate methods of analysis, obtained either from geometric morphometry or from other analytical methods, could be used. Optical instruments provide a larger amount of information but they cannot assess all the actual anatomical landmarks obtained by contact instruments. Motion artifacts are more common with contact instruments, but they can be easily transported, and they are less expensive. Overall, contact instruments seem sufficiently reliable, simple and fast to be used also in a clinical context, thus providing useful quantitative information to allow a better patient care, without submitting the subjects to potentially harmful procedures.


title: Evaluating the privacy of Android mobile applications under forensic analysis

abstract: Abstract In this paper, we investigate and evaluate through experimental analysis the possibility of recovering authentication credentials of mobile applications from the volatile memory of Android mobile devices. Throughout the carried experiments and analysis, we have, exclusively, used open-source and free forensic tools. Overall, the contribution of this paper is threefold. First, it thoroughly, examines thirteen (13) mobile applications, which represent four common application categories that elaborate sensitive users' data, whether it is possible to recover authentication credentials from the physical memory of mobile devices, following thirty (30) different scenarios. Second, it explores in the considered applications, if we can discover patterns and expressions that indicate the exact position of authentication credentials in a memory dump. Third, it reveals a set of critical observations regarding the privacy of Android mobile applications and devices.


title: Pleasures of the brain

abstract: Abstract How does the brain cause positive affective reactions to sensory pleasure? An answer to pleasure causation requires knowing not only which brain systems are activated by pleasant stimuli, but also which systems actually cause their positive affective properties. This paper focuses on brain causation of behavioral positive affective reactions to pleasant sensations, such as sweet tastes. Its goal is to understand how brain systems generate ‘liking,’ the core process that underlies sensory pleasure and causes positive affective reactions. Evidence suggests activity in a subcortical network involving portions of the nucleus accumbens shell, ventral pallidum, and brainstem causes ‘liking’ and positive affective reactions to sweet tastes. Lesions of ventral pallidum also impair normal sensory pleasure. Recent findings regarding this subcortical network’s causation of core ‘liking’ reactions help clarify how the essence of a pleasure gloss gets added to mere sensation. The same subcortical ‘liking’ network, via connection to brain systems involved in explicit cognitive representations, may also in turn cause conscious experiences of sensory pleasure.


title: Integrating paper and digital information on EnhancedDesk: a method for realtime finger tracking on an augmented desk system

abstract: This article describes a design and implementation of an augmented desk system, named EnhancedDesk, which smoothly integrates paper and digital information on a desk. The system provides users an intelligent environment that automatically retrieves and displays digital information corresponding to the real objects (e.g., books) on the desk by using computer vision. The system also provides users direct manipulation of digital information by using the users’ own hands and fingers for more natural and more intuitive interaction. Based on the experiments with our first prototype system, some critical issues on augmented desk systems were identified when trying to pursue rapid and fine recognition of hands and fingers. To overcome these issues, we developed a novel method for realtime finger tracking on an augmented desk system by introducing a infrared camera, pattern matching with normalized correlation, and a pan-tilt camera. We then show an interface prototype on EnhancedDesk. It is an application to a computer-supported learning environment, named Interactive Textbook. The system shows how effective the integration of paper and digital information is and how natural and intuitive direct manipulation of digital information with users’ hands and fingers is.


title: A data mining approach to the prediction of corporate failure

abstract: This paper uses a data mining approach to the prediction of corporate failure. Initially, we use four single classifiers — discriminant analysis, logistic regression, neural networks and C5.0 — each based on two feature selection methods for predicting corporate failure. Of the two feature selection methods — human judgement based on financial theory and ANOVA statistical method — we found the ANOVA method performs better than the human judgement method in all classifiers except discriminant analysis. Among the individual classifiers, decision trees and neural networks were found to provide better results. Finally, a hybrid method that combines the best features of several classification models is developed to increase the prediction performance. The empirical tests show that such a hybrid method produces higher prediction accuracy than individual classifiers.


title: Automatic Model Generation for Virtual Commissioning based on Plant Engineering Data

abstract: Abstract The complexity of modern process plants is steadily increasing – and thus the demands placed on automation systems. Today, the validation and testing of an automation system is a demanding task, usually executed under very tight schedules. Therefore, the use of simulations to check-out the automation system earlier in the engineering phase can be very beneficial. This paper investigates the possibility of automatically generating simulation models based on plant engineering data, usable for virtual commissioning. The proposed mechanism is usable without any specific additional application and can be realized with the plant engineering and simulation tool alone. The concept and workflow will be described in detail, and the paper concludes with a case study based on a prototypical implementation. Based on the proposed concept, the reuse of engineering data to generate simulation models, and thus the broader use of simulation throughout the lifecycle, becomes easier and therefore more feasible.


title: Understanding Pharmaceutical Quality by Design

abstract: This review further clarifies the concept of pharmaceutical quality by design (QbD) and describes its objectives. QbD elements include the following: (1) a quality target product profile (QTPP) that identifies the critical quality attributes (CQAs) of the drug product; (2) product design and understanding including identification of critical material attributes (CMAs); (3) process design and understanding including identification of critical process parameters (CPPs), linking CMAs and CPPs to CQAs; (4) a control strategy that includes specifications for the drug substance(s), excipient(s), and drug product as well as controls for each step of the manufacturing process; and (5) process capability and continual improvement. QbD tools and studies include prior knowledge, risk assessment, mechanistic models, design of experiments (DoE) and data analysis, and process analytical technology (PAT). As the pharmaceutical industry moves toward the implementation of pharmaceutical QbD, a common terminology, understanding of concepts and expectations are necessary. This understanding will facilitate better communication between those involved in risk-based drug development and drug application review.


title: Challenges in risk assessment of multiple mycotoxins in food

abstract: Most fungi are able to produce several mycotoxins simultaneously and, consequently, to contaminate a wide variety of foodstuffs. Therefore, the risk of human co-exposure to multiple mycotoxins is real, raising a growing concern about their potential impact on human health. Besides, government and industry regulations are usually based on individual toxicities, and do not take into account the complex dynamics associated with interactions between co-occurring groups of mycotoxins. The present work assembles, for the first time, the challenges posed by the likelihood of human co-exposure to these toxins and the possibility of interactive effects occurring after absorption, towards knowledge generation to support a more accurate human risk assessment. Regarding hazard assessment, a physiologically-based framework is proposed in order to infer the health effects from exposure to multiple mycotoxins in food, including knowledge on the bioaccessibility, toxicokinetics and toxicodynamics of single and combined t...


title: Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images

abstract: Like computer vision before, remote sensing has been radically changed by the introduction of deep learning and, more notably, Convolution Neural Networks. Land cover classification, object detection and scene understanding in aerial images rely more and more on deep networks to achieve new state-of-the-art results. Recent architectures such as Fully Convolutional Networks can even produce pixel level annotations for semantic mapping. In this work, we present a deep-learning based segment-before-detect method for segmentation and subsequent detection and classification of several varieties of wheeled vehicles in high resolution remote sensing images. This allows us to investigate object detection and classification on a complex dataset made up of visually similar classes, and to demonstrate the relevance of such a subclass modeling approach. Especially, we want to show that deep learning is also suitable for object-oriented analysis of Earth Observation data as effective object detection can be obtained as a byproduct of accurate semantic segmentation. First, we train a deep fully convolutional network on the ISPRS Potsdam and the NZAM/ONERA Christchurch datasets and show how the learnt semantic maps can be used to extract precise segmentation of vehicles. Then, we show that those maps are accurate enough to perform vehicle detection by simple connected component extraction. This allows us to study the repartition of vehicles in the city. Finally, we train a Convolutional Neural Network to perform vehicle classification on the VEDAI dataset, and transfer its knowledge to classify the individual vehicle instances that we detected.


title: The Possible Role of Flavonoids in the Prevention of Diabetic Complications

abstract: Type 2 diabetes mellitus is a disease that affects many metabolic pathways. It is associated with insulin resistance, impaired insulin signaling, β-cell dysfunction, abnormal glucose levels, altered lipid metabolism, sub-clinical inflammation and increased oxidative stress. These and other unknown mechanisms lead to micro- and macro-complications, such as neuropathy, retinopathy, nephropathy and cardiovascular disease. Based on several in vitro animal models and some human studies, flavonoids appear to play a role in many of the metabolic processes involved in type 2 diabetes mellitus. In this review, we seek to highlight the most recent papers focusing on the relationship between flavonoids and main diabetic complications.


THU_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning


title: CoupledLP: Link Prediction in Coupled Networks

abstract: We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task? We propose a unified framework, CoupledLP, to solve the problem. Given two coupled networks, we first leverage atomic propagation rules to automatically construct implicit links in the target network for addressing the challenge of target network incompleteness, and then propose a coupled factor graph model to incorporate the meta-paths extracted from the coupled part of the two networks for transferring heterogeneous knowledge. We evaluate the proposed framework on two different genres of datasets: disease-gene (DG) and mobile social networks. In the DG networks, we aim to use the disease network to predict the associations between genes. In the mobile networks, we aim to use the mobile communication network of one mobile operator to infer the network structure of its competitors. On both datasets, the proposed CoupledLP framework outperforms several alternative methods. The proposed problem of coupled link prediction and the corresponding framework demonstrate both the scientific and business applications in biology and social networks.


title: Multipurpose autonomous agricultural robot

abstract: More than 40 percent of the population in the world chooses agriculture as the primary occupation. In recent years, increased interest has grown for the development of the autonomous vehicles like robots in the agriculture. The existing agricultural robot performs basic elementary functions like harvesting, planting and spreading the pesticides. The Proposed system aims at designing multipurpose autonomous agricultural robotic vehicle which can be controlled through Bluetooth for ploughing, seeding and irrigation systems. This is especially important for the workers in the area of potentially harmful for the safety and health of the workers. These robots are used to reduce human intervention, ensuring proper irrigation and efficient utilization of resources. These robots are mainly useful in automated weed control; usage of fertilizers based on soil condition, soil sensors for drip irrigation in rain feed areas.


title: Cell Tracking via Proposal Generation and Selection

abstract: Microscopy imaging plays a vital role in understanding many biological processes in development and disease. The recent advances in automation of microscopes and development of methods and markers for live cell imaging has led to rapid growth in the amount of image data being captured. To efficiently and reliably extract useful insights from these captured sequences, automated cell tracking is essential. This is a challenging problem due to large variation in the appearance and shapes of cells depending on many factors including imaging methodology, biological characteristics of cells, cell matrix composition, labeling methodology, etc. Often cell tracking methods require a sequence-specific segmentation method and manual tuning of many tracking parameters, which limits their applicability to sequences other than those they are designed for. In this paper, we propose 1) a deep learning based cell proposal method, which proposes candidates for cells along with their scores, and 2) a cell tracking method, which links proposals in adjacent frames in a graphical model using edges representing different cellular events and poses joint cell detection and tracking as the selection of a subset of cell and edge proposals. Our method is completely automated and given enough training data can be applied to a wide variety of microscopy sequences. We evaluate our method on multiple fluorescence and phase contrast microscopy sequences containing cells of various shapes and appearances from ISBI cell tracking challenge, and show that our method outperforms existing cell tracking methods. ::: Code is available at: this https URL


title: Determining the Influence of Reddit Posts on Wikipedia Pageviews

abstract: The activity of passive content consumers on social media sites is typically difficult to measure. This paper explores the activity of a subset of such consumers by looking at the influence on Wikipedia pageviews of one large Reddit community which frequently links to Wikipedia articles. The subreddit used in this analysis, /r/todayilearned (TIL), features a large number of posts on an eclectic set of topics, but excludes current events, which helps rule out the primary threat to being able to make causal statements. Wikipedia's public hourly pageview data provides a unique opportunity to study the influence of a Reddit post on a Wikipedia page at different time horizons. We here present analyses using posts from 2012 in TIL, showing that the week in which a post references a specific Wikipedia article is associated with a substantial increase in pageviews relative to prior and successive weeks. We then apply functional PCA to the dataset in order to characterize pageview dynamics. We also provide a qualitative analysis of the subset of Wikipedia topics posted to Reddit.


title: Review Quantitative RT-PCR: Pitfalls and Potential

abstract: Reverse transcription PCR (RT-PCR) represents a sensitive and powerful tool for analyzing RNA. While it has tremendous potential for quantitative applications, a comprehensive knowledge of its tech...


title: Quantum computers

abstract: Using atoms as digital bits will start a completely new era in computer design. Atoms cannot be simply manipulated and used like the bits built with transistors. The behavior of matter on the atomic scale follows the rules of modern physics. This behavior cannot be understood in terms of our classical description of the world (i. e. Newtonian mechanics or Maxwell's equations in electromagnetics). The physical theory dealing with such behavior is called quantum mechanics. Its use in the computer industry will most probably cause a revolution in the way we use and understand computers. The author describes how such a quantum computer-a computer based on the rules of quantum mechanics-may work, and how it is going to give incredible speed and problem-solving power.


MIMO Space-Time Block Coding ( STBC ) : Simulations and Results


title: ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories

abstract: Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.


title: Reduce the cogging torque of Axial Flux Permanent Magnet synchronous motor for light electric vehicle applications

abstract: This paper proposes a method to reduce cogging torque of a single side Axial Flux Permanent Magnet (AFPM) motor according to analysis results of finite element analysis (FEA) method. First, the main cause of generated cogging torque will be studied using three dimensional FEA method. In order to reduce the cogging torque, a dual layer magnet step skewed (DLMSS) method is proposed to determine the shape of dual layer magnets. The skewed angle of magnetic poles between these two layers is determined using equal air gap flux of inner and outer layers. Finally, a single-sided AFPM motor based on the proposed methods is built as experimental platform to verify the effectiveness of the design. Meanwhile, the differences between design and tested results will be analyzed for future research and improvement.


title: Comparison by Computed Tomographic Angiography—The Presence and Extent of Coronary Arterial Atherosclerosis in South Asians Versus Caucasians With Diabetes Mellitus

abstract: South Asians in the Western world have a high prevalence of diabetes mellitus (DM) and an increased risk of coronary artery disease (CAD) and mortality compared with Caucasians. CAD in asymptomatic South Asian patients with type 2 DM has not been investigated. The aim of this observational cohort study was to investigate CAD in asymptomatic South Asian patients with type 2 DM and to compare with matched Caucasian patients. A total of 120 asymptomatic South Asian patients with type 2 DM and matched Caucasian patients (mean age 52 years, 55% men) were derived from an ongoing registry of cardiovascular risk stratification in asymptomatic patients with DM. Cardiovascular risk assessment included multidetector row coronary computed tomography angiography. CAD was assessed as the coronary artery calcium score and classified into no signs of atherosclerosis or minor wall irregularities


title: Education Still Needs Artificial Intelligence to Support Personalized Motor Skill Learning: Aikido as a Case Study

abstract: Motor skill learning is hardly considered in current AIED literature. However, there are many learning tasks that require consolidating motor tasks into memory through repetition towards accurate movements, such as learning to write, to draw, to play a musical instrument, to practice a sport technique, to dance, to use sign language or to train for surgery. The field of Artificial Intelli- gence (AI) needs new sap to cope with the challenges in the Educational (ED) domain aimed to support psychomotor learning. This new sap can be provided by novel interactive technologies around the Internet of the Things that deal with Quantified-self wearable devices, 3D modelling, Big Data processing, etc. The paper aims to identify opportunities and challenges for AI + ED that can be discussed during the workshop. Some of the issues raised are illustrated within a case study instantiated in the Aikido practice, a defensive martial art that in- volves learning skilled movements by training both the body and the mind, and which is not only part of extra-curricular activity in many schools, but has also been reported of value for teaching in STEM (Science, Technology, Engineer- ing and Mathematics) education, in particular, some laws of mechanics.


title: Sixth sense technology: Comparisons and future predictions

abstract: This paper discusses a technology that is expected to widely spread in the near future, which is the Sixth Sense Technology. With this new technology yet to be introduced in the market, different implementation approaches are discussed. Such approaches are demonstrated through other inventions similar to the Sixth Sense (SS). They all fall into the same category, which is Augmented Reality Technologies. The paper focuses on the possible applications and opportunities of such technology. Furthermore, the implementation approaches are compared and the pros/cons of each approach are explored. Most importantly, technical challenges and open issues regarding each implementation approach are brought out. Furthermore, predictions are made on which approach is expected to succeed in the coming years. Finally, solutions are discussed to improve the Sixth Sense Technology with regards to its implementation approaches to insure new and better ways of human-computer interaction.


title: On Learning and Learned Representation with Dynamic Routing in Capsule Networks

abstract: Capsule Networks (CapsNet) are recently proposed multi-stage computational models specialized for entity representation and discovery in image data. CapsNet employs iterative routing that shapes how the information cascades through different levels of interpretations. In this work, we investigate i) how the routing affects the CapsNet model fitting, ii) how the representation by capsules helps discover global structures in data distribution and iii) how learned data representation adapts and generalizes to new tasks. Our investigation shows: i) routing operation determines the certainty with which one layer of capsules pass information to the layer above, and the appropriate level of certainty is related to the model fitness, ii) in a designed experiment using data with a known 2D structure, capsule representations allow more meaningful 2D manifold embedding than neurons in a standard CNN do and iii) compared to neurons of standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.


title: Multiple Kernel Learning for Hyperspectral Image Classification: A Review

abstract: With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective approaches to fully use the spatial–spectral information provided by the data cube. Multiple kernel learning (MKL) has been successfully applied to HSI classification due to its capacity to handle heterogeneous fusion of both spectral and spatial features. This approach can generate an adaptive kernel as an optimally weighted sum of a few fixed kernels to model a nonlinear data structure. In this way, the difficulty of kernel selection and the limitation of a fixed kernel can be alleviated. Various MKL algorithms have been developed in recent years, such as the general MKL, the subspace MKL, the nonlinear MKL, the sparse MKL, and the ensemble MKL. The goal of this paper is to provide a systematic review of MKL methods, which have been applied to HSI classification. We also analyze and evaluate different MKL algorithms and their respective characteristics in different cases of HSI classification cases. Finally, we discuss the future direction and trends of research in this area.


title: A bandwidth-efficient architecture for media processing

abstract: Media applications are characterized by large amounts of available parallelism, little data reuse, and a high computation to memory access ratio. While these characteristics are poorly matched to conventional microprocessor architectures, they are a good fit for modern VLSI technology with its high arithmetic capacity but limited global bandwidth. The stream programming model, in which an application is coded as streams of data records passing through computation kernels, exposes both parallelism and locality in media applications that can be exploited by VLSI architectures. The Imagine architecture supports the stream programming model by providing a bandwidth hierarchy tailored to the demands of media applications. Compared to a conventional scalar processor. Imagine reduces the global register and memory bandwidth required by typical applications by factors of 13 and 21 respectively. This bandwidth efficiency enables a single chip Imagine processor to achieve a peak performance of 16.2GFLOPS (single-precision floating point) and sustained performance of up to 8.5GFLOPS on media processing kernels.


title: A Single-Event-Hardened Phase-Locked Loop Fabricated in 130 nm CMOS

abstract: A radiation-hardened-by-design phase-locked loop (PLL)-designed and fabricated in 130 nm CMOS-is shown to mitigate single-event transients (SETs). Two-photon-absorption (TPA) laser tests were used to characterize the error signatures of the PLL and to perform single-event upset (SEU) mapping of the PLL sub-components. Results show that a custom, voltage-based charge pump reduces the error response of the PLL over conventional designs by more than two orders of magnitude as measured by the number of erroneous PLL clock pulses following a single-event. Additionally, SEU mapping indicates a 99% reduction in the vulnerable area of the radiation-hardened-by-design (RHBD) charge pump over a conventional design. Furthermore, the TPA experiments highlight the importance of the voltage-controlled oscillator in the overall SET response of the PLL implementing the RHBD charge pump.


Neural Sequence Chunkers


title: Deep Stereo: Learning to Predict New Views from the World's Imagery

abstract: Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [22, 33], but their use in graphics problems has been limited ([23, 7] are notable recent exceptions). In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches, which consist of multiple complex stages of processing, each of which requires careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network, which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system, which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. We show view interpolation results on imagery from the KITTI dataset [12], from data from [1] as well as on Google Street View images. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.


title: Non-Bayesian Additive Regularization for Multimodal Topic Modeling of Large Collections

abstract: Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis based on the preferential use of graphical models and Bayesian learning. Additive regularization for topic modeling (ARTM) is a recent semiprobabilistic approach, which provides a simpler inference for many models previously studied only in the Bayesian settings. ARTM reduces barriers to entry into topic modeling research field and facilitates combination of topic models. In this paper we develop the multimodal extension of ARTM approach and implement it in BigARTM open source project for online parallelized topic modeling. We demonstrate the ability of non-Bayesian regularization to combine modalities, languages and multiple criteria to find sparse, diverse, and interpretable topics.


title: Comparative Analysis of Traditional and Object-Oriented Software Testing

abstract: Software testing is an important phase of the software development life cycle. Testing is the process of executing the program with the goal of finding errors. The purpose of testing is not only to find the bugs in software but also to improve the quality of software. This paper compares traditional and object-oriented software testing. Today, object-oriented concepts are used throughout software engineering. Testing of object-oriented software is different from testing of traditional software. Object-oriented software testing offers new challenges to the software testing team, because the object-oriented approach supports several new and powerful features. This paper focuses on the need for adopting an object-oriented software-testing approach and the challenges that occur in object-oriented testing.


title: Effect of low-energy laser irradiation on colony formation capability in different human tumor cells in vitro.

abstract: Fibroblasts and lymphocytes are the most widely used cells for studying the so-called biostimulative effect of low-power laser in vitro. In contrast, stimulation of cancer cells by laser light has not been investigated extensively. The present study attempted to evaluate whether or not human tumor cells could exhibit an increase in colony-forming capability following low-watt laser irradiation. LoVo and HT29 (colon carcinoma), MCF7 (breast carcinoma), M14 and JR1 (malignant melanoma) cell lines were irradiated at different doses of light delivered from an argon or an argon-dye laser. Radiant exposures between 4.2 and 150 kJ/m2 at irradiances ranging from 35 to 500 W/m2 were delivered. Results were mixed. Of the 41 experiments performed, five showed a significant statistical increase in the number of colonies (P less than 0.05), whereas three showed a decrease (P less than 0.05). Nevertheless, the trend of most data was toward an increase in colony formation, and Wilcoxon's signed-ranks test suggested that light increases tumor cell culture growth (P less than 0.03).


THE ARENA GAMES IN THE ROMAN EMPIRE: A CONTRIBUTION TO THE EXPLANATION OF THE HISTORY OF MORALS AND HUMANITY


title: Handwritten Chinese character recognition with spatial transformer and deep residual networks

abstract: This paper considers using deep neural networks for handwritten Chinese character recognition (HCCR) with arbitrary position, scale, and orientations. To solve this problem, we combine the recently proposed spatial transformer network (STN) with the deep residual network (DRN). The STN acts like a character shape normalization procedure. Different from the traditional heuristic shape normalization methods, STN is learned directly from the data. Furthermore, the DRN makes the training of very deep network to be both efficient and effective. With the combination of STN and DRN, the whole model can be trained jointly in an end-to-end manner. In this paper, new state-of-the-art performance has been achieved by our proposed model on the offline ICDAR-2013 Chinese handwriting competition database. Moreover, the experiment on randomly distorted samples shows that the STN is very effective for robust HCCR in rectifying the shape of distorted characters.


Moving Object Detection, Tracking and Classification for Smart Video Surveillance


title: Color by correlation: A simple, unifying framework for color constancy

abstract: The paper considers the problem of illuminant estimation: how, given an image of a scene, recorded under an unknown light, we can recover an estimate of that light. Obtaining such an estimate is a central part of solving the color constancy problem. Thus, the work presented will have applications in fields such as color-based object recognition and digital photography. Rather than attempting to recover a single estimate of the illuminant, we instead set out to recover a measure of the likelihood that each of a set of possible illuminants was the scene illuminant. We begin by determining which image colors can occur (and how these colors are distributed) under each of a set of possible lights. We discuss how, for a given camera, we can obtain this knowledge. We then correlate this information with the colors in a particular image to obtain a measure of the likelihood that each of the possible lights was the scene illuminant. Finally, we use this likelihood information to choose a single light as an estimate of the scene illuminant. Computation is expressed and performed in a generic correlation framework which we develop. We propose a new probabilistic instantiation of this correlation framework and show that it delivers very good color constancy on both synthetic and real images. We further show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it: the gray-world and gamut-mapping algorithms are presented in this framework and we also explore the relationship of these algorithms to other probabilistic and neural network approaches to color constancy.


title: Globally Optimal DLS Method for PnP Problem with Cayley parameterization.

abstract: The perspective-n-point (PnP) problem, which estimates 3D rotation and translation of a calibrated camera from n pairs of known 3D points and corresponding 2D image points, is a classical problem but still fundamental in the computer vision community. It is well studied that the PnP problem can be solved by at least three points [1]. If n ≥ 4, the PnP problem becomes a nonlinear problem where the number of the solutions depend on n and the shape of the scene. This paper proposes an efficient, scalable, and globally optimal DLS method parameterized by Cayley representation, which has been regarded as a unsuitable parametrization due to its singularity. First we derive a new optimality condition without Lagrange multipliers. Letting pi = [xi,yi,zi] be an i-th 3D point and mi = [ui,vi,1] be the corresponding calibrated image point in homogeneous coordinates, the PnP problem can be formulated as a nonlinear optimization


title: Encoding of emotional memories depends on amygdala and hippocampus and their interactions

abstract: We have studied patients with variable degrees of left hippocampal and amygdala pathology who performed a verbal encoding task during functional magnetic resonance imaging (fMRI) to assess the impact of pathology on emotional-memory performance and encoding-evoked activity. The severity of left hippocampal pathology predicted memory performance for neutral and emotional items alike, whereas the severity of amygdala pathology predicted memory performance for emotional items alone. Encoding-related hippocampal activity for successfully remembered emotional items correlated with the degree of left amygdala pathology. Conversely, amygdala-evoked activity with respect to subsequently remembered emotional items correlated with the degree of left hippocampal pathology. Our data indicate a reciprocal dependence between amygdala and hippocampus during the encoding of emotional memories.


title: Domain Adaptation via Pseudo In-Domain Data Selection

abstract: We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large general-domain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are not themselves identical to the in-domain data, we call them pseudo in-domain subcorpora. These subcorpora -- 1% the size of the original -- can then used to train small domain-adapted Statistical Machine Translation (SMT) systems which outperform systems trained on the entire corpus. Performance is further improved when we use these domain-adapted models in combination with a true in-domain model. The results show that more training data is not always better, and that best results are attained via proper domain-relevant data selection, as well as combining in- and general-domain systems during decoding.


title: Muscle motor point identification is essential for optimizing neuromuscular electrical stimulation use

abstract: Transcutaneous neuromuscular electrical stimulation applied in clinical settings is currently characterized by a wide heterogeneity of stimulation protocols and modalities. Practitioners usually refer to anatomic charts (often provided with the user manuals of commercially available stimulators) for electrode positioning, which may lead to inconsistent outcomes, poor tolerance by the patients, and adverse reactions. Recent evidence has highlighted the crucial importance of stimulating over the muscle motor points to improve the effectiveness of neuromuscular electrical stimulation. Nevertheless, the correct electrophysiological definition of muscle motor point and its practical significance are not always fully comprehended by therapists and researchers in the field. The commentary describes a straightforward and quick electrophysiological procedure for muscle motor point identification. It consists in muscle surface mapping by using a stimulation pen-electrode and it is aimed at identifying the skin area above the muscle where the motor threshold is the lowest for a given electrical input, that is the skin area most responsive to electrical stimulation. After the motor point mapping procedure, a proper placement of the stimulation electrode(s) allows neuromuscular electrical stimulation to maximize the evoked tension, while minimizing the dose of the injected current and the level of discomfort. If routinely applied, we expect this procedure to improve both stimulation effectiveness and patient adherence to the treatment.The aims of this clinical commentary are to present an optimized procedure for the application of neuromuscular electrical stimulation and to highlight the clinical implications related to its use.


title: A low-power dual-mode 20-Gb/s NRZ and 28-Gb/s PAM-4 voltage-mode transmitter

abstract: A dual-mode NRZ/PAM-4 differential low-swing voltage-mode transmitter employs a quarter-rate output multiplexing architecture for low-power operation. In NRZ mode, 2-tap feedforward equalization is realized with analog replica-bias tap control that is configurable in high-performance controlled-impedance or energy-efficient impedance-modulated settings. This analog control also allows for efficient generation of the middle levels in PAM-4 operation. Fabricated in GP 65nm CMOS, the transmitter supports an output swing range of 100–400mV P pd with up to 12dB of equalization in NRZ mode and achieves energy efficiencies of 1.48 and 0.91pJ/b at 20-Gb/s NRZ and 28-Gb/s PAM-4 data rates. Operation in the NRZ energy-efficient impedance-modulation setting allows for power savings of up to 32% relative to the controlled-impedance setting.


title: Software development methods and usability: Perspectives from a survey in the software industry in Norway

abstract: This paper investigates the relationship between software development methodologies and usability. The point of departure is the assumption that two important disciplines in software development, one of software development methods (SDMs) and one of usability work, are not integrated in industrial software projects. Building on previous research we investigate two questions; (1) Will software companies generally acknowledge the importance of usability, but not prioritise it in industrial projects? and (2) To what degree are software development methods and usability perceived by practitioners as being integrated? To this end a survey in the Norwegian IT industry was conducted. From a sample of 259 companies we received responses from 78 companies. In response to our first research question, our findings show that although there is a positive bias towards usability, the importance of usability testing is perceived to be much less than that of usability requirements. Given the strong time and cost pressures associated with the software industry, we believe that these results highlight that there is a gap between intention and reality. Regarding our second research question our survey revealed that companies perceive usability and software development methods to be integrated. This is in contrast to earlier research, which, somewhat pessimistically, has argued for the existence of two different cultures, one of software development and one of usability. The findings give hope for the future, in particular because the general use of system development methods are pragmatic and adaptable.


title: Incivility, retention and new graduate nurses: an integrated review of the literature

abstract: AIM ::: To evaluate the influence of incivility on the new graduate nurse transition experience. ::: ::: ::: BACKGROUND ::: Incivility in the work environment is a major source of dissatisfaction and new graduate nurses are especially vulnerable. Incivility contributes to the high levels of turnover associated within the first 2 years of new graduate nurse employment. ::: ::: ::: EVALUATION ::: An integrated review of the literature was conducted using MEDLINE-EBSCOhost, PsycInfo and CINAHL databases. Relevant articles were reviewed for appropriateness related to inclusion/exclusion criteria and for quality using established criteria. Sixteen studies were included in the final analysis. ::: ::: ::: KEY ISSUES ::: Themes that emerged included workplace incivility, nurse residency programmes, mentoring through preceptors and empowerment/work environment. Findings indicated that incivility in the workplace was a significant predictor of low job satisfaction in new graduate nurses transitioning into practice. ::: ::: ::: CONCLUSIONS ::: While graduate nurse transition programmes are associated with improved satisfaction and retention, they appear to address incivility by acculturating new graduate nurses to the experience of incivility. There is little evidence that the culture of incivility has been addressed. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Nurse managers have the responsibility to be aware of the prevalence of incivility, assess for its occurrence, and implement strategies which eliminate workplace incivility and tolerance for uncivil behaviours.


title: Significance of nonaromatic organic acids in honey.

abstract: Although organic acids represent < 0.5% of honey's constituents, they make important contributions to the organoleptic, physical, and chemical properties of honey. To date, approximately 30 nonaromatic organic acids have been identified in honey, but relatively little attention has been paid to these components. This article reviews the current literature related to the significance of nonaromatic organic acids in honey; it was written with a goal of attracting researchers to study these interesting honey components. Previous research contributions on nonaromatic organic acids in honey may be classified into five main areas: (i) the antibacterial activities of these acids, (ii) the antioxidant activities of these acids, (iii) the use of these acids as possible indicators of incipient fermentation, (iv) the use of these acids for treatment of Varroa infestation, and (v) the use of these acids as factors for the characterization of both botanical and geographical origins of honeys. We conclude that nonaromatic organic acids are of interest for diverse reasons and that there is a particular need for studies regarding their possible antibacterial and antioxidant activities.


title: An experimental study comparing linguistic phylogenetic reconstruction methods

abstract: This paper reports a simulation study comparing and evaluating the performance of different linguistic phylogeny reconstruction methods on model datasets for which the true trees are known. UPGMA performed least well, then (in ascending order) neighbor joining, the method of Gray & Atkinson and finally maximum parsimony. Weighting characters greatly improves the accuracy of maximum parsimony and maximum compatibility if the characters with high weights exhibit low homoplasy.


title: Mind's eye: A recurrent visual representation for image caption generation

abstract: In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.


title: Spatial-Depth Super Resolution for Range Images

abstract: We present a new post-processing step to enhance the resolution of range images. Using one or two registered and potentially high-resolution color images as reference, we iteratively refine the input low-resolution range image, in terms of both its spatial resolution and depth precision. Evaluation using the Middlebury benchmark shows across-the-board improvement for sub-pixel accuracy. We also demonstrated its effectiveness for spatial resolution enhancement up to 100 times with a single reference image.


Managing Online Trolling: From Deviant to Social and Political Trolls


title: PS language definition

abstract: The last few years has brought a number of changes to the digital system designer's parts inventory - mainly the addition of large scale integration components such as memories and processors. The solution of an information processing problem involves making the right choices from the variety of components offered and designing the hardware/software interface mechanisms between the components. Methods of simulating this new generation of digital systems are needed as tools. The demands placed on such a tool are great: it must (1) facilitate modeling of a digital system at various levels of architectural detail; (2) allow within a single model two components at quite different specification levels; (3) as a design progresses, allow replacement of models by more detailed models; (4) facilitate modeling of all types of components - hardware, firmware, and software; and (5) have a readily changeable parts library. PS is a tool for simulation of digital systems which meets these demands. In addition PS is designed to case the problem of communicating hardware/software mechanisms between people by automatically producing pictures of a system in its various states. These pictures can be used as a means of describing the system. The models produced by PS are called picture-system models [1, 2, 3].


title: An Iterative Approach to Text Segmentation

abstract: We present divSeg, a novel method for text segmentation that iteratively splits a portion of text at its weakest point in terms of the connectivity strength between two adjacent parts. To search for the weakest point, we apply two different measures: one is based on language modeling of text segmentation and the other, on the interconnectivity between two segments. Our solution produces a deep and narrow binary tree - a dynamic object that describes the structure of a text and that is fully adaptable to a user's segmentation needs. We treat it as a separate task to flatten the tree into a broad and shallow hierarchy either through supervised learning of a document set or explicit input of how a text should be segmented. The rich structure of our created tree further allows us to segment documents at varying levels such as topic, sub-topic, etc. We evaluated our new solution on a set of 265 articles from Discover magazine where the topic structures are unknown and need to be discovered. Our experimental results show that the iterative approach has the potential to generate better segmentation results than several leading baselines, and the separate flattening step allows us to adapt the results to different levels of details and user preferences.


title: Influences of physical training on the heart of dogs.

abstract: To investigate the effects of physical training on cardiac dimensions and function, eight dogs were exercised for 12 weeks by treadmill running 1 hour/ day, 5 days/week. Five dogs were confined in cages as controls for an 8-week period. Heart rates were monitored by telemetry during rest and exercise. Maximum QRS spatial magnitudes were calculated from records of McFee lead electrocardiograms. Left ventricular end-diastolic dimensions were determined radiographically by the bead and clip technique. No statistically significant changes occurred during the control period. Training produced statistically significant decreases in heart rate at rest (72 beats/min to 49 beats/min, P < 0.005) and at a standard work load of 6.1 mph on a level treadmill (205 beats/ min to 158 beats/min, P < 0.005) and statistically significant increases in work load (5.4 mph to 9.1 mph, P < 0.005) at a standard heart rate of 194 beats/min. Improvements were rapid during the first 4 weeks of training but gradual during the remaining 8 weeks. Training caused small but statistically significant increases in left ventricular end-diastolic wall thickness (8.7 mm to 9.3 mm, P < 0.0025), estimated left ventricular mass (83.6 g to 91.2 g, P < 0.01), and maximum (McFee) QRS spatial magnitude (4.0 mv to 4.8 mv, P < 0.05).


title: Monitoring Breathing via Signal Strength in Wireless Networks

abstract: Systems and methods are disclosed for the use of sensor links in a network to estimate the breathing rate of a breathing subject within a structure, estimate the location of the subject within the structure, and detect if the subject is breathing. The structure may be a bed, a building, or a room in the building. The received signal strength of the sensor links is obtained and is then used in various breathing models to determine the breathing rate estimation, the location estimation, and the breathing detection.


title: Detection and classification of Malaria in thin blood slide images

abstract: In this work an image processing system was developed to identify malaria parasites in thin blood smears and to classify them into one of the four different species of malaria. Many techniques were implemented in the preprocessing stage to enhance the images. In the first part of the system morphological processing is applied to extract the Red Blood Cells (RBC) from blood images. The developed algorithm picks the suspicious regions and detects the parasites in the images including the overlapped cells. Accordingly, the RBCs are classified into infected and non-infected cells and the number of RBCs in each image is calculated. The second part of the system uses the Normalized Cross-Correlation function to classify the parasite into one of the four species namely, Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale. Compared to manual results, the system achieved 95 % accuracy for detection and counting of RBCs and 100% for detection and classifying the parasite into one of its four types.


title: Ads by whom? ads about what?: exploring user influence and contents in social advertising

abstract: Despite the growing interest in using online social networking services (OSNS) for advertising, little is understood about what contributes to the social advertising performance. In this research, we pose following questions: How many clicks do social advertisements actually receive? What are the characteristics of the advertisements that receive many clicks? What factors contribute to the clicks on advertisements? In order to answer these questions, we collect data from AdbyMe, a social media advertisement platform that connects businesses, or advertisers, with users of online social network services. Businesses can reach a large target audience through AdbyMe users who publish the advertisements on their social networks. We analyze the factors that may affect the clicks on advertisements being published on OSNS. In particular, we look into the advertised contents as well as the characteristics of users who publish the advertisements. We find that the traditional advertisement content analysis alone cannot fully explain the effectiveness of social advertisements. More importantly, we discover that in a social advertising paradigm, social influence of a publisher has a strong impact on the number of clicks on the advertisements. Our findings suggest that considering both the advertised contents and the influence of advertising publishers allows better understanding of the social advertisement phenomenon.


title: Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

abstract: Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods require a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys et al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.


title: Minimum entropy, k-means, spectral clustering

abstract: This paper addresses an information-theoretic aspect of k-means and spectral clustering. First, we revisit the k-means clustering and show that its objective function is approximately derived from the minimum entropy principle when the Renyi's quadratic entropy is used. Then we present a maximum within-clustering association that is derived using a quadratic distance measure in the framework of minimum entropy principle, which is very similar to a class of spectral clustering algorithms that is based on the eigen-decomposition method.


title: Empirical evaluation of software maintainability based on a manually validated refactoring dataset

abstract: Abstract Context Refactoring is a technique for improving the internal structure of software systems. It has a solid theoretical background while being used in development practice also. However, we lack empirical research results on the real effect of code refactoring and its application. Objective This paper presents a manually validated subset of a previously published dataset containing the refactorings extracted by the RefFinder tool, code metrics, and maintainability of 7 open-source systems. We found that RefFinder had around 27% overall average precision on the subject systems, thus our manually validated subset has substantial added value. Using the dataset, we studied several aspects of the refactored and non-refactored source code elements (classes and methods), like the differences in their maintainability and source code metrics. Method We divided the source code elements into a group containing the refactored elements and a group with non-refactored elements. We analyzed the elements’ characteristics in these groups using correlation analysis, Mann–Whitney U test and effect size measures. Results Source code elements subjected to refactorings had significantly lower maintainability than elements not affected by refactorings. Moreover, refactored elements had significantly higher size related metrics, complexity, and coupling. Also these metrics changed more significantly in the refactored elements. The results are mostly in line with our previous findings on the not validated dataset, with the difference that clone metrics had no strong connection with refactoring. Conclusions Compared to the preliminary analysis using a not validated dataset, the manually validated dataset led to more significant results, which suggests that developers find targets for refactorings based on some internal quality properties of the source code, like their size, complexity or coupling, but not clone related metrics as reported in our previous studies. They do not just use these properties for identifying targets, but also control them with refactorings.


title: Adjusting story points calculation in scrum effort & time estimation

abstract: Effort and time estimation at the initial stage of an agile project is a difficult and challenging task due to volatility and changing in customer requirements. Scrum remains the most used agile project management method. However effort estimation in scrum is mainly based on story points which are subjective measures and mostly lead to inaccurate estimates. In this paper we propose to adjust this measure using three adjustment factors. Moreover this paper will show how to use also this adjusted story point in velocity calculation to ameliorate scrum time estimation.


title: Comparative Analysis of 6 Bit Thermometer-to-Binary Decoders for Flash Analog-to-Digital Converter

abstract: In the design of high speed Flash ADC selection of Thermometer to Binary decoder plays an important role. This paper describes different decoder topologies suitable for Flash ADCs. Comparative analysis between them is presented in terms of hardware required, propagation delay & power consumption. Result shows that fat tree & Mux based topologies are suitable for high speed conversions, but Mux based topology is effective in terms of power consumption. Presence of bubble error reduces output correction capability. In this paper advance scheme is proposed for correction of bubble error in Mux based decoder.


title: Internal Representations of the Therapeutic Relationship Among Adolescents in Psychodynamic Psychotherapy

abstract: This study examined changes in adolescents' internal representations of their relationship with their therapist and the extent to which these changes were related to changes in their representations of their relationship with their parents and to treatment outcomes. ::: ::: Method ::: Thirty adolescents (aged 15–18 years, 70% women) undergoing psychodynamic psychotherapy participated in relationship anecdote paradigms interviews based on the core conflictual relationship theme method and completed outcome measures at the beginning of treatment and a year later. ::: ::: Results ::: Adolescents' positive representations of their therapists increased throughout the year of treatment, whereas their negative representations did not change. There was an association between the development of the therapeutic relationship and improvement in the perception of the relationship with parents over the course of therapy. Increases in the level of positive representations and decreases in the level of negative representations of the therapist were associated with greater satisfaction with treatment but not with the other outcome measures. These results support the centrality of the therapeutic relationship in the process of change during adolescents' psychodynamic psychotherapy. Copyright © 2014 John Wiley & Sons, Ltd. ::: ::: Key Practitioner Message ::: The finding that positive representations of the therapist increased throughout treatment but that negative representations remained steady suggests that therapists who treat adolescents should expect and be able to hear adolescent clients' positive and negative internal representations of themselves. Therapists need to realize that although adolescents often experience negative emotions and perceptions in therapy as in other significant relationships, this does not necessarily block the development of positive emotions. ::: The finding that changes in the representations of the therapist are associated with changes in the representations of parents is in line with psychodynamic theory, which posits that psychotherapy facilitates new interpersonal experiences and new insights through the exploration of the therapeutic relationship. Working in the ‘here and now’ may eventually impact the nature of other significant relationships, particularly with parents in the case of adolescents.