title: Robust Facial Expression Recognition Based on Local Directional Pattern

abstract: Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.


title: Intrusion Detection System Using Genetic Algorithm for Cloud

abstract: Cloud and its transactions have emerged as a major challenge. This paper aims to come up with an efficient and best possible way to transfer data in cloud computing environment. This goal is achieved with the help of Soft Computing Techniques. Of the various techniques such as fuzzy logic, genetic algorithm or neural network, the paper proposes an effective method of intrusion detection using genetic algorithm. The selection of the optimized path for the data transmission proved to be effective method in cloud computing environment. Network path optimization increases data transmission speed making intrusion in network nearly impossible. Intruders are forced to act quickly for intruding the network which is quite a tough task for them in such high speed data transmission network.


title: A method of requirements elicitation and analysis for Global Software Development

abstract: To perform requirements elicitation and analysis, effective communication and collaboration between stakeholders are necessary. Global Software Development (GSD), where software teams are located in different parts of the world, has become increasingly popular. However, geographical distance, cultural diversity, differences in time zones, and language barriers create difficulties for GSD stakeholders in engaging in effective communication. Taking into consideration the factors involved in GSD, previous research showed that the ways by which requirements are gathered and analyzed for collocated software development cannot be used effectively for GSD. Thus, in this paper, we present a method of requirements elicitation and analysis for GSD. The method consists of 4 stages: (1) data collection; (2) educating stakeholders about GSD issues; (3) post-education assessment; and (4) requirements elicitation and analysis. Past researchers used student groups in a university environment to play the roles of stakeholders in experiments in GSD studies. Likewise, we preliminarily validate our method by applying it to a case study of an online shopping system, where the roles of client, requirements engineer, project analyst, and designers were played by a group of students.


title: Medusa: Simplified Graph Processing on GPUs

abstract: Graphs are common data structures for many applications, and efficient graph processing is a must for application performance. Recently, the graphics processing unit (GPU) has been adopted to accelerate various graph processing algorithms such as BFS and shortest paths. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To simplify graph processing on GPUs, we propose a programming framework called Medusa which enables developers to leverage the capabilities of GPUs by writing sequential C/C++ code. Medusa offers a small set of user-defined APIs and embraces a runtime system to automatically execute those APIs in parallel on the GPU. We develop a series of graph-centric optimizations based on the architecture features of GPUs for efficiency. Additionally, Medusa is extended to execute on multiple GPUs within a machine. Our experiments show that 1) Medusa greatly simplifies implementation of GPGPU programs for graph processing, with many fewer lines of source code written by developers and 2) the optimization techniques significantly improve the performance of the runtime system, making its performance comparable with or better than manually tuned GPU graph operations.


title: Recent advances in 3D SEM surface reconstruction

abstract: The scanning electron microscope (SEM), as one of the most commonly used instruments in biology and material sciences, employs electrons instead of light to determine the surface properties of specimens. However, the SEM micrographs still remain 2D images. To effectively measure and visualize the surface attributes, we need to restore the 3D shape model from the SEM images. 3D surface reconstruction is a longstanding topic in microscopy vision as it offers quantitative and visual information for a variety of applications consisting medicine, pharmacology, chemistry, and mechanics. In this paper, we attempt to explain the expanding body of the work in this area, including a discussion of recent techniques and algorithms. With the present work, we also enhance the reliability, accuracy, and speed of 3D SEM surface reconstruction by designing and developing an optimized multi-view framework. We then consider several real-world experiments as well as synthetic data to examine the qualitative and quantitative attributes of our proposed framework. Furthermore, we present a taxonomy of 3D SEM surface reconstruction approaches and address several challenging issues as part of our future work.


title: Myopathy in broiler chickens: a role for Ca(2+)-activated phospholipase A2?

abstract: The role of Ca(2+)-dependent phospholipase A2 (PLA2) in the mechanism of skeletal muscle damage in broiler chickens was examined in vitro using a novel, synthetic, PLA2-specific inhibitor Ro31-499/001 (Ro31). Muscle damage was assessed by measurement of creatine kinase (CK) efflux from isolated muscles into the incubation medium. Treatment with the specific Ca(2+)-ionophore 4-Br-A23187 (5 microM) caused a 72% elevation (P<0.05) in muscle 45Ca2+ accumulation, which was associated with a marked increase (P<0.001) in muscle CK efflux (7.6-fold). Incubation with Ro31 (50 microM) reduced (P<0.001) CK efflux from muscles treated with ionophore (45%) but was without effect on 45Ca accumulation. Treatment with the Na+ ionophore monensin (100 microM) induced 55% (P< 0.05) elevation in 45Ca2+ accumulation with a concomitant 2.5-fold increase (P<0.001) in CK loss. Muscles incubated with monensin in the presence of Ro31 exhibited a 49% reduction (P<0.001) in CK leakage but showed no change in 45Ca2+ uptake. The results indicate that increasing external Ca2+ entry, directly or indirectly, and elevation of intracellular Ca2+, significantly alters sarcolemmal integrity resulting in increased CK efflux from broiler skeletal muscle. This process is, at least in part, dependent upon activation of PLA2 activity and thus inhibitable by Ro31. It is further proposed that muscle damage in poultry induced by a range of stresses, and insults may also be mediated by a Ro31 sensitive, PLA2-dependent component. The findings have implications for strategies to reduce or prevent myopathies in poultry affecting bird welfare and product quality.


title: Towards adaptive classification for BCI

abstract: Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain–computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.


title: Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

abstract: In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance.


title: Upregulated Seizure-Related 6 Homolog-Like 2 Is a Prognostic Predictor of Hepatocellular Carcinoma

abstract: Seizure-related 6 homolog-like 2 (SEZ6L2), which is localized on the cell surface, has been found to be associated with tumor angiogenesis and lung cancer progression. However, the role of SEZ6L2 in hepatocellular carcinoma (HCC) is still unclear. We obtained data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to investigate SEZ6L2 expression and regulation in HCC. Then, HCC tissue samples were collected to verify SEZ6L2 by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical staining (IHC). Patient information was collected for survival and prognosis analysis. qRT-PCR, IHC, and bioinformatics analysis showed that the SEZ6L2 protein was highly expressed in HCC samples. Clinical data showed that high SEZ6L2 protein expression was correlated with tumor-node-metastasis (TNM) stages ( ), tumor number ( ), and tumor size ( ). Meanwhile, SEZ6L2 overexpression was closely associated with poor overall survival and disease-free survival in HCC patients. Moreover, SEZ6L2 is an independent prognostic predictor for the survival of HCC patients. This study suggests a significant correlation between SEZ6L2 and HCC, which means that SEZ6L2 may potentially serve as a useful prognostic biomarker for HCC patients.


Automatic Translation of English Text to Indian Sign Language Synthetic Animations.


title: Compact X-band FMCW sensor module for fast and accurate vehicle occupancy detection

abstract: This paper presents an X-band FMCW sensor module for vehicle occupancy detection with compact size of only 4.5 cm × 4 cm. The transmitting and receiving antenna arrays are co-designed with the sensor to achieve optimal RF performances using less circuit area. Measurement results demonstrate successful and accurate identification of vehicles with fast response time less than 0.5 second.


title: Validation of the dutch version of the health education impact questionnaire (HEIQ) and comparison of the Dutch translation with the English, German and French HEIQ

abstract: Background ::: The Health Education Impact Questionnaire (heiQ) evaluates the effectiveness of health education and self-management programs provided to people dealing with a wide range of conditions. Aim of this study was to translate, culturally adapt and validate the Dutch translation of the heiQ and to compare the results with the English, German and French translations.


title: The Promise of Edge Computing

abstract: The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. Edge computing could address concerns such as latency, mobile devices' limited battery life, bandwidth costs, security, and privacy.


Semantic Information Retrieval Architecture for Unstructured Data using NLS


title: Heuristic Evaluation Functions for General Game Playing

abstract: A general game playing program plays games that it has not previously encountered. A game manager program sends the game playing programs a description of a game's rules and objectives in a well-defined game description language. A central challenge in creating effective general game playing programs is that of constructing heuristic evaluation functions from game descriptions. This paper describes a method for constructing evaluation functions that represent exact values of simplified games. The simplified games are abstract models that incorporate the most essential aspects of the original game, namely payoff, control, and termination. Results of applying this method to a sampling of games suggest that heuristic evaluation functions based on our method are both comprehensible and effective.


title: Narrow-leafed lupin (Lupinus angustifolius L.) functional identification and characterization of the aldehyde dehydrogenase (ALDH) gene superfamily

abstract: Aldehyde dehydrogenases (ALDHs) constitute a NAD(P)+-dependent superfamily of proteins, which catalyze the irreversible oxidation of aldehydes molecules to non-toxic carboxylic acids. This superfamily in legume seeds has a primary role in oxidative stress response under biotic and abiotic stresses as desiccation, cold, salinity, and heat shock factors that largely affect seed germination and seedling growth. ::: ::: Up to date only a study has been dedicated to identify and characterize the complete ALDH superfamily in a legume crop, Glycine max, and other few partial studies focusing on their sequence cloning in pea, and fenugreek, despite the importance of legume crops for food sustainability. The consecution of the narrow-leafed lupin (NLL) (Lupinus angustifolius L.) genome sequencing project have resulted in the identification of ALDH genes that still need to be comparatively analyzed for systematic identification and ALDH gene families functional characterization. ::: ::: In this study, nineteen unique ALDH sequences that encode for 10 ALDH families were identified in L. angustifolius. 5 additional ALDH families (ALDH5, ALDH6, ALDH12, ALDH18, and ALDH22) are present in this specie in comparison to Glycine max. Phylogenetic analysis showed that ALDHs were split into 11 clades containing at least one ALDH gene of NLL. The relationship among families is quite well conserved between legume species and among monocot and dicot plants. Structure–functional analysis of ALDH5 and ALDH6 showed a catalytic mechanism which involved C330–E427 couple, and C320/R143-R319, respectively in a specific molecular (catalytic) environment. The structure–functional analysis identified potential neo-functionalities only in families ALDH2C4 and ALDH7B4. ::: ::: Overall, this study reveals new insights into the functional–structure features and evolution of ALDH gene families, particularly concerning NLL, a legume crop. These ALDH functional identification and characterization are valuable in molecular breeding strategies applied to legume for improvement of stress tolerance and yield.


title: Fake News in Social Networks

abstract: We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors' past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary's attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents' network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary's attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users' private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.


title: When Does Virtual Embodiment Change Our Minds?

abstract: Can an avatar's body movements change a person's perception of good and bad? We discuss virtual embodiment according to theories of embodied cognition EC, and afferent and sensorimotor correspondences. We present an example study using virtual reality VR to test EC theory, testing the effect of altered virtual embodiment on perception. Participants either controlled an avatar whose arm movements were similar to their own or reflected the mirror opposite of their arm movements. We measured their associations of "good" and "bad" with the left and right i.e., space-valence associations. This study demonstrated how VR could be used to examine the possible ways that systems of the body e.g., visual, motor may interact to influence cognition. The implications of this research suggest that visual feedback alone is not enough to alter space-valence associations. Multiple sensory experiences of media i.e., sensorimotor feedback may be necessary to influence cognition, not simply visual feedback.


title: Major physical and psychological harms of methamphetamine use.

abstract: ISSUES ::: The major physical and psychological health effects of methamphetamine use, and the factors associated with such harms. ::: ::: ::: APPROACH ::: Comprehensive review. ::: ::: ::: KEY FINDINGS ::: Physical harms reviewed included toxicity and mortality, cardiovascular/cerebrovascular pathology, dependence and blood-borne virus transmission. Psychological harms include methamphetamine psychosis, depression, suicide, anxiety and violent behaviours. ::: ::: ::: IMPLICATIONS ::: While high-profile health consequences, such as psychosis, are given prominence in the public debate, the negative sequelae extend far beyond this. This is a drug class that causes serious heart disease, has serious dependence liability and high rates of suicidal behaviours. ::: ::: ::: CONCLUSION ::: The current public image of methamphetamine does not portray adequately the extensive, and in many cases insidious, harms caused.


title: Self-Compassion , Stress , and Coping

abstract: People who are high in self-compassion treat themselves with kindness and concern when they experience negative events. The present article examines the construct of self-compassion from the standpoint of research on coping in an effort to understand the ways in which people who are high in self-compassion cope with stressful events. Self-compassionate people tend to rely heavily on positive cognitive restructuring but do not appear to differ from less self-compassionate people in the degree to which they cope through problem-solving and distraction. Existing evidence does not show clear differences in the degree to which people who are low vs. high in self-compassion seek support as a coping strategy, but more research is needed.


Early Mortality Syndrome (EMS) as new Emerging Threat in Shrimp Industry


title: Investigation of some medicinal plants traditionally used for treatment of malaria in Kenya as potential sources of antimalarial drugs.

abstract: Malaria is a major public health problem in many tropical and subtropical countries and the burden of this disease is getting worse, mainly due to the increasing resistance of Plasmodium falciparum against the widely available antimalarial drugs. There is an urgent need for discovery of new antimalarial agents. Herbal medicines for the treatment of various diseases including malaria are an important part of the cultural diversity and traditions of which Kenya's biodiversity has been an integral part. Two major antimalarial drugs widely used today came originally from indigenous medical systems, that is quinine and artemisinin, from Peruvian and Chinese ancestral treatments, respectively. Thus ethnopharmacology is a very important resource in which new therapies may be discovered. The present review is an analysis of ethnopharmacological publications on antimalarial therapies from some Kenyan medicinal plants.


title: Neural and biological soldier enhancement : From SciFi to deployment

abstract: We discuss selected highlights of future human performance enhancement and their potential impact as for example envisioned by authors of Science Fiction and Fantasy literature, coming into reach of man now. With reference to a new taxonomy of ergonomics as introduced by Kenneth R. Boff, we discern “technointegration” and “stimulated bioenhancement” and allocate these to the realms of neural respectively biological ergonomics, i.e. ergonomics of generation 3 and 4. Tools of knowledge generation including the Disruptive Technology Assessment Game approach are addressed. A brief comment on the perceivable shift in human-machine-boundary by society is included.


title: "Alone together?": exploring the social dynamics of massively multiplayer online games

abstract: Massively Multiplayer Online Games (MMOGs) routinely attract millions of players but little empirical data is available to assess their players' social experiences. In this paper, we use longitudinal data collected directly from the game to examine play and grouping patterns in one of the largest MMOGs: World of Warcraft. Our observations show that the prevalence and extent of social activities in MMOGs might have been previously over-estimated, and that gaming communities face important challenges affecting their cohesion and eventual longevity. We discuss the implications of our findings for the design of future games and other online social spaces.


title: When and why are log-linear models self-normalizing?

abstract: Several techniques have recently been proposed for training “self-normalized” discriminative models. These attempt to find parameter settings for which unnormalized model scores approximate the true label probability. However, the theoretical properties of such techniques (and of self-normalization generally) have not been investigated. This paper examines the conditions under which we can expect self-normalization to work. We characterize a general class of distributions that admit self-normalization, and prove generalization bounds for procedures that minimize empirical normalizer variance. Motivated by these results, we describe a novel variant of an established procedure for training self-normalized models. The new procedure avoids computing normalizers for most training examples, and decreases training time by as much as factor of ten while preserving model quality.


InSilicoLab - A User Workspace Implementing e-Science Principles


title: A decision support system for public logistics information service management and optimization

abstract: Transportation optimization usually aims at minimizing the empty load ratios (ELRs) of vehicles. Most Chinese vehicles for logistics are owned by individual entrepreneurs. Because China is very large, transport distances are typically long, and thus the ELR is very high. The ELR is the primary reason for high transport costs, considerable pollution, and high energy consumption. Many Chinese local governments try to build public transport information services that decrease the ELR. This work proposes a decision support system (DSS) for public logistics information service management and optimization (PLISMO) for vehicle drivers and owners, logistics customers and related logistics service providers and management institutes. The dynamic and real-time matching model between goods and vehicles, and the enabling technologies are important issues for the DSS for PLISMO. Therefore, intelligent positioning technologies are employed to acquire and manage the vehicle status. A model matching vehicles with goods is developed based on an assessment model of transport capability and service priority criteria. A multi-objective real-time scheduling model is devised to minimize the ELR. Based on the concepts and decision-making models for PLISMO, a DSS is created and the architecture of the system is investigated. The effectiveness of the DSS and decision-making models is demonstrated by a case of finished vehicle logistics (FVL). Analytical results show that the proposed DSS can reduce the ELR and logistics cost. This system helps governments construct DSSs for general PLISMO.


title: Instant tactile-audio map: enabling access to digital maps for people with visual impairment

abstract: In this paper, we propose an automatic approach, complete with a prototype system, for supporting instant access to maps for local navigation by people with visual impairment. The approach first detects and segments texts from a map image and recreates the remaining graphical parts in a tactile form which can be reproduced immediately through a tactile printer. Then, it generates an SVG (Scalable Vector Graphics) file, which integrates both text and graphical information. The tactile hardcopy and the SVG file together are used to provide a user with interactive access to the map image through a touchpad, resulting in a tactile-audio representation of the original input image. This supports real-time access to the map without tedious conversion by a sighted professional. Evaluations with six users who are blind show that the created tactile-audio maps from our prototype system convey the most important map information and are deemed as potentially useful for local navigation.


The Influences of Perceived Value on Consumer Purchase Intention : The Moderating Effect of Advertising Endorser


title: Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text

abstract: This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with ICON-2016. The shared task was to predict Part of Speech (POS) tag at word level for a given text. The code-mixed text is generated mostly on social media by multilingual users. The presence of the multilingual words, transliterations, and spelling variations make such content linguistically complex. In this paper, we propose an approach to POS tag code-mixed social media text using Recurrent Neural Network Language Model (RNN-LM) architecture. We submitted the results for Hindi-English (hi-en), Bengali-English (bn-en), and Telugu-English (te-en) code-mixed data.


Implementation of Hybrid Switch Based Soft-Switching Inverter for UHE Traction Motor Drives


title: Automated breast cancer detection and classification using ultrasound images: A survey

abstract: Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.


title: Back to Thin-Core Massively Parallel Processors

abstract: Examination of the innovations of the past three decades that brought chips to the point at which many-core processors are possible reveals that there are multiple roads ahead, and each is full of challenges.


title: Deep residual learning for image steganalysis

abstract: Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.


title: RSSI informed phase method for distance calculations

abstract: In an attempt to find an accurate, environmentally robust, and fast process for RFID distance estimations, a method was developed called RSSI Informed Phase. This method uses both phase and RSSI measurements to estimate the distance, and potentially location, of a moving RFID tag. RSSI is initially used to find an approximate reader to tag separation distance. This distance value is applied to find an approximate slope of the phase angle vs. frequency curve. The estimated slope can inform the phase distance calculation, meaning fewer reads are required to find the actual phase angle vs. frequency slope. The reduction in the number of necessary reads accelerates the localization process and makes this method more robust for dynamic environments.


title: Super Mario as a String: Platformer Level Generation Via LSTMs

abstract: The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long Short-Term Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario Brothers levels.


title: Maintaining Engagement in Long-term Interventions with Relational Agents

abstract: We discuss issues in designing virtual humans for applications that require long-term voluntary use and the problem of maintaining engagement with users over time. Concepts and theories related to engagement from a variety of disciplines are reviewed. We describe a platform for conducting studies into long-term interactions between humans and virtual agents and present the results of two longitudinal, randomized, controlled experiments in which the effect of manipulations of agent behavior on user engagement was assessed.


title: A contactless electrical energy transmission system for portable-telephone battery chargers

abstract: A high-performance contactless electrical energy transmission (CEET) technique which employs the inductive energy transmission principle is described. The proposed technique enables the implementation of high-efficiency high-power-density fully regulated CEET systems suitable for applications with a wide input and load range. A high efficiency of the system is achieved by recovering the energy stored in the leakage inductances of the transformer by incorporating them in the operation of the circuit, and by employing high-frequency-inverter and controlled-rectifier topologies that allow a controlled bidirectional power flow through the transformer. In addition, a feedforward variable-switching-frequency control of the inverter is used to maintain approximately constant power transfer through the transformer with the input voltage changes, whereas the output-side rectifier employs a local pulsewidth-modulation control to achieve a tight regulation of the output in the presence of load variations. Specifically, the described CEET system is suitable for use in universal-input battery chargers.


title: Active learning with support vector machines in the drug discovery process

abstract: We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called “active learning paradigm” from Machine Learning for selecting the successive batches. Our main selection strategy is based on the maximum margin hyperplanegenerated by “Support Vector Machines”. This hyperplane separates the current set of active from the inactive compounds and has the largest possible distance from any labeled compound. We perform a thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals and show that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.


title: Driver inattention detection methods: A review

abstract: In recent years, driver inattention or distraction is one of the major causes for road accidents. If the distracted driver is alerted on time, most of the physical injuries, deaths, and economic losses can be avoided. To find if the driver is distracted, an efficient inattention detection system has to be developed. Towards the same, researchers have measured driver inattention level based on any one of the following four measures: (1) subjective ratings from the driver, (2) vehicle based measures, (3) physiological measures and (4) behavioral measures. In this paper, we aim to review the above measures used to detect driver inattention based on their advantages and limitations. The issues to be considered while developing an efficient inattention detection system are discussed. The inattention detection system should be designed in such a way that it triggers the alert system only when the driver is distracted beyond the acceptable limit.


title: A particle filtering approach to FM-band passive radar tracking and automatic target recognition

abstract: We present two stochastic filters for an FM-band passive air surveillance radar. The first system uses an extended Kalman filter and delay-Doppler measurements to track targets. The second system uses a particle filter to simultaneously track and classify targets. Automatic target recognition is made possible by the inclusion of radar cross section (RCS) in the measurement vector. The extended Kalman filter cannot take advantage of radar cross section measurements because there is no closed-form relationship between the state elements which determine target aspect and the resulting RCS measurement. We believe that this is the first work to propose the use of RCS for the purpose of target recognition within a passive radar system. We also present many simulation results for a challenging 2-target 3-sensor task involving trajectories which nearly coincide for a portion of their track length.


title: Analyzing the input stream for character- level errors in unconstrained text entry evaluations

abstract: Recent improvements in text entry error rate measurement have enabled the running of text entry experiments in which subjects are free to correct errors (or not) as they transcribe a presented string. In these “unconstrained” experiments, it is no longer necessary to force subjects to unnaturally maintain synchronicity with presented text for the sake of performing overall error rate calculations. However, the calculation of character-level error rates, which can be trivial in artificially constrained evaluations, is far more complicated in unconstrained text entry evaluations because it is difficult to infer a subject's intention at every character. For this reason, prior character-level error analyses for unconstrained experiments have only compared presented and transcribed strings, not input streams. But input streams are rich sources of character-level error information, since they contain all of the text entered (and erased) by a subject. The current work presents an algorithm for the automated analysis of character-level errors in input streams for unconstrained text entry evaluations. It also presents new character-level metrics that can aid method designers in refining text entry methods. To exercise these metrics, we perform two analyses on data from an actual text entry experiment. One analysis, available from the prior work, uses only presented and transcribed strings. The other analysis uses input streams, as described in the current work. The results confirm that input stream error analysis yields richer information for the same empirical data. To facilitate the use of these new analyses, we offer pseudocode and downloadable software for performing unconstrained text entry experiments and analyzing data.


title: Provable Bayesian Inference via Particle Mirror Descent

abstract: Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters. However, when Bayes' rule does not result in tractable closed-form, most approximate inference algorithms lack either scalability or rigorous guarantees. To tackle this challenge, we propose a simple yet provable algorithm, \emph{Particle Mirror Descent} (PMD), to iteratively approximate the posterior density. PMD is inspired by stochastic functional mirror descent where one descends in the density space using a small batch of data points at each iteration, and by particle filtering where one uses samples to approximate a function. We prove result of the first kind that, with $m$ particles, PMD provides a posterior density estimator that converges in terms of $KL$-divergence to the true posterior in rate $O(1/\sqrt{m})$. We demonstrate competitive empirical performances of PMD compared to several approximate inference algorithms in mixture models, logistic regression, sparse Gaussian processes and latent Dirichlet allocation on large scale datasets.


Determination of N-butyl-scopolamine bromide in human plasma and urine by a HPLC method. Evidence of negligible absorption after oral administration.


CORRELATION ENERGY DENSITIES: E PLURIBUS UNUM


title: Determination of 2-keto-l-gulonic, 2-keto-d-gluconic and 2,5-diketo-d-gluconic acids by capillary zone electrophoresis

abstract: Abstract During the biosynthetic processing of 2-keto- l -gulonic acid (2-KLG) from 2,5-diketo- d -gluconic acid (2,5-DKG) by Corynebacterium sp., 2-keto- d -glutonic acid (2-KDG) is produced as a byproduct. These organic acids have been analyzed by high-performance liquid chromatography (HPLC) on an Aminex HPX-87H column, but the resolution was not good enough for quantitative analysis. We investigated the quantitation of 2-KLG, 2-KDG and 2,5-DKG using capillary electrophoresis (CE) and the results were compared with those of HPLC. With CE, in contrast to HPLC, good resolution, efficiency and rapid analysis were demonstrated as well as low consumption of solvent and samples. The CE system was applied at 15 kV with UV detection at 195 nm using 100 m M sodium borate (pH 8.4) as an electrolyte. The results were shown within 5 min with efficiency approaching 100 000 theoretical plates. The relative standard deviations of migration time and peak area were less than 0.9% and 1.6%, respectively. The detection limits for quantitative determination were 0.5–1.3 μM level. The above compounds, in fermentation broth, were analyzed under the optimum conditions. Considering the results of our study, the CE method should be highly suitable for the separation of 2-KLG, 2-KDG and 2,5-DKG in the fermentation broth.


Channel Estimation and Equalization for 5 G Wireless Communication Systems


title: Land-Vehicle Navigation Using GPS

abstract: The Global Positioning System (GPS) has made navigation systems practical for a number of land-vehicle navigation applications. Today, GPS-based navigation systems can be found in motor vehicles, farming and mining equipment, and a variety of other land-based vehicles (e.g., golf carts and mobile robots). Each of these applications is discussed and the reader is introduced to some of the issues involved with each one. One particular technical aspect of navigation for land vehicles is discussed. Specifically, the research discussed in this paper presents a quantitative examination of the impact that individual navigation sensors have on the perfomance of a land-vehicle navigation system. A range of navigation sensor performance levels and their influence on vehicle positioning accuracy are examined. Results show that, for a typical navigation system, positioning error is dominated by the accuracy of the position fixes provided by the GPS receiver when GPS position fixes are available and by the rate gyro's bias drift when GPS position fixes are not available. Furthermore, results show that the accuracy of the GPS position fixes has a significant impact on the relative contributions that each dead-reckoning navigation sensor error makes. The implications of these results for navigation system design and sensor design are discussed.


title: A Second-Order Method for Convex $\ell_1$-Regularized Optimization with Active Set Prediction

abstract: We describe an active-set method for the minimization of an objective function $\phi$ that is the sum of a smooth convex function and an $\ell_1$-regularization term. A distinctive feature of the method is the way in which active-set identification and {second-order} subspace minimization steps are integrated to combine the predictive power of the two approaches. At every iteration, the algorithm selects a candidate set of free and fixed variables, performs an (inexact) subspace phase, and then assesses the quality of the new active set. If it is not judged to be acceptable, then the set of free variables is restricted and a new active-set prediction is made. We establish global convergence for our approach, and compare the new method against the state-of-the-art code LIBLINEAR.


title: Descriptive anatomy of the human auditory tube

abstract: The aim of this study was to correlate current morphologic data relating to the lumen of the auditory tube. Four methods were used: dissection under the operating microscope; microendoscopy of the tubal lumen; optical and electron microscope histology; and MR or CT imaging. The auditory tube consists of two unequal cones, a small posterior third, fixed and osseous (protympanum), and a mobile fibrocartilaginous anterior two-thirds, both joined by the tubal isthmus, a short constriction which is pseudosphincteric at endoscopy. The tensor veli palatini muscle (TVPM) and the levator veli palatini muscle (LVPM) are the chief muscles that vary the tubal lumen of the fibrocartilaginous portion, which is collapsed at rest. CT and especially MR imaging allows their observation in static conditions. Serial histologic sections reveal the continuity between the TVPM and the tensor tympani muscle. The main cartilage framing the lumen varies in shape according to the level surveyed. The tubal mucosa is lined with an epithelium combining ciliated and mucus cells, involved in mucociliary drainage and gas exchanges in the auditory tube. These morphologic elements represent a basis for study of tubal physiology and for planning treatment in dysfunctions of the auditory tube.


title: An integrative imputation method based on multi-omics datasets

abstract: Background ::: Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from missing values due to technical limitations and various constrains in experiments. These missing values severely hinder integrative analysis of multi-omics data. Current imputation methods mainly focus on using single omics data while ignoring biological interconnections and information imbedded in multi-omics data sets.


FAST BAYESIAN COMPRESSIVE SENSING USING LAPLACE PRIORS S .


title: An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation and Evaluation Protocol

abstract: Face anti-spoofing problem can be quite challenging due to various factors including diversity of face spoofing attacks, any new means of spoofing, the problem of imaging sensor interoperability and other environmental factors in addition to the small sample size. Taking into account these observations, in this work, first, a new evaluation protocol called “innovative attack evaluation protocol” to study the effect of occurrence of unseen attack types is proposed which better reflects the realistic conditions in spoofing attacks. Second, a new formulation of the problem based on the anomaly detection concept is proposed where the training data comes from the positive class only. The test data, of course, may come from the positive or negative class. Finally, a thorough evaluation and comparison of 20 different one-class and two-class systems is performed and demonstrated that the anomaly-based formulation is not inferior as compared with the conventional two-class approach.


title: Forcing Neurocontrollers to Exploit Sensory Symmetry

abstract: Several attempts have been made in the past to construct encoding schemes that allow modularity to emerge in evolving systems, but success is limited. We believe that in order to create successful and scalable encodings for emerging modularity, we first need to explore the benefits of different types of modularity by hard-wiring these into evolvable systems. In this paper we explore different ways of exploiting sensory symmetry inherent in the agent in the simple game Cellz by evolving symmetrically identical modules. It is concluded that significant increases in both speed of evolution and final fitness can be achieved relative to monolithic controllers. Furthermore, we show that a simple function approximation task that exhibits sensory symmetry can be used as a quick approximate measure of the utility of an encoding scheme for the more complex game-playing task.


title: Adaptive Optical Self-Interference Cancellation Using a Semiconductor Optical Amplifier

abstract: We experimentally demonstrate an optical system that uses a semiconductor optical amplifier (SOA) to perform adaptive, analog self-interference cancellation for radio-frequency signals. The system subtracts a known interference signal from a corrupted received signal to recover a weak signal of interest. The SOA uses a combination of slow and fast light and cross-gain modulation to perform precise amplitude and phase matching to cancel the interference. The system achieves 38 dB of cancellation across 60-MHz instantaneous bandwidth and 56 dB of narrowband cancellation, limited by noise. The Nelder-Mead simplex algorithm is used to adaptively minimize the interference power through the control of the semiconductor's bias current and input optical power.


Logic and logic programming


Triplet Extraction from Sentences


title: A Proposal of a Secure P2P-Type Storage Scheme by Using the Secret Sharing and the Blockchain

abstract: We propose a new secure online storage scheme based on an open P2P network without a central server. In the proposed scheme, attackers cannot detect target user data in the online storage because the user data is divided into some parts by the secret sharing, and they are distributed to P2P nodes via the anonymous communication. The proposed scheme can prevent the direct attack on the target user node because metadata for the reconstruction also hidden in the online storage, hence nothing remains in the user node. Even if the state of the P2P network varies over time between the storing and restoring operation, the proposed scheme ensures that the user can identify the target nodes which stored his metadata by utilizing the Blockchain technology with only memorable secure information for user authentication. Furthermore, a malicious node to attack on others can be detected and ruled out by the mutual monitoring among the nodes and the majority decision rule.


title: An Optimal Calibration Method for a MEMS Inertial Measurement Unit

abstract: An optimal calibration method for a micro-electro-mechanical inertial measurement unit (MIMU) is presented in this paper. The accuracy of the MIMU is highly dependent on calibration to remove the deterministic errors of systematic errors, which also contain random errors. The overlapping Allan variance is applied to characterize the types of random error terms in the measurements. The calibration model includes package misalignment error, sensor-to-sensor misalignment error and bias, and a scale factor is built. The new concept of a calibration method, which includes a calibration scheme and a calibration algorithm, is proposed. The calibration scheme is designed by D-optimal and the calibration algorithm is deduced by a Kalman filter. In addition, the thermal calibration is investigated, as the bias and scale factor varied with temperature. The simulations and real tests verify the effectiveness of the proposed calibration method and show that it is better than the traditional method.


title: Deep Diffeomorphic Normalizing Flows

abstract: The Normalizing Flow (NF) models a general probability density by estimating an invertible transformation applied on samples drawn from a known distribution. We introduce a new type of NF, called Deep Diffeomorphic Normalizing Flow (DDNF). A diffeomorphic flow is an invertible function where both the function and its inverse are smooth. We construct the flow using an ordinary differential equation (ODE) governed by a time-varying smooth vector field. We use a neural network to parametrize the smooth vector field and a recursive neural network (RNN) for approximating the solution of the ODE. Each cell in the RNN is a residual network implementing one Euler integration step. The architecture of our flow enables efficient likelihood evaluation, straightforward flow inversion, and results in highly flexible density estimation. An end-to-end trained DDNF achieves competitive results with state-of-the-art methods on a suite of density estimation and variational inference tasks. Finally, our method brings concepts from Riemannian geometry that, we believe, can open a new research direction for neural density estimation.


title: Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models

abstract: We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes.


title: Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor

abstract: The latest technological progress in sensors, actuators and energy storage devices enables the developments of miniature VTOL systems. In this paper we present the results of two nonlinear control techniques applied to an autonomous micro helicopter called Quadrotor. A backstepping and a sliding-mode techniques. We performed various simulations in open and closed loop and implemented several experiments on the test-bench to validate the control laws. Finally, we discuss the results of each approach. These developments are part of the OS4 project in our lab.


title: A robot exploration strategy based on Q-learning network

abstract: This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB-D sensor only. The robot controller achieves obstacle avoidance ability by pre-training of feature maps using the depth information. The system is based on the recent Deep Q-Network (DQN) framework where a convolution neural network structure was adopted in the Q-value estimation of the Q-learning method. We separate the DQN into a supervised deep learning structure and a Q-learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre-training deep learning structure. Note that the robot is traveling in environments which are different from the pre-training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.


title: Processing scene context: Fast categorization and object interference

abstract: The extent to which object identification is influenced by the background of the scene is still controversial. On the one hand, the global context of a scene might be considered as an ultimate representation, suggesting that object processing is performed almost systematically before scene context analysis. Alternatively, the gist of a scene could be extracted sufficiently early to be able to influence object categorization. It is thus essential to assess the processing time of scene context. In the present study, we used a go/no-go rapid visual categorization task in which subjects had to respond as fast as possible when they saw a ‘‘man-made environment’’, or a ‘‘natural environment’’, that was flashed for only 26 ms. ‘‘Man-made’’ and ‘‘natural’’ scenes were categorized with very high accuracy (both around 96%) and very short reaction times (median RT both around 390 ms). Compared with previous results from our group, these data demonstrate that global context categorization is remarkably fast: (1) it is as fast as object categorization [Fabre-Thorpe, M., Delorme, A., Marlot, C., & Thorpe, S. (2001). A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. Journal of Cognitive Neuroscience, 13(2), 171– 180]; (2) it is faster than contextual categorization at more detailed levels such as sea, mountain, indoor or urban contexts [Rousselet, G. A., Joubert, O. R., & Fabre-Thorpe, M. (2005). How long to get to the ‘‘gist’’ of real-world natural scenes? Visual Cognition, 12(6), 852– 877]. Further analysis showed that the efficiency of contextual categorization was impaired by the presence of a salient object in the scene especially when the object was incongruent with the context. Processing of natural scenes might thus involve in parallel the extraction of the global gist of the scene and the concurrent object processing leading to categorization. These data also suggest early interactions between scene and object representations compatible with contextual influences on object categorization in a parallel network. � 2007 Elsevier Ltd. All rights reserved.


title: Physiological regulation of phospholipid methylation alters plasma homocysteine in mice.

abstract: Biological methylation reactions and homocysteine (Hcy) metabolism are intimately linked. In previous work, we have shown that phosphatidylethanolamine N-methyltransferase, an enzyme that methylates phosphatidylethanolamine to form phosphatidylcholine, plays a significant role in the regulation of plasma Hcy levels through an effect on methylation demand (Noga, A. A., Stead, L. M., Zhao, Y., Brosnan, M. E., Brosnan, J. T., and Vance, D. E. (2003) J. Biol. Chem. 278, 5952-5955). We have further investigated methylation demand and Hcy metabolism in liver-specific CTP:phosphocholine cytidylyltransferase-alpha (CTalpha) knockout mice, since flux through the phosphatidylethanolamine N-methyltransferase pathway is increased 2-fold to meet hepatic demand for phosphatidylcholine. Our data show that plasma Hcy is elevated by 20-40% in mice lacking hepatic CTalpha. CTalpha-deficient hepatocytes secrete 40% more Hcy into the medium than do control hepatocytes. Liver activity of betaine:homocysteine methyltransferase and methionine adenosyltransferase are elevated in the knockout mice as a mechanism for maintaining normal hepatic S-adenosylmethionine and S-adenosylhomocysteine levels. These data suggest that phospholipid methylation in the liver is a major consumer of AdoMet and a significant source of plasma Hcy.


title: NLP-based approaches for malware classification from API sequences

abstract: In the field of malware analysis, two basic types, which are static analysis and dynamic analysis, are involved in the process of understanding on how particular malware functions. By using dynamic analysis, malware researchers could collect API call sequences that are very valuable sources of information for identifying malware behavior. The proposed malware classification procedures introduced in this paper use API call sequences as inputs to classifiers. In addition, taking advantage of the development in Natural Language Processing field, we use some methods such as n-gram, doc2vec (or Paragraph vectors), TF-IDF to convert those API sequences to numeric vectors before feeding to the classifiers. Our proposed approaches are divided into 3 different methods to classify malware, that is TF-IDF, Paragraph Vector with Distributed Bag of Words and Paragraph Vector with Distributed Memory. Each of them provides us a very good accuracy.


Discourse Parsing: A Decision Tree Approach


title: Multi-Vendor Loyalty Programs: Influencing Customer Behavioral Loyalty?

abstract: Loyalty programs are a consolidated marketing instrument whose adoption in many sectors has not been associated with appropriate comprehension of either their management elements or their effects. The purpose of this research is to contribute to knowledge about the effect of loyalty programs on repeat purchase behavior. More specifically, it seeks to see discover whether joining a program changes the buying behavior of its members, and, if so, to study the profile of those whose behavior changes most. The intention was also to provide new study variables pertaining to multi-vendor loyalty programs, such as where they are joined or purchases in associated outlets as a result of behavioral loyalty. Research was carried out using a sample of 1,200 individuals (31,746 purchases) belonging to a multi-vendor loyalty program. The study period was 13 years, 4 months, and split into two phases: before and after the joining the program. Different methodological approaches, such as the use of transactional databases that included pre-program-enrollment data and of the same sampling units throughout the study, were incorporated into the research with the aim of advancing academic knowledge regarding multi-vendor loyalty programs. Moreover, a type of program and market hardly dealt with in the relevant literature was analyzed. The results showed while the loyalty program had managed to reduce the time between purchases, it had not affected purchase volume or average expenditure. They also demonstrated the existence of a differential profile of customers who had changed their buying behavior to a greater extent. Finally, recency was identified as being the decisive variable in behavioral change.


title: Automatic Speech Recognition and Translation of a Swiss German Dialect: Walliserdeutsch

abstract: Walliserdeutsch is a Swiss German dialect spoken in the south west of Switzerland. To investigate the potential of automatic speech processing of Walliserdeutsch, a small database was collected based mainly on broadcast news from a local radio station. Experiments suggest that automatic speech recognition is feasible: use of another (Swiss German) database shows that the small data size lends itself to bootstrapping from other data; use of Kullback-Leibler HMM suggests that phoneme mapping techniques can compensate for a grapheme-based dictionary. Experiments also indicate that statistical machine translation is feasible; the difficulty of small data size is offset by the close proximity to (high) German.


title: The Holy Cross College football team hepatitis outbreak.

abstract: During a 15-day period in September and October 1969, an outbreak of infectious hepatitis affected the members of a college football team. Of 97 persons exposed, 90 were infected, 32 experienced typical icteric disease, 22 were anicteric but symptomatic, and 36 asymptomatic players were recognized as having significantly elevated serum glutamic pyruvic transaminase values (> 100 units). Other athletes, using the same facilities but arriving six days after the established date of exposure, were unaffected. The decision to obtain blood samples from the entire team, as soon as the initial cases were recognized, resulted in the demonstration of an unexpectedly high attack rate of 93%. Epidemiologic investigation revealed that an infected group of children in the neighborhood, an imperfect drinking water supply, a warm August day, a football team in training, and a local fire were links in the chain which resulted in this most unusual outbreak of infectious hepatitis.


title: Employing machine learning techniques for detection and classification of phishing emails

abstract: A phishing email is a legitimate-looking email which is designed to fool the recipient into believing that it is a genuine email, and either reveals sensitive information or downloads malicious software through clicking on malicious links contained in the body of the email. Given that phishing emails cost UK consumers £174m in 2015, this paper proposal is driven by a problem whose resolution will have a great impact on people's lives in the UK and in the world. In this paper, we proposed a Neural Network (NN)-based model for detections and classifications of phishing emails using publically available email datasets for both benign and phishing emails. The results of the experiments are presented in order to demonstrate the effectiveness of the model in terms of accuracy, true-positive rate, false-positive rate, network performance and error histogram.


title: A soft switching bidirectional DC-DC converter based on three-state switching cell

abstract: This paper presents a three-port soft switching dc-dc converter topology based on the bidirectional version of the three-state switching cell (3SSC) and dual active bridge (DAB), feasible to interconnect battery banks with a dc link and photovoltaic panels. The converter features high voltage gain and current reduced stress in the converter primary switches. All active switches are designed to operate in Zero Voltage Switching (ZVS). The theoretical analysis and experimental results a 2 kW prototype are presented. The experimental results are presented in two modes: Mode Discharge Battery Bank (MDBB) and Mode Charge Battery Bank (MDBB). The maximum efficiency in MDBB is 96% and in MCBB is 95.5%.


An efficient method of determining relationships among compound keywords using machine-AC


title: Towards a universal neural network encoder for time series

abstract: We study the use of a time series encoder to learn representations that are useful on data set types with which it has not been trained on. The encoder is formed of a convolutional neural network whose temporal output is summarized by a convolutional attention mechanism. This way, we obtain a compact, fixed-length representation from longer, variable-length time series. We evaluate the performance of the proposed approach on a well-known time series classification benchmark, considering full adaptation, partial adaptation, and no adaptation of the encoder to the new data type. Results show that such strategies are competitive with the state-of-the-art, often outperforming conceptually-matching approaches. Besides accuracy scores, the facility of adaptation and the efficiency of pre-trained encoders make them an appealing option for the processing of scarcely- or non-labeled time series.


title: Are Trust and Distrust Distinct Constructs? An Empirical Study of the Effects of Trust and Distrust among Online Banking Users

abstract: This study provides insights on the trust-related factors that influence consumer intentions to use online banking. Specifically, the study examines the relationships between trustworthiness, trust, distrust, and user intentions to use online banking services. Over 500 college students located across two different universities completed a survey designed to examine the effects of trustworthiness perceptions on trust and distrust, and the downstream influence of trust and distrust on intention to use. As hypothesized, results indicate that trust and distrust are distinct constructs, and that the established nomological network between trustworthiness, trust, and intention hold in the current context. Also supported is the new contention that trustworthiness is negatively related to distrust, and that distrust has a negative effect on intention to use. Finally, our hypothesis examining the relative contribution of trust and distrust on user intentions was not supported. In the context of this study, trust overwhelmed distrust.


title: Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations

abstract: A humanoid walking robot is a highly nonlinear dynamical system that relies strongly on contact forces between its feet and the ground in order to realize stable motions, but these contact forces are unfortunately severely limited. Model predictive control, also known as receding horizon control, is a general control scheme specifically designed to deal with such constrained dynamical systems, with the potential ability to react efficiently to a wide range of situations. Apart from the question of computation time which needs to be taken care of carefully (these schemes can be highly computation intensive), the initial question of which optimal control problems should be considered to be solved online in order to lead to the desired walking movements is still unanswered. A key idea for answering to this problem can be found in the ZMP preview control scheme. After presenting here this scheme with a point of view slightly different from the original one, we focus on the problem of compensating strong perturbations of the dynamics of the robot and propose a new linear model predictive control scheme which is an improvement of the original ZMP preview control scheme.


title: Towards GPU-Accelerated Large-Scale Graph Processing in the Cloud

abstract: Recently, we have witnessed that cloud providers start to offer heterogeneous computing environments. There have been wide interests in both clusters and cloud of adopting graphics processors (GPUs) as accelerators for various applications. On the other hand, large-scale graph processing is important for many data-intensive applications in the cloud. In this paper, we propose to leverage GPUs to accelerate large-scale graph processing in the cloud. Specifically, we develop an in-memory graph processing engine G2 with three non-trivial GPU-specific optimizations. Firstly, we adopt fine-grained APIs to take advantage of the massive thread parallelism of the GPU. Secondly, G2 embraces a graph partition based approach for load balancing on heterogeneous CPU/GPU architectures. Thirdly, a runtime system is developed to perform transparent memory management on the GPU, and to perform scheduling for an improved throughput of concurrent kernel executions from graph tasks. We have conducted experiments on an Amazon EC2 virtual cluster of eight nodes. Our preliminary results demonstrate that 1) GPU is a viable accelerator for cloud-based graph processing, and 2) the proposed optimizations improve the performance of GPU-based graph processing engine. We further present the lessons learnt and open problems towards large-scale graph processing with GPU accelerations.


TagATune: A Game for Music and Sound Annotation


title: Economic scarcity alters the perception of race.

abstract: When the economy declines, racial minorities are hit the hardest. Although existing explanations for this effect focus on institutional causes, recent psychological findings suggest that scarcity may also alter perceptions of race in ways that exacerbate discrimination. We tested the hypothesis that economic resource scarcity causes decision makers to perceive African Americans as “Blacker” and that this visual distortion elicits disparities in the allocation of resources. Studies 1 and 2 demonstrated that scarcity altered perceptions of race, lowering subjects’ psychophysical threshold for seeing a mixed-race face as “Black” as opposed to “White.” In studies 3 and 4, scarcity led subjects to visualize African American faces as darker and more “stereotypically Black,” compared with a control condition. When presented to naive subjects, face representations produced under scarcity elicited smaller allocations than control-condition representations. Together, these findings introduce a novel perceptual account for the proliferation of racial disparities under economic scarcity.


title: On parallelizability of stochastic gradient descent for speech DNNS

abstract: This paper compares the theoretical efficiency of model-parallel and data-parallel distributed stochastic gradient descent training of DNNs. For a typical Switchboard DNN with 46M parameters, the results are not pretty: With modern GPUs and interconnects, model parallelism is optimal with only 3 GPUs in a single server, while data parallelism with a minibatch size of 1024 does not even scale to 2 GPUs. We further show that data-parallel training efficiency can be improved by increasing the minibatch size (through a combination of AdaGrad and automatic adjustments of learning rate and minibatch size) and data compression. We arrive at an estimated possible end-to-end speed-up of 5 times or more. We do not address issues of robustness to process failure or other issues that might occur during training, nor of speed of convergence differences between ASGD and SGD parameter update patterns.


title: Empathy development in adolescence predicts social competencies in adulthood

abstract: This 23-year study explored the predictive associations between empathy development in adolescence and self-reported social competencies and outcomes in adulthood. Participants were 1,527 adults aged 35 years (48.3% female). The predictor variable (adolescent empathy) was measured yearly at the ages of 12 to 16 years. The outcome variables (adult empathy, communication skills, social integration, relationship satisfaction, and conflicts in relationships) were measured at the age of 35 years. Five important results stand out. First, longitudinal measurement invariance was established for the measure of adolescent empathy. Second, empathy tended to increase during the adolescent years. Third, significant interindividual differences in level and change of adolescent empathy were found. Fourth, gender was related to level of adolescent empathy, favoring girls over boys. Fifth, not only level but also change in adolescent empathy predicted individual differences in social competencies in adulthood two decades later. These findings demonstrate that developmental processes that are relevant for adjustment reveal long-term social consequences beyond the adolescent years.


title: Flame Retardant Effects in PVC-Epoxidised Natural Rubber Miscible Blends: Halogen and Non-Halogen Based Additives

abstract: Effects of four flame retardant additives, (1) antimony trioxide, (2) zinc borate, (3) chlorinated paraffin wax and (4) decabromodiphenyl ether in a PVC-epoxidised natural rubber miscible composition in the presence of a phthalate plasticizer and a phosphate plasticizer were studied. Mixing was done in a Brabender plasticorder at 180 °C. Incorporation of additives did not affect the miscibility of the blend components, and the strength property re tained between 83% and 91%. The processibility of the blend was improved due to chlorinated paraffin wax and slightly impaired due to zinc borate. Antimony trioxide and decabromodiphenyl ether had little influence on the processibility. The flame retardant additives were more effective in a phthalate plasticizer medium than in a phosphate plasticizer medium. The thermal stability of com ponents was mutually influenced by the additive and the matrix. The vapor phase flame inhibiting action of antimony trioxide in PVC compositions was more pronounced in a phthalat...


title: AIDA: An Online Tool for Accurate Disambiguation of Named Entities in Text and Tables

abstract: We present AIDA, a framework and online tool for entity detection and disambiguation. Given a natural-language text or a Web table, we map mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base like DBpedia, Freebase, or YAGO. AIDA is a robust framework centred around collective disambiguation exploiting the prominence of entities, similarity between the context of the mention and its candidates, and the coherence among candidate entities for all mentions. We have developed a Web-based online interface for AIDA where different formats of inputs can be processed on the fly, returning proper entities and showing intermediate steps of the disambiguation process.


title: LineSwitch: Tackling Control Plane Saturation Attacks in Software-Defined Networking

abstract: Software defined networking (SDN) is a new networking paradigm that in recent years has revolutionized network architectures. At its core, SDN separates the data plane, which provides data forwarding functionalities, and the control plane, which implements the network control logic. The separation of these two components provides a virtually centralized point of control in the network, and at the same time abstracts the complexity of the underlying physical infrastructure. Unfortunately, while promising, the SDN approach also introduces new attacks and vulnerabilities. Indeed, previous research shows that, under certain traffic conditions, the required communication between the control and data plane can result in a bottleneck. An attacker can exploit this limitation to mount a new, network-wide, type of denial of service attack, known as the control plane saturation attack . This paper presents LineSwitch, an efficient and effective data plane solution to tackle the control plane saturation attack. LineSwitch employs probabilistic proxying and blacklisting of network traffic to prevent the attack from reaching the control plane, and thus preserve network functionality. We implemented LineSwitch as an extension of the reference SDN implementation, OpenFlow, and run a thorough set of experiments under different traffic and attack scenarios. We compared LineSwitch to the state of the art, and we show that it provides at the same time, the same level of protection against the control plane saturation attack, and a reduced time overhead by up to 30%.


title: DATA STRUCTURES AND ALGORITHMS FOR TILINGS I. OLAF DELGADO-FRIEDRICHS

abstract: Based on the mathematical theory of Delaney symbols, data structures and algorithms are presented for the analysis and manipulation of generalized periodic tilings in arbitrary dimensions.


title: Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data

abstract: Contingency tables summarize the relations between categorical variables and arise in both scientific and business domains. Asymmetrically large two-way contingency tables pose a problem for common visualization methods. The Contingency Wheel has been recently proposed as an interactive visual method to explore and analyze such tables. However, the scalability and readability of this method are limited when dealing with large and dense tables. In this paper we present Contingency Wheel++, new visual analytics methods that overcome these major shortcomings: (1) regarding automated methods, a measure of association based on Pearson's residuals alleviates the bias of the raw residuals originally used, (2) regarding visualization methods, a frequency-based abstraction of the visual elements eliminates overlapping and makes analyzing both positive and negative associations possible, and (3) regarding the interactive exploration environment, a multi-level overview+detail interface enables exploring individual data items that are aggregated in the visualization or in the table using coordinated views. We illustrate the applicability of these new methods with a use case and show how they enable discovering and analyzing nontrivial patterns and associations in large categorical data.


title: An assistive tabletop keyboard for stroke rehabilitation

abstract: We propose a tabletop keyboard that assists stroke patients in using computers. Using computers for purposes such as paying bills, managing bank accounts, sending emails, etc., which all include typing, is part of Activities of Daily Living (ADL) that stroke patients wish to recover. To date, stroke rehabilitation research has greatly focused on using computer-assisted technology for rehabilitation. However, working with computers as a skill that patients need to recover has been neglected. The conventional human computer interfaces are mouse and keyboard. Using keyboard stays the main challenge for hemiplegic stroke patients because typing is usually a bimanual task. Therefore, we propose an assistive tabletop keyboard which is not only a novel computer interface that is specially designed to facilitate patient-computer interaction but also a rehab medium through which patients practice the desired arm/hand functions.


title: Estimation of the Doppler spread and time delay spread for the wireless communication channel

abstract: The performance of the wireless communication channel including HF channel is strongly affected by the short term variations. It is due to the multipath fading which introduces time and frequency selective fading. By characterizing the wireless channel, the information can be used to increase the performance of the wireless communication. Doppler spread and time delay spread are parameters to be investigated where they can be used to characterize the wireless channel. Scattering function via the ambiguity function estimator is adopted in this work. PSK signal with two signal lengths (1 second and 2 seconds) are used in the analysis. The analysis is performed in the absent of the white Gaussian noise. The Doppler spread is estimated from the spread of the scattering plot at the peak of each path while the time delay spread is estimated from the time lag marginal of the scattering function derivation. BPSK having duration of more than 1 second can be used to estimate the Doppler spread and the time delay in the multipath environment. The information on the number of path and their attenuations can also be obtained. Besides, it can give information on the frequency selective fading and the multipath fading condition of the channel.


title: Information Retrieval by Semantic Similarity

abstract: Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.


title: Test Case Prioritization Using Requirements-Based Clustering

abstract: The importance of using requirements information in the testing phase has been well recognized by the requirements engineering community, but to date, a vast majority of regression testing techniques have primarily relied on software code information. Incorporating requirements information into the current testing practice could help software engineers identify the source of defects more easily, validate the product against requirements, and maintain software products in a holistic way. In this paper, we investigate whether the requirements-based clustering approach that incorporates traditional code analysis information can improve the effectiveness of test case prioritization techniques. To investigate the effectiveness of our approach, we performed an empirical study using two Java programs with multiple versions and requirements documents. Our results indicate that the use of requirements information during the test case prioritization process can be beneficial.


title: Death Due to Decapitation in Two Motorcyclists: A Description of a Unique Case and a Brief Review of the Literature†

abstract: Deaths due to decapitation, especially related to traffic accidents, are rarely found in forensic practice. The present case involves a man and a woman who died from decapitation due to an unusual mechanism while they were riding on a motorbike down a mountain road. The autopsy, which was completed as a physics study, allowed the accident to be reconstructed as follows: A plastic cable that had detached from a timber-transporting machine whipped the road and hit the two motorcyclists. The impact resulted in the complete severing of both riders’ heads. Involving different scientists in this accident investigation was crucial to understanding the dynamics of the accident. In fact, in addition to scene inspection and autopsy, a physics study was carried out on the cable and the clamp involved, which led to an explanation for the abrupt movement of the cable and, thus, to a thorough reconstruction of the accident.


A study on scar revision


title: Design and implementation of a rectenna for satellite application

abstract: This paper addresses the microwave energy harvesting for powering wireless sensors on board of geostationary satellites. The electromagnetic environment existing on such satellites is first investigated and experimental results in K-band demonstrate for the first time the feasibility of such energy harvesting in satellite applications for powering autonomous wireless sensors. Experimental results demonstrates that a DC voltage up to 4.5 V and a DC power up to 2.25 mW can be harvested on board of geostationary satellites by using a simple rectenna topology.


title: Software defined network — Architectures

abstract: Software Defined Network (SDN) is the latest network architecture in which the data and control planes do not reside on the same networking element. The control of packet forwarding in this architecture is taken out and is carried out by a programmable software component, the controller, whereas the forwarding elements are only used as packet moving devices that are driven by the controller. SDN architecture also provides Open APIs from both control and data planes. In order to provide communication between the controller and the forwarding hardware among many available protocols, OpenFlow (OF), is generally regarded as a standardized protocol for SDN. Open APIs for communication between the controller and applications enable development of network management applications easy. Therefore, SDN makes it possible to program the network thus provide numerous benefits. As a result, various vendors have developed SDN architectures. This paper summarizes as well as compares most of the common SDN architectures available till date.


title: Deep neural network based speech separation for robust speech recognition

abstract: In this paper, a novel deep neural network (DNN) architecture is proposed to generate the speech features of both the target speaker and interferer for speech separation without using any prior information about the interfering speaker. DNN is adopted here to directly model the highly nonlinear relationship between speech features of the mixed signals and the two competing speakers. Experimental results on a monaural speech separation and recognition challenge task show that the proposed DNN framework enhances the separation performance in terms of different objective measures under the semi-supervised mode where the training data of the target speaker is provided while the unseen interferer in the separation stage is predicted by using multiple interfering speakers mixed with the target speaker in the training stage. Furthermore, as a preprocessing step in the testing stage for robust speech recognition, our speech separation approach can achieve significant improvements of the recognition accuracy over the baseline system with no source separation.


title: Friction compensation control for electric power steering systems

abstract: This paper considers friction compensation control problems for electric power steering systems (EPSs) of vehicle. According to the requirement analysis of vehicle assistant control, a mathematical model of EPSs is established, and a double closed-loop control system structure is proposed. Based on the assist characteristic of the desired steering wheel torque, we design a torque control law with mechanical friction compensator and a current control law and with electrical friction compensator respectively. Theory analyses shows the double closed-loop control system structure can make the output torque applied to the steering wheel at different mechanical friction torques are always close to the desired steering wheel torque, and the control laws with friction compensators can make starting steering wheel is smooth. Simulation results show the control system structure and the friction compensation control strategy are easy to implement and the control effect is better.


CONSTRAINTS AND APPROACHES FOR DISTRIBUTED SENSOR NETWORK SECURITY


title: A simple low voltage, high output impedance resistor based current mirror with extremely low input and output voltage requirements

abstract: A New CMOS resistor based current mirror with extremely low voltage requirement at input (in the range of a few mV) and output voltage requirement of about one V ds is presented. The proposed structure combines the advantages of wide input swing, wide output swing, large output impedance, low input impedance and high linearity altogether, which make it attractive for high performance low-voltage and low power applications. Pspice simulations show a very low THD of −42.2dB from a single 0.9V supply in 0.18µm, input impedance of 239Ω, output impedance of 1.2MΩ and power dissipation of only 180µW. Interestingly, the input and output voltage requirements are only 10mV and 0.1V respectively.


title: Empirically Supported Psychological Interventions: Controversies and Evidence

abstract: ▪ Abstract Efforts to increase the practice of evidence-based psychotherapy in the United States have led to the formation of task forces to define, identify, and disseminate information about empirically supported psychological interventions. The work of several such task forces and other groups reviewing empirically supported treatments (ESTs) in the United States, United Kingdom, and elsewhere is summarized here, along with the lists of treatments that have been identified as ESTs. Also reviewed is the controversy surrounding EST identification and dissemination, including concerns abou research methodology, external validity, and utility of EST research, as well as the reliability and transparency of the EST review process.


title: Smart homes and home health monitoring technologies for older adults: A systematic review

abstract: Abstract Background Around the world, populations are aging and there is a growing concern about ways that older adults can maintain their health and well-being while living in their homes. Objectives The aim of this paper was to conduct a systematic literature review to determine: (1) the levels of technology readiness among older adults and, (2) evidence for smart homes and home-based health-monitoring technologies that support aging in place for older adults who have complex needs. Results We identified and analyzed 48 of 1863 relevant papers. Our analyses found that: (1) technology-readiness level for smart homes and home health monitoring technologies is low; (2) the highest level of evidence is 1b (i.e., one randomized controlled trial with a PEDro score ≥6); smart homes and home health monitoring technologies are used to monitor activities of daily living, cognitive decline and mental health, and heart conditions in older adults with complex needs; (3) there is no evidence that smart homes and home health monitoring technologies help address disability prediction and health-related quality of life, or fall prevention; and (4) there is conflicting evidence that smart homes and home health monitoring technologies help address chronic obstructive pulmonary disease. Conclusions The level of technology readiness for smart homes and home health monitoring technologies is still low. The highest level of evidence found was in a study that supported home health technologies for use in monitoring activities of daily living, cognitive decline, mental health, and heart conditions in older adults with complex needs.


title: Player Behavior and Optimal Team Composition for Online Multiplayer Games

abstract: We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to find behavior clusters, which are, in turn, used with classification algorithms to learn the outcome predictor. For our numerical experiments, we consider League of Legends, a popular team-based role-playing game developed by Riot Games. We observe the learned clusters to not only corroborate well with game knowledge, but also provide insights surprising to expert players. We also demonstrate that game outcomes can be predicted with fairly high accuracy given team composition-based features.


title: Design-for-reliability and implementation on power converters

abstract: Reliability is a complex task that needs an integrated teamwork to accomplish. The reliability work of a power converter starts from topology design, component selection and qualification, design, testing, manufacturing, and does not end with the first customer shipment. Reliability has to be designed in, tested in, and built in. This paper provides a comprehensive study on reliability issues related to DC-DC power converter design and a Design-for-Reliability (DFR) practice. First of all, a reliability sensitivity analysis is conducted for a typical DC-DC converter design using an interactive design-for-reliability tool created by the author and colleagues at HP. The design parameters studied include thermal design, component derating, topology design, mechanical layout, and component quality and selections. Then, some common reliability issues of DC-DC converter design are discussed and the component reliability issues and applications in DC-DC converters are discussed in details. In the last part of the paper, an integrated reliability qualification testing matrix is presented using highly accelerated testing methods.


title: Statistical topological data analysis using persistence landscapes

abstract: We define a new topological summary for data that we call the persistence landscape. Since this summary lies in a vector space, it is easy to combine with tools from statistics and machine learning, in contrast to the standard topological summaries. Viewed as a random variable with values in a Banach space, this summary obeys a strong law of large numbers and a central limit theorem. We show how a number of standard statistical tests can be used for statistical inference using this summary. We also prove that this summary is stable and that it can be used to provide lower bounds for the bottleneck and Wasserstein distances.


title: Practical Strategies for Power-Efficient Computing Technologies

abstract: After decades of continuous scaling, further advancement of silicon microelectronics across the entire spectrum of computing applications is today limited by power dissipation. While the trade-off between power and performance is well-recognized, most recent studies focus on the extreme ends of this balance. By concentrating instead on an intermediate range, an ~ 8× improvement in power efficiency can be attained without system performance loss in parallelizable applications-those in which such efficiency is most critical. It is argued that power-efficient hardware is fundamentally limited by voltage scaling, which can be achieved only by blurring the boundaries between devices, circuits, and systems and cannot be realized by addressing any one area alone. By simultaneously considering all three perspectives, the major issues involved in improving power efficiency in light of performance and area constraints are identified. Solutions for the critical elements of a practical computing system are discussed, including the underlying logic device, associated cache memory, off-chip interconnect, and power delivery system. The IBM Blue Gene system is then presented as a case study to exemplify several proposed directions. Going forward, further power reduction may demand radical changes in device technologies and computer architecture; hence, a few such promising methods are briefly considered.


SDN Architecture Impact on Network Security.


title: Prevalence of unrecognized depression and associated factors among patients attending medical outpatient department in Adare Hospital, Hawassa, Ethiopia

abstract: Depression is an illness that involves the body, mood, and thoughts and that affects the way a person eats, sleeps, feels about him or herself and thinks about things. Depression is one of the most common mental disorders affecting 121 million people in the world, and it frequently goes unrecognized among patients. It is estimated that 5%–10% of the population at any given time is suffering from identifiable depression needing psychiatric or psychosocial intervention. An institution-based cross-sectional study design was implemented to determine the magnitude and associated factors of unrecognized depression among patients attending the adult medical outpatient department in Adare Hospital, Hawassa, Southern Nations, Nationalities, and Peoples’ Region, Ethiopia, among 326 patients selected using systematic random sampling technique. Data were collected using the interviewer-administered technique. A structured questionnaire was used to collect data on sociodemographic characteristics and other independent variables. Depression was assessed using the Patient Health Questionnaire 9. Data were entered and ana-lyzed using SPSS 20. The level of significance was determined at P 60 years (AOR =4.14 [1.87–9.14]), being divorced (AOR =3.13 [1.70–5.76]), and having an average monthly income <750 ETB (AOR =1.92 [1.119–3.195]). The findings of this study revealed that the prevalence of depression among patients attending general medical outpatient department was highly underrecognized and unmanaged. On the basis of these results, we recommended that health-care providers perform a thorough assessment to address common mental disorders, especially depression, and suggest that training to recognize and manage depression appropriately be given.


title: Group-sparse subspace clustering with missing data

abstract: This paper explores algorithms for subspace clustering with missing data. In many high-dimensional data analysis settings, data points Lie in or near a union of subspaces. Subspace clustering is the process of estimating these subspaces and assigning each data point to one of them. However, in many modern applications the data are severely corrupted by missing values. This paper describes two novel methods for subspace clustering with missing data: (a) group-sparse sub-space clustering (GSSC), which is based on group-sparsity and alternating minimization, and (b) mixture subspace clustering (MSC), which models each data point as a convex combination of its projections onto all subspaces in the union. Both of these algorithms are shown to converge to a local minimum, and experimental results show that they outperform the previous state-of-the-art, with GSSC yielding the highest overall clustering accuracy.


Graal IR : An Extensible Declarative Intermediate Representation