title: Improving Customer Relationship Management Through Business Intelligence

abstract: This paper examines empirically the role of business intelligence (BI) in customer relationship management (CRM). Drawing on relevant literature on BI and CRM, the research model for the current investigation proposes that BI approaches of an organisation and its competition influence organisational business strategy which in turn impacts its customer strategy. The model is tested empirically using survey data of 165 respondents from 73 different private and state owned businesses operating in a transitional economy of East Europe. Empirical evidence confirms a key role of BI in CRM through its impact on organisational business and customer strategies development. Such findings make two important contributions. For research, they provide an improved understanding of the factors and processes involved in realising benefits from BI. For practice, they show managers how BI can be leveraged to achieve performance gains through competitive actions. Further research is recommended to confirm and extend the current investigation.


title: Estimation Stability with Cross Validation (ESCV)

abstract: Cross-validation (CV) is often used to select the regularization parameter in high dimensional problems. However, when applied to the sparse modeling method Lasso, CV leads to models that are unstable in high-dimensions, and consequently not suited for reliable interpretation. In this paper, we propose a model-free criterion ESCV based on a new estimation stability (ES) metric and CV. Our proposed ESCV finds a locally ES-optimal model smaller than the CV choice so that the it fits the data and also enjoys estimation stability property. We demonstrate that ESCV is an effective alternative to CV at a similar easily parallelizable computational cost. In particular, we compare the two approaches with respect to several performance measures when applied to the Lasso on both simulated and real data sets. For dependent predictors common in practice, our main finding is that, ESCV cuts down false positive rates often by a large margin, while sacrificing little of true positive rates. ESCV usually outperforms CV in terms of parameter estimation while giving similar performance as CV in terms of prediction. For the two real data sets from neuroscience and cell biology, the models found by ESCV are less than half of the model sizes by CV. Judged based on subject knowledge, they are more plausible than those by CV as well. We also discuss some regularization parameter alignment issues that come up in both approaches.


title: Hierarchical entity-relationship diagrams: the model, method of creation and experimental evaluation

abstract: A bottom-up method for creating a hierarchy of entity-relationship diagrams (HERD) from a given, "flat" ER diagram (ERD) is proposed. The hierarchy consists of simple and interrelated diagrams--ER structures--with external relationships to other structures. The HERD-tree diagram, which provides the most general view of the conceptual schema, is located at the top of the hierarchy. The method is based on packaging operations, which group entities and relationships according to certain criteria. These operations are applied in several steps on a given (presumably large-scale) ERD. We describe the new constructs, which are added to the ER model to enable the creation of HERD, and a bottom-up method for creating HERD. We also evaluate HERD from the point of view of user comprehension and preference, based on an experimental comparison to flat ERDs.


title: A field study of refactoring challenges and benefits

abstract: It is widely believed that refactoring improves software quality and developer productivity. However, few empirical studies quantitatively assess refactoring benefits or investigate developers' perception towards these benefits. This paper presents a field study of refactoring benefits and challenges at Microsoft through three complementary study methods: a survey, semi-structured interviews with professional software engineers, and quantitative analysis of version history data. Our survey finds that the refactoring definition in practice is not confined to a rigorous definition of semantics-preserving code transformations and that developers perceive that refactoring involves substantial cost and risks. We also report on interviews with a designated refactoring team that has led a multi-year, centralized effort on refactoring Windows. The quantitative analysis of Windows 7 version history finds that the binary modules refactored by this team experienced significant reduction in the number of inter-module dependencies and post-release defects, indicating a visible benefit of refactoring.


title: Smart Memories: a modular reconfigurable architecture

abstract: Trends in VLSI technology scaling demand that future computing devices be narrowly focused to achieve high performance and high efficiency, yet also target the high volumes and low costs of widely applicable general purpose designs. To address these conflicting requirements, we propose a modular reconfigurable architecture called Smart Memories, targeted at computing needs in the 0.1μ technology generation. A Smart Memories chip is made up of many processing tiles, each containing local memory, local interconnect, and a processor core. For efficient computation under a wide class of possible applications, the memories, the wires, and the computational model can all be altered to match the applications. To show the applicability of this design, two very different machines at opposite ends of the architectural spectrum, the Imagine stream processor and the Hydra speculative multiprocessor, are mapped onto the Smart Memories computing substrate. Simulations of the mappings show that the Smart Memories architecture can successfully map these architectures with only modest performance degradation.


title: Aggregating individual judgments and priorities with the analytic hierarchy process

abstract: The Analytic Hierarchy Process (AHP) is often used in group settings where group members either engage in discussion to achieve a consensus or express their own preferences. Individual judgments can be aggregated in different ways. Two of the methods that have been found to be most useful are the aggregation of individual judgments (AIJ) and the aggregation of individual priorities (AIP). We propose that the choice of method depends on whether the group is assumed to act together as a unit or as separate individuals and explain why AIJ is appropriate for the former while AIP is appropriate for the latter. We also address the relationships between the choice of method, the applicability of the Pareto principle, and the use of arithmetic or geometric means in aggregation. Finally, we discuss Ramanathan and Ganesh's method to derive priorities for individual decision-makers that can be used when aggregate group preferences of individuals whose judgments are not all equally weighted. We conclude that while this method can be useful, it is applicable only in special circumstances.


title: CPOE-Enabled Coordination: Appropriation for Deep Structure Use and Impacts on Patient Outcomes

abstract: In the United States, the Centers for Medicare & Medicaid Services (CMS) has begun instituting pay-for-performance incentives that reward hospitals based on patient-centric outcomes such as patient satisfaction. Further, to promote the “meaningful use” of health information technology (HIT), CMS has been prompting hospitals to adopt and use HITs. Computerized provider order entry (CPOE) is one such HIT and is designed to improve coordination in patient care teams and consequently patient outcomes. We explore the impact of CPOE-enabled coordination on patient satisfaction with the care team. In a departure from prior research that has tended to treat the team as all clinicians within a hospital unit/clinic, we conceptualize (and operationalize) patient care teams as ad hoc and patient-specific and thus comprised of those clinicians having direct contact with the patient. In a further departure from prior research that has employed lean measures of IS use (e.g., use intentions, duration, or frequency of use), we respond to the call for rich measures of IS use by conceptualizing deep structure use (DSU) of CPOE as patient care team-level usage of CPOE features. ::: ::: We draw upon adaptive structuration theory (AST) to identify faithfulness of appropriation (FOA) and consensus on appropriation (COA) as two related, but distinct, aspects of CPOE appropriation by patient care teams that affect DSU. We also draw on relational coordination theory to conceptualize communicative coordination (CC) as team communication for coordination purposes and theorize that DSU affects patient satisfaction through CC and informating the patient differentially across high/low patient mortality risk conditions. ::: ::: Based on data from 224 patient care teams caring for both low and high patient mortality risk conditions, our results indicate that FOA and COA are salient predictors of DSU, and that the effect of COA on DSU is mediated by FOA. We also observed a significant indirect effect of DSU on patient satisfaction (as mediated by communicative coordination and patient informating), but only for high patient mortality risk conditions. Our findings are important because they show that by using CPOE in a comprehensive manner, patient care team members are better able to coordinate patient care and are able to better inform the patient about their care, ultimately leading to improved patient satisfaction. Additional implications for HIT research and practice are discussed.


title: SOAP-Based vs. RESTful Web Services: A Case Study for Multimedia Conferencing

abstract: RESTful Web services are now emerging as an alternative to SOAP-based Web services and might be a more suitable choice in some cases. A comparison of two Web programming interfaces - the standard Parlay-X multimedia SOAP-based Web service and a RESTful Web service that offers the same functionalities - for developing multimedia conferencing applications shows that the RESTful Web interface offers better performance.


title: Defining Attributes and Metrics of Effective Research Mentoring Relationships

abstract: Despite evidence of mentoring’s importance in training researchers, studies to date have not yet determined which mentoring relationships have the most impact and what specific factors in those mentoring relationships contribute to key outcomes, such as the commitment to and persistence in research career paths for emerging researchers from diverse populations. Efforts to broaden participation and persistence in biomedical research careers require an understanding of why and how mentoring relationships work and their impact, not only to research training but also to promoting career advancement. This paper proposes core attributes of effective mentoring relationships, as supported by the literature and suggested by theoretical models of academic persistence. In addition, both existing and developing metrics for measuring the effectiveness of these attributes within mentoring relationships across diverse groups are presented, as well as preliminary data on these metrics from the authors’ work.


title: The role of interaction in virtual embodiment: Effects of the virtual hand representation

abstract: How do people appropriate their virtual hand representation when interacting in virtual environments? In order to answer this question, we conducted an experiment studying the sense of embodiment when interacting with three different virtual hand representations, each one providing a different degree of visual realism but keeping the same control mechanism. The main experimental task was a Pick-and-Place task in which participants had to grasp a virtual cube and place it to an indicated position while avoiding an obstacle (brick, barbed wire or fire). An additional task was considered in which participants had to perform a potentially dangerous operation towards their virtual hand: place their virtual hand close to a virtual spinning saw. Both qualitative measures and questionnaire data were gathered in order to assess the sense of agency and ownership towards each virtual hand. Results show that the sense of agency is stronger for less realistic virtual hands which also provide less mismatch between the participant's actions and the animation of the virtual hand. In contrast, the sense of ownership is increased for the human virtual hand which provides a direct mapping between the degrees of freedom of the real and virtual hand.


title: Semantic Structural Alignment of Neural Representational Spaces Enables Translation between English and Chinese Words

abstract: Two sets of items can share the same underlying conceptual structure, while appearing unrelated at a surface level. Humans excel at recognizing and using alignments between such underlying structures in many domains of cognition, most notably in analogical reasoning. Here we show that structural alignment reveals how different people's neural representations of word meaning are preserved across different languages, such that patterns of brain activation can be used to translate words from one language to another. Groups of Chinese and English speakers underwent fMRI scanning while reading words in their respective native languages. Simply by aligning structures representing the two groups' neural semantic spaces, we successfully infer all seven Chinese-English word translations. Beyond language translation, conceptual structural alignment underlies many aspects of high-level cognition, and this work opens the door to deriving many such alignments directly from neural representational content.


title: Comparision Between Neocone, Alvogyl and Zinc Oxide Eugenol Packing for the Treatment of Dry Socket: A Double Blind Randomised Control Trial

abstract: Objectives ::: To prospectively evaluate and compare the effectiveness of Neocone, Alvogyl and Zinc Oxide Eugenol (ZOE) intra alveolar dressings for the management of dry socket and to study the epidemiological factors associated with the condition.


Enhancing Cortana User Experience Using Machine Learning


title: Cloud Log Forensics Metadata Analysis

abstract: The increase in the quantity and questionable quality of the forensic information retrieved from the current virtualized data cloud system architectures has made it extremely difficult for law enforcement to resolve criminal activities within these logical domains. This paper poses the question of what kind of information is desired from virtual machine (VM) hosted operating systems (OS) investigated by a cloud forensic examiner. The authors gives an overview of the information that exists on current VM OS by looking at it's kernel hypervisor logs and discusses the shortcomings. An examination of the role that the VM kernel hypervisor logs provide as OS metadata in cloud investigations is also presented.


title: Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance

abstract: article i nfo In this study, the individually determined upper alpha frequency band in EEG (electroencephalogram) was investigated as a neurofeedback parameter. Fourteen subjects were trained on five sessions within 1 week by means of feedback dependent on the current upper alpha amplitude. On the first and fifth session, cognitive ability was tested by a mental rotation test. As a result, eleven of the fourteen subjects showed significant training success. Individually determined upper alpha was increased independently of other frequency bands. The enhancement of cognitive performance was significantly larger for the neurofeedback group than for a control group who did not receive feedback. Thus, enhanced cognitive control went along with an increased upper alpha amplitude that was found in the neurofeedback group only.


title: Framing Refugees as Clients.

abstract: The refugee problem is typically viewed in terms of filling only the immediate requirements of a needy people.2 The collection of documents and reports about refugees reflects with few exceptions, a logistical, legalistic concern, with litde supporting qualitative scholarly material. Similarly, current work supports vast misconceptions about refugees as people with particular psychocultural histories. Understanding the im? pact of the event of flight in the lives of these people rarely enters the analysis of refugee needs, problems and programs. It is as if culture were a novelty easily dismissed in the face of the events which have turned persons into victims. Yet, the events and the aftermath have meaning beyond the functional, instrumental forms of physical survival: it is particularly clear in statements from those who have lived in refugee status for several years (Tibetans and Palestinians) that the survival of their ethnic identity or ethos is essential, and is threatened not only by the event of flight, but also, its aftermath. Quick, tragic historical events muster odd unions of sympathy and concern for their victims. The character of such events is devastating: there is the event itself, with the true threat of death by enemy and element; and there is the aftermath, marking the course of refugee life, reshaping?perhaps destroying the totality of meaning of predictable patterns of culture. "Survivors" and "refugees" are victims well beyond the event labeling them as such.


title: Machine learning techniques applied to prepaid subscribers: Case study on the telecom industry of Morocco

abstract: In order to survive the fierce competition in today's telecommunication industry, it is mandatory to understand the need of customers who might think to move toward another competitor. Thus, assessing the churn prediction, which becomes a real concern in the telecom industry, is critical in predicting future trends of the industry. In this work, we wanted to determine the best and reliable prediction model in improving the competitiveness of Moroccan telecommunication sector. We focused on prepaid costumers, a group of subscribers that are unreliable, less committed to a provider, and they can end their services without prior notices. We used various machine learning techniques applied to Call Details Records (CDR) to analyze a dataset of 635997 subscribers and monitored their usage behaviors over a period of three months. The Visitor Location Register ID, which describes the locations where calls/SMS/Recharge and Data events were performed was also used to investigate the churning rate per different regions. Among models tested, we found that the Gradient Boosting Classifier (GBC) is the best classifier model with accuracy of 91% and f1 score of 89%. This model fits better the Churn prediction use case.


title: Extreme Learning Machine for Regression and Multiclass Classification

abstract: Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the “generalized” single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.


title: The enactment of socio-technical transition pathways: A reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990–2014)

abstract: This paper aims to make two contributions to the sustainability transitions literature, in particular the Geels and Schot (2007. Res. Policy 36(3), 399) transition pathways typology. First, it reformulates and differentiates the typology through the lens of endogenous enactment, identifying the main patterns for actors, formal institutions, and technologies. Second, it suggests that transitions may shift between pathways, depending on struggles over technology deployment and institutions. Both contributions are demonstrated with a comparative analysis of unfolding low-carbon electricity transitions in Germany and the UK between 1990–2014. The analysis shows that Germany is on a substitution pathway, enacted by new entrants deploying small-scale renewable electricity technologies (RETs), while the UK is on a transformation pathway, enacted by incumbent actors deploying large-scale RETs. Further analysis shows that the German transition has recently shifted from a ‘stretch-and-transform’ substitution pathway to a ‘fit-and-conform’ pathway, because of a fightback from utilities and altered institutions. It also shows that the UK transition moved from moderate to substantial incumbent reorientation, as government policies became stronger. Recent policy changes, however, substantially downscaled UK renewables support, which is likely to shift the transition back to weaker reorientation.


Towards Automatically-Tuned Neural Networks.


title: Secure User Authentication on Smartphones via Sensor and Face Recognition on Short Video Clips

abstract: Smartphones play a key role in our daily life, they can replace our watch, calendar, and mail box but also our credit card, house keys and in the near future our identity documents. Their increasing use in storing sensitive information, has raised the need to protect users and their data through secure authentication protocols. The main achievement of this work is to make the smartphone not only the cause of the problem but also part of the solution. Here, the Sensor Pattern Noise of the smartphone embedded camera and the HOG features of the user’s face are combined for a double check of user identity.


title: BayesDB: A probabilistic programming system for querying the probable implications of data

abstract: Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? This paper describes BayesDB, a probabilistic programming platform that aims to enable users to query the probable implications of their data as directly as SQL databases enable them to query the data itself. This paper focuses on four aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data analysis, that answers queries by averaging over an implicit space of probabilistic models; (ii) techniques for implementing BQL using a broad class of multivariate probabilistic models; (iii) a semi-parametric Bayesian model-builder that auomatically builds ensembles of factorial mixture models to serve as baselines; and (iv) MML, a "meta-modeling" language for imposing qualitative constraints on the model-builder and combining baseline models with custom algorithmic and statistical models that can be implemented in external software. BayesDB is illustrated using three applications: cleaning and exploring a public database of Earth satellites; assessing the evidence for temporal dependence between macroeconomic indicators; and analyzing a salary survey.


title: Composable, distributed-state models for high-dimensional time series

abstract: In this thesis we develop a class of nonlinear generative models for high-dimensional time series. The first key property of these models is their distributed, or “componential” latent state, which is characterized by binary stochastic variables which interact to explain the data. The second key property is the use of an undirected graphical model to represent the relationship between latent state (features) and observations. The final key property is composability: the proposed class of models can form the building blocks of deep networks by successively training each model on the features extracted by the previous one. ::: We first propose a model based on the Restricted Boltzmann Machine (RBM) that uses an undirected model with binary latent variables and real-valued “visible” variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few timesteps. This “conditional” RBM (CRBM) makes on-line inference efficient and allows us to use a simple approximate learning procedure. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. We also explore CRBMs as priors in the context of Bayesian filtering applied to multi-view and monocular 3D person tracking. ::: We extend the CRBM in a way that preserves its most important computational properties and introduces multiplicative three-way interactions that allow the effective interaction weight between two variables to be modulated by the dynamic state of a third variable. We introduce a factoring of the implied three-way weight tensor to permit a more compact parameterization. The resulting model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve its ability to blend motion styles or to transition smoothly among them. ::: In separate but related work, we revisit Products of Hidden Markov Models (PoHMMs). We show how the partition function can be estimated reliably via Annealed Importance Sampling. This enables us to demonstrate that PoHMMs outperform various flavours of HMMs on a variety of tasks and metrics, including log likelihood.


title: Design of phase II clinical trials in cancer using decision theory.

abstract: An application of decision theory to the design of phase II clinical trials in cancer is presented. A risk function which depends on the prior distribution of the response rate, the costs of treating a patient, and the gains or losses resulting from the decision taken upon completion of the study is derived and optimal restricted sampling plans for a response rate of 20% are provided.


title: Efficient Single-Switch Boost-Dual-Input Flyback PFC Converter With Reduced Switching Loss

abstract: This paper presents an efficient single-switch boost-dual-input flyback power factor correction (PFC) converter. To achieve a high power factor, a boost PFC cell operating in discontinuous conduction mode is presented. A dual-input flyback dc–dc module, which consists of parallel primary windings and serial secondary windings, operating in critical conduction mode is introduced for galvanic isolation and valley-switching operation. In addition, the proposed converter features high efficiency because the input power is equally divided, and some power is directly transferred to the load by the dual-input flyback dc–dc module. The remaining power is stored in dc-bus capacitors, and it is closely related to the holdup time. Moreover, the currents in the leakage inductors are absorbed by the dc-bus capacitors through a snubber diode, and they are reprocessed by the dual-input flyback dc–dc module. Consequently, the peak voltage in the MOSFET drain is clamped, and the total power conversion efficiency is improved. To verify the performance of the proposed converter, the operating principles, steady-state analysis, and experimental results from a 48 V, 100 W prototype are presented.


title: DroidFax: A Toolkit for Systematic Characterization of Android Applications

abstract: As the Android app market keeps growing, there is a pressing need for automated tool supports to empower Android developers to produce quality apps with higher productivity. Yet existing tools for Android mostly aim at security and privacy protection, primarily targeting end users and security analysts. Towards filling this gap, we present DROIDFAX, a toolkit that targets the developers to help them comprehensively understand Android apps regarding their code structure and behavioral traits. To that end, DROIDFAX features a systematic app characterization in multiple dimensions and views, through lightweight code analysis and profiling of both ordinary method calls (including those via reflection and exceptional control flows) and inter-component communications (including those within and across apps). The toolkit also includes a statement coverage tracker that works directly on bytecode and a dedicated tracer of events occurred during app executions. Applying DROIDFAX in two use cases has resulted in important findings about app behavioral patterns and an advanced security defense technique for Android. Empirical results also showed promising efficiency and scalability of DROIDFAX for practical adoption. A demo video for DROIDFAX can be viewed here or downloaded here.


title: Case report: HER2 amplification as a resistance mechanism to crizotinib in NSCLC with MET exon 14 skipping

abstract: ABSTRACTPatients with non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping can benefit from crizotinib treatment. Currently, the main resistance mechanisms to crizotinib are MET D1228N...


A Neural Model for Joint Event Detection and Summarization.


title: Objective evidence of cognitive complaints in Chronic Fatigue Syndrome : A BOLD fMRI study of verbal working memory

abstract: Abstract Individuals with Chronic Fatigue Syndrome (CFS) often have difficulties with complex auditory information processing. In a series of two Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) studies, we compared BOLD signal changes between Controls and individuals with CFS who had documented difficulties in complex auditory information processing (Study 1) and those who did not (Study 2) in response to performance on a simple auditory monitoring and a complex auditory information processing task (mPASAT). We hypothesized that under conditions of cognitive challenge: (1) individuals with CFS who have auditory information processing difficulties will utilize frontal and parietal brain regions to a greater extent than Controls and (2) these differences will be maintained even when objective difficulties in this domain are controlled for. Using blocked design fMRI paradigms in both studies, we first presented the auditory monitoring task followed by the mPASAT. Within and between regions of interest (ROI), group analyses were performed for both studies with statistical parametric mapping (SPM99). Findings showed that individuals with CFS are able to process challenging auditory information as accurately as Controls but utilize more extensive regions of the network associated with the verbal WM system. Individuals with CFS appear to have to exert greater effort to process auditory information as effectively as demographically similar healthy adults. Our findings provide objective evidence for the subjective experience of cognitive difficulties in individuals with CFS.


title: Prediction and analysis of Pakistan election 2013 based on sentiment analysis

abstract: The significance of social media has already been proven in provoking transformation of public opinion for developed countries in improving democratic process of elections. On the contrary, developing countries lacking basic necessities of life possess monopolistic electoral system in which candidates are elected based on tribes, family backgrounds, or landlord influences. They extort voters to cast votes against their promises for the provision of basic needs. Similarly voters also poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. People of Pakistan utilized social media to garner support and campaign for political parties in General Elections 2013. Political leaders, parties, and people of Pakistan disseminated party's agenda and advocacy of party's ideology on Twitter without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties Positive, negative and neutral behavior of the party followers as well as party's campaign impact can be predicted from the analysis. The analytical findings proved to be having considerable correspondence with actual results as published by Election Commission of Pakistan..


title: � Human Brain Mapping 23:200–209(2004) � Brain Activity Underlying Emotional Valence and Arousal: A Response-Related fMRI Study

abstract: Emotional behavior is organized along two psychophysiologic dimensions: (1) valence, varying from negative to positive, and (2) arousal, varying from low to high. Behavioral responses along these dimensions are assumed to be mediated by different brain circuits. We recorded startle reflex modulation and skin conductance responses in healthy volunteers during functional magnetic resonance imaging (fMRI) while they viewed a set of emotional pictures and took verbal ratings of the emotional valence and arousal of each picture after scanning. Response-related multiple correlation analysis revealed differential brain activity in five brain regions. Startle reflex changes, associated with the valence of a stimulus, correlated with activity in the amygdala, while verbal reports of negative emotional valence varied with insular activity. Peripheral physiologic and verbal responses along the arousal dimension varied with thalamic and frontomedial activity. Peripheral physiologic responses along both dimensions correlated with activity in somatosensory association areas in the anterior parietal cortex. In the valence dimension, activity in the left anterior parietal cortex was associated with highly correlating peripheral physiologic and verbal responses, suggesting that verbal reports of emotional valence might depend partly on brain circuits representing peripheral physiologic changes. Our data provide direct evidence for a functional segregation of brain structures underlying peripheral physiologic responses and verbal ratings along the emotional dimensions of valence and arousal. Hum Brain Mapp 23:200 -209, 2004. © 2004 Wiley-Liss, Inc.


title: Exploring Differential Topic Models for Comparative Summarization of Scientific Papers

abstract: This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differentially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summarization methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.


title: RAP-CLA: A Reconfigurable Approximate Carry Look-Ahead Adder

abstract: In this brief, we propose a fast yet energy-efficient reconfigurable approximate carry look-ahead adder (RAP-CLA). This adder has the ability of switching between the approximate and exact operating modes making it suitable for both error-resilient and exact applications. The structure, which is more area and power efficient than state-of-the-art reconfigurable approximate adders, is achieved by some modifications to the conventional carry look ahead adder (CLA). The efficacy of the proposed RAP-CLA adder is evaluated by comparing its characteristics to those of two state-of-the-art reconfigurable approximate adders as well as the conventional (exact) CLA in a 15 nm FinFET technology. The results reveal that, in the approximate operating mode, the proposed 32-bit adder provides up to 55% and 28% delay and power reductions compared to those of the exact CLA, respectively, at the cost of up to 35.16% error rate. It also provides up to 49% and 19% lower delay and power consumption, respectively, compared to other approximate adders considered in this brief. Finally, the effectiveness of the proposed adder on two image processing applications of smoothing and sharpening is demonstrated.


title: Classification of Building Information Model (BIM) Structures with Deep Learning

abstract: In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.


title: Power flow management of a grid tied PV-battery powered fast electric vehicle charging station

abstract: The prospective spread of Electric vehicles (EV) and plug-in hybrid electric vehicles arises the need for fast charging rates. High required charging rates lead to high power demands, which may not be supported by the grid. In this paper, an optimal power flow technique of a PV-battery powered fast EV charging station is presented to minimize the operation cost. The objective is to help the penetration of PV-battery systems into the grid to support the growing need for fast charging of EVs. An optimization problem is formulated along with the required constraints and the operating cost function is chosen as a combination of electricity grid prices and the battery degradation cost. In the first stage of the proposed optimization procedure, an offline particle swarm optimization (PSO) is performed as a prediction layer. In the second stage, dynamic programming (DP) is performed as an online reactive management layer. Forecasted system data is utilized in both stages to find the optimal solution for the power management. In the reactive management layer, the outputs of the PSO are used to limit the available state trajectories used in the DP and, accordingly, improve the system computation time and efficiency. Online error compensation is implemented into the DP and fed back to the prediction layer for necessary prediction adjustments. Simulation and experimental results are successfully implemented to validate the effectiveness of the proposed management system.


title: Knowledge Graph Embedding with Iterative Guidance from Soft Rules

abstract: Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from this https URL


title: Very-low-voltage and ultra-low-power analog circuits for nomadic applications

abstract: This paper gives an overview on the key features of the several building blocks constituting modern nomadic systems. Different micro-power energy harvesting techniques are described and discussed. In addition, this paper reviews the state of the art and provides recently published examples of low power low voltage analog circuits, such as operational amplifiers, reference generators, and data converters.


title: Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

abstract: In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.


Security in Near Field Communication ( NFC ) Strengths and Weaknesses


title: The true sample complexity of active learning

abstract: We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally believed that active learning does not help, we show that active learning does help in the limit, often with exponential improvements in sample complexity. This contrasts with the traditional analysis of active learning problems such as non-homogeneous linear separators or depth-limited decision trees, in which Ω(1/?) lower bounds are common. Such lower bounds should be interpreted carefully; indeed, we prove that it is always possible to learn an ?-good classifier with a number of samples asymptotically smaller than this. These new insights arise from a subtle variation on the traditional definition of sample complexity, not previously recognized in the active learning literature.


title: Selection of inorganic-based draw solutions for forward osmosis applications

abstract: In this investigation, a protocol for the selection of optimal draw solutions for forward osmosis (FO) applications was developed and the protocol was used to determine the most appropriate draw solutions for specific FO applications using a currently available FO membrane. The protocol includes a desktop screening process and laboratory and modeling analyses. The desktop screening process resulted in 14 draw solutions suitable for FO applications. The 14 draw solutions were then tested in the laboratory to evaluate water flux and reverse salt diffusion through the FO membrane. Internal concentration polarization was found to lower both water flux and reverse salt diffusion by reducing the draw solution concentration at the interface between the support and dense layers of the membrane. Draw solution reconcentration was evaluated using reverse osmosis (RO) system design software. Analysis of experimental data and model results, combined with consideration of the costs associated with the FO and RO processes showed that a small group of seven draw solutions appeared to be the most suitable. The different characteristics of these draw solutions highlighted the importance of considering the specific FO application and membrane types being used prior to selecting the most appropriate draw solution.


title: Interaction design qualities: theory and practice

abstract: This paper reports the results of an action research project investigating the articulation of interaction design qualities for a web portal for urban planning and development. A framework for analyzing interaction design qualities is presented. The framework consists of the practical, the social, the aesthetic, the structural and the ethical quality dimensions, and it was tried out in practice with developers and designers of the portal. This provided experiences used to revise the framework. The results indicate that the framework can be improved by splitting the social quality dimension into a communicational dimension and an organizational dimension. The structural dimension is also renamed to the technical dimension.


title: Implementation of children tracking system on android mobile terminals

abstract: Recently, all over the world crime against children is increasing at higher rates and it is high time to offer safety support system for the children going to schools. This paper focuses on implementing children tracking system for every child attending school. However the existing systems are not powerful enough to prevent the crime against children since these systems give information about the children group and not about each child resulting in low assurance about their child safety to parents and also does not concentrate on sensing the cry of the child and intimating the same to its parents. The proposed system includes a child module and two receiver modules for getting the information about the missed child on periodical basis. The child module includes ARM7 microcontroller (1pc 2378) Global positioning system (GPS) Global system for mobile communication (GSM) Voice playback circuit and the receiver module includes Android mobile device in parent's hand and the other as monitoring database in control room of the school. Finally implementation results for the proposed system are provided in this paper.


title: Fast shortest path distance estimation in large networks

abstract: In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. We therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. The efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. While theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy, our methods require as much as 250 times less space than selecting landmarks at random. In addition, we demonstrate that at a very small accuracy loss our techniques are several orders of magnitude faster than the state-of-the-art exact methods. Finally, we study an application of our methods to the task of social search in large graphs.


title: Improved Hysteresis and Reliability of MoS2 Transistors With High-Quality CVD Growth and Al2O3 Encapsulation

abstract: We report considerable improvement in the hysteresis and reliability of MoS2 field-effect transistors (FETs) achieved by chemical vapor deposition (CVD) of single-layer MoS2 and dielectric encapsulation. Our results show that a high-quality 15-nm thick Al2O3 layer allows for an efficient protection of the devices from adsorbent-type trapping sites. Combined use of the CVD-grown MoS2 as a channel and encapsulation simultaneously leads to at least an order of magnitude smaller hysteresis and up to two orders of magnitude lower long-term drifts of the transistor characteristics. Together with high on/off current ratios ( $\sim 10^{\textsf {9}}$ ) achieved in our devices, this presents a considerable advance in the technology of MoS2 FETs. As such, we conclude that both CVD growth of MoS2 and encapsulation present important technological steps toward reaching commercial quality standards of next-generation two-dimensional (2D) material technologies.


title: Batch based Monocular SLAM for Egocentric Videos

abstract: Simultaneous Localization and Mapping (SLAM) from a monocular camera has been a well researched area. However, estimating camera pose and 3d geometry reliably for egocentric videos still remain a challenge. Some of the common causes of failures are dominant 3D rotations and low parallax between successive frames, resulting in unreliable pose and 3d estimates. For forward moving cameras, with no opportunities for loop closures, the drift leads to eventual failures for traditional feature based and direct SLAM techniques. We propose a novel batch mode structure from motion based technique for robust SLAM in such scenarios. In contrast to most of the existing techniques, we process frames in short batches, wherein we exploit short loop closures arising out of to-and-fro motion of wearer's head, and stabilize the egomotion estimates by 2D batch mode techniques such as motion averaging on pairwise epipolar results. Once pose estimates are obtained reliably over a batch, we refine the 3d estimate by triangulation and batch mode Bundle Adjustment (BA). Finally, we merge the batches using 3D correspondences and carry out a BA refinement post merging. We present both qualitative and quantitative comparison of our method on various public first and third person video datasets, to establish the robustness and accuracy of our algorithm over the state of the art.


title: Modeling and control of a new quadrotor manipulation system

abstract: This paper introduces a new quadrotor manipulation system that consists of a 2-link manipulator attached to the bottom of a quadrotor. This new system presents a solution for the drawbacks found in the current quadrotor manipulation system which uses a gripper fixed to a quadrotor. Unlike the current system, the proposed system enables the end-effector to achieve any arbitrary orientation and thus increases its degrees of freedom from 4 to 6. Also, it provides enough distance between the quadrotor and the object to be manipulated. This is useful in some applications such as demining applications. System kinematics and dynamics are derived which are highly nonlinear. Controller is designed based on feedback linearization to track desired trajectories. Controlling the movements in the horizontal directions is simplified by utilizing the derived nonholonmic constraints. Finally, the proposed system is simulated using MATLAB/SIMULINK program. The simulation results show the effectiveness of the proposed controller.


title: Deep architectures for Neural Machine Translation

abstract: It has been shown that increasing model depth improves the quality of neural machine translation. However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study. ::: In this work, we describe and evaluate several existing approaches to introduce depth in neural machine translation. Additionally, we explore novel architectural variants, including deep transition RNNs, and we vary how attention is used in the deep decoder. We introduce a novel "BiDeep" RNN architecture that combines deep transition RNNs and stacked RNNs. ::: Our evaluation is carried out on the English to German WMT news translation dataset, using a single-GPU machine for both training and inference. We find that several of our proposed architectures improve upon existing approaches in terms of speed and translation quality. We obtain best improvements with a BiDeep RNN of combined depth 8, obtaining an average improvement of 1.5 BLEU over a strong shallow baseline. ::: We release our code for ease of adoption.


title: Microencapsulation of human growth hormone within biodegradable polyester microspheres: Protein aggregation stability and incomplete release mechanism

abstract: Recombinant human growth hormone (rhGH) was encapsulated within poly(D,L-lactic-co-glycolic acid) microspheres by a double emulsion solvent evaporation method. A mixture of methylene chloride and ethyl acetate in varying volume ratios was used for the microsphere preparation. Protein release profiles from three different microsphere formulations demonstrated initial burst effects ranging from 28.2% to 54.7% after a 1-day incubation and exhibited no further significant releases up to 19 days. This was because the encapsulated rhGH with the microspheres was largely aggregated in a noncovalent fashion during the formulation. Nonaggregated water soluble rhGH species within the microspheres are likely to be responsible for the rapid release upon incubation. The initially released rhGH in the incubation medium, however, was composed of mostly monomer species with a small amount of dimer as probed by size-exclusion chromatography. Circular dichroism spectra of the initially released rhGH in the medium revealed that the conformation of the released rhGH was correctly folded relative to that of native rhGH, with little variation in alpha-helix contents depending on the formulations. The "nonrelease" mechanism after the initial burst release was attributed to nonspontaneously dissociable noncovalent protein aggregation and surface adsorption of rhGH present within the microspheres.


title: Overfitting and neural networks: conjugate gradient and backpropagation

abstract: Methods for controlling the bias/variance tradeoff typically assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (MLP) neural networks, global parameters such as the training time, network size, or the amount of weight decay are commonly used to control the bias/variance tradeoff. However, the degree of overfitting can vary significantly throughout the input space of the model. We show that overselection of the degrees of freedom for an MLP trained with backpropagation can improve the approximation in regions of underfitting, while not significantly overfitting in other regions. This can be a significant advantage over other models. Furthermore, we show that "better" learning algorithms such as conjugate gradient can in fact lead to worse generalization, because they can be more prone to creating varying degrees of overfitting in different regions of the input space. While experimental results can not cover all practical situations, our results do help to explain common behavior that does not agree with theoretical expectations. Our results suggest one important reason for the relative success of MLPs, bring into question common beliefs about neural network training regarding training algorithms, overfitting, and optimal network size, suggest alternate guidelines for practical use, and help to direct future work.


title: Plasmodium falciparum parasitaemia and clinical malaria among school children living in a high transmission setting in western Kenya

abstract: BackgroundMalaria among school children is increasingly receiving attention, yet the burden of malaria in this age group is poorly defined. This study presents data on malaria morbidity among school children in Bungoma county, western Kenya.MethodThis study investigated the burden and risk factors of Plasmodium falciparum infection, clinical malaria, and anaemia among 2346 school children aged 5–15 years, who were enrolled in an individually randomized trial evaluating the effect of anthelmintic treatment on the risks of malaria. At baseline, children were assessed for anaemia and nutritional status and information on household characteristics was collected. Children were followed-up for 13 months to assess the incidence of clinical malaria by active detection, and P. falciparum infection and density evaluated using repeated cross-sectional surveys over 15 months.ResultsOn average prevalence of P. falciparum infection was 42 % and ranged between 32 and 48 % during the five cross-sectional surveys. Plasmodium falciparum prevalence was significantly higher among boys than girls. The overall incidence of clinical malaria was 0.26 episodes per person year (95 % confidence interval, 0.24–0.29) and was significantly higher among girls (0.23 versus 0.31, episodes per person years). Both infection prevalence and clinical disease varied by season. In multivariable analysis, P. falciparum infection was associated with being male, lower socioeconomic status and stunting. The risk of clinical malaria was associated with being female.ConclusionThese findings show that the burden of P. falciparum parasitaemia, clinical malaria and anaemia among school children is not insignificant, and suggest that malaria control programmes should be expanded to include this age group.


title: Attentive Relational Networks for Mapping Images to Scene Graphs

abstract: Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome Dataset, and the results demonstrate the effectiveness and superiority of our model.


title: An interactive approach for the teaching of virtual memory using open educational resources

abstract: The teaching of computing involves many basic subjects, which are the basis for an effective learning. The teaching of memory hierarchy and, particularly, virtual memory is a great challenge, because both consider different structures, functionalities and performance. Open Educational Resources (OER) have been successfully applied to help the teaching and learning of challenging subjects. This paper addresses a novel approach for the teaching of virtual memory with OER Amnesia, which simulates the main elements of a memory hierarchy. It also describes class plans and their application in classes with Amnesia. Our best results show improvements of up to 180% in scores of students when they use Amnesia to learn virtual memory.


title: A Model-Based Fuzz Framework to the Security Testing of TCG Software Stack Implementations

abstract: Fuzz testing is an effective technique for finding security vulnerabilities in software. Traditionally fuzz testing tools use random inputs and watch the resulting values. In this paper, we present a model-based fuzz framework for systematic automated testing of a TCG Trusted Software Stack implementation. This framework is based on blackbox fuzz testing methods, integrated with target profiling, data modeling and test algorithm etc. With the generation of smart, semantic-aware test cases, a more complete and deep testing can be provided. We also demonstrate the use of our model-based fuzz framework which can identity several vulnerabilities in some form of TSS implementation.


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

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


title: Modular equalization architecture using inter-module and switchless intra-module equalizer for energy storage system

abstract: This paper proposes a novel modular equalization architecture using inter-module and intra-module equalizers for energy storage systems comprising multiple cells/modules connected in series. In the proposed equalization architecture, cells in a module is equalized by a series-resonant voltage multiplier (SRVM)-based intra-module equalizer while module voltages are unified by a switched capacitor converter (SCC)-based inter-module equalizer. A square wave voltage generated at a switching node of the SCC-based inter-module equalizer is utilized to drive the SRVM-based intra-module equalizer so that the SRVM-based intra-module equalizer can be totally switchless topology. The required switch count of the proposed equalization architecture can be dramatically reduced compared to conventional architectures, allowing simplified circuitry. The proposed equalization architecture is modular and allows different equalizer topologies to be used for inter- and intra-module equalizers, offering good modularity and design flexibility. The derivation procedure of the proposed equalization architecture is explained, followed by separate analyses for the SCC-based inter-module equalizer and SRVM-based intra-module equalizer. An experimental equalization test for three supercapacitor (SC) modules, each consisting of four cells in series, was performed from an initially voltage-imbalanced condition. Module and cell voltages were equalized at different rates, and the equalization performance of the proposed modular equalization architecture was successfully demonstrated.


title: Outrageous but meaningful coincidences: dependent type-safe syntax and evaluation

abstract: Tagless interpreters for well-typed terms in some object language are a standard example of the power and benefit of precise indexing in types, whether with dependent types, or generalized algebraic datatypes. The key is to reflect object language types as indices (however they may be constituted) for the term datatype in the host language, so that host type coincidence ensures object type coincidence. Whilst this technique is widespread for simply typed object languages, dependent types have proved a tougher nut with nontrivial computation in type equality. In their type-safe representations, Danielsson [2006] and Chapman [2009] succeed in capturing the equality rules, but at the cost of representing equality derivations explicitly within terms. This article constructs a type-safe representation for a dependently typed object language, dubbed KIPLING, whose computational type equality just appropriates that of its host, Agda. The KIPLING interpreter example is not merely de rigeur - it is key to the construction. At the heart of the technique is that key component of generic programming, the universe.


title: FOXO1 mediates RANKL-induced osteoclast formation and activity.

abstract: We have previously shown that the transcription factor FOXO1 is elevated in conditions with high levels of bone resorption. To investigate the role of FOXO1 in the formation of osteoclasts, we examined mice with lineage-specific deletion of FOXO1 in osteoclast precursors and by knockdown of FOXO1 with small interfering RNA. The receptor activator for NF-κB ligand (RANKL), a principal bone-resorbing factor, induced FOXO1 expression and nuclear localization 2 d after stimulation in bone marrow macrophages and RAW264.7 osteoclast precursors. RANKL-induced osteoclast formation and osteoclast activity was reduced in half in vivo and in vitro with lineage-specific FOXO1 deletion (LyzM.Cre(+)FOXO1(L/L)) compared with matched controls (LyzM.Cre(-)FOXO1(L/L)). Similar results were obtained by knockdown of FOXO1 in RAW264.7 cells. Moreover, FOXO1-mediated osteoclast formation was linked to regulation of NFATc1 nuclear localization and expression as well as a number of downstream factors, including dendritic cell-specific transmembrane protein, ATP6vod2, cathepsin K, and integrin αv. Lastly, FOXO1 deletion reduced M-CSF-induced RANK expression and migration of osteoclast precursors. In the present study, we provide evidence that FOXO1 plays a direct role in osteoclast formation by mediating the effect of RANKL on NFATc1 and several downstream effectors. This is likely to be significant because FOXO1 and RANKL are elevated in osteolytic conditions.


title: The impact of technology on the buying behaviour in an online retail environment in India

abstract: Among the BRIC Nations, India has been the fastest growing online market adding over 18 million Internet users growing at an annual rate of 41%. India is also among the top 3 fastest growing markets worldwide in the last 12 months. A study [37] by The Associated Chamber of Commerce and Industry of India (ASSOCHAM) and ComScore has suggested online retail has grown 43% and has a 60% market penetration.


title: A Flexible Approach to Improving System Reliability with Virtual Lockstep

abstract: There is an increasing need for fault tolerance capabilities in logic devices brought about by the scaling of transistors to ever smaller geometries. This paper presents a hypervisor-based replication approach that can be applied to commodity hardware to allow for virtually lockstepped execution. It offers many of the benefits of hardware-based lockstep while being cheaper and easier to implement and more flexible in the configurations supported. A novel form of processor state fingerprinting is also presented, which can significantly reduce the fault detection latency. This further improves reliability by triggering rollback recovery before errors are recorded to a checkpoint. The mechanisms are validated using a full prototype and the benchmarks considered indicate an average performance overhead of approximately 14 percent with the possibility for significant optimization. Finally, a unique method of using virtual lockstep for fault injection testing is presented and used to show that significant detection latency reduction is achievable by comparing only a small amount of data across replicas.


title: Optimizing the Vehicle Routing Problem With Time Windows: A Discrete Particle Swarm Optimization Approach

abstract: Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.


title: Pregel: a system for large-scale graph processing

abstract: Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.


title: CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks

abstract: Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.


title: The Academic Resilience Scale (ARS-30): A New Multidimensional Construct Measure

abstract: Resilience is a psychological construct observed in some individuals that accounts for success despite adversity. Resilience reflects the ability to bounce back, to beat the odds and is considered an asset in human characteristic terms. Academic resilience contextualises the resilience construct and reflects an increased likelihood of educational success despite adversity. The paper provides an account of the development of a new measure of academic resilience. The 30 item Academic Resilience Scale (ARS-30) explores process—as opposed to outcome—aspects of resilience, providing a new multidimensional construct measure of academic resilience based on students’ specific adaptive cognitive-affective and behavioural responses to academic adversity. Findings from the study involving a sample of undergraduate students (N=532) demonstrate that the ARS-30 has good internal reliability and construct validity. It is suggested that a measure such as the ARS-30, which is based on adaptive responses, aligns more closely with the conceptualisation of resilience and provides a valid construct measure of academic resilience relevant for research and practice in university student populations.


title: A method to predict different mechanisms for blood-brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine.

abstract: The blood-brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.


title: The iterative time reversal process: Analysis of the convergence

abstract: The efficiency of a time reversal acoustic mirror to focus on a reflective target through an inhomogeneous media has been demonstrated. In a multitarget media, the ability of such a mirror to work in an iterative mode in order to focus selectively on the strongest target was shown [C. Prada, F. Wu, and M. Fink, J. Acoust. Soc. Am. 90, 1119 (1991)]. The theory of how the iterative time reversal process is built is based on a matrix formalism and treats the array of L transducers in a given medium as a linear system of L inputs/L outputs. The system is characterized at each frequency by its transfer matrix K and the time reversal iterative process is then described by a time reversal operator K*K. Because of the reciprocity principle, this operator is Hermitian. The following result is shown: If the scattering medium is a set of well resolved targets of different reflectivities then each eigenvector of the operator K*K with nonzero eigenvalue corresponds to one of the targets in the set and provides the opt...


title: The self-organizing map, the Geo-SOM, and relevant variants for geosciences

abstract: In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknesses of the different variants proposed are shown through a set of tests based on artificial data. A real world application of these techniques is given through the analysis of geodemographic data from Lisbon's metropolitan area.


Web-based e-catalog systems in B2B procurement


title: Data analysis and visualization of sales data

abstract: Data is being generated very rapidly due to increase in information in everyday life. Huge amount of data get accumulated from various organizations that is difficult to analyze and exploit. Data created by an expanding number of sensors in the environment such as traffic cameras and satellites, internet activities on social networking sites, healthcare database, government database, sales data etc., are example of huge data. Processing, analyzing and communicating this data are a challenge. Online shopping websites get flooded with voluminous amount of sales data every day. Analyzing and visualizing this data for information retrieval is a difficult task. Therefore a system is required which will effectively analyze and visualize data. This paper focuses on a system which will visualize sales data which will help users in applying intelligence in business, revenue generation, and decision making, managing business operation and tracking progress of tasks.


title: Image Retrieval based on Bag-of-Words model

abstract: This article gives a survey for bag-of-words (BoW) or bag-of-features model in image retrieval system. In recent years, large-scale image retrieval shows significant potential in both industry applications and research problems. As local descriptors like SIFT demonstrate great discriminative power in solving vision problems like object recognition, image classification and annotation, more and more state-of-the-art large scale image retrieval systems are trying to rely on them. A common way to achieve this is first quantizing local descriptors into visual words, and then applying scalable textual indexing and retrieval schemes. We call this model as bag-of-words or bag-of-features model. The goal of this survey is to give an overview of this model and introduce different strategies when building the system based on this model.


title: Faster Fully Homomorphic Encryption

abstract: We describe two improvements to Gentry’s fully homomorphic scheme based on ideal lattices and its analysis: we provide a more aggressive analysis of one of the hardness assumptions (the one related to the Sparse Subset Sum Problem) and we introduce a probabilistic decryption algorithm that can be implemented with an algebraic circuit of low multiplicative degree. Combined together, these improvements lead to a faster fully homomorphic scheme, with a O(λ 3.5) bit complexity per elementary binary add/mult gate, where λ is the security parameter. These improvements also apply to the fully homomorphic schemes of Smart and Vercauteren [PKC’2010] and van Dijk et al. [Eurocrypt’2010].


title: Passive online rogue access point detection using sequential hypothesis testing with TCP ACK-pairs

abstract: Rogue (unauthorized) wireless access points pose serious security threats to local networks. In this paper, we propose two online algorithms to detect rogue access points using sequential hypothesis tests applied to packet-header data collected passively at a monitoring point. One algorithm requires training sets, while the other does not. Both algorithms extend our earlier TCP ACK-pair technique to differentiate wired and wireless LAN TCP traffic, and exploit the fundamental properties of the 802.11 CSMA/CA MAC protocol and the half duplex nature of wireless channels. Our algorithms make prompt decisions as TCP ACK-pairs are observed, and only incur minimum computation and storage overhead. We have built a system for online rogue-access-point detection using these algorithms and deployed it at a university gateway router. Extensive experiments in various scenarios have demonstrated the excellent performance of our approach: the algorithm that requires training provides rapid detection and is extremely accurate (the detection is mostly within 10 seconds, with very low false positive and false negative ratios); the algorithm that does not require training detects 60%-76% of the wireless hosts without any false positives; both algorithms are light-weight (with computation and storage overhead well within the capability of commodity equipment).


title: vStarCloud: An operating system architecture for Cloud computing

abstract: In recent years, cloud computing is increasingly abstracting away the operating system, allowing developers to focus higher up the stack on applications, not infrastructure. In this position paper, we analyzed the deficiencies of the traditional operating systems in the cloud environment and the features of existing cloud operating systems. We have proposed an operating system architecture for cloud computing which is called vStarCloud, and implemented a prototype to show that our approach is promising. We describe how common problems for the cloud environment can be effectively solved by POSIV (Portable Operating System Interface of vStarCloud) interface. The prototype of vStarCloud provides the ability of virtual machine management and MapReduce job management. Then we discussed how to cope with the challenges of the new cloud operating systems.


title: Color detection and segmentation for road and traffic signs

abstract: This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University/Sweden in the field of the ITS


title: Rapid Clothing Retrieval via Deep Learning of Binary Codes and Hierarchical Search

abstract: This paper deals with the problem of clothing retrieval in a recommendation system. We develop a hierarchical deep search framework to tackle this problem. We use a pre-trained network model that has learned rich mid-level visual representations in module 1. Then, in module 2, we add a latent layer to the network and have neurons in this layer to learn hashes-like representations while fine-tuning it on the clothing dataset. Finally, module 3 achieves fast clothing retrieval using the learned hash codes and representations via a coarse-to-fine strategy. We use a large clothing dataset where 161,234 clothes images are collected and labeled. Experiments demonstrate the potential of our proposed framework for clothing retrieval in a large corpus.


title: Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : Application to fNIRS signals

abstract: Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.


title: Unsupervised adaptation for deep neural networks using Alternating Direction Method of Multipliers

abstract: In this paper, we continue our work on linear least squares based adaptation (LLS) for deep neural networks. We show that our previously proposed algorithm is a special case of an optimization algorithm called Alternating Direction Method of Multipliers (ADMM). We demonstrate that the adaptation algorithm can improve the performance on various deep neural networks including the bidirectional long short term memory (BLSTM). On the Switchboard subset of the Hub5 2000 evaluation set, we show that LLS adaptation can achieve 6 to 9% relative word error rate (WER) reduction, and improve our two-pass system to 7.5% WER. In this paper, we also analyze the factors that could contribute to the success of an adaptation algorithm. This helps us to understand under what circumstances, adaptation could improve the system performance.


title: When and how to update systematic reviews.

abstract: BACKGROUND ::: Systematic reviews are most helpful if they are up-to-date. We did a systematic review of strategies and methods describing when and how to update systematic reviews. ::: ::: ::: OBJECTIVES ::: To identify, describe and assess strategies and methods addressing: 1) when to update systematic reviews and 2) how to update systematic reviews. ::: ::: ::: SEARCH STRATEGY ::: We searched MEDLINE (1966 to December 2005), PsycINFO, the Cochrane Methodology Register (Issue 1, 2006), and hand searched the 2005 Cochrane Colloquium proceedings. ::: ::: ::: SELECTION CRITERIA ::: We included methodology reports, updated systematic reviews, commentaries, editorials, or other short reports describing the development, use, or comparison of strategies and methods for determining the need for updating or updating systematic reviews in healthcare. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: We abstracted information from each included report using a 15-item questionnaire. The strategies and methods for updating systematic reviews were assessed and compared descriptively with respect to their usefulness, comprehensiveness, advantages, and disadvantages. ::: ::: ::: MAIN RESULTS ::: Four updating strategies, one technique, and two statistical methods were identified. Three strategies addressed steps for updating and one strategy presented a model for assessing the need to update. One technique discussed the use of the "entry date" field in bibliographic searching. Statistical methods were cumulative meta-analysis and predicting when meta-analyses are outdated. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Little research has been conducted on when and how to update systematic reviews and the feasibility and efficiency of the identified approaches is uncertain. These shortcomings should be addressed in future research.


title: Expressive Time Series Querying with Hand-Drawn Scale-Free Sketches

abstract: We present Qetch, a tool where users freely sketch patterns on a scale-less canvas to query time series data without specifying query length or amplitude. We study how humans sketch time series patterns --- humans preserve visually salient perceptual features but often non-uniformly scale and locally distort a pattern --- and we develop a novel matching algorithm that accounts for human sketching errors. Qetch enables the easy construction of complex and expressive queries with two key features: regular expressions over sketches and relative positioning of sketches to query multiple time-aligned series. Through user studies, we demonstrate the effectiveness of Qetch's different interaction features. We also demonstrate the effectiveness of Qetch's matching algorithm compared to popular algorithms on targeted, and exploratory query-by-sketch search tasks on a variety of data sets.


title: Learning personal + social latent factor model for social recommendation

abstract: Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship. In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.


title: Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior

abstract: Research on the neural systems underlying emotion in animal models over the past two decades has implicated the amygdala in fear and other emotional processes. This work stimulated interest in pursuing the brain mechanisms of emotion in humans. Here, we review research on the role of the amygdala in emotional processes in both animal models and humans. The review is not exhaustive, but it highlights five major research topics that illustrate parallel roles for the amygdala in humans and other animals, including implicit emotional learning and memory, emotional modulation of memory, emotional influences on attention and perception, emotion and social behavior, and emotion inhibition and regulation.


title: Post-operative medical and surgical complications after primary total joint arthroplasty in solid organ transplant recipients: a case series

abstract: PURPOSE ::: In a series of solid organ transplant (SOT) recipients who underwent a subsequent primary total joint arthroplasty (TJA) procedure, this study aimed to determine: (1) 90-day morbidity and mortality after primary total knee or hip arthroplasty (TKA and THA), (2) overall post-operative infection rates, and (3) how complication and infection rates compared across primary TJA procedure and type of transplant organ. ::: ::: ::: METHODS ::: The University of Michigan Health System database was retrospectively searched using current procedural terminology codes for any primary TKA or THA performed at the institution in years 2000-2012 in a patient who previously received a successful SOT at any hospital. ::: ::: ::: RESULTS ::: The search yielded 44 arthroplasties performed in 29 SOT recipients (average age 54.8 years, average follow-up about 30 months for both groups). No deaths were reported, but 13/27 (48.1%) THA patients and 2/6 (33.3%) TKA patients experienced a total of 29 complications within 90 days of surgery. One patient (3.7%) [1/27 patients, 1/37 joints] underwent revision hip arthroplasty to correct limb length. One THA patient and two TKA patients developed infection requiring revision surgery (3.7% and 33%, respectively). Type of transplant did not affect complication rates (P=0.65), and infection was more common after TKA (P=0.01). ::: ::: ::: CONCLUSIONS ::: A series of SOT recipients demonstrated increased rates of infection and other complications following TJA. Surgical and medical teams should work closely to optimize this population for TJA surgery and minimize peri-operative complications. ::: ::: ::: LEVEL OF EVIDENCE & STUDY DESIGN ::: Level IV, Prognostic Case-Series.


title: Toolkits and Libraries for Deep Learning

abstract: Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.


title: Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

abstract: Recently, convolutional neural networks (CNNs) have been used as a powerful tool to solve many problems of machine learning and computer vision. In this paper, we aim to provide insight on the property of convolutional neural networks, as well as a generic method to improve the performance of many CNN architectures. Specifically, we first examine existing CNN models and observe an intriguing property that the filters in the lower layers form pairs (i.e., filters with opposite phase). Inspired by our observation, we propose a novel, simple yet effective activation scheme called concatenated ReLU (CRelu) and theoretically analyze its reconstruction property in CNNs. We integrate CRelu into several state-of-the-art CNN architectures and demonstrate improvement in their recognition performance on CIFAR-10/100 and ImageNet datasets with fewer trainable parameters. Our results suggest that better understanding of the properties of CNNs can lead to significant performance improvement with a simple modification.


title: Relational dialectics and social networking sites: The role of Facebook in romantic relationship escalation, maintenance, conflict, and dissolution

abstract: Abstract Due to their prevalence and unique affordances, social networking sites such as Facebook have the potential to influence offline relationships. This study employed Baxter’s (2011) refinement of relational dialectics theory to explore Facebook’s role in emerging adults’ romantic relationships. Data from ten focus groups revealed that Facebook contributes to and provides a forum for discursive struggles related to the integration–separation, expression–privacy, and stability–change dialectics. Romantic partners are able to connect with each other and integrate their social networks on Facebook, but some struggle to maintain privacy and independence. As such, SNSs can be a site of and trigger for romantic conflict. Participants’ responses indicated that Facebook is interwoven with the experience of these dialectics due to its affordances, specifically the semi-public nature of relationship activities on Facebook and the shift in control over relational information from individuals to network members.


title: A dynamic model for advertising and pricing competition between national and store brands

abstract: We study the relationship between the pricing and advertising decisions in a channel where a national brand is competing with a private label. We consider a differential game that incorporates the carryover effects of brand advertising over time for both the manufacturer and the retailer and we account for the complementary and competitive roles of advertising. Analysis of the obtained equilibrium Markov strategies shows that the relationship between advertising and pricing decisions in the channel depends mainly on the nature of the advertising effects. In particular, the manufacturer reacts to higher competitive retailer's advertising levels by offering price concessions and limiting his advertising expenditures. The retailer's optimal reaction to competitive advertising effects in the channel depends on two factors: (1) the price competition level between the store and the national brands and (2) the strength of the competitive advertising effects. For example, in case of intense price competition between the two brands combined with a strong manufacturer's competitive advertising effect, the retailer should lower both the store and the national brands' prices as a reaction to higher manufacturer's advertising levels. For the retailer, the main advantage from boosting his competitive advertising investments seems to be driven by increased revenues from the private label. The retailer should however limit his investments in advertising if the latter generates considerable competitive effects on the national brand's sales.


Learning Hypernymy over Word Embeddings


title: Loop closure detection for visual SLAM systems using deep neural networks

abstract: The detection of loop closure is of essential importance in visual simultaneous localization and mapping systems. It can reduce the accumulating drift of localization algorithms if the loops are checked correctly. Traditional loop closure detection approaches take advantage of Bag-of-Words model, which clusters the feature descriptors as words and measures the similarity between the observations in the word space. However, the features are usually designed artificially and may not be suitable for data from new-coming sensors. In this paper a novel loop closure detection approach is proposed that learns features from raw data using deep neural networks instead of common visual features. We discuss the details of the method of training neural networks. Experiments on an open dataset are also demonstrated to evaluate the performance of the proposed method. It can be seen that the neural network is feasible to solve this problem.