title: Shear velocity estimation by the combined use of supervised and unsupervised neural networks

abstract: A neural estimator composed of two different neural networks, namely, a perceptron an a Kohonen map, is proposed. The training algorithm of the resulting combined network is very simple since the component networks are trained in succession, starting from the Kohonen network, and the algorithms originally developed for them are used. The estimator is fully developed in connection with a determination of the shear velocity of the formation surrounding a fluid-filled borehole. The neural estimator is applied to synthetic seismograms out of the training set. In most of the cases a considerable improvement in estimation accuracy is obtained with respect to the Kohonen map. However, only a slight improvement in estimation accuracy is obtained with respect to the Kohonen map. However, only a slight improvement was noted for perceptrons which suffered convergence problems during the training phase. >


title: A Dual-Band Polarization Reconfigurable Antenna for WLAN Systems

abstract: A new microstrip dual-band polarization reconfigurable antenna is presented for wireless local area network (WLAN) systems operating at 2.4 and 5.8 GHz. The antenna consists of a square microstrip patch that is aperture coupled to a microstrip line located along the diagonal line of the patch. The dual-band operation is realized by employing the TM10 and TM30 modes of the patch antenna. Four shorting posts are inserted into the patch to adjust the frequency ratio of the two modes. The center of each edge of the patch is connected to ground via a PIN diode for polarization switching. By switching between the different states of PIN diodes, the proposed antenna can radiate either horizontal, vertical, or 45° linear polarization in the two frequency bands. Measured results on reflection coefficients and radiation patterns agree well with numerical simulations.


title: GeoMoB — A geo location based browser for secured mobile banking

abstract: With banks reaching its users via mobile banking, it is becoming one of the essential feature that is demanded by almost every smartphone user. Mobile banking via a mobile browser is similar to internet banking. Browsing-based threats for smartphones are just the same as those for personal computers, elevating the need to focus on mobile security. Among the several authentication schemes, geolocation authentication is gaining importance as it is found most suitable for mobile devices. In this paper, GeoMoB, a dedicated secure mobile browser for mobile banking that makes use of multifactor authentication is designed and developed. GeoMoB features a geolocation based authentication scheme which ensures security of mobile transactions based on the user location. In addition to the existing two factor authentication scheme using user ID, password and OTP, the mobile number and geolocation is used to authenticate the user. The geolocation intimates the banks location from where the transaction is going to be performed thus helping banks to ensure secure transactions. The geolocation of the user is acquired through the network provider and hence the need for using GSM is eliminated. The multifactor authentication used in GeoMoB ensures security while performing mobile transaction and prevents users from various attacks.


title: Reliable Decision Support using Counterfactual Models.

abstract: Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely future outcomes, but we show that this approach is unreliable, and sometimes even dangerous. The key issue is that supervised learning algorithms are highly sensitive to the policy used to choose actions in the training data, which causes the model to capture relationships that do not generalize. We propose using a different learning objective that predicts counterfactuals instead of predicting outcomes under an existing action policy as in supervised learning. To support decision-making in temporal settings, we introduce the Counterfactual Gaussian Process (CGP) to predict the counterfactual future progression of continuous-time trajectories under sequences of future actions. We demonstrate the benefits of the CGP on two important decision-support tasks: risk prediction and "what if?" reasoning for individualized treatment planning.


title: The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

abstract: Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients’ independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients’ or caregivers’ attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients , for short overnight control to supplement conventional insulin delivery , and for short periods where patients rest and follow a prescribed food re...


title: A survey on concept drift adaptation

abstract: Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.


External Memory Enhanced Sequence-to-Sequence Dialogue Systems


title: Use of lean and six sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center.

abstract: BACKGROUND ::: Operating rooms (ORs) are resource-intense and costly hospital units. Maximizing OR efficiency is essential to maintaining an economically viable institution. OR efficiency projects often focus on a limited number of ORs or cases. Efforts across an entire OR suite have not been reported. Lean and Six Sigma methodologies were developed in the manufacturing industry to increase efficiency by eliminating non-value-added steps. We applied Lean and Six Sigma methodologies across an entire surgical suite to improve efficiency. ::: ::: ::: STUDY DESIGN ::: A multidisciplinary surgical process improvement team constructed a value stream map of the entire surgical process from the decision for surgery to discharge. Each process step was analyzed in 3 domains, ie, personnel, information processed, and time. Multidisciplinary teams addressed 5 work streams to increase value at each step: minimizing volume variation; streamlining the preoperative process; reducing nonoperative time; eliminating redundant information; and promoting employee engagement. Process improvements were implemented sequentially in surgical specialties. Key performance metrics were collected before and after implementation. ::: ::: ::: RESULTS ::: Across 3 surgical specialties, process redesign resulted in substantial improvements in on-time starts and reduction in number of cases past 5 pm. Substantial gains were achieved in nonoperative time, staff overtime, and ORs saved. These changes resulted in substantial increases in margin/OR/day. ::: ::: ::: CONCLUSIONS ::: Use of Lean and Six Sigma methodologies increased OR efficiency and financial performance across an entire operating suite. Process mapping, leadership support, staff engagement, and sharing performance metrics are keys to enhancing OR efficiency. The performance gains were substantial, sustainable, positive financially, and transferrable to other specialties.


title: Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis

abstract: The importance of building sentiment analysis tools for Arabic social media has been recognized during the past couple of years, especially with the rapid increase in the number of Arabic social media users. One of the main difficulties in tackling this problem is that text within social media is mostly colloquial, with many dialects being used within social media platforms. In this paper, we present a set of features that were integrated with a machine learning based sentiment analysis model and applied on Egyptian, Saudi, Levantine, and MSA Arabic social media datasets. Many of the proposed features were derived through the use of an Arabic Sentiment Lexicon. The model also presents emoticon based features, as well as input text related features such as the number of segments within the text, the length of the text, whether the text ends with a question mark or not, etc. We show that the presented features have resulted in an increased accuracy across six of the seven datasets we've experimented with and which are all benchmarked. Since the developed model out-performs all existing Arabic sentiment analysis systems that have publicly available datasets, we can state that this model presents state-of-the-art in Arabic sentiment analysis.


title: scBatch: Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment

abstract: MOTIVATION ::: Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single cell RNA sequencing (RNA-seq) data. ::: ::: ::: RESULTS ::: We present scBatch, a numerical algorithm for batch effect correction on bulk and single cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch effect correction methods. ::: ::: ::: AVAILABILITY ::: The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this paper is available at github.com/tengfei-emory/scBatch-paper-scripts. ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online.


title: A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation

abstract: Customers 'purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.


title: Plug-and-Play Distributed Algorithms for Optimized Power Generation in a Microgrid

abstract: This paper introduces distributed algorithms that share the power generation task in an optimized fashion among the several Distributed Energy Resources (DERs) within a microgrid. We borrow certain concepts from communication network theory, namely Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, which are known to be convenient in terms of communication requirements and network efficiency. We adapt the synchronized version of AIMD to minimize a cost utility function of interest in the framework of smart grids. We then implement the AIMD utility optimisation strategies in a realistic power network simulation in Matlab-OpenDSS environment, and we show that the performance is very close to the full-communication centralized case.


title: An Empirical Study of Real-World SPARQL Queries

abstract: Understanding how users tailor their SPARQL queries is crucial when designing query evaluation engines or fine-tuning RDF stores with performance in mind. In this paper we analyze 3 million real-world SPARQL queries extracted from logs of the DBPedia and SWDF public endpoints. We aim at finding which are the most used language elements both from syntactical and structural perspectives, paying special attention to triple patterns and joins, since they are indeed some of the most expensive SPARQL operations at evaluation phase. We have determined that most of the queries are simple and include few triple patterns and joins, being Subject-Subject, Subject-Object and Object-Object the most common join types. The graph patterns are usually star-shaped and despite triple pattern chains exist, they are generally short.


title: Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

abstract: Abstract The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.


title: Parent Praise to 1‐ to 3‐Year‐Olds Predicts Children's Motivational Frameworks 5 Years Later

abstract: In laboratory studies, praising children's effort encourages them to adopt incremental motivational frameworks—they believe ability is malleable, attribute success to hard work, enjoy challenges, and generate strategies for improvement. In contrast, praising children's inherent abilities encourages them to adopt fixed-ability frameworks. Does the praise parents spontaneously give children at home show the same effects? Although parents' early praise of inherent characteristics was not associated with children's later fixed-ability frameworks, parents' praise of children's effort at 14–38 months (N = 53) did predict incremental frameworks at 7–8 years, suggesting that causal mechanisms identified in experimental work may be operating in home environments.


title: Asphyxial Suicide with Helium and a Plastic Bag

abstract: Suicide by helium inhalation inside a plastic bag has recently been publicized by right-to-die proponents in “how to” print and videotape materials. This article reports a suicide performed according to this new and highly lethal technique, which is also a potentially undetectable cause of death. Toxicology information could not determine helium inhalation, and drug screening did not reveal data of significance. The cause of death could be determined only by the physical evidence at the scene of death. Helium inhalation can easily be concealed when interested parties remove or alter evidence. To ensure that their deaths are not documented as suicide, some individuals considering assisted suicide may choose helium methods and assistance from helpers. Recent challenges to Oregon’s physician-assisted suicide law may increase interest in helium instead of barbiturates for assisted suicide.


title: Partitioned residual echo power estimation for frequency‐domain acoustic echo cancellation and postfiltering

abstract: Residual echo arises in hands-free telephony equipment due to insufficient echo canceler convergence, but can be suppressed using a postfilter. The residual echo power spectral density is the most crucial control parameter for both frequency-domain acoustic echo cancellation and combined residual echo and noise postfiltering. In this contribution we present and compare residual echo power spectral estimation techniques. We introduce a new partitioned block-adaptive estimation technique delivering considerably improved residual echo estimates in strongly reverberant and noisy acoustic environments. We show that the adaptation loop of the frequency-domain adaptive filter (FDAF) can be used simultaneously for residual echo power estimation and tracking of the echo path impulse response. In this way, the FDAF and the postfilter concept supplement each other in a true synergy with low complexity. The resulting echo and noise control system proves to be robust in double talk situations as well.


title: What factors facilitate teacher skill , teacher morale , and perceived student learning in technology-using classrooms ?

abstract: Abstract Based on a comprehensive study of 94 classrooms from four states in different geographic regions of the country, this quantitative study investigated the impact of seven factors related to school technology (planning, leadership, curriculum alignment, professional development, technology use, teacher openness to change, and teacher non-school computer use) on five dependent measures in the areas of teacher skill (technology competency and technology integration), teacher morale, and perceived student learning (impact on student content acquisition and higher order thinking skills acquisition). Stepwise regression resulted in models to explain each of the five dependent measures. Teacher technology competency was predicted by teacher openness to change. Technology integration was predicted by teacher openness to change and the percentage of technology use with others. Teacher morale was predicted by professional development and constructivist use of technology. Technology impact on content acquisition was predicted by the strength of leadership, teacher openness to change, and negatively influenced by teacher non-school computer use. Technology impact on higher-order thinking skills was predicted by teacher openness to change, the constructivist use of technology, and negatively influenced by percentage of technology use where students work alone. Implications for the adoption and use of school technologies are discussed.


title: Measuring mindfulness—the Freiburg Mindfulness Inventory (FMI)

abstract: Mindfulness, a concept originally derived from Buddhist psychology, is essential for some well-known clinical interventions. Therefore an instrument for measuring mindfulness is useful. We report here on two studies constructing and validating the Freiburg Mindfulness Inventory (FMI) including a short form. A preliminary questionnaire was constructed through expert interviews and extensive literature analysis and tested in 115 subjects attending mindfulness meditation retreats. This psychometrically sound 30-item scale with an internal consistency of Cronbach alpha = .93 was able to significantly demonstrate the increase in mindfulness after the retreat and to discriminate between experienced and novice meditators. In a second study we broadened the scope of the concept to 86 subjects without meditation experience, 117 subjects with clinical problems, and 54 participants from retreats. Reducing the scale to a short form with 14 items resulted in a semantically robust and psychometrically stable (alpha = .86) form. Correlation with other relevant constructs (self-awareness, dissociation, global severity index, meditation experience in years) was significant in the medium to low range of correlations and lends construct validity to the scale. Principal Component Analysis suggests one common factor. This short scale is sensitive to change and can be used also with subjects without previous meditation experience.


title: QoE in Video Transmission: A User Experience-Driven Strategy

abstract: The increasing popularity of video (i.e., audio-visual) applications or services over both wired and wireless links has prompted recent growing interests in the investigations of quality of experience (QoE) in online video transmission. Conventional video quality metrics, such as peak-signal-to-noise-ratio and quality of service, only focus on the reception quality from the systematic perspective. As a result, they cannot represent the true visual experience of an individual user. Instead, the QoE introduces a user experience-driven strategy which puts special emphasis on the contextual and human factors in addition to the transmission system. This advantage has raised the popularity and widespread usage of QoE in video transmission. In this paper, we present an overview of selected issues pertaining to QoE and its recent applications in video transmission, with consideration of the compelling features of QoE (i.e., context and human factors). The selected issues include QoE modeling with influence factors in the end-to-end chain of video transmission, QoE assessment (including subjective test and objective QoE monitoring) and QoE management of video transmission over different types of networks. Through the literature review, we observe that the context and human factors in QoE-aware video transmission have attracted significant attentions since the past two to three years. A vast number of high quality works were published in this area, and will be highlighted in this survey. In addition to a thorough summary of recent progresses, we also present an outlook of future developments on QoE assessment and management in video transmission, especially focusing on the context and human factors that have not been addressed yet and the technical challenges that have not been completely solved so far. We believe that our overview and findings can provide a timely perspective on the related issues and the future research directions in QoE-oriented services over video communications.


title: Identification of many microRNAs that copurify with polyribosomes in mammalian neurons

abstract: Localized translation in mammalian dendrites may play a role in synaptic plasticity and contribute to the molecular basis for learning and memory. The regulatory mechanisms that control localized translation in neurons are not well understood. We propose a role for microRNAs (miRNAs), a class of noncoding RNAs, as mediators of neuronal translational regulation. We have identified 86 miRNAs expressed in mammalian neurons, of which 40 have not previously been reported. A subset of these miRNAs exhibits temporally regulated expression in cortical cultures. Moreover, all of the miRNAs that were tested cofractionate with polyribosomes, the sites of active translation. These findings indicate that a large, diverse population of miRNAs may function to regulate translation in mammalian neurons.


title: Reverse Analysis and Vulnerability Detection for Network System Software

abstract: In information era advocating ubiquitous computing, computing resources are susceptible to attack without guaranteeing security of network system. It is necessary and desirable for network system to employ powerful safeguard to protect itself against diversified vulnerabilities. In this paper, we present reverse analysis and vulnerability detection for network system software (RAVDNSS), a novel approach which uses reverse analysis and vulnerability detection technologies to deal with security problems on critical network system. Adaptive reverse analysis we propose is used to dig out potential vulnerabilities, which might be abused by unauthorized and unlawful communities. A new vulnerability detection model is designed to provide safety precautions through detecting vulnerabilities and monitoring program behaviors. Our investigation aims to improve the ability to guard network system against malicious attacks. The proposed schemes demonstrate that our approach can effectively perform security detection and management of network system software.


title: Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

abstract: The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics--techniques for analyzing large quantities of data and gleaning new insights from that analysis--which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases--that is, key examples--where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure--analytics, algorithms, registries, assessment scores, monitoring devices, and so forth--that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.


title: Double Tree: An Advanced KWIC Visualization for Expert Users

abstract: In this paper we present Double Tree, a new visualization of Key Word In Context (KWIC) displays targeted to support linguistic analysis. Inspired by Wattenberg’s and Viegas’ [1] Word Tree visualization, Double Tree extends the idea of representing KWIC results as trees. We address several issues with Word Trees with respect to the specific demands of linguists and discuss the design decisions and implementation details that we chose for Double Tree. In particular we present our approach for displaying a two-sided tree. We describe details of the layout, including how frequency and linguistic information is incorporated, and what user interaction is supported. We conclude with some consideration on possible next steps for Double Tree.


title: Probabilistic graph and hypergraph matching

abstract: We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyper-edges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyper-edge weight matrix and the desired vertex-to-vertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn [15] row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straight-forward scalability from graphs to hyper-graphs.


title: Sustainable E-Governance: The Relationship among Trust, Digital Divide, and E-Government

abstract: This study empirically examines the correlation between the quality of e-government and trust in government. It used survey data collected in 2013 from the metropolitan areas of Seoul. An index was developed to measure the quality of e-government services, and the Gov 3.0 values were reflected in the analysis, including openness, sharing, communication, and collaboration. The results show a partial correlation between the quality of e-government service and trust in government. In addition, the level of trust varied according to the different type of the digital divide groups. It suggests that as ICT (Information Communication Technology) has become more sophisticated, a willingness to share information among organizations and stakeholders may become a major factor to thoseactively seeking information and resources to make value-added products. It also suggests that more integrated data management including network securityand an open attitude toward information sharing will be more important beyond the level of technical issues.


title: Novel Silicon-Controlled Rectifier (SCR) for High-Voltage Electrostatic Discharge (ESD) Applications

abstract: Electrostatic discharge (ESD) protection for high-voltage integrated circuits is challenging due to the requirement of high holding voltage to minimize the risk of ESD-induced latchup and electrical overstress. In this letter, a new silicon-controlled rectifier (SCR) is developed for this particular application. The SCR is designed based on the concept that the holding voltage can be increased by reducing the emitter injection efficiency in the SCR. This is accomplished by using a segmented emitter topology. Experimental data show that the new SCR can possess a holding voltage that is larger than 40 V and a failure current It2 that is higher than 28 mA/mum.


title: Integrating three dimensional visualization and additive manufacturing into K-12 classrooms

abstract: With the increased availability and affordability of three dimensional visualization and additive manufacturing tools, the opportunity to bring these technologies into the classroom has never been greater. Utilizing 3D scanners, 3D modeling software and 3D printers in the classroom opens the door for hands-on STEM and STEaM education. This paper outlines methods and approaches to introduce these technologies into K-12 classrooms. Including technologies available to educators, methods and approaches to bring these technologies into the classroom and results and lessons learned from in-school pilot programs related to these technologies. Overall this paper is intended to aid educators in bringing these technologies into the classroom to broaden STEM and STEaM education.


title: Lip and vermilion reconstruction with the facial artery musculomucosal flap.

abstract: The lips are a complex laminated structure. When lost through injury or disease, they present a complex reconstructive challenge. The facial artery musculomucosal (FAMM) flap is a composite flap with features similar to those of lip tissue. In this article, the anatomy, dissection, and clinical applications for the use of the FAMM flap in lip and vermilion reconstruction are discussed. A series of 16 FAMM flaps in 13 patients is presented. Seven patients had upper-lip reconstruction and six had lower-lip reconstruction. Superiorly based FAMM flaps were used in eight patients, and eight inferiorly based flaps were performed in five patients. Three patients had bilateral, inferiorly based flaps. In summary, the FAMM flap is a local flap that can be used for lip and vermilion reconstruction. Although not identical to the lip, it has many similar features, which make it an excellent option for lip reconstruction.


title: Proactive Thermal-Aware Resource Management in Virtualized HPC Cloud Datacenters

abstract: Clouds provide the abstraction of nearly-unlimited computing resources through the elastic use of federated resource pools (virtualized datacenters). They are being increasingly considered for HPC applications, which have traditionally targeted grids and supercomputing clusters. However, maximizing energy efficiency and utilization of cloud datacenter resources, avoiding undesired thermal hotspots (due to overheating of over-utilized computing equipment), and ensuring quality of service guarantees for HPC applications are all conflicting objectives, which require joint consideration of multiple pairwise tradeoffs. An innovative proactive thermal-aware virtual machine consolidation (involving allocations as well as migrations) technique is proposed to maximize computing resource utilization, to minimize datacenter energy consumption for computing, and to improve the efficiency of heat extraction. The capability to migrate virtual machines away from lightly-loaded servers in a thermal-aware manner opens up opportunity to improve resource consolidation over time and, hence, achieve the aforementioned goals. The effectiveness of the proposed technique is verified through experimental evaluations with HPC workload traces under single- as well as federated-datacenter scenarios.


title: Active Transfer Learning for Cross-System Recommendation

abstract: Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previous transfer learning models assume that entity-correspondences across different systems are given as input, which means that for any entity (e.g., a user or an item) in a target system, its corresponding entity in a source system is known. This assumption can hardly be satisfied in real-world scenarios where entity-correspondences across systems are usually unknown, and the cost of identifying them can be expensive. For example, it is extremely difficult to identify whether a user A from Facebook and a user B from Twitter are the same person. In this paper, we propose a framework to construct entity correspondence with limited budget by using active learning to facilitate knowledge transfer across recommender systems. Specifically, for the purpose of maximizing knowledge transfer, we first iteratively select entities in the target system based on our proposed criterion to query their correspondences in the source system. We then plug the actively constructed entity-correspondence mapping into a general transferred collaborative-filtering model to improve recommendation quality. We perform extensive experiments on real world datasets to verify the effectiveness of our proposed framework for this crosssystem recommendation problem.


title: Statistical parsing of Bangla sentences by CYK algorithm

abstract: Statistical parsing is the task of enabling the parser to find the most probable parse of a sentence according to probabilistic context-free grammar. Crucial use of statistical parser is to solve the disambiguation problem. This paper proposes a statistical parser using probabilistic version of Cocke-Younger-Kasami (CYK) algorithm to parse different kinds of Bangla sentences. For improving parsing efficiency, this model also uses left binarization technique to grammar. Rule probability and word probability is used to generate different probabilities for the same structure of a sentence. Experiment results with different kinds of sentence shows the effectiveness of the propose parser with reasonable accuracy.


title: Accent: A communication oriented network operating system kernel

abstract: Accent is a communication oriented operating system kernel being built at Carnegie-Mellon University to support the distributed personal computing project, Spice, and the development of a fault-tolerant distributed sensor network (DSN). Accent is built around a single, powerful abstraction of communication between processes, with all kernel functions, such as device access and virtual memory management accessible through messages and distributable throughout a network. In this paper, specific attention is given to system supplied facilities which support transparent network access and fault-tolerant behavior. Many of these facilities are already being provided under a modified version of VAX/UNIX. The Accent system itself is currently being implemented on the Three Rivers Corp. PERQ.


title: Grid-Tied PV system energy smoothing

abstract: Grid-tied PV energy smoothing was implemented by using a valve regulated lead-acid (VRLA) battery as a temporary energy storage device to both charge and discharge as required to smooth the inverter energy output from the PV array. Inverter output was controlled by the average solar irradiance over the previous 1h time interval. On a clear day the solar irradiance power curve is offset by about 1h, while on a variable cloudy day the inverter output power curve will be smoothed based on the average solar irradiance. Test results demonstrate that this smoothing algorithm works very well. Battery state of charge was more difficult to manage because of the variable system inefficiencies. Testing continued for 30-days and established consistent operational performance for extended periods of time under a wide variety of resource conditions. Both battery technologies from Exide (Absolyte) and East Penn (Advanced Valve Regulated Lead-Acid) proved to cycle well at a partial state of charge over the time interval tested.


title: Ontology Patterns and Beyond - Towards a Universal Pattern Language

abstract: In this paper we argue for a broader view of ontology patterns and therefore present different use-cases where drawbacks of the current declarative pattern languages can be seen. We also discuss usecases where a declarative pattern approach can replace procedural-coded ontology patterns. With previous work on an ontology pattern language in mind we argue for a general pattern language.


title: Block based curvelet feature extraction for face recognition

abstract: In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed for face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA and Boosted LDA (BLDA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet algorithm. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.


title: Singing Voice Separation and Pitch Extraction from Monaural Polyphonic Audio Music via DNN and Adaptive Pitch Tracking

abstract: With the explosive growth of audio music everywhere over the Internet, it is becoming more important to be able to classify or retrieve audio music based on their key components, such as vocal pitch for common popular music. This paper proposes a novel and effective two-stage approach to singing pitch extraction, which involves singing voice separation and pitch tracking for monaural polyphonic audio music. The first stage extracts singing voice from the songs by using deep neural networks in a supervised setting. Then the second stage estimates the pitch based on the extracted singing voice in a robust manner. Experimental results based on MIR-1K showed that the proposed approach outperforms a previous state-of-the-art approach in raw-pitch accuracy. Moreover, the proposed approach has been submitted to the singing voice separation and audio melody extraction tasks of Music Information Retrieval Evaluation eXchange (MIREX) in 2015. The results of the competition shows that the proposed approach is superior to other submitted algorithms, which demonstrates the feasibility of the method for further applications in music processing.


title: Compact band-notched ultra wideband printed monopole antenna

abstract: A simple and compact microstrip-fed ultra wideband (UWB) printed monopole antenna with band-notched performance is proposed in this paper. The antenna is composed of a cirque ring with a small strip bar, so that the antenna occupies about 8.29 GHz bandwidth covering 3.18-11.47 GHz with expected band rejection of 5.09 GHz to 5.88 GHz. A quasi-omnidirectional and quasi-symmetrical radiation pattern is also obtained. This kind of band-notched UWB antenna requires no external filters and thus greatly simplifies the system design of UWB wireless communication.


title: A comprehensive method for multilingual video text detection, localization, and extraction

abstract: Text in video is a very compact and accurate clue for video indexing and summarization. Most video text detection and extraction methods hold assumptions on text color, background contrast, and font style. Moreover, few methods can handle multilingual text well since different languages may have quite different appearances. This paper performs a detailed analysis of multilingual text characteristics, including English and Chinese. Based on the analysis, we propose a comprehensive, efficient video text detection, localization, and extraction method, which emphasizes the multilingual capability over the whole processing. The proposed method is also robust to various background complexities and text appearances. The text detection is carried out by edge detection, local thresholding, and hysteresis edge recovery. The coarse-to-fine localization scheme is then performed to identify text regions accurately. The text extraction consists of adaptive thresholding, dam point labeling, and inward filling. Experimental results on a large number of video images and comparisons with other methods are reported in detail.


title: Algoritmic music composition based on artificial intelligence: A survey

abstract: We present a taxonomy of the Artificial Intelligence (AI) methods currently applied for algorithmic music composition. The area known as algorithmic music composition concerns the research on processes of composing music pieces automatically by a computer system. The use of AI for algorithmic music consists on the application of AI techniques as the main tools for the composition generation. There are several models of AI used in music composition such as: heuristics in evolutionary algorithms, neural networks, stochastic methods, generative models, agents, decision trees, declarative programming and grammatical representation. In this survey we present the trending in techniques for automatic music composition. We summarized several research projects of the last seven years and highlight the directions of music composition based on AI techniques.


title: An Efficient Multiplication Algorithm Using Nikhilam Method

abstract: Multiplication is one of the most important operation in computer arithmetic. Many integer operations such as squaring, division and computing reciprocal require same order of time as multiplication whereas some other operations such as computing GCD and residue operation require at most a factor of log n time more than multiplication. We propose an integer multiplication algorithm using Nikhilam method of Vedic mathematics which can be used to multiply two binary numbers efficiently.


Encoding Causality via Modal Formulae


Reactive Extrusion Systems


title: Adaptive Voltage Control of the DC/DC Boost Stage in PV Converters With Small Input Capacitor

abstract: In the case of photovoltaic (PV) systems, an adequate PV voltage regulation is fundamental in order to both maximize and limit the power. For this purpose, a large input capacitor has traditionally been used. However, when reducing that capacitor's size, the nonlinearities of the PV array make the performance of the voltage regulation become highly dependent on the operating point. This paper analyzes the nonlinear characteristics of the PV generator and clearly states their effect on the control of the dc/dc boost stage of commercial converters by means of a linearization around the operating point. Then, it proposes an adaptive control, which enables the use of a small input capacitor preserving at the same time the performance of the original system with a large capacitor. Experimental results are carried out for a commercial converter with a 40 μF input capacitor, and a 4 kW PV array. The results corroborate the theoretical analysis; they evidence the problems of the traditional control, and validate the proposed control with such a small capacitor.


title: Joint Training of Cascaded CNN for Face Detection

abstract: Cascade has been widely used in face detection, where classifier with low computation cost can be firstly used to shrink most of the background while keeping the recall. The cascade in detection is popularized by seminal Viola-Jones framework and then widely used in other pipelines, such as DPM and CNN. However, to our best knowledge, most of the previous detection methods use cascade in a greedy manner, where previous stages in cascade are fixed when training a new stage. So optimizations of different CNNs are isolated. In this paper, we propose joint training to achieve end-to-end optimization for CNN cascade. We show that the back propagation algorithm used in training CNN can be naturally used in training CNN cascade. We present how jointly training can be conducted on naive CNN cascade and more sophisticated region proposal network (RPN) and fast R-CNN. Experiments on face detection benchmarks verify the advantages of the joint training.


title: Deep Residual Learning for Image Recognition

abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.


title: Color-Guided Depth Map Super Resolution Using Convolutional Neural Network

abstract: With the development of 3-D applications, such as 3-D reconstruction and object recognition, accurate and high-quality depth map is urgently required. Recently, depth cameras have been affordable and widely used in daily life. However, the captured depth map always owns low resolution and poor quality, which limits its practical application. This paper proposes a color-guided depth map super resolution method using convolutional neural network. First, a dual-stream convolutional neural network, which integrates the color and depth information simultaneously, is proposed for depth map super resolution. Then, the optimized edge map generated by the high resolution color image and low resolution depth map is used as additional information to refine the object boundary in the depth map. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.


title: A cognitive-motivational analysis of anxiety.

abstract: Evidence of preattentive and attentional biases in anxiety is evaluated from a cognitive-motivational perspective. According to this analysis, vulnerability to anxiety stems mainly from a lower threshold for appraising threat, rather than a bias in the direction of attention deployment. Thus, relatively innocuous stimuli are evaluated as having higher subjective threat value by high than low trait anxious individuals, and it is further assumed that everyone orients to stimuli that are judged to be significantly threatening. This account is contrasted with other recent cognitive models of anxiety, and implications for the etiology, maintenance and treatment of anxiety disorders are discussed.


The Unconscious Eye Opener: Pupil Dilation Reveals Strategic Recruitment of Resources Upon Presentation of Subliminal Reward Cues


Allan Variance Analysis on Error Characters of MEMS Inertial Sensors for an FPGA-based GPS / INS System


A client-side analysis of TLS usage in mobile apps


title: Proofs of Proofs of Work with Sublinear Complexity

abstract: In the setting of blockchain based transaction ledgers we study the problem of “simplified payment verification” (SPV) which refers to the setting of a transaction verifier that wishes to examine the last k blocks of the blockchain (e.g., for the purpose of verification of a certain transaction) using as only advice the genesis block (or some “checkpoint” block that is known to it).


title: A multi-label classification based approach for sentiment classification

abstract: A multilabel classification based approach for sentiment analysis is proposed.Empirical comparisons of different multilabel classification methods are performed.Three different sentiment dictionaries are compared.Dalian University of Technology Sentiment Dictionary is the best in the comparisons. A multi-label classification based approach for sentiment analysis is proposed in this paper. To the best of our knowledge, this work is the first to propose to use multi-label classification for sentiment classification of microblogs. The proposed prototype has three main components, text segmentation, feature extraction, and multi-label classification.Raw segmented words and sentiment features based on the three different sentiment dictionaries, Dalian University of Technology Sentiment Dictionary, National Taiwan University Sentiment Dictionary and HowNet Dictionary, are the features and the bag of words is the feature representation.A detailed empirical study of different multi-label classification methods on sentiment classification is conducted to compare their classification performances. Specifically, total 11 state of the art multi-label classification methods are compared on two microblog datasets and 8 evaluation metrics are used. The effects of the three sentiment dictionaries for multi-label classification are empirically studied and compared, which, to the best of our knowledge, have not been performed. The performed empirical comparisons show that Dalian University of Technology Sentiment Dictionary has the best performance among the three different sentiment dictionaries.


title: Catfish Sampling Techniques: Where We Are Now and Where We Should Go

abstract: ABSTRACT We review the peer-reviewed literature regarding sampling of the three most commonly managed ictalurids: Channel Catfish, Blue Catfish, and Flathead Catfish. For each species, we summarize what is known about data quality (accuracy and precision) and sampling efficiency of the most commonly used gears for surveying these species. We identify research needs and provide information to guide gear selection based on different sampling objectives. To rank gear-specific sampling efficiency (catch/h and catch/person-h), we report median catch rates and the interpolated 25th and 75th percentiles of published means. We also describe the accuracy of relative abundance and size-related metrics for each gear. For Channel Catfish, tandem baited hoop nets provide the most efficient (11–24 fish/net/tandem set, 20–60 fish/person-h) and accurate samples. Low-frequency electrofishing provides the most efficient samples of Blue Catfish (23–373 fish/h, 2.1–11.3 fish/person-h) and Flathead Catfish (19–62 fish/h, 2.1–...


title: Are you close with me? are you nearby?: investigating social groups, closeness, and willingness to share

abstract: As ubiquitous computing becomes increasingly mobile and social, personal information sharing will likely increase in frequency, the variety of friends to share with, and range of information that can be shared. Past work has identified that whom you share with is important for choosing whether or not to share, but little work has explored which features of interpersonal relationships influence sharing. We present the results of a study of 42 participants, who self-report aspects of their relationships with 70 of their friends, including frequency of collocation and communication, closeness, and social group. Participants rated their willingness to share in 21 different scenarios based on information a UbiComp system could provide. Our findings show that (a) self-reported closeness is the strongest indicator of willingness to share, (b) individuals are more likely to share in scenarios with common information (e.g. we are within one mile of each other) than other kinds of scenarios (e.g. my location wherever I am), and (c) frequency of communication predicts both closeness and willingness to share better than frequency of collocation.


title: Depth Images Could Tell us More: Enhancing Depth Discriminability for RGB-D Scene Recognition

abstract: Recently depth-modal information has been witnessed effectively in computer vision community, especially for scene analysis related tasks. However, it still suffers severely from depth data scarcity as well as improperly transferring pre-trained RGB models to fit depth-modal data. In this study, we propose a novel two-step training strategy to address these problems and focus on enhancing the recognition power for depth-modal images in RGB-D scene recognition task. Specifically, we build an effective “Res-U” architecture on a GAN (generative adversarial networks) based RGB-to-depth modality translation model, which is endowed with both short and long skips for residual learning. On one hand, this could first well pre-train a depth-modal-specific discriminator network from scratch in an unsupervised manner, which is effectively transformed for the subsequent recognition task instead of directly fitting pre-trained RGB model to depth-specific one. On the other hand, new depth images with helpful perturbations, generated from the modality translation model, help argument the original training set and regularize the learning process in some sense. This two-step training strategy makes it more effective for training a modal-specific network to discriminate depth scenes. Besides, we extensively explore the modality translation network to investigate the effects in recognizing depth-modal scenes, which encourages a reasonable way to take full advantage of multi-modalities. The proposed method achieves state-of-the-art accuracy on NYU Depth v2 and SUN RGB-D benchmark datasets, especially on depth data only evaluation.


title: Microsphere-Coupled Scanning Laser Confocal Nanoscope for Sub-Diffraction-Limited Imaging at 25 nm Lateral Resolution in the Visible Spectrum

abstract: We report a direct optical super-resolution imaging approach with 25 nm (∼λ/17) lateral resolution under 408 nm wavelength illumination by combining fused silica and polystyrene microspheres with a conventional scanning laser confocal microscope (SLCM). The microsphere deposited on the target surface generates a nanoscale central lobe illuminating a sub-diffraction-limited cross-section located on the target surface. The SLCM confocal pinhole isolates the reflected light from the near-field subdiffractive cross-section and suppresses the noises from the side lobe and the far-field paraxial focal point. The structural detail of the subdiffractive cross-section is therefore captured, and the 2D target surface near the bottom of microspheres can be imaged by intensity-based point scanning.


title: Continuous-time common-mode feedback for high-speed switched-capacitor networks

abstract: This paper deals with the design of a continuous-time common-mode feedback (CMFB) for switched-capacitor networks. Its reduced input capacitance decreases the capacitive load at the output of the fully differential amplifier, improving its achievable gain-bandwidth (GBW) product and slew rate. This topology is more suitable for high-speed switched-capacitor applications when compared to a conventional switched-capacitor CMFB, enabling operation at higher clock frequencies. Additionally, it provides a superior rejection to the negative power supply noise (PSRR/sup -/). The performance of the CMFB is demonstrated in the implementation of a second-order 10.7-MHz bandpass switched-capacitor filter and compared with that of an identical filter using the conventional switched-capacitor CMFB (SC-CMFB). The filter using the continuous-time CMFB reduces the error due to finite GBW and slew rate to less than 1% for clock frequencies up to 72 MHz while providing a dynamic range of 59 dB and a PSRR/sup -/>22 dB. Both circuits were fabricated in 0.35-/spl mu/m CMOS technology.


title: Behavioral software engineering - guidelines for qualitative studies

abstract: Researchers are increasingly recognizing the importance of human aspects in software development and since qualitative methods are used to, in-depth, explore human behavior, we believe that studies using such techniques will become more common. ::: Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and knowledge on using them found in the social sciences. The aim of this study was thus to extend the software engineering research community's current body of knowledge regarding available qualitative methods and provide recommendations and guidelines for their use. ::: With the support of a literature review, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. ::: We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. ::: Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.


title: Translating Video Content to Natural Language Descriptions

abstract: Humans use rich natural language to describe and communicate visual perceptions. In order to provide natural language descriptions for visual content, this paper combines two important ingredients. First, we generate a rich semantic representation of the visual content including e.g. object and activity labels. To predict the semantic representation we learn a CRF to model the relationships between different components of the visual input. And second, we propose to formulate the generation of natural language as a machine translation problem using the semantic representation as source language and the generated sentences as target language. For this we exploit the power of a parallel corpus of videos and textual descriptions and adapt statistical machine translation to translate between our two languages. We evaluate our video descriptions on the TACoS dataset, which contains video snippets aligned with sentence descriptions. Using automatic evaluation and human judgments we show significant improvements over several baseline approaches, motivated by prior work. Our translation approach also shows improvements over related work on an image description task.


title: The challenge of decentralized marketplaces

abstract: Online trust systems are playing an important role in to-days world and face various challenges in building them. Billions of dollars of products and services are traded through electronic commerce, files are shared among large peer-to-peer networks and smart contracts can potentially replace paper contracts with digital contracts. These systems rely on trust mechanisms in peer-to-peer networks like reputation systems or a trustless public ledger. In most cases, reputation systems are build to determine the trustworthiness of users and to provide incentives for users to make a fair contribution to the peer-to-peer network. The main challenges are how to set up a good trust system, how to deal with security issues and how to deal with strategic users trying to cheat on the system. The Sybil attack, the most important attack on reputation systems is discussed. At last match making in two sided markets and the strategy proofness of these markets are discussed.


title: SURGICAL MANAGEMENT OF VULVAR CANCER

abstract: Vulvar cancer is an uncommon disease, marked by typical long delays in diagnosis due to lack of awareness by doctors and patients. The most common histology is squamous, although melanoma, sarcoma and adenocarcinoma occur less frequently. The predictable spread pattern of vulvar cancer to regional then distant lymphatics has allowed for improvements in survival largely due to radical surgical intervention. However, the significant morbidity from radical surgery has led to the search for better prognostic indicators and complementary therapeutic modalities to modify the extent of surgery in both early and advanced disease. En bloc radical vulvectomy and bilateral inguinal-femoral lymphadenectomy are rarely performed today: an early invasive stage has been defined where only limited excision is required. The extent of and the indications for inguinal lymphadenectomy for various clinical tumors and role of separate incisions have been clarified. When disease has spread to more than one inguinal node, adjuvant radiotherapy has replaced pelvic lymphadenectomy as the standard. Inguinal radiotherapy without groin dissection does not appear to be adequate therapy for most patients. The use of chemotherapy and radiation to shrink large tumors to allow surgical resection continues to be evaluated but has demonstrated excellent results to date. The utility of newer techniques of sentinel node mapping is also being evaluated in squamous cancers and melanoma to limit the extent of lymphadenectomy in patients with clinically normally lymph nodes.


title: Smoothed analysis of tensor decompositions

abstract: Low rank decomposition of tensors is a powerful tool for learning generative models. The uniqueness results that hold for tensors give them a significant advantage over matrices. However, tensors pose serious algorithmic challenges; in particular, much of the matrix algebra toolkit fails to generalize to tensors. Efficient decomposition in the overcomplete case (where rank exceeds dimension) is particularly challenging. We introduce a smoothed analysis model for studying these questions and develop an efficient algorithm for tensor decomposition in the highly overcomplete case (rank polynomial in the dimension). In this setting, we show that our algorithm is robust to inverse polynomial error -- a crucial property for applications in learning since we are only allowed a polynomial number of samples. While algorithms are known for exact tensor decomposition in some overcomplete settings, our main contribution is in analyzing their stability in the framework of smoothed analysis. Our main technical contribution is to show that tensor products of perturbed vectors are linearly independent in a robust sense (i.e. the associated matrix has singular values that are at least an inverse polynomial). This key result paves the way for applying tensor methods to learning problems in the smoothed setting. In particular, we use it to obtain results for learning multi-view models and mixtures of axis-aligned Gaussians where there are many more "components" than dimensions. The assumption here is that the model is not adversarially chosen, which we formalize by thinking of the model parameters as being perturbed. We believe this an appealing way to analyze realistic instances of learning problems, since this framework allows us to overcome many of the usual limitations of using tensor methods.


title: Assumptions about Word Meaning: Individuation and Basic‐Level Kinds

abstract: In 2 experiments, 3 1/2-year-old children interpreted a novel count noun (e.g., “This is a murvil”) applied to an unfamiliar stuffed animal as referring to a basic-level kind, rather than to a kind that individuates its members by type of situation (context or life-phase). For example, children made interpretations akin to PERSON (a basic-level kind) rather than PASSENGER (a context-restricted kind), and DOG (a basic-level kind) rather than PUPPY (a life-phase-restricted kind). These experiments also document the role of object familiarity (previous knowledge of a basic-level count noun for the animal) and explicit information (about the relevance of the animal's situation) in the learning of count nouns for situation-restricted kinds. We note that children readily learn the meanings of basic-level count nouns through ostensive definitions (e.g., “This is an X”), although ostensive definitions do not distinguish basic-level kinds from situation-restricted kinds. Therefore, we suggest that children make an implicit assumption that a count noun applied to an unfamiliar solid object refers to a basic-level kind of object, and not to a kind that individuates its members by type of situation. We illustrate the importance of this assumption by showing how it bears directly on individuation, and therefore, on quantification (e.g., counting).


title: From Non-Functional Requirements to Design through Patterns

abstract: Design patterns aid in documenting and communicating proven design solutions to recurring problems. They describe not only how to solve design problems, but also why a solution is chosen over others and what trade-offs are made. Non-functional requirements (NFRs) are pervasive in descriptions of design patterns. They play a crucial role in understanding the problem being addressed, the trade-offs discussed, and the design solution proposed. However, since design patterns are mostly expressed as informal text, the structure of the design reasoning is not systematically organised. This paper proposes a systematic treatment of NFRs in descriptions of patterns and when applying patterns during design. The approach organises, analyses and refines non-functional requirements, and provides guidance and reasoning support when applying patterns during the design of a software system. Three design patterns taken from the literature are used to illustrate this approach.


title: Deep Learning for Medical Image Segmentation

abstract: This report provides an overview of the current state of the art deep learning architectures and optimisation techniques, and uses the ADNI hippocampus MRI dataset as an example to compare the effectiveness and efficiency of different convolutional architectures on the task of patch-based 3-dimensional hippocampal segmentation, which is important in the diagnosis of Alzheimer's Disease. We found that a slightly unconventional "stacked 2D" approach provides much better classification performance than simple 2D patches without requiring significantly more computational power. We also examined the popular "tri-planar" approach used in some recently published studies, and found that it provides much better results than the 2D approaches, but also with a moderate increase in computational power requirement. Finally, we evaluated a full 3D convolutional architecture, and found that it provides marginally better results than the tri-planar approach, but at the cost of a very significant increase in computational power requirement.


title: Platform Criminalism: The 'Last-Mile' Geography of the Darknet Market Supply Chain

abstract: Does recent growth of darknet markets signify a slow reorganisation of the illicit drug trade? Where are darknet markets situated in the global drug supply chain? In principle, darknet markets allow producers to sell directly to end users, bypassing traditional trafficking routes. And yet, there is evidence that many offerings originate from a small number of highly active consumer countries, rather than from countries that are primarily known for drug production. In a large-scale empirical study, we determine the darknet trading geography of three plant-based drugs across four of the largest darknet markets, and compare it to the global footprint of production and consumption for these drugs. We present strong evidence that cannabis and cocaine vendors are primarily located in a small number of consumer countries, rather than producer countries, suggesting that darknet trading happens at the `last mile’, possibly leaving old trafficking routes intact. A model to explain trading volumes of opiates is inconclusive. We cannot find evidence for significant production-side offerings across any of the drug types or marketplaces. Our evidence further suggests that the geography of darknet market trades is primarily driven by existing consumer demand, rather than new demand fostered by individual markets.


title: Intentional meaning of programs

abstract: In this dissertation we present a new approach for bridging the gap between the domain knowledge and programs by defining explicit mappings between program ::: entities and domain concepts shared within domain ontologies. We call these mappings the ``intentional interpretation'' and they define explicitly the ::: ``intentional meaning'' of a program. We use the intentional meaning to characterize the degree in which the code reflects the domain knowledge. We investigate how do the mismatches between the programs and the domain knowledge affect different quality attributes of programs. In order to ::: automate our analyses we present a technique for automatic recovery of the intentional meaning based on the similarities between the names of concepts and ::: program identifiers. In order to obtain knowledge for our analyses, we develop a method for automatically extracting fragments of domain ontologies by analyzing similarities of domain specific APIs.


title: Multi-view stacking for activity recognition with sound and accelerometer data

abstract: Abstract Many Ambient Intelligence (AmI) systems rely on automatic human activity recognition for getting crucial context information, so that they can provide personalized services based on the current users’ state. Activity recognition provides core functionality to many types of systems including: Ambient Assisted Living, fitness trackers, behavior monitoring, security, and so on. The advent of wearable devices along with their diverse set of embedded sensors opens new opportunities for ubiquitous context sensing. Recently, wearable devices such as smartphones and smart-watches have been used for activity recognition and monitoring. Most of the previous works use inertial sensors (accelerometers, gyroscopes) for activity recognition and combine them using an aggregation approach, i.e., extract features from each sensor and aggregate them to build the final classification model. This is not optimal since each sensor data source has its own statistical properties. In this work, we propose the use of a multi-view stacking method to fuse the data from heterogeneous types of sensors for activity recognition. Specifically, we used sound and accelerometer data collected with a smartphone and a wrist-band while performing home task activities. The proposed method is based on multi-view learning and stacked generalization, and consists of training a model for each of the sensor views and combining them with stacking. Our experimental results showed that the multi-view stacking method outperformed the aggregation approach in terms of accuracy, recall and specificity.


title: CASSANDRA: audio-video sensor fusion for aggression detection

abstract: This paper presents a smart surveillance system named CASSANDRA, aimed at detecting instances of aggressive human behavior in public environments. A distinguishing aspect of CASSANDRA is the exploitation of the complimentary nature of audio and video sensing to disambiguate scene activity in real-life, noisy and dynamic environments. At the lower level, independent analysis of the audio and video streams yields intermediate descriptors of a scene like: "scream", "passing train" or "articulation energy". At the higher level, a Dynamic Bayesian Network is used as a fusion mechanism that produces an aggregate aggression indication for the current scene. Our prototype system is validated on a set of scenarios performed by professional actors at an actual train station to ensure a realistic audio and video noise setting.


title: Reward-based training of recurrent neural networks for cognitive and value-based tasks

abstract: Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.


title: Visemenet: audio-driven animator-centric speech animation

abstract: We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.


title: A survey of deep neural network architectures and their applications

abstract: This work was supported in part the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374010, and 61403319, and the Alexander von Humboldt Foundation of Germany.


title: Deep Neural Networks with Elastic Rectified Linear Units for Object Recognition

abstract: Abstract Rectified Linear Unit (ReLU) is crucial to the recent success of deep neural networks (DNNs). In this paper, we propose a novel Elastic Rectified Linear Unit (EReLU) that focuses on processing the positive part of input. Unlike previous variants of ReLU that typically adopt linear or piecewise linear functions to represent the positive part, EReLU is characterized by that each positive value scales within a moderate range like a spring during training stage. On test time, EReLU becomes standard ReLU. EReLU improves model fitting with no extra parameters and little overfitting risk. Furthermore, we propose Elastic Parametric Rectified Linear Unit (EPReLU) by taking advantage of EReLU and parametric ReLU (PReLU). EPReLU is able to further improve the performance of networks. In addition, we present a new training strategy to train DNNs with EPReLU. Experiments on four benchmarks including CIFAR10, CIFAR10, SVHN and ImageNet 2012 demonstrate the effectiveness of both EReLU and EPReLU.


title: McLuhan and the "Toronto School of Communication"

abstract: How does one recognize a "school" of thought ? And why should one? These are questions that, concerning a truly distinctive and now distinguished intellectual trend originating in Toronto, I have entertained since the death of Marshall McLuhan on the last day of 1980. At the time I was impressed by the fact that Harold Innis, Eric Havelock and McLuhan, the three main scholars who taught that communication systems create definite psychological and social "states", had all been at the university of Toronto. The most significant common thread was that all three had explored different implications of ancient Greek literacy to support their theoretical approach. Even if they had not directly collaborated with each other, they had known each other's work and been inspired by common perceptions.


title: New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease

abstract: Parkinsons disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting about 1% of the people older than 65 and about 89% of the people with PD develop different speech disorders. Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in speech such as phonation, articulation, prosody and intelligibility. The study of phonation and articulation has been addressed mainly considering sustained vowels; however, the analysis of prosody and intelligibility requires the inclusion of words, sentences and monologue. In this paper we present a new database with speech recordings of 50 patients with PD and their respective healthy controls, matched by age and gender. All of the participants are Spanish native speakers and the recordings were collected following a protocol that considers both technical requirements and several recommendations given by experts in linguistics, phoniatry and neurology. This corpus includes tasks such as sustained phonations of the vowels, diadochokinetic evaluation, 45 words, 10 sentences, a reading text and a monologue. The paper also includes results of the characterization of the Spanish vowels considering different measures used in other works to characterize different speech impairments.


title: An Experimental Analysis of Change Propagation in Dynamic Trees

abstract: Change propagation is a technique for automatically adjusting the output of an algorithm to changes in the input. The idea behind change propagation is to track the dependences between data and function calls, so that, when the input changes, functions affected by that change can be re-executed to update the computation and the output. Change propagation makes it possible for a compiler to dynamize static algorithms. The practical effectiveness of change propagation, however, is not known. In particular, the cost of dependence tracking and change propagation may seem significant. The contributions of the paper are twofold. First, we present some experimental evidence that change propagation performs well when compared to direct implementations of dynamic algorithms. We implement change propagation on tree-contraction as a solution to the dynamic trees problem and present an experimental evaluation of the approach. As a second contribution, we present a library for dynamic-trees that support a general interface and present an experimental evaluation by considering a broad set of applications. The dynamic-trees library relies on change propagation to handle edge insertions/deletions. The applications that we consider include path queries, subtree queries, leastcommon-ancestor queries, maintenance of centers and medians of trees, nearest-marked-vertex queries, semidynamic minimum spanning trees, and the max-flow algorithm of Sleator and Tarjan.


Exploiting Morphological Regularities in Distributional Word Representations.


title: TimedTextRank: adding the temporal dimension to multi-document summarization

abstract: Graph-ranking based algorithms (e.g. TextRank) have been proposed for multi-document summarization in recent years. However, these algorithms miss an important dimension, the temporal dimension, for summarizing evolving topics. For an evolving topic, recent documents are usually more important than earlier documents because recent documents contain much more novel information than earlier documents and a novelty-oriented summary should be more appropriate to reflect the changing topic. We propose the TimedTextRank algorithm to make use of the temporal information of documents based on the graph-ranking based algorithm. A preliminary study is performed to demonstrate the effectiveness of the proposed TimedTextRank algorithm for dynamic multi-document summarization.


title: A Novel On-Road Vehicle Detection Method Using $\pi$HOG

abstract: In this paper, a new on-road vehicle detection method is presented. First, a new feature named the Position and Intensity-included Histogram of Oriented Gradients (PIHOG or $\pi$ HOG) is proposed. Unlike the conventional HOG, $\pi$ HOG compensates the information loss involved in the construction of a histogram with position information, and it improves the discriminative power using intensity information. Second, a new search space reduction (SSR) method is proposed to speed up the detection and reduce the computational load. The SSR additionally decreases the false positive rate. A variety of classifiers, including support vector machine, extreme learning machine, and $k$ -nearest neighbor, are used to train and classify vehicles using $\pi$ HOG. The validity of the proposed method is demonstrated by its application to Caltech, IR, Pittsburgh, and Kitti datasets. The experimental results demonstrate that the proposed vehicle detection method not only improves detection performance but also reduces computation time.


title: Optimizing the Energy and Throughput of a Water-Quality Monitoring System

abstract: This work presents a new approach to the maximization of energy and throughput in a wireless sensor network (WSN), with the intention of applying the approach to water-quality monitoring. Water-quality monitoring using WSN technology has become an interesting research area. Energy scarcity is a critical issue that plagues the widespread deployment of WSN systems. Different power supplies, harvesting energy from sustainable sources, have been explored. However, when energy-efficient models are not put in place, energy harvesting based WSN systems may experience an unstable energy supply, resulting in an interruption in communication, and low system throughput. To alleviate these problems, this paper presents the joint maximization of the energy harvested by sensor nodes and their information-transmission rate using a sum-throughput technique. A wireless information and power transfer (WIPT) method is considered by harvesting energy from dedicated radio frequency sources. Due to the doubly near–far condition that confronts WIPT systems, a new WIPT system is proposed to improve the fairness of resource utilization in the network. Numerical simulation results are presented to validate the mathematical formulations for the optimization problem, which maximize the energy harvested and the overall throughput rate. Defining the performance metrics of achievable throughput and fairness in resource sharing, the proposed WIPT system outperforms an existing state-of-the-art WIPT system, with the comparison based on numerical simulations of both systems. The improved energy efficiency of the proposed WIPT system contributes to addressing the problem of energy scarcity.


title: Cluster head selection using hesitant fuzzy in Wireless Sensor Networks

abstract: Since Wireless Sensor Networks (WSNs) are composed of a set of sensor nodes that limit resource constraints such as energy constraints, energy consumption in WSNs is one of the challenges of these networks. One of the solutions to reduce energy consumption in WSNs is to use clustering. In clustering, cluster members send their data to their Cluster Head (CH), and the CH after collecting the data, sends them to the Base Station (BS). In clustering, choosing CHs is very important; so many methods have proposed to choose the CH. In this study, a hesitant fuzzy method with three input parameters namely, remaining energy, distance to the BS, distance to the center of cluster is proposed for efficient cluster head selection in WSNs. We define different scenarios and simulate them, then investigate the results of simulation.


title: Topological Spatial Verification for Instance Search

abstract: This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a “sketch-and-match” scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a triangulated graph, whose edges essentially capture the topological layout of the corresponding keypoints. Next, the spatial consistency is efficiently estimated as the number of common edges between the triangulated graphs. Compared to the existing methods, our technique is much more effective in modeling the complex spatial transformations of non-planar and non-rigid instances, while being compatible to instances with simple linear transformations. Moreover, our method is by nature more robust in spatial verification by considering the locations, rather than the local geometry of keypoints, which are sensitive to motions and viewpoint changes. We evaluate our method extensively on three years of TRECVID datasets, as well as our own dataset MQA, showing large improvement over other methods for the task of Instance Search.


title: Lexical Categories: Verbs, Nouns and Adjectives

abstract: Acknowledgements List of abbreviations 1. The problem of the lexical categories 2. Verbs as licensers of subjects 3. Nouns as bearers of a referential index 4. Adjectives as neither nouns nor verbs 5. Lexical categories and the nature of the grammar Appendix: Adpositions as functional categories References Index.


title: The Design of Cyber Threat Hunting Games: A Case Study

abstract: Cyber Threat Hunting is an emerging cyber security activity. Recent studies show that, although similar actions like threat hunting are being actively practiced in some organization, security administrator and policy makers are far from being satisfied with their effectiveness. Most security professionals lack expertise in data analytics while most people with data analytics skills lack security knowledge. To understand the necessity of threat hunting education at university level, we organized a \textit{Threat Hunting Competition} on campus with generated logs. In this paper, we identify skills needed for cyber threat hunting, describe the data generation process as well as the usage of logs to teach threat hunting at universities.


title: Multi-octave planar microwave slot-coupled directional coupler up to 28 GHz with novel phase velocity compensation

abstract: A nonuniform symmetrical QTEM-mode directional slot-line coupler operating in a multi-octave frequency range from 2.5 GHz up to 28.6GHz is presented. The calculated continuously varying even- and odd-mode line impedances are realized by a 3D electromagnetic (EM) field simulation of the slot-coupled microstrip cross section. A new method of even- and odd-mode phase velocity compensation, based on a continuously structured prepreg, is suggested. A -8.34 dB coupling factor is achieved, showing a low amplitude and phase imbalance up to 28 GHz. Furthermore, a tandem configuration is evaluated by utilizing two -8.34 dB directional coupler to form a wideband -3.01 dB coupler.


title: Autonomic Communications in Software-Driven Networks

abstract: Autonomic communications aim to provide the quality-of-service in networks using self-management mechanisms. It inherits many characteristics from autonomic computing, in particular, when communication systems are running as specialized applications in software-defined networking (SDN) and network function virtualization (NFV)-enabled cloud environments. This paper surveys autonomic computing and communications in the context of software-driven networks, i.e., networks based on SDN/NFV concepts. Autonomic communications create new challenges in terms of security, operations, and business support. We discuss several goals, research challenges, and development issues on self-management mechanisms and architectures in software-driven networks. This paper covers multiple perspectives of autonomic communications in software-driven networks, such as automatic testing, integration, and deployment of network functions. We also focus on self-management and optimization, which make use of machine learning techniques.


title: The Prevalence of High Anxiety and Substance Use in University Students in the Republic of Macedonia

abstract: Abs tract Aim: The aim of the study was to determine the prevalence of high anxiety and substance use among university students in the Republic of Macedonia. Material and methods: The sample comprised 742 students, aged 18-22 years, who attended the first (188 students) and second year studies at theMedical Faculty (257), Faculty of Dentistry (242), and Faculty of Law (55) within Ss. Cyril and Methodius Universityin Skopje. As a psychometric test the Beck Anxiety Inventory (BAI) was used. It is a selfrating questionnaire used for measuring the severity of anxiety. A psychiatric interview was performed with students with BAI scores > 25. A selfadministered questionnaire consisted of questions on the habits of substance (alcohol, nicotine, sedativehypnotics, and illicit drugs) use and abuse was also used. For statistical evaluation Statistica 7 software was used. Results: The highest mean BAI scores were obtained by first year medical students (16.8 ± 9.8). Fifteen percent of all students and 20% of first year medical students showed high levels of anxiety. Law students showed the highest prevalence of substance use and abuse. Conclusion: High anxiety and substance use as maladaptive behaviours among university students are not systematically investigated in our country. The study showed that students show these types of unhealthy reactions, regardless of the curriculum of education. More attention should be paid to students in the early stages of their education. A student counselling service which offers mental health assistance needs to be established within University facilities in R. Macedonia alongside the existing services in our health system.


title: Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval

abstract: Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient, since the classification model has to be retrained for every acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user's relevance feedback is often provided for the top K retrieved items. In this paper, we present a framework for batch mode active learning, which selects a number of informative examples for manual labeling in each iteration. The key feature of batch mode active learning is to reduce the redundancy among the selected examples such that each example provides unique information for model updating. To this end, we employ the Fisher information matrix as the measurement of model uncertainty, and choose the set of unlabeled examples that can efficiently reduce the Fisher information of the classification model. We apply our batch mode active learning framework to both text categorization and image retrieval. Promising results show that our algorithms are significantly more effective than the active learning approaches that select unlabeled examples based only on their informativeness for the classification model.


title: Kinetic Energy of Wind-Turbine Generators for System Frequency Support

abstract: As wind power penetration increases and fossil plants are retired, it is feared that there will be insufficient kinetic energy (KE) from the plants to support the system frequency. This paper shows the fear is groundless because the high inertias (H cong 4 seconds) of wind turbine-generators (WTGs) can be integrated to provide frequency support during generation outage.


title: Building Ontologies with Basic Formal Ontology

abstract: In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.


title: CARDINAL: Computer Assisted Authoring of Movie Scripts

abstract: We present Cardinal, a tool for computer-assisted authoring of movie scripts. Cardinal provides a means of viewing a script through a variety of perspectives, for interpretation as well as editing. This is made possible by virtue of intelligent automated analysis of natural language scripts and generating different intermediate representations. Cardinal generates 2-D and 3-D visualizations of the scripted narrative and also presents interactions in a timeline-based view. The visualizations empower the scriptwriter to understand their story from a spatial perspective, and the timeline view provides an overview of the interactions in the story. The user study reveals that users of the system demonstrated confidence and comfort using the system.