title: Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis

abstract: An important challenge in understanding climate change is to uncover the dependency relationships between various climate observations and forcing factors. Graphical lasso, a recently proposed l1 penalty based structure learning algorithm, has been proven successful for learning underlying dependency structures for the data drawn from a multivariate Gaussian distribution. However, climatological data often turn out to be non-Gaussian, e.g. cloud cover, precipitation, etc. In this paper, we examine nonparametric learning methods to address this challenge. In particular, we develop a methodology to learn dynamic graph structures from spatial-temporal data so that the graph structures at adjacent time or locations are similar. Experimental results demonstrate that our method not only recovers the underlying graph well but also captures the smooth variation properties on both synthetic data and climate data.


title: miR-34 is maternally inherited in Drosophila melanogaster and Danio rerio

abstract: MicroRNAs (miRNAs) are small, endogenous, regulatory RNA molecules that can bind to partially complementary regions on target messenger RNAs and impede their expression or translation. We rationalized that miRNAs, being localized to the cytoplasm, will be maternally inherited during fertilization and may play a role in early development. Although Dicer is known to be essential for the transition from single-celled zygote to two-cell embryo, a direct role for miRNAs has not yet been demonstrated. We identified miRNAs with targets in zygotically expressed transcripts in Drosophila using a combination of transcriptome analysis and miRNA target prediction. We experimentally established that Drosophila miRNA dme-miR-34, the fly homologue of the cancer-related mammalian miRNA miR-34, involved in somatic-cell reprogramming and having critical role in early neuronal differentiation, is present in Drosophila embryos before initiation of zygotic transcription. We also show that the Drosophila miR-34 is dependent on maternal Dicer-1 for its expression in oocytes. Further, we show that miR-34 is also abundant in unfertilized oocytes of zebrafish. Its temporal expression profile during early development showed abundant expression in unfertilized oocytes that gradually decreased by 5 days post-fertilization (dpf). We find that knocking down the maternal, but not the zygotic, miR-34 led to developmental defects in the neuronal system during early embryonic development in zebrafish. Here, we report for the first time, the maternal inheritance of an miRNA involved in development of the neuronal system in a vertebrate model system.


title: Speaker identification on the SCOTUS corpus

abstract: This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near‐100% text‐independent identification accuracy on utterances that are longer than one second; (2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; (3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (one hour). These findings and their significance are discussed.


title: Multiple sclerosis: a primary care perspective.

abstract: Multiple sclerosis (MS) is the most common permanently disabling disorder of the central nervous system in young adults. Relapsing remitting MS is the most common type, and typical symptoms include sensory disturbances, Lhermitte sign, motor weakness, optic neuritis, impaired coordination, and fatigue. The course of disease is highly variable. The diagnosis is clinical and involves two neurologic deficits or objective attacks separated in time and space. Magnetic resonance imaging is helpful in confirming the diagnosis and excluding mimics. Symptom exacerbations affect 85% of patients with MS. Corticosteroids are the treatment of choice for patients with acute, significant symptoms. Disease-modifying agents should be initiated early in the treatment of MS to forestall disease and preserve function. Two immunomodulatory agents (interferon beta and glatiramer) and five immunosuppressive agents (fingolimod, teriflunomide, dimethyl fumarate, natalizumab, and mitoxantrone) are approved by the U.S. Food and Drug Administration for the treatment of MS, each with demonstrated effectiveness and unique adverse effect profiles. Symptom management constitutes a large part of care; neurogenic bladder and bowel, sexual dysfunction, pain, spasticity, and fatigue are best treated with a multidisciplinary approach to improve quality of life.


title: 10.7 Low-Power mm-Wave Components up to 104GHz in 90nm CMOS

abstract: A customized 90nm device layout yields an extrapolated fmax of 300GHz. The device is incorporated into a low-power 60GHz amplifier consuming 10.5mW, providing 12dB of gain, and an output P1dB of 4dBm. An experimental 3-stage 104GHz amplifier has a measured peak gain of 9.3dB. Finally, a Colpitts oscillator at 104GHz delivers up to -5dBm of output power while consuming 6mW.


title: Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement

abstract: Online metrics measured through A/B tests have become the gold standard for many evaluation questions. But can we get the same results as A/B tests without actually fielding a new system? And can we train systems to optimize online metrics without subjecting users to an online learning algorithm? This tutorial summarizes and unifies the emerging body of methods on counterfactual evaluation and learning. These counterfactual techniques provide a well-founded way to evaluate and optimize online metrics by exploiting logs of past user interactions. In particular, the tutorial unifies the causal inference, information retrieval, and machine learning view of this problem, providing the basis for future research in this emerging area of great potential impact. Supplementary material and resources are available online at http://www.cs.cornell.edu/~adith/CfactSIGIR2016.


title: A Bidirectional Wireless Power Transfer System for Roadway Powered Electric Vehicles

abstract: A wireless power transfer system for roadway powered ::: electric vehicles (EVs) is presented. The system concept is ::: using inductive coupling of primary coils arranged in a linear ::: array in the roadway to secondary coils in each EV. The system ::: is geared to minimize loss of the stored energy in the primary ::: coils and compensation capacitors by passing the energy on ::: to the subsequent coil and capacitor of the linear array. The ::: inductive power transfer (IPT) system is operated as a switched ::: resonant inverter controlled by the primary and secondary coil ::: currents. By this way power transfer in both directions can ::: be accomplished. We describe an extension of the moving field ::: inductive power transfer (MFIPT) system by introducing a ::: synchronous switched DC-DC converter. A rectangular AC power ::: supply voltage is provided by a switched inverter from the underroad ::: DC power supply. An automatic switching adapts to load ::: changes and switches in optimum phase. Also the analysis of a ::: basic IPT cell is discussed and efficiency considerations are given.


title: Lange K: Genomewide Association Analysis by Lasso Penalized Logistic Regression

abstract: Motivation: In ordinary regression, imposition of a lasso penalty makes continuous model selection straightforward. Lasso penalized regression is particularly advantageous when the number of predictors far exceeds the number of observations. ::: ::: Method: The present article evaluates the performance of lasso penalized logistic regression in case–control disease gene mapping with a large number of SNPs (single nucleotide polymorphisms) predictors. The strength of the lasso penalty can be tuned to select a predetermined number of the most relevant SNPs and other predictors. For a given value of the tuning constant, the penalized likelihood is quickly maximized by cyclic coordinate ascent. Once the most potent marginal predictors are identified, their two-way and higher order interactions can also be examined by lasso penalized logistic regression. ::: ::: Results: This strategy is tested on both simulated and real data. Our findings on coeliac disease replicate the previous SNP results and shed light on possible interactions among the SNPs. ::: ::: Availability: The software discussed is available in Mendel 9.0 at the UCLA Human Genetics web site. ::: ::: Contact: klange@ucla.edu ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Building legal ontologies with methontology and webode

abstract: This paper presents how to build an ontology in the legal domain following the ontology development methodology METHONTOLOGY and using the ontology engineering workbench WebODE. Both of them have been widely used to develop ontologies in many other domains. The ontology used to illustrate this paper has been extracted from an existing class taxonomy proposed by Breuker, and adapted to the Spanish legal domain.


title: Portable Features for Classifying Emotional Text

abstract: Are word-level affect lexicons useful in detecting emotions at sentence level? Some prior research finds no gain over and above what is obtained with ngram features---arguably the most widely used features in text classification. Here, we experiment with two very different emotion lexicons and show that even in supervised settings, an affect lexicon can provide significant gains. We further show that while ngram features tend to be accurate, they are often unsuitable for use in new domains. On the other hand, affect lexicon features tend to generalize and produce better results than ngrams when applied to a new domain.


title: Intrahepatic cholangiocarcinoma: pathogenesis and rationale for molecular therapies

abstract: Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with very poor prognosis. Genome-wide, high-throughput technologies have made major advances in understanding the molecular basis of this disease, although important mechanisms are still unclear. Recent data have revealed specific genetic mutations (for example, KRAS, IDH1 and IDH2), epigenetic silencing, aberrant signaling pathway activation (for example, interleukin (IL)-6/signal transducer and activator of transcription 3 (STAT3), tyrosine kinase receptor-related pathways) and molecular subclasses with unique alterations (for example, proliferation and inflammation subclasses). In addition, some ICCs share common genomic traits with hepatocellular carcinoma. All this information provides the basis to explore novel targeted therapies. Currently, surgery at early stage is the only effective therapy. At more advanced stages, chemotherapy regimens are emerging (that is, cisplatin plus gemcitabine), along with molecular targeted agents tested in several ongoing clinical trials. Nonetheless, a first-line conclusive treatment remains an unmet need. Similarly, there are no studies assessing tumor response related with genetic alterations. This review explores the recent advancements in the knowledge of the molecular alterations underlying ICC and the future prospects in terms of therapeutic strategies leading towards a more personalized treatment of this neoplasm.


title: Assessing the validity of single-item life satisfaction measures: results from three large samples

abstract: Purpose ::: The present paper assessed the validity of single-item life satisfaction measures by comparing single-item measures to the Satisfaction with Life Scale (SWLS)—a more psychometrically established measure.


title: Enhancing the quality of life through wearable technology

abstract: t is hard to place a price tag either on human life or on the quality of life. This has become starkly evident since the attack on the World Trade Center on September 11, 2001. A new “normal” has emerged for people around the world and our “way of life,” as we have known it, has been changed forever. There is a similar, yet silent, crisis that is dramatically affecting the quality of life for individuals—lack of access to affordable and high-quality healthcare. Technology is the key to enhancing the quality of life for everyone in the continuum of life from newborns to senior citizens—whether it is the safe delivery and care of undernourished premature babies or extending the life of a senior citizen through exploratory treatments and procedures. Technology is indeed the catalyst that can rapidly transform healthcare and the practice of medicine. So, any technology to minimize the loss of human life and/or enhance the quality of life has a value that is priceless. Moreover, the healthcare industry must meet the challenge of balancing cost containment with maintenance of desired patient outcomes, and this can be accomplished through the adoption of technology. An overview of the key challenges facing the practice of medicine today is presented in this article along with the need for technological solutions that can “prevent” problems. Then, the development of the Wearable Motherboard ™ as a “platform” for sensors and monitoring devices that can unobtrusively monitor the health and well being of individuals (directly and/or remotely) is described. This is followed by a discussion of the applications and impact of this technology in the continuum of life—from preventing SIDS to facilitating independent living for senior citizens. Finally, the future advancements in the area of wearable, yet comfortable, systems that can continue the transformation of healthcare—all aimed at enhancing the quality of life for humans—are presented.


title: A self-powered sensor patch for glucose monitoring in sweat

abstract: We developed a paper-based self-powered sensor patch for detecting glucose levels in sweat. This wearable, non-invasive biosensor made use of the integration of a vertically stacked paper-based glucose/oxygen enzymatic fuel cell into a standard Band-Aid patch. The paper-based device, attached directly to human skin, wicked sweat from human skin and monitored an electrochemical energy conversion as a transducing element for glucose monitoring in sweat, thus obviating the requirement of external power sources and sophisticated readout instrumentation. The device features (i) a 3-D origami paper-based fuel cell structure for easy system integration on paper, (ii) an electrically conducting paper microfluidic reservoir for high anode surface area and efficient mass transfer, and (iii) a direct electron transfer between enzymes and electrodes for enhanced electron discharge properties. The sensor showed a linear range of output current at 0.02–1.0mg/mL glucose (R2=0.989) with a high sensitivity.


title: Predicting the popularity of online content

abstract: We present a method for accurately predicting the long time popularity of online content from early measurements of user access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors.


title: Time series analysis of landslide dynamics using an Unmanned Aerial Vehicle (UAV)

abstract: In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) was applied to create Digital Surface Models (DSMs) of the landslide surface with an accuracy of 4–5 cm in the horizontal and 3–4 cm in the vertical direction. The accuracy of the co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, which minimized alignment errors to a mean of 0.07 m. Variables such as landslide area and the leading edge slope were measured and temporal patterns were discovered. Volumetric changes of particular areas of the landslide were measured over the time series. Surface movement of the landslide was tracked and quantified with the COSI-Corr image correlation algorithm but without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6630 m3. This study has demonstrated a robust and repeatable algorithm that allows a landslide’s dynamics to be mapped and monitored with a UAV over a relatively long time series.


title: Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis

abstract: Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the assumption that the sentiment expressed in a sentence is unified and consistent, which does not hold in the reality. As a fine-grained alternative of the task, analyzing the sentiment towards a specific target and aspect has drawn much attention from the community for its more practical assumption that sentiment is dependent on a particular set of aspects and entities. Recently, deep neural models have achieved great successes on sentiment analysis. As a functional simulation of the behavior of human brains and one of the most successful deep neural models for sequential data, long short-term memory (LSTM) networks are excellent in learning implicit knowledge from data. However, it is impossible for LSTM to acquire explicit knowledge such as commonsense facts from the training data for accomplishing their specific tasks. On the other hand, emerging knowledge bases have brought a variety of knowledge resources to our attention, and it has been acknowledged that incorporating the background knowledge is an important add-on for many NLP tasks. In this paper, we propose a knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model. To explicitly model the inference of the dependent sentiment, we augment the LSTM with a stacked attention mechanism consisting of attention models for the target level and sentence level, respectively. In order to explicitly integrate the explicit knowledge with implicit knowledge, we propose an extension of LSTM, termed Sentic LSTM. The extended LSTM cell includes a separate output gate that interpolates the token-level memory and the concept-level input. In addition, we propose an extension of Sentic LSTM by creating a hybrid of the LSTM and a recurrent additive network that simulates sentic patterns. In this paper, we are mainly concerned with a joint task combining the target-dependent aspect detection and targeted aspect-based polarity classification. The performance of proposed methods on this joint task is evaluated on two benchmark datasets. The experiment shows that the combination of proposed attention architecture and knowledge-embedded LSTM could outperform state-of-the-art methods in two targeted aspect sentiment tasks. We present a knowledge-rich solution for the task of targeted aspect-based sentiment analysis. Our model can effectively incorporate the commonsense knowledge into the deep neural network and be trained in an end-to-end manner. We show that the two-step attentive neural architecture as well as the proposed Sentic LSTM and H-Sentic-LSTM can achieve an improved performance on resolving the aspect categories and sentiment polarity for a targeted entity in its context over state-of-the-art systems.


title: Regulation of secondary metabolism in streptomycetes.

abstract: While the biological functions of most of the secondary metabolites made by streptomycetes are not known, it is inconceivable that they do not play an adaptive ecological role. The biosynthesis of secondary metabolites under laboratory conditions usually occurs in a growth phase or developmentally controlled manner, but is also influenced by a wide variety of environmental and physiological signals, presumably reflecting the range of conditions that trigger their production in nature. The expression of secondary metabolic gene clusters is controlled by many different families of regulatory proteins, some of which are found only in actinomycetes, and is elicited by both extracellular and intracellular signalling molecules. The application of a variety of genetic and molecular approaches is now beginning to reveal fascinating insights into the complex regulatory cascades that govern this process.


title: Ontologies for Privacy Requirements Engineering: A Systematic Literature Review

abstract: Privacy has been frequently identified as a main concern for system developers while dealing with/managing personal information. Despite this, most existing work on privacy requirements deals with them as a special case of security requirements. Therefore, key aspects of privacy are, usually, overlooked. In this context, wrong design decisions might be made due to insufficient understanding of privacy concerns. In this paper, we address this problem with a systematic literature review whose main purpose is to identify the main concepts/relations for capturing privacy requirements. In addition, the identified concepts/relations are further analyzed to propose a novel privacy ontology to be used by software engineers when dealing with privacy requirements.


title: Enterprise resource planning: Managing the implementation process

abstract: Abstract Over the past few years, thousands of companies around the world have implemented enterprise resource planning (ERP) systems. Implementing an ERP system is generally a formidable challenge, with a typical ERP implementation taking anywhere from one to five years. The story of the success of ERP systems in achieving the stated objectives is mixed. Some companies have had very successful implementations while others have struggled. This paper empirically investigates and identifies key differences in the approaches used by companies that managed their implementations on-time and/or on/under-budget versus the ones that did not using data collected through a survey of US manufacturing companies that have implemented ERP systems. Logistic regressions are used to classify on-time and on/under-budget firm groups based on the survey responses and to identify the significant variables that contribute to on-time and on/under-budget implementation performance. The results indicate that many different factors ranging from pre-implementation planning to system configuration influence performance, which managers should be sensitive about when implementing major systems like ERP.


title: Collective Content Selection For Concept-To-Text Generation

abstract: A content selection component determines which information should be conveyed in the output of a natural language generation system. We present an efficient method for automatically learning content selection rules from a corpus and its related database. Our modeling framework treats content selection as a collective classification problem, thus allowing us to capture contextual dependencies between input items. Experiments in a sports domain demonstrate that this approach achieves a substantial improvement over context-agnostic methods.


title: Query expansion for mixed-script information retrieval

abstract: For many languages that use non-Roman based indigenous scripts (e.g., Arabic, Greek and Indic languages) one can often find a large amount of user generated transliterated content on the Web in the Roman script. Such content creates a monolingual or multi-lingual space with more than one script which we refer to as the Mixed-Script space. IR in the mixed-script space is challenging because queries written in either the native or the Roman script need to be matched to the documents written in both the scripts. Moreover, transliterated content features extensive spelling variations. In this paper, we formally introduce the concept of Mixed-Script IR, and through analysis of the query logs of Bing search engine, estimate the prevalence and thereby establish the importance of this problem. We also give a principled solution to handle the mixed-script term matching and spelling variation where the terms across the scripts are modelled jointly in a deep-learning architecture and can be compared in a low-dimensional abstract space. We present an extensive empirical analysis of the proposed method along with the evaluation results in an ad-hoc retrieval setting of mixed-script IR where the proposed method achieves significantly better results (12% increase in MRR and 29% increase in MAP) compared to other state-of-the-art baselines.


title: The role mining problem: finding a minimal descriptive set of roles

abstract: Devising a complete and correct set of roles has been recognized as one of the most important and challenging tasks in implementing role based access control. A key problem related to this is the notion of goodness/interestingness -- when is a role good/interesting? In this paper, we define the role mining problem (RMP) as the problem of discovering an optimal set of roles from existing user permissions. The main contribution of this paper is to formally define RMP, and analyze its theoretical bounds. In addition to the above basic RMP, we introduce two different variations of the RMP, called the δ-approx RMP and the Minimal Noise RMP that have pragmatic implications. We reduce the known "set basis problem" to RMP to show that RMP is an NP-complete problem. An important contribution of this paper is also to show the relation of the role mining problem to several problems already identified in the data mining and data analysis literature. By showing that the RMP is in essence reducible to these known problems, we can directly borrow the existing implementation solutions and guide further research in this direction.


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

abstract: The work proposes an Event Popularity Algorithm for Financial Trading.The approach is based on sentiment analysis to the social network Twitter.Planning and performing a financial community for the extraction of analyzed tweets.The events are focused on the thirty companies that compose the Dow Jones index. The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies.


title: The second generation of autonomous surface vessels: Optimized performance for AUVs assistance at Sea

abstract: The next generation of Autonomous Surface Vessels (ASV) will be optimized for optimum performance at sea. The design of an innovative unconventional SWATH-ASV family, patented by the author and under further investigation at the MIT innovative Ship design lab is described in its main features. The new unconventional design of the hull is the key to superior resistance, powering and seakeeping performance. The achievement of these excellent results is a direct consequence of the extensive use of new computational fluid dynamic tools integrated into full parametric geometry optimization methods. The main modeling capabilities of these numerical methods are presented. As a result, the new ASV-SWATH is able to demand 20% to 40% less propulsion power than conventional catamarans at high speeds and it is subject to significantly lower motions in waves: 50% less heave and 60% pitch in a wide range of incoming wave frequencies. The new ASV platform is the ideal, yet missing, element to realize a true persistent and portable network of AUVs at sea. The characteristics of onboard systems for AUV launching/recovering and wireless recharging are finally discussed and their feasibility assessed in the light of the seakeeping predictions performed both for the AUV and the ASV in the same sea state.


An Image Steganography Scheme Using 3 D-Sudoku


title: Self-biasing low power adaptive photoreceptor

abstract: This report describes a self-biased 5-transistor, 2-capacitor, adaptive logarithmic photoreceptor circuit. The photoreceptor uses its photocurrent, multiplied by a constant factor, to bias its forward amplifier. As a result, power consumption scales with illumination and takes on low values under typical illumination levels. This scaling enables solar powered operation, because power consumption scales with available power. As in earlier circuits, the photoreceptor has an adaptive logarithmic response: it has a bandpass characteristic with low gain for DC (including mismatch) and a higher gain for transient changes in contrast. The gain-bandwidth product is higher than for a passive architecture by a desired multiple. Stability criteria, sharing the bias among many photoreceptors, and measurements from a fabricated chip are shown.


title: A behavioral planning framework for autonomous driving

abstract: In this paper, we propose a novel planning framework that can greatly improve the level of intelligence and driving quality of autonomous vehicles. A reference planning layer first generates kinematically and dynamically feasible paths assuming no obstacles on the road, then a behavioral planning layer takes static and dynamic obstacles into account. Instead of directly commanding a desired trajectory, it searches for the best directives for the controller, such as lateral bias and distance keeping aggressiveness. It also considers the social cooperation between the autonomous vehicle and surrounding cars. Based on experimental results from both simulation and a real autonomous vehicle platform, the proposed behavioral planning architecture improves the driving quality considerably, with a 90.3% reduction of required computation time in representative scenarios.


title: Efficient Confidence Measures for Embedded Stereo

abstract: The advent of embedded stereo cameras based on low-power and compact devices such as FPGAs (Field Programmable Gate Arrays) has enabled to effectively address several computer vision problems. However, being the depth data generated by stereo algorithms affected by errors, reliable strategies to detect wrong disparity assignments by means of confidence measures are desirable. Recent works proved that confidence measures are also a powerful cue to improve the overall accuracy of stereo. Most approaches aimed at predicting match reliability rely on cost volume analysis, an information seldom available as output of most embedded depth sensors. Therefore, in this paper we analyze and evaluate strategies compatible with the constraints of embedded stereo cameras. In particular, we focus our attention on methods to infer match reliability inside depth sensors based on highly constrained computing architectures such as FPGAs. We quantitatively assess, on Middlebury 2014 and KITTI 2015 datasets, the impact of different design strategies for 16 confidence measures from the literature, suited for implementation on such embedded systems. Our evaluation shows that, compared to the confidence measures typically deployed in this context and based on storing intermediate results, other approaches yield much more accurate predictions with negligible computing requirements and memory footprint. This enables for their implementation even on highly constrained architectures.


title: Motivational Profiles from a Self-Determination Perspective: The Quality of Motivation Matters.

abstract: The present research complements extant variable-centered research that focused on the dimensions of autonomous and controlled motivation through adoption of a person-centered approach for identifying motivational profiles. Both in high school students (Study 1) and college students (Study 2), a cluster analysis revealed 4 motivational profiles: a good quality motivation group (i.e., high autonomous, low controlled); a poor quality motivation group (i.e., low autonomous, high controlled); a low quantity motivation group (i.e., low autonomous, low controlled); and a high quantity motivation group (i.e., high autonomous, high controlled). To compare the 4 groups, the authors derived predictions from qualitative and quantitative perspectives on motivation. Findings generally favored the qualitative perspective; compared with the other groups, the good quality motivation group displayed the most optimal learning pattern and scored highest on perceived need-supportive teaching. Theoretical and practical implications of the findings are discussed.


title: Rule weight specification in fuzzy rule-based classification systems

abstract: This paper shows how the rule weight of each fuzzy rule can be specified in fuzzy rule-based classification systems. First, we propose two heuristic methods for rule weight specification. Next, the proposed methods are compared with existing ones through computer simulations on artificial numerical examples and real-world pattern classification problems. Simulation results show that the proposed methods outperform the existing ones in the case of multiclass pattern classification problems with many classes.


title: A New Temperature Compensation Method for a 2.5 GHz Integrated VCO

abstract: Summary: A�2.5�GHzfullyintegratedvoltagecontrolledoscil lator�(VCO)� forwirelessapplicationhasbeendesignedina�0.3 55mCMOS� technology.� Amethodforcompensatingtheeffectof � temperatureonthecarrieroscillationfrequencyha sbeen� presentedinthiswork.� WecomparealsodifferentV COs� topologiesinordertoselectonewithlowphaseno ise,�low� supplysensitivityandlargetuningfrequency.�Good �resultsare� obtainedwithasimpleNMOS�-GmVCO.�Thisproposed� VCO� hasawideoperatingrangefrom� 300� MHzwithagood � linearitybetweentheoutputfrequencyandthecont rolinput� voltage,�withatemperaturecoefficientof�85�ppm/° Cfrom�8 20°Cto�120°Crange.�Thephasenoiseisabout�8135. 2dBc/Hz� at�1MHzfromthecarrierwithapowerconsumptiono f�5mW.�


title: Anomaly detection using call stack information

abstract: The call stack of a program execution can be a very good information source for intrusion detection. There is no prior work on dynamically extracting information from the call stack and effectively using it to detect exploits. In this paper we propose a new method to do anomaly detection using call stack information. The basic idea is to extract return addresses from the call stack, and generate an abstract execution path between two program execution points. Experiments show that our method can detect some attacks that cannot be detected by other approaches, while its convergence and false positive performance is comparable to or better than the other approaches. We compare our method with other approaches by analyzing their underlying principles and thus achieve a better characterization of their performance, in particular on what and why attacks will be missed by the various approaches.


title: The influence of futures markets on real time price stabilization in electricity markets

abstract: Markets can interact with power systems in ways that can render an otherwise stable market and an otherwise stable power system into an unstable overall system. This unstable system will be characterized not only by fluctuating prices that do not settle to constant values, but, more worrisome, it creates the possibility of inducing slow electromechanical oscillations if left unchecked. This will tend to happen as a result of "price chasing" on the part of suppliers that can react (and over-react) to changing system prices. This paper examines the role that futures markets may have on the clearing prices and on altering the volatility and potential instability of real time prices and generator output.


Mitigating Catastrophic Forgetting in Temporal Difference Learning with Function Approximation


SciPass : a 100 Gbps capable secure Science DMZ using OpenFlow and Bro


title: Critical and Creative Thinking in the English Language Classroom

abstract: In recent years the importance of promoting critical thinking skills and creative thinking in education has been widely acknowledged. More specifically, it is believed that the successful incorporation of these skills in the teaching of English as a second or foreign language have a central role. This belief is based on the premise that there is a close relationship between knowledge and thinking and between language learning and thinking processes. The first part of this article presents the research findings and a short literature review on critical thinking skills and creative thinking. We then present suggestions for their successful incorporation in the English language classroom. We hope that this article will serve as a starting point for English teachers to evaluate their current teaching practices, and motivation to incorporate critical and creative thinking skills in their classroom.


title: Information-theoretic algorithm for feature selection

abstract: Abstract Feature selection is used to improve the efficiency of learning algorithms by finding an optimal subset of features. However, most feature selection techniques can handle only certain types of data. Additional limitations of existing methods include intensive computational requirements and inability to identify redundant variables. In this paper, we present a novel, information-theoretic algorithm for feature selection, which finds an optimal set of attributes by removing both irrelevant and redundant features. The algorithm has a polynomial computational complexity and is applicable to datasets of a mixed nature. The method performance is evaluated on several benchmark datasets by using a standard classifier (C4.5).


title: High precision grasp pose detection in dense clutter

abstract: This paper considers the problem of grasp pose detection in point clouds. We follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network. We propose two new representations of grasp candidates, and we quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as well as pretraining and prior knowledge significantly improve grasp detection. We evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work.


title: Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts

abstract: Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.


title: JESC: Japanese-English Subtitle Corpus

abstract: In this paper we describe the Japanese-English Subtitle Corpus (JESC). JESC is a large Japanese-English parallel corpus covering the underrepresented domain of conversational dialogue. It consists of more than 3.2 million examples, making it the largest freely available dataset of its kind. The corpus was assembled by crawling and aligning subtitles found on the web. The assembly process incorporates a number of novel preprocessing elements to ensure high monolingual fluency and accurate bilingual alignments. We summarize its contents and evaluate its quality using human experts and baseline machine translation (MT) systems.


title: Estimating and Interpreting the Instantaneous Frequency of a Signal

abstract: For pt.I see ibid., vol.80, no.4, p.520-38 (1992). The concept of instantaneous frequency (IF) is extended to discrete-time signals. The specific problem explored is that of estimating the IF of frequency-modulated (FM) discrete-time signals embedded in Gaussian noise. Well-established methods for estimating the IF include differentiation of the phase and smoothing thereof, adaptive frequency estimation techniques such as the phase locked loop (PLL), and extraction of the peak from time-varying spectral representations. More recently, methods based on a modeling of the signal phase as a polynomial have been introduced. These methods are reviewed, and their performance compared on both simulated and real data. Guidelines are given as to which estimation method should be used for a given signal class and signal-to-noise ratio (SNR). >


title: WebVR——Web Virtual Reality Engine Based on P2P network

abstract: WebVR, a multi-user online virtual reality engine, is introduced. The main contributions are mapping the geographical space and virtual space to the P2P overlay network space, and dividing the three spaces by quad-tree method. The geocoding is identified with Hash value, which is used to index the user list, terrain data, and the model object data. Sharing of data through improved Kademlia network model is designed and implemented. In this model, XOR algorithm is used to calculate the distance of the virtual space. The model greatly improves the hit rate of 3D geographic data search under P2P overlay network. Some data preprocessing methods have been adopted to accelerate the data transfer. 3D Global data is used for testing the engine. The test result indicates that, without considering the client bandwidth limit, the more users, the faster loading.


title: TasteWeights: a visual interactive hybrid recommender system

abstract: This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recommendation process and elicit preferences from the end user. We present an evaluation that compares different interactive and non-interactive hybrid strategies for computing recommendations across diverse social and semantic web APIs. Results of the study indicate that explanation and interaction with a visual representation of the hybrid system increase user satisfaction and relevance of predicted content.


title: More is Less: On the End-to-End Security of Group Chats in Signal, WhatsApp, and Threema

abstract: Secure instant messaging is utilized in two variants: one-to-one communication and group communication. While the first variant has received much attention lately (Frosch et al., EuroS Cohn-Gordon et al., EuroS Kobeissi et al., EuroS&P17), little is known about the cryptographic mechanisms and security guarantees of secure group communication in instant messaging. To approach an investigation of group instant messaging protocols, we first provide a comprehensive and realistic security model. This model combines security and reliability goals from various related literature to capture relevant properties for communication in dynamic groups. Thereby the definitions consider their satisfiability with respect to the instant delivery of messages. To show its applicability, we analyze three widely used real-world protocols: Signal, WhatsApp, and Threema. By applying our model, we reveal several shortcomings with respect to the security definition. Therefore we propose generic countermeasures to enhance the protocols regarding the required security and reliability goals. Our systematic analysis reveals that (1) the communications' integrity – represented by the integrity of all exchanged messages – and (2) the groups' closeness – represented by the members' ability of managing the group – are not end-to-end protected. We additionally show that strong security properties, such as Future Secrecy which is a core part of the one-to-one communication in the Signal protocol, do not hold for its group communication.


title: Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction

abstract: Current research on traffic flow prediction mainly concentrates on generating accurate prediction results based on intelligent or combined algorithms but ignores the interpretability of the prediction model. In practice, however, the interpretability of the model is equally important for traffic managers to realize which road segment in the road network will affect the future traffic state of the target segment in a specific time interval and when such an influence is expected to happen. In this paper, an interpretable and adaptable spatiotemporal Bayesian multivariate adaptive-regression splines (ST-BMARS) model is developed to predict short-term freeway traffic flow accurately. The parameters in the model are estimated in the way of Bayesian inference, and the optimal models are obtained using a Markov chain Monte Carlo (MCMC) simulation. In order to investigate the spatial relationship of the freeway traffic flow, all of the road segments on the freeway are taken into account for the traffic prediction of the target road segment. In our experiments, actual traffic data collected from a series of observation stations along freeway Interstate 205 in Portland, OR, USA, are used to evaluate the performance of the model. Experimental results indicate that the proposed interpretable ST-BMARS model is robust and can generate superior prediction accuracy in contrast with the temporal MARS model, the parametric model autoregressive integrated moving averaging (ARIMA), the state-of-the-art seasonal ARIMA model, and the kernel method support vector regression.


title: Electromagnetic Energy Harvesting Using Full-Wave Rectification

abstract: This paper presents a new approach to realizing full-wave rectification for electromagnetic energy harvesting. Instead of using one antenna to feed one rectifier circuit, we propose utilizing two antennas connected to one rectifier to form a full-wave rectenna. This balanced configuration allows the received power to be rectified and transferred to a dc load between two antennas, making it very easy to channel the harvested power in rectenna arrays. The proposed concept is demonstrated in the microwave regime using an array of full-wave rectennas optimized for 2.45 GHz. The full-wave rectenna array is compared with an array of half-wave rectennas that occupy the same footprint and are optimized to maximize power absorption at the same frequency. Measurements showed that the proposed full-wave rectifier performed better than the half-wave rectifier and achieved 74% radiation-to-dc conversion efficiency. Here, without loss of generality, the proposed concept is demonstrated using T-matched dipole antennas at a specific frequency, but the concept can be applied to other antenna types and other frequencies.


title: A 0.4-V Wide Temperature Range All-MOSFET Subthreshold Voltage Reference With 0.027%/V Line Sensitivity

abstract: This brief presents a CMOS voltage reference for ultra-low-power applications, such as in implementable medical devices and energy harvesting-based wireless sensor nodes. In these applications, excellent capabilities to reject interference from power sources and work in low voltage to extend survival periods are critical for the voltage references. A transistor size-ratio determined current generator with high process and voltage independence is implemented by two same-threshold-voltage NMOS transistors, hence obtaining the precise reference voltage with an active load. The proposed circuit is fabricated in a standard 0.18- ${\mu }\text{m}$ CMOS process. Measurement results show that the prototype design provides a 210-mV reference with only 0.027% line sensitivity with a supply voltage from 1.8 V down to 0.4 V. The power supply ripple rejection at 10 Hz, 1 KHz, and 1 MHz is −59, 47, and 53 dB, respectively. The temperature coefficient is 82 ppm/°C in the temperature range from −40°C to +140 °C. Furthermore, the power consumption is only 9.6 nW at room temperature and the active area is 0.021 mm2.


title: DSAN: Double Supervised Network with Attention Mechanism for Scene Text Recognition

abstract: In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision comes from context-level modelling branch and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method achieves the current state-of-the-art results on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.


An Analytical Solution to the Stance Dynamics of Passive Spring-Loaded Inverted Pendulum with Damping


title: The big data challenges of connectomics

abstract: Due to recent technological developments in acquisition techniques, the field of electron microscopy-based connectomics now produces colossal amounts of data. Here, the authors discuss the practical and analytical challenges associated with such large amounts of data and propose some solutions to surmount them.


title: Effects of The "What is Beautiful is Good" Stereotype on Perceived Trustworthiness

abstract: Attractive individuals are favored in various evaluative and judgmental ways. This study examined the effects of the “what is beautiful is good” stereotype on the specific area of perceived trustworthiness. 284 undergraduate students enrolled in an introductory level psychology class at the University of Wisconsin-La Crosse participated in an online survey, which rated one of three sets of four attractive and unattractive photographs of both men and women with respect to how likable, attractive, and trustworthy they perceived the stimulus to be. Next, the participants chose which stimulus they would trust the most in a hypothetical situation. Results indicated that attractive individuals were seen as more trustworthy than unattractive individuals and women were seen as more trustworthy than men. When participants were forced to choose among the four pictures in a trust scenario, they overwhelmingly chose the attractive females. Findings from this study should help inform others of possible gender and attractiveness judgmental biases being made regarding trustworthiness.


title: /spl delta/-clusters: capturing subspace correlation in a large data set

abstract: Clustering has been an active research area of great practical importance for recent years. Most previous clustering models have focused on grouping objects with similar values on a (sub)set of dimensions (e.g., subspace cluster) and assumed that every object has an associated value on every dimension (e.g., bicluster). These existing cluster models may not always be adequate in capturing coherence exhibited among objects. Strong coherence may still exist among a set of objects (on a subset of attributes) even if they take quite different values on each attribute and the attribute values are not fully specified. This is very common in many applications including bio-informatics analysis as well as collaborative filtering analysis, where the data may be incomplete and subject to biases. In bio-informatics, a bicluster model has recently been proposed to capture coherence among a subset of the attributes. We introduce a more general model, referred to as the /spl delta/-cluster model, to capture coherence exhibited by a subset of objects on a subset of attributes, while allowing absent attribute values. A move-based algorithm (FLOC) is devised to efficiently produce a near-optimal clustering results. The /spl delta/-cluster model takes the bicluster model as a special case, where the FLOC algorithm performs far superior to the bicluster algorithm. We demonstrate the correctness and efficiency of the /spl delta/-cluster model and the FLOC algorithm on a number of real and synthetic data sets.


title: Overview of the EVS codec architecture

abstract: The recently standardized 3GPP codec for Enhanced Voice Services (EVS) offers new features and improvements for low-delay real-time communication systems. Based on a novel, switched low-delay speech/audio codec, the EVS codec contains various tools for better compression efficiency and higher quality for clean/noisy speech, mixed content and music, including support for wideband, super-wideband and full-band content. The EVS codec operates in a broad range of bitrates, is highly robust against packet loss and provides an AMR-WB interoperable mode for compatibility with existing systems. This paper gives an overview of the underlying architecture as well as the novel technologies in the EVS codec and presents listening test results showing the performance of the new codec in terms of compression and speech/audio quality.


title: A Guide to Visual Multi-Level Interface Design from Synthesis of Empirical Study Evidence

abstract: Displaying multiple levels of data visually has been proposed to address the challenge of limited screen space. Although many previous empirical studies have addressed different aspects of this question, the information visualization research community does not currently have a clearly articulated consensus on how, when, or even if displaying data at multiple levels is effective. To shed more light on this complex topic, we conducted a systematic review of 22 existing multi-level interface studies to extract high-level design guidelines. To facilitate discussion, we cast our analysis findings into a four-point decision tree: (1) When are multi-level displays useful? (2) What should the higher visual levels display? (3) Should the different visual levels be displayed simultaneously, or one at a time? (4) Should the visual levels be embedded in a single display, or separated into multiple displays? Our analysis resulted in three design guidelines: (1) the number of levels in display and data should match; (2) high visual levels should only display task-relevant information; (3) simultaneous display, rather than temporal switching, is suitable for tasks with multi-level answers. Table of Contents: Introduction / Terminology / Methodology / Summary of Studies / Decision 1: Single or Multi-level Interface? / Decision 2: How to Create the High-Level Displays? / Decision 3: Simultaneous or Temporal Displays of the Multiple Visual Levels / Decision 4: How to Spatially Arrange the Visual Levels, Embedded or Separate? / Limitations of Study / Design Recommendations / Discussion and Future Work


title: Distributed Adaptive Control: A theory of the Mind, Brain, Body Nexus

abstract: Abstract Distributed Adaptive Control (DAC) is a theory of the design principles underlying the Mind, Brain, Body Nexus (MBBN) that has been developed over the last 20 years. DAC assumes that the brain maintains stability between an embodied agent and its environment through action. It postulates that in order to act, or know how, the brain has to answer four fundamental questions: why, what, where, when. Thus the function of the brain is to continuously solve the, so called, H4W problem. The DAC theory is expressed as a robot based neural architecture organized in two complementary structures: layers and columns. The organizational layers are called: reactive, adaptive and contextual and its columnar organization defines the processing of states of the world, the self and the generation of action. Each layer is described with respect to its key hypotheses, implementation and specific benchmarks. After this overview of the key elements of DAC, the mapping of its key assumptions towards the invertebrate and mammalian brain is described. In particular, this review will focus on the systems involved in realizing the core principles underlying the reactive layer: the allostatic control of fundamental behavior systems in the vertebrate brain and the emergent non-linearity through neuronal mass action in the locust brain. The adaptive layer will be analyzed in terms of the classical conditioning paradigm and its neuronal substrate the amygdala-cerebellum-neocortex complex together with episodic memory and the formation of sense-act couplets in the hippocampus. For the contextual layer the ability of circuits in the prefrontal cortex to acquire and express contextual plans for action is described. The general overview of DAC’s explanation of MBBN is combined by examples of application scenarios in which DAC has been validated including mobile and humanoid robots, neurorehabilitation and the large-scale interactive space Ada. After 20 years of research DAC can be considered a mature theory of MBBN. It has build up a track record of explaining core aspects of mind, brain and behavior, has made testable and verified predictions at the level of behavior, physiology and anatomy, has been shown to be able to control complex real-world artefacts and has been successfully applied to brain repair and neurorehabilitation. Currently DAC is extended to capture the phenomenon of consciousness, the ultimate challenge in the study of the Mind, Brain, Body Nexus.


The eyeBook – Using Eye Tracking to Enhance the Reading Experience


title: Learning Text-Line Localization with Shared and Local Regression Neural Networks

abstract: Text line detection and localisation is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a novel approach for text line localisation based on Convolutional Neural Networks and Multidimensional Long Short-Term Memory cells as a regressor in order to predict the coordinates of the text line bounding boxes directly from the pixel values. Targeting typically large images in document image analysis, we propose a new model using weight sharing over local blocks. We compare two strategies: directly predicting the four coordinates or predicting lower-left and upper-right points separately followed by matching. We evaluate our work on the highly unconstrained Maurdor dataset and show that our method outperforms both other machine learning and image processing methods.


title: (sp)iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers

abstract: Mobile phones are increasingly equipped with a range of highly responsive sensors. From cameras and GPS receivers to three-axis accelerometers, applications running on these devices are able to experience rich interactions with their environment. Unfortunately, some applications may be able to use such sensors to monitor their surroundings in unintended ways. In this paper, we demonstrate that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard. Note that unlike previous emanation recovery papers, the accelerometers on such devices sample at near the Nyquist rate, making previous techniques unworkable. Our application instead detects and decodes keystrokes by measuring the relative physical position and distance between each vibration. We then match abstracted words against candidate dictionaries and record word recovery rates as high as 80%. In so doing, we demonstrate the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage (e.g., microphone, camera).


title: Simultaneous localization and map building – a guided tour

abstract: Simultaneous Localization and Mapping (SLAM) has been one of the active research areas in robotic community for the past decade of years. SLAM addresses the problem of a robot navigating and building a map of an unknown environment, without an initial map or an absolute localization means. This paper attempts to provide a comprehensive overview of the SLAM problem. Successful SLAM implementations using laser, sonar and radar can be found in the literature. However, recent extensions to the general SLAM problem has looked into the possibility of using 3-dimensional features and the use of vision sensors. We will focus on these two approaches to the SLAM problem using vision: one with single or monocular camera and another with stereovision. Current applications and future challenges will also be discussed.


title: Acupuncture for Pediatric Conditions: A Narrative Review

abstract: Abstract Background: The use of acupuncture for common pediatric conditions continues to grow. Three previous overviews have summarized the research literature. Objective: The aim of this article is to provide an updated narrative review of the literature on the use of acupuncture within the pediatric population. Methods: A systematic search was performed to identify clinical trials and reviews published through July 2015. Trials were excluded if they were not: (1) acupuncture-specific; (2) limited to pediatric patients; (3) published in English. The literature was assessed and synthesized into a narrative review and commentary. Results: Seventy-one publications were identified (59 clinical trials and 12 reviews) that represented 20 conditions. Since the prior overviews, 36 new publications (33 clinical trials) were identified and represented nine countries; the top three were China, Norway, and the United States. The top five areas of research at that timepoint were autism spectrum disorder, cerebral pal...


title: Haptic Perception of Material Properties and Implications for Applications

abstract: Perceiving the material properties of objects through touch is generally superior to the perception of shape. We review major material properties accessible through haptic interaction, along with theoretical accounts of the underlying perceptual processes. These include roughness, friction, compliance, and thermal properties. Subsequently, we describe algorithms that have been used to render these same material properties on haptic devices. We then point to applications that have capitalized on the accessibility of material through touch, including tactile displays, simulation of mechanical mechanisms in the automobile, and medical training simulators.


title: A comparative study of anomaly detection schemes in network intrusion detection

abstract: Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of network connections [9] as well as on real network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA’98 data and real network data.


title: Teaching software architecture to undergraduate students: an experience report

abstract: Software architecture lies at the heart of system thinking skills for software. Teaching software architecture requires contending with the problem of how to make the learning realistic -- most systems which students can learn quickly are too simple for them to express architectural issues. We address here the ten years' history of teaching an undergraduate software architecture course, as a part of a bachelor's program in software engineering. Included are descriptions of what we perceive the realistic goals to be, of teaching software architecture at this level. We go on to analyze the successes and issues of various approaches we have taken over the years. We finish with recommendations for others who teach this same subject, either as a standalone undergraduate course or integrated into a software engineering course.


title: Reducing peak electricity demand in building climate control using real-time pricing and model predictive control

abstract: A method to reduce peak electricity demand in building climate control by using real-time electricity pricing and applying model predictive control (MPC) is investigated. We propose to use a newly developed time-varying, hourly-based electricity tariff for end-consumers, that has been designed to truly reflect marginal costs of electricity provision, based on spot market prices as well as on electricity grid load levels, which is directly incorporated into the MPC cost function. Since this electricity tariff is only available for a limited time window into the future we use least-squares support vector machines for electricity tariff price forecasting and thus provide the MPC controller with the necessary estimated time-varying costs for the whole prediction horizon. In the given context, the hourly pricing provides an economic incentive for a building controller to react sensitively with respect to high spot market electricity prices and high grid loading, respectively. Within the proposed tariff regime, grid-friendly behaviour is rewarded. It can be shown that peak electricity demand of buildings can be significantly reduced. The here presented study is an example for the successful implementation of demand response (DR) in the field of building climate control.


title: Empirical studies of agile software development : A systematic review

abstract: Agile software development represents a major departure from traditional, plan-based approaches to software engineering. A systematic review of empirical studies of agile software development up to and including 2005 was conducted. The search strategy identified 1996 studies, of which 36 were identified as empirical studies. The studies were grouped into four themes: introduction and adoption, human and social factors, perceptions on agile methods, and comparative studies. The review investigates what is currently known about the benefits and limitations of, and the strength of evidence for, agile methods. Implications for research and practice are presented. The main implication for research is a need for more and better empirical studies of agile software development within a common research agenda. For the industrial readership, the review provides a map of findings, according to topic, that can be compared for relevance to their own settings and situations.


title: Single neurons in prefrontal cortex encode abstract rules

abstract: The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the ‘rules’ for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules1. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.


title: Kernel Flows: from learning kernels from data into the abyss

abstract: Learning can be seen as approximating an unknown function by interpolating the training data. Kriging offers a solution to this problem based on the prior specification of a kernel. We explore a numerical approximation approach to kernel selection/construction based on the simple premise that a kernel must be good if the number of interpolation points can be halved without significant loss in accuracy (measured using the intrinsic RKHS norm $\|\cdot\|$ associated with the kernel). We first test and motivate this idea on a simple problem of recovering the Green's function of an elliptic PDE (with inhomogeneous coefficients) from the sparse observation of one of its solutions. Next we consider the problem of learning non-parametric families of deep kernels of the form $K_1(F_n(x),F_n(x'))$ with $F_{n+1}=(I_d+\epsilon G_{n+1})\circ F_n$ and $G_{n+1} \in \operatorname{Span}\{K_1(F_n(x_i),\cdot)\}$. With the proposed approach constructing the kernel becomes equivalent to integrating a stochastic data driven dynamical system, which allows for the training of very deep (bottomless) networks and the exploration of their properties. These networks learn by constructing flow maps in the kernel and input spaces via incremental data-dependent deformations/perturbations (appearing as the cooperative counterpart of adversarial examples) and, at profound depths, they (1) can achieve accurate classification from only one data point per class (2) appear to learn archetypes of each class (3) expand distances between points that are in different classes and contract distances between points in the same class. For kernels parameterized by the weights of Convolutional Neural Networks, minimizing approximation errors incurred by halving random subsets of interpolation points, appears to outperform training (the same CNN architecture) with relative entropy and dropout.


title: Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment

abstract: Face alignment acts as an important task in computer vision. Regression-based methods currently dominate the approach to solving this problem, which generally employ a series of mapping functions from the face appearance to iteratively update the face shape hypothesis. One keypoint here is thus how to perform the regression procedure. In this work, we formulate this regression procedure as a sparse coding problem. We learn two relational dictionaries, one for the face appearance and the other one for the face shape, with coupled reconstruction coefficient to capture their underlying relationships. To deploy this model for face alignment, we derive the relational dictionaries in a stage-wised manner to perform close-loop refinement of themselves, i.e., the face appearance dictionary is first learned from the face shape dictionary and then used to update the face shape hypothesis, and the updated face shape dictionary from the shape hypothesis is in return used to refine the face appearance dictionary. To improve the model accuracy, we extend this model hierarchically from the whole face shape to face part shapes, thus both the global and local view variations of a face are captured. To locate facial landmarks under occlusions, we further introduce an occlusion dictionary into the face appearance dictionary to recover face shape from partially occluded face appearance. The occlusion dictionary is learned in a data driven manner from background images to represent a set of elemental occlusion patterns, a sparse combination of which models various practical partial face occlusions. By integrating all these technical innovations, we obtain a robust and accurate approach to locate facial landmarks under different face views and possibly severe occlusions for face images in the wild. Extensive experimental analyses and evaluations on different benchmark datasets, as well as two new datasets built by ourselves, have demonstrated the robustness and accuracy of our proposed model, especially for face images with large view variations and/or severe occlusions.


title: Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey

abstract: Wireless sensor networks (WSNs) security is a topic of great importance because of advancement of many security sensitive applications in diverse fields involving WSNs. Compared to traditional wireless and wired networks, WSNs also possess numerous additional vulnerabilities such as dynamic network topology, broadcast nature of the medium, resource constrained nodes, immense network scale and lack of physical infrastructure. The open communication environment makes WSNs more vulnerable than wired communications to several types of attacks including the passive type of eavesdropping attack leading to intercepted transmissions and active type of jamming attack leading to disrupted transmissions. These additional vulnerabilities enable the adversary to launch severe and even more complicated attacks. Thus a thorough investigation of attacks that can be launched against WSNs is required. Therefore, this article is motivated for examining the wireless security vulnerabilities and the imposed threats to devise reliable and efficient defence technique for improvement of WSNs security. We first summarize the security challenges and the security requirements of wireless networks. The paper then throws light on security vulnerabilities in wireless networks and classifies various attacks in WSNs according to different OSI protocol layers. Finally, some open technical challenges which are still unsolved are summarized and future works in WSNs security are discussed.


title: The Foundations of Deep Learning with a Path Towards General Intelligence

abstract: Like any field of empirical science, AI may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and tacit assumptions in deep learning research. Deep Learning methodology seeks to overcome limitations in traditional machine learning research as it combines facets of model richness, generality, and practical applicability. The methodology so far has produced outstanding results due to a productive synergy of function approximation, under plausible assumptions of irreducibility and the efficiency of back-propagation family of algorithms. We examine these winning traits of deep learning, and also observe the various known failure modes of deep learning. We conclude by giving recommendations on how to extend deep learning methodology to cover the postulates of general-purpose AI including modularity, and cognitive architecture. We also relate deep learning to advances in theoretical neuroscience research.


title: Location of banking automatic teller machines based on convolution

abstract: In this paper, the problem of determining the optimum number and locations of banking automatic teller machines (ATMs) is considered. The objective is to minimize the total number of ATMs to cover all customer demands within a given geographical area. First, a mathematical model of this optimization problem is formulated. A novel heuristic algorithm with unique features is then developed to efficiently solve this problem. Finally, simulation results show the effectiveness of this algorithm in solving the ATM placement problem.


Novel Heteroanalogues of Methyl Maltoside Containing Sulfur and Selenium as Potential Glycosidase Inhibitors.


title: Measuring E-Learning System Success (Research In Progress)

abstract: Education is considered to be one of the sectors that have been radically affected by developments in information technology. E-learning is believed to be the main outcome of adopting and using the new and more advanced information technology in the education sector. In spite of this rapid growth in the e-learning field there still exists a range of issues facing the stakeholders of e-learning systems. One of the key issues is measuring e-learning system success. Although considerable attention has been paid to the information systems success issue, there remain arguments about the factors which are most effective in measuring information system success. The issue of measuring information system success has an impact on evaluating e-learning systems success as well. ::: This study aims to fill this void by proposing an evaluation methodology model to assess e-learning systems success. The contribution of this study is the proposed model to assess e-learning systems success. The model is based on a thorough review of the e-learning success literature and existing IS success models. A range of stakeholders such as Academic staff, students, and ICT staff are considered in this model.


title: Tensor field design in volumes

abstract: 3D tensor field design is important in several graphics applications such as procedural noise, solid texturing, and geometry synthesis. Different fields can lead to different visual effects. The topology of a tensor field, such as degenerate tensors, can cause artifacts in these applications. Existing 2D tensor field design systems cannot be used to handle the topology of a 3D tensor field. In this paper, we present to our knowledge the first 3D tensor field design system. At the core of our system is the ability to edit the topology of tensor fields. We demonstrate the power of our design system with applications in solid texturing and geometry synthesis.


title: Image denoising through multi-scale learnt dictionaries

abstract: Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large smooth areas. In this paper we present a patch-based denoising algorithm relying on a sparsity-inspired model (K-SVD), which uses a multi-scale analysis framework. This allows us to overcome some of the disadvantages of the popular algorithms. We look for a sparse representation under an already sparsifying wavelet transform by adaptively training a dictionary on the different decomposition bands of the noisy image itself, leading to a multi-scale version of the K-SVD algorithm. We then combine the single scale and multi-scale approaches by merging both outputs by weighted joint sparse coding of the images. Our experiments on natural images indicate that our method is competitive with state of the art algorithms in terms of PSNR while giving superior results with respect to visual quality.


title: The mechanical design of snake-arm robot

abstract: Snake-arm robots with a slender and flexible body have many potential applications in aerospace, nuclear and surgery industry. In this paper, we presented a snake-arm robot. This robot has three hollow slender segments composed of ball-joints driven by steel cables. A cage-like structure is contrived to drive the steel cables, and a special motor-driven control system is proposed for the control of this robot. A kinematics model has been built with a new moving decomposition method which significantly simplifies the transformation matrix. In the end, the prototype of this robot is performed, and the design of this robot is proved to be feasible by experimental results that have been shown in this paper.


title: All your browser-saved passwords could belong to us: a security analysis and a cloud-based new design

abstract: Web users are confronted with the daunting challenges of creating, remembering, and using more and more strong passwords than ever before in order to protect their valuable assets on different websites. Password manager is one of the most popular approaches designed to address these challenges by saving users' passwords and later automatically filling the login forms on behalf of users. Fortunately, all the five most popular Web browsers have provided password managers as a useful built-in feature. Unfortunately, the designs of all those Browser-based Password Managers (BPMs) have severe security vulnerabilities. In this paper, we uncover the vulnerabilities of existing BPMs and analyze how they can be exploited by attackers to crack users' saved passwords. Moreover, we propose a novel Cloud-based Storage-Free BPM (CSF-BPM) design to achieve a high level of security with the desired confidentiality, integrity, and availability properties. We have implemented a CSF-BPM system into Firefox and evaluated its correctness and performance. We believe CSF-BPM is a rational design that can also be integrated into other popular Web browsers.


Statistical Learning and Kernel Methods in Bioinformatics


title: Emotional abilities as predictors of risky driving behavior among a cohort of middle aged drivers

abstract: Abstract The aim of this study is to analyze the relationship between emotional abilities and the influence of this relationship on self reported drivers’ risky attitudes. The risky driving attitudes and emotional abilities of 177 future driving instructors were measured. The results demonstrate that risky attitudes correlate negatively with emotional abilities. Regression analysis showed that adaptability and interpersonal abilities explained the differences observed in the global risk attitude index. There were some differences in the specific risk factors. The variability observed in the speed and distraction and fatigue factors could also be explained by interpersonal and adaptability abilities. Nevertheless the tendency to take risks was explained by stress management and also interpersonal components. Emotional abilities have the weakest relation with alcohol and drugs factor, and in this case the variability observed was explained by the adaptability component. The results obtained highlight the importance take off including emotional abilities in prevention programs to reduce risky driving behaviors.


title: Effect of Motor Control Exercises Versus Graded Activity in Patients With Chronic Nonspecific Low Back Pain: A Randomized Controlled Trial

abstract: Background. Motor control exercises to improve control and coordination of trunk muscles and graded activity under the principles of cognitive-behavioral therapy are 2 commonly used exercise therapies, yet there is little evidence to support the use of one intervention over the other. Objective. The objective of this study was to compare the effectiveness of motor control exercises and graded activity for patients with chronic nonspecific low back pain. Design. This study was a prospectively registered randomized controlled trial with outcome assessment and statistical analyses conducted blind to group. Setting. The study was conducted in primary care settings. Patients. The participants were 172 patients with chronic (12 weeks) nonspecific low back pain. Interventions. Patients were randomly assigned to receive either motor control exercises or graded activity. There was no attempt to subclassify patients to match them to a treatment. Patients in both groups received 14 sessions of individualized, supervised exercise therapy. Measurements. Primary outcomes were average pain over the previous week (numeric rating scale) and function (Patient-Specific Functional Scale); secondary outcomes were disability (24-item Roland-Morris Disability Questionnaire), global impression of change (Global Perceived Effect Scale), and quality of life (36-Item Short-Form Health Survey questionnaire [SF-36]). Outcome measures were collected at baseline and at 2, 6, and 12 months after intervention. Results. A linear mixed models analysis showed that there were no significant differences between treatment groups at any of the time points for any of the outcomes studied. For example, the effect for pain at 2 months was 0.0 (0.7 to 0.8). Limitations. Clinicians could not be blinded to the interventions. Conclusion. The results of this study suggest that motor control exercises and graded activity have similar effects for patients with chronic nonspecific low back pain.


title: Reduction of Torque Ripple in Inset Permanent Magnet Synchronous Motor by Magnets Shifting

abstract: This paper proposes a method of reducing total torque ripple by magnets shifting. The key of this method is to consider the reluctance torque ripple, because the reluctance torque ripple is the main source of torque ripple in inset permanent magnet synchronous motor. This method is realized by appropriately choosing Repeating Unit, which indicates a group of poles producing torques with consistency in waveforms and phases. Meanwhile, the uniform analytical expressions of torques with magnets shifting are established, including cogging torque, reluctance torque, and total torque. Moreover, the total torque ripple can be reduced greatly, and the average total torque loss is acceptable when one pole-pair is chosen as one Repeating Unit and the shifting angles of Repeating Unit are 3.75 and 1.875 mechanical degrees. These theoretical analyses are verified by the finite-element method.


title: Open-source Hardware: Opportunities and Challenges

abstract: Innovation in hardware is slowing due to rising costs of chip design and diminishing benefits from Moore's law and Dennard scaling. Software innovation, on the other hand, is flourishing, helped in good measure by a thriving open-source ecosystem. We believe that open source can similarly help hardware innovation, but has not yet due to several reasons. We identify these reasons and how the industry, academia, and the hardware community at large can come together to address them.


title: Salient object detection by composition

abstract: Conventional saliency analysis methods measure the saliency of individual pixels. The resulting saliency map inevitably loses information in the original image and finding salient objects in it is difficult. We propose to detect salient objects by directly measuring the saliency of an image window in the original image and adopt the well established sliding window based object detection paradigm.


title: A Total-Evidence Approach to Dating with Fossils, Applied to the Early Radiation of the Hymenoptera

abstract: Phylogenies are usually dated by calibrating interior nodes against the fossil record. This relies on indirect methods that, in the worst case, misrepresent the fossil information. Here, we contrast such node dating with an approach that includes fossils along with the extant taxa in a Bayesian total-evidence analysis. As a test case, we focus on the early radiation of the Hymenoptera, mostly documented by poorly preserved impression fossils that are difficult to place phylogenetically. Specifically, we compare node dating using nine calibration points derived from the fossil record with total-evidence dating based on 343 morphological characters scored for 45 fossil (4--20 complete) and 68 extant taxa. In both cases we use molecular data from seven markers (∼5 kb) for the extant taxa. Because it is difficult to model speciation, extinction, sampling, and fossil preservation realistically, we develop a simple uniform prior for clock trees with fossils, and we use relaxed clock models to accommodate rate variation across the tree. Despite considerable uncertainty in the placement of most fossils, we find that they contribute significantly to the estimation of divergence times in the total-evidence analysis. In particular, the posterior distributions on divergence times are less sensitive to prior assumptions and tend to be more precise than in node dating. The total-evidence analysis also shows that four of the seven Hymenoptera calibration points used in node dating are likely to be based on erroneous or doubtful assumptions about the fossil placement. With respect to the early radiation of Hymenoptera, our results suggest that the crown group dates back to the Carboniferous, ∼309 Ma (95% interval: 291--347 Ma), and diversified into major extant lineages much earlier than previously thought, well before the Triassic. [Bayesian inference; fossil dating; morphological evolution; relaxed clock; statistical phylogenetics.].


title: Comparative Experiments on Sentiment Classification for Online Product Reviews

abstract: Evaluating text fragments for positive and negative subjective expressions and their strength can be important in applications such as single- or multi- document summarization, document ranking, data mining, etc. This paper looks at a simplified version of the problem: classifying online product reviews into positive and negative classes. We discuss a series of experiments with different machine learning algorithms in order to experimentally evaluate various trade-offs, using approximately 100K product reviews from the web.


title: Image super-resolution using multi-layer support vector regression

abstract: Existing support vector regression (SVR) based image superresolution (SR) methods always utilize single layer SVR model to reconstruct source image, which are incapable of restoring the details and reduce the reconstruction quality. In this paper, we present a novel image SR approach, where a multi-layer SVR model is adopted to describe the relationship between the low resolution (LR) image patches and the corresponding high resolution (HR) ones. Besides, considering the diverse content in the image, we introduce pixel-wise classification to divide pixels into different classes, such as horizontal edges, vertical edges and smooth areas, which is more conductive to highlight the local characteristics of the image. Moreover, the input elements to each SVR model are weighted respectively according to their corresponding output pixel's space positions in the HR image. Experimental results show that, compared with several other learning-based SR algorithms, our method gains high-quality performance. © 2014 IEEE.


title: Does the EU sugar policy reform increase added sugar consumption? An empirical evidence on the soft drink market

abstract: National Health authorities recommend a decrease in the consumption of ‘added’ sugar. At the same moment, a reform of the Common Organisation of the Sugar Market will lead to a decrease by more than 30% of the sugar price in the EU. Using French data on the soft drinks purchases, this paper investigates the impact of this reform on the consumption of sugar sweetened beverages and on added sugar consumption. The soft drink market is composed of highly differentiated products with different sugar content. Hence the reform of the EU sugar policy leads to a decrease in regular soft drink prices by more than 3% in average and varies from 1.7% to 6.5% according to the brand. To assess substitution within the food category of sugar sweetened beverages, we use a structural econometric model, the random-coefficients logit model. Our model also takes into account observed and unobserved heterogeneity in the consumers’ behavior and then allows to estimate the impact of the sugar price decrease on the soft drink consumption according to the type of consumers. Results suggest that price changes would lead to an increase in market shares of regular products by 7.5% and a decrease in market share of diet products by 3.5%. On the whole, it would rise the consumption of regular soft drinks by more that 1 liter per year and per person and the consumption of added sugar by 124 grams per year and per person. Moreover, the reform leads to substitution between brands at the benefit of products with the highest sugar content. The increase in per person consumption is larger in households composed of overweight and obese individuals.


title: Hierarchical Relative Entropy Policy Search

abstract: Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks that are strongly structured. Such task structures can be exploited by incorporating hierarchical policies that consist of gating networks and sub-policies. However, this concept has only been partially explored for real world settings and complete methods, derived from first principles, are needed. Real world settings are challenging due to large and continuous state-action spaces that are prohibitive for exhaustive sampling methods. We define the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-policies for execution by the agent. In order to efficiently share experience with all sub-policies, also called inter-policy learning, we treat these sub-policies as latent variables which allows for distribution of the update information between the sub-policies. We present three different variants of our algorithm, designed to be suitable for a wide variety of real world robot learning tasks and evaluate our algorithms in two real robot learning scenarios as well as several simulations and comparisons.


title: A Machine Learning Approach to Demographic Prediction using Geohashes

abstract: With the rapid proliferation of smartphones, human beings act as social sensors by means of carrying GPS-enabled devices that share location data. This has resulted in an abundance of sensor data gathered over long periods of time. Gaining meaningful insights from such massive amounts of spatio-temporal data accumulated by several disparate sources is often a challenge for organizations. Identifying demographics of mobile phone users by telecommunication providers is one such example. Demographic information plays a very significant role in targeting online advertisements to focused user groups by gaining insights about userfis mobility patterns. However, in practice, demographic information such as age and gender are mostly unavailable to app developers for open access due to privacy concerns. In this paper, we try to address the gap of how to enrich location data with demographics, which could be valuable for app developers. In our study, we use a machine learning approach to predict the gender and age of mobile phone users from a set of 3,252,950 anonymised GPS trajectories with 60,865 unique devices using a predictive model which is based upon the concept of Geohashes. We study to what extent usersfi demographics could be inferred from their frequently visited locations by encoding by formulating a multi-level classification algorithm to find the most frequently visited Geohashes and associating them with nearest points of interests which would enable predicting age-group and gender of the users who prefer to visit a specific location in a sequential manner. Experiments are conducted on a real dataset of mobile phone users collected and shared by a telecommunication provider. Th The experimental results show that the proposed algorithm can achieve mean prediction accuracy scores of 71.62% and 96.75% for predicting gender and age groups of the users respectively.


title: The Improvement for Rete Algorithm

abstract: Rete algorithm is the most efficient forward inference algorithm, widely used in rule engine systems. The Rete algorithm can be divided into two parts: rule compilation and runtime execution. Rule compilation describes how the rules in the production memory generate an efficient discrimination network - Rete network, while runtime execution describes how the asserted facts propagate through the network and fire the rule execution. In the rule compilation part, this paper introduces 'or' connective between conditions, then proposes an algorithm that interprets a single production containing multiple 'or' connectives as the equivalent of multiple productions. In runtime execution part, a specific rule engine handles common objects as facts to provide easy integration, and this paper introduces the 'update' primitive to update facts in the working memory of the rule engine and describes how to implement it through a shadow proxy mechanism.