title: User Modeling for Task Oriented Dialogues

abstract: We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and system turns into fixed length representations using Recurrent Neural Networks (RNN). It then encodes the dialogue history using another RNN layer. At each turn, user responses are decoded from the hidden representations of the dialogue level RNN. This hierarchical user simulator (HUS) approach allows the model to capture undiscovered parts of the user goal without the need of an explicit dialogue state tracking. We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal. We evaluate the proposed models on movie ticket booking domain by systematically interacting each user simulator with various dialogue system policies trained with different objectives and users.


title: Research and development of a microgrid control and monitoring system for the remote community of Bella Coola: Challenges, solutions, achievements and lessons learned

abstract: Reliance on costly and polluting diesel generators is a major difficulty common to almost all the remote off-grid communities. However, there are oftentimes opportunities to replace at least a part of it with clean renewable energy. This can be achieved by incorporating appropriate energy storage technologies for shifting the energy as well as smart control and monitoring systems. Bella Coola in British Columbia has been the first remote community in Canada that initiated the replacement of a part of its diesel consumption with clean energy. This paper attempts to describe the technical challenges in Bella Coola, proposed solutions with emphasis on the control and monitoring part, a summary of open and closed-loop tests, and the lessons learned from this microgrid project that can be inspiring for future projects.


Fast & Dynamic Image Restoration using Laplace equation Based Image Inpainting


title: Uncovering intimate and casual relationships from mobile phone communication

abstract: We analyze a large-scale mobile phone call dataset with the metadata of the mobile phone users, including age, gender, and billing locality, to uncover the nature of relationships between peers or individuals of similar ages. We show that in addition to the age and gender of users, the information about the ranks of users to each other in their egocentric networks is crucial in characterizing intimate and casual relationships of peers. The opposite-gender pairs in intimate relationships are found to show the highest levels of call frequency and daily regularity, consistent with small-scale studies on romantic partners. This is followed by the same-gender pairs in intimate relationships, while the lowest call frequency and daily regularity are observed for the pairs in casual relationships. We also find that older pairs tend to call less frequently and less regularly than younger pairs, while the average call durations exhibit a more complex dependence on age. We expect that a more detailed analysis can help us better characterize the nature of peer relationships and distinguish various types of relations, such as siblings, friends, and romantic partners, more clearly.


title: Automated pain detection in facial videos of children using human-assisted transfer learning

abstract: Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.


title: Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space

abstract: Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research.


title: Emerging trends in the enterprise data analytics: connecting Hadoop and DB2 warehouse

abstract: Enterprises are dealing with ever increasing volumes of data, reaching into the petabyte scale. With many of our customer engagements, we are observing an emerging trend: They are using Hadoop-based solutions in conjunction with their data warehouses. They are using Hadoop to deal with the data volume, as well as the lack of strict structure in their data to conduct various analyses, including but not limited to Web log analysis, sophisticated data mining, machine learning and model building. This first stage of the analysis is off-line and suitable for Hadoop. But, once their data is summarized or cleansed enough, and their models are built, they are loading the results into a warehouse for interactive querying and report generation. At this later stage, they leverage the wealth of business intelligence tools, which they are accustomed to, that exist for warehouses. In this paper, we outline this use case and discuss the bidirectional connectors we developed between IBM DB2 and IBM InfoSphere BigInsights.


title: Comparing In Situ and Multidimensional Relevance Judgments

abstract: To address concerns of TREC-style relevance judgments, we explore two improvements. The first one seeks to make relevance judgments contextual, collecting in situ feedback of users in an interactive search session and embracing usefulness as the primary judgment criterion. The second one collects multidimensional assessments to complement relevance or usefulness judgments, with four distinct alternative aspects examined in this paper - novelty, understandability, reliability, and effort. We evaluate different types of judgments by correlating them with six user experience measures collected from a lab user study. Results show that switching from TREC-style relevance criteria to usefulness is fruitful, but in situ judgments do not exhibit clear benefits over the judgments collected without context. In contrast, combining relevance or usefulness with the four alternative judgments consistently improves the correlation with user experience measures, suggesting future IR systems should adopt multi-aspect search result judgments in development and evaluation. We further examine implicit feedback techniques for predicting these judgments. We find that click dwell time, a popular indicator of search result quality, is able to predict some but not all dimensions of the judgments. We enrich the current implicit feedback methods using post-click user interaction in a search session and achieve better prediction for all six dimensions of judgments.


title: High-level synthesis for FPGA design based-SLAM application

abstract: The development of SLAM algorithms in the era of autonomous navigation and the growing demand for autonomous robot in place of human being, has put into question how to reduce the computational complexity and make use of these algorithms to operate in real time. Our work aims to take advantage of the high level synthesis (HLS) on FPGAs to design a real time SLAM application. Precisely, we evaluate the promess-held by the new modern low power FPGAs in accelerating SLAM algorithms. Throughtout this, we will attempt to implement a well-known algorithms (FastSLAM2.0), on a new modern FPGA using OpenCL, a standard high level language. Our implementation results show a significant improvement of the algorithm processing time on an FPGA device over a modern powerful embedded GPGPU.


title: Implications of free breathing motion assessed by 4D-computed tomography on the delivered dose in radiotherapy for esophageal cancer.

abstract: The aim of this study was to assess the effect of breathing motion on the delivered dose in esophageal cancer 3-dimensional (3D)-conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT), and volumetric modulated arc therapy (VMAT). We assessed 16 patients with esophageal cancer. All patients underwent 4D-computed tomography (4D-CT) for treatment planning. For each of the analyzed patients, 1 3D-CRT, 1 IMRT, and 1 VMAT (RapidArc-RA) plan were calculated. Each of the 3 initial plans was recalculated on the 4D-CT (for the maximum free inspiration and maximum free expiration) to assess the effect of breathing motion. We assessed the minimum dose (Dmin) and mean dose (Dmean) to the esophagus within the planning target volume, the volume changes of the lungs, the Dmean and the total lung volume receiving at least 40Gy (V40), and the V30, V20, V10, and V5. For the heart we assessed the Dmean and the V25. Over all techniques and all patients the change in Dmean as compared with the planned Dmean (planning CT [PCT]) to the esophagus was 0.48% in maximum free inspiration (CT_insp) and 0.55% in maximum free expiration (CT_exp). The Dmin CT_insp change was 0.86% and CT_exp change was 0.89%. The Dmean change of the lungs (heart) was in CT_insp 1.95% (2.89%) and 3.88% (2.38%) in CT_exp. In all, 4 patients had a clinically relevant change of the dose (≥ 5% Dmean to the heart and the lungs) between inspiration and expiration. These patients had a very cranially or caudally situated tumor. There are no relevant differences in the delivered dose to the regions of interest among the 3 techniques. Breathing motion management could be considered to achieve a better sparing of the lungs or heart in patients with cranially or caudally situated tumors.


title: Collaborative Service Caching for Edge Computing in Dense Small Cell Networks

abstract: Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading has been extensively studied in the literature, service caching is an equally, if not more, important design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related data (libraries/databases) in the edge server, e.g. MEC-enabled Base Station (BS), enabling corresponding computation tasks to be executed. Since only a small number of services can be cached in resource-limited edge server at the same time, which services to cache has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service caching in MEC-enabled dense small cell (SC) networks. We propose an efficient decentralized algorithm, called CSC (Collaborative Service Caching), where a network of small cell BSs optimize service caching collaboratively to address a number of key challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. Our algorithm is developed based on parallel Gibbs sampling by exploiting the special structure of the considered problem using graphing coloring. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSC is further extended to the SC network with selfish BSs, where a coalitional game is formulated to incentivize collaboration. A coalition formation algorithm is developed by employing the merge-and-split rules and ensures the stability of the SC coalitions.


Attack net penetration testing


title: Link level performance comparison between LTE V2X and DSRC

abstract: Applications of VANETs (Vehicular Ad hoc Networks) have their own requirements and challenges in wireless communication technology. Although regarded as the first standard for VANETs, IEEE 802.11p is still in the field-trial stage. Recently, LTE V2X (Long-Term Evolution Vehicular to X) appeared as a systematic V2X solution based on TD-LTE (Time Division Long-Term Evolution) 4G. It is regarded as the most powerful competitor to 802.11p. We conduct link level simulations of LTE V2X and DSRC (Dedicated Short-Range Communication) for several different types of scenarios. Simulation results show that LTE V2X can achieve the same BLER (Block Error Ratio) with a lower SNR (Signal Noise Ratio) than DSRC. A more reliable link can be guaranteed by LTE V2X, which can achieve the same BLER with lower receiving power than DSRC. The coverage area of LTE V2X is larger than that of DSRC.


title: 3D reality modelling: photo-realistic 3D models of real world scenes

abstract: This paper presents a 3D reality modelling system, applying lasers and digital photography to create super-accurate photo-realistic three-dimensional worlds. A key feature of the 3D reality system is its flexibility. It can be used with all common long-range scanners and digital or video cameras, without the need of any pre-calibration or static arrangement between the different sensors. The system can be used for a wide range of applications in industry and science, as well as for environmental modelling and virtual reality presentations. The 3D models are sufficiently lifelike to be used in sectors such as real estate, cultural heritage and construction. Results can be exported to all major 3D software and CAD packages or viewed via a Web browser.


title: Regulation of aromatic amino acid transport by tRNA: role of 2-methylthio-N6-(delta2-isopentenyl)-adenosine.

abstract: E. coli growing rapidly in media where ferric iron is not freely available contain a population of specifically undermodified tRNAs. These tRNAs contain isopentenyl adenosine instead of the usual methylthioisopentenyl adenosine adjacent to the 3' end of the anticodon. Iron restricted E. coli also show an enhanced capacity to transport aromatic amino acids into the cell. Our work shows that undermodified tRNAs for phe, tyr and trp can function as positive regulatory elements of the aromatic amino acid transport system in E. coli. This iron related metabolic control, mediated through a specific post-transcriptional modification of the tRNAs, may be an important mechanism for adapting E. coli for growth in an iron restricted environment.


title: Protective Role of Tetrahydrocurcumin: an Active Polyphenolic Curcuminoid on Cadmium-InducedOxidative Damage in Rats

abstract: In the present work, protective effect of tetrahydrocurcumin (THC) against oxidative damages in cadmium (Cd)-induced toxicity in rats was evaluated. Cd is an important environmental and industrial toxicant that affects almost all the organs, especially liver. Liver is the major organ responsible for the metabolism and the primary target for many toxic chemicals and drugs. Effect of THC, the curcumin-derived polyphenolic compound on Cd-induced oxidative stress and hepatic damage was evaluated using male albino Wistar rats. In Cd-administered rats (5 mg/kg body weight (b.w.), orally for 4 weeks), activities of aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) and gamma glutamyl transferase (GGT) were significantly increased in serum with the elevated level of bilirubin. Red blood cells (RBC), haemoglobin contents and haematocrit values were also significantly decreased in Cd-treated rats. In addition, the levels of lipid peroxidation markers like thiobarbituric acid reactive substances (TBARS), lipid hydroperoxides (LHP), protein carbonyl contents (PCC) and conjugated dienes (CD) were significantly increased followed by the significant decrease in the levels of reduced glutathione (GSH), total sulphydryl groups (TSH), ascorbic acid (vitamin C) and vitamin E in liver of Cd-administered rats. Oral administration of THC (20, 40 and 80 mg/kg b.w.) followed by Cd for 4 weeks showed a significant restoration of the above changes to near normal. Histopathological changes observed in Cd intoxicated hepatic tissues were minimized on treatment with THC. This study suggests that THC at the dose of 80 mg/kg b.w. effectively subdues the Cd-induced toxicity and controls the free radical-induced liver damage in rats.


title: Cannabis use and later life outcomes

abstract: AIM: To examine the associations between the extent of cannabis use during adolescence and young adulthood and later education, economic, employment, relationship satisfaction and life satisfaction outcomes. DESIGN: A longitudinal study of a New Zealand birth cohort studied to age 25 years. MEASUREMENTS: Measures of: cannabis use at ages 14-25; university degree attainment to age 25; income at age 25; welfare dependence during the period 21-25 years; unemployment 21-25 years; relationship quality; life satisfaction. Also, measures of childhood socio-economic disadvantage, family adversity, childhood and early adolescent behavioural adjustment and cognitive ability and adolescent and young adult mental health and substance use. FINDINGS: There were statistically significant bivariate associations between increasing levels of cannabis use at ages 14-21 and: lower levels of degree attainment by age 25 (P Language: en


title: An Empirical Study of User Behaviors on Pinterest Social Network

abstract: Many previous research works have focused on analyzing online social networks in many dimensions, as they bring large commercial value and provide significant references for many other subjects. Pinterest, a pinboard-style image sharing social service, has attracted much attention recently and become one of the most popular social networks. The special mechanism and style of Pinterest distinguish it from other previous popular social networks and shape totally different user behaviors, which makes it worth launching a new study on Pinterest. In this paper, we have an empirical study of user behaviors on Pinterest, based on a large Pinterest dataset containing 1.13 million users, 57 million following relations, 19 million boards and 933 million pins. We focus on studying the characteristics, manifestations and overall effects of user behaviors from many aspects, including the user interests, correlations between neighboring users, the common features of the most popular users, the topology of the network structure, etc. The most distinguishing characteristic of Pinterest is that users focus on everyday lives and are willing to collect many images about decoration, food, fashion, etc, which makes Pinterest an ideal advertising and marketing platform for retail companies. Many other qualitative and quantitative analysis on user behaviors have been discussed in our paper. We provide a comprehensive understanding of user behaviors on the Pinterest social network, which is our main contribution.


title: Dual-Band Dual-Polarized Compact Log-Periodic Dipole Array for MIMO WLAN Applications

abstract: A compact dual-band dual-polarized log-periodic dipole array with a dimension of $160 \times 160 \times 60~\hbox{mm}^3$ for multiple-input–multiple-output (MIMO) WLAN applications is proposed. In this antenna, there are 12 antennas: six for horizontal polarization and six for vertical polarization. In order to achieve dual linear polarizations and beam switching, six horizontal antennas are placed in sequential, rotating arrangement on a horizontal substrate panel with an equal inclination angle of $60^\circ$ to form a symmetrical structure, while the other six antennas for vertical polarization are inserted through slots made on the horizontal substrate panel. Furthermore, six pairs of double T-type slits are introduced to mainly reduce the mutual coupling between the horizontal antennas. The proposed array is manufactured and exhibits the characteristics of high isolation, good front-to-back ratio, and average gains of 5 and 6 dBi over the 2.4- and 5-GHz band, respectively. The MIMO performance of the array is analyzed and evaluated by mutual coupling and the envelope correlation coefficient.


Differential regulation of gene expression in mouse parotid glands in response to isoprenaline treatment.


title: Federated Identity Management Systems: A Privacy-Based Characterization

abstract: Identity management systems store attributes associated with users and employ these attributes to facilitate authorization. The authors analyze existing systems and describe a privacy-driven taxonomy of design choices, which can help technical experts consulting on public policy relating to identity management. The US National Strategy for Trusted Identities in Cyberspace initiative is discussed to illustrate how this taxonomy helps analyze public policy options.


title: ASR error management for improving spoken language understanding

abstract: This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .


title: A Level-Based Learning Swarm Optimizer for Large-Scale Optimization

abstract: In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.


title: Exploiting Human Mobility Patterns for Point-of-Interest Recommendation

abstract: Point-of-interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). Unlike traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user»s availability. While there are some prior studies which consider temporal effects by solely using check-in timestamps for modeling, they suffer from check-in data sparsity. Recent years, the advent in positioning technology has accumulated a variety of urban data related to human mobility. There is a potential to exploit human mobility patterns from heterogeneous information sources for improving POI recommendation. To this end, we propose a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we profile the temporal popularity of POIs, learn the latent regularity to characterize users, and conduct comprehensive experiments with real-world data. Evaluation results demonstrate the effectiveness of the proposed method.


title: Biometric identification through hand geometry measurements

abstract: A work in defining and implementing a biometric system based on hand geometry identification is presented here. Hand features are extracted from a color photograph taken when the user has placed his hand on a platform designed for such a task. Different pattern recognition techniques have been tested to be used in classification and/or verification from Euclidean distance to neural networks. Experimental results, up to a 97 percent rate of success in classification, will show the possibility of using this system in medium/high security environments with full acceptance from all users.


title: Clinical utility of circulating non-coding RNAs — an update

abstract: Over the past decade, the amount of research and the number of publications on associations between circulating small and long non-coding RNAs (ncRNAs) and cancer have grown exponentially. Particular focus has been placed on the development of diagnostic and prognostic biomarkers to enable efficient patient management — from early detection of cancer to monitoring for disease recurrence or progression after treatment. Owing to their high abundance and stability, circulating ncRNAs have potential utility as non-invasive, blood-based biomarkers that can provide information on tumour biology and the effects of treatments, such as targeted therapies and immunotherapies. Increasing evidence highlights the roles of ncRNAs in cell-to-cell communication, with a number of ncRNAs having the capacity to regulate gene expression outside of the cell of origin through extracellular vesicle-mediated transfer to recipient cells, with implications for cancer progression and therapy resistance. Moreover, ‘foreign’ microRNAs (miRNAs) encoded by non-human genomes (so-called xeno-miRNAs), such as viral miRNAs, have been shown to be present in human body fluids and can be used as biomarkers. Herein, we review the latest developments in the use of circulating ncRNAs as diagnostic and prognostic biomarkers and discuss their roles in cell-to-cell communication in the context of cancer. We provide a compendium of miRNAs and long ncRNAs that have been reported in the literature to be present in human body fluids and that have the potential to be used as diagnostic and prognostic cancer biomarkers. Evidence of the functional roles of non-coding RNAs in cancer is expanding, and the potential of these RNAs as diagnostic and prognostic biomarkers is increasingly recognized. Herein, the authors review the recent developments in these areas and provide compendiums of circulating microRNAs and long non-coding RNAs that have promise as diagnostic and prognostic cancer biomarkers. Non-coding RNAs (ncRNAs) have important roles in regulating the expression of genes that control fundamental biological functions; altered levels of ncRNAs are frequently detected in cancer cells compared with nonmalignant cells. ncRNAs can be secreted into extracellular space as cell-free RNAs either encapsulated inside macrovesicles (such as extracellular vesicles) or in association with proteins (as RNA–protein complexes) or high-density lipoproteins (HDLs), and can subsequently be detected in blood samples as circulating ncRNAs. Altered levels of circulating ncRNAs are found in body fluids, including serum, plasma, urine, and saliva, from patients with cancer compared with individuals without cancer and have potential clinical applications as diagnostic and prognostic blood-based biomarkers. Circulating ncRNAs can mediate cell-to-cell communication and regulate gene expression in recipient cells, thereby acting similarly to hormones (mediating communication between cells within an individual) or as exogenous messengers (participating in inter-individual and cross-species molecular communication). Non-coding RNAs (ncRNAs) have important roles in regulating the expression of genes that control fundamental biological functions; altered levels of ncRNAs are frequently detected in cancer cells compared with nonmalignant cells. ncRNAs can be secreted into extracellular space as cell-free RNAs either encapsulated inside macrovesicles (such as extracellular vesicles) or in association with proteins (as RNA–protein complexes) or high-density lipoproteins (HDLs), and can subsequently be detected in blood samples as circulating ncRNAs. Altered levels of circulating ncRNAs are found in body fluids, including serum, plasma, urine, and saliva, from patients with cancer compared with individuals without cancer and have potential clinical applications as diagnostic and prognostic blood-based biomarkers. Circulating ncRNAs can mediate cell-to-cell communication and regulate gene expression in recipient cells, thereby acting similarly to hormones (mediating communication between cells within an individual) or as exogenous messengers (participating in inter-individual and cross-species molecular communication).


title: The iNaturalist Species Classification and Detection Dataset

abstract: Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.


title: Performance and Scalability of GPU-Based Convolutional Neural Networks

abstract: In this paper we present the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a derivative of standard Multilayer Perceptron (MLP) neural networks optimized for two-dimensional pattern recognition problems such as Optical Character Recognition (OCR) or face detection. We describe the basic parts of a CNN and demonstrate the performance and scalability improvement that can be achieved by shifting the computation-intensive tasks of a CNN to the GPU. Depending on the network topology training and classification on the GPU performs 2 to 24 times faster than on the CPU. Furthermore, the GPU version scales much better than the CPU implementation with respect to the network size.


title: All Words Unsupervised Semantic Category Labeling for Hindi

abstract: In the task of semantic category labeling, given a text, every word in it has to be assigned a semantic category. Our language of interest is Hindi. We use the ontological categories defined in Hindi Wordnet as semantic category inventories. In this paper we present two unsupervised approaches namely Flat Semantic Category Labeler (FSCL) and Hierarchical Semantic Category Labeler (HSCL ). The former method treats semantic categories as a flat list, whereas the latter one exploits the hierarchy among the semantic categories in a top down manner. Further our methods use simple probabilistic models, using which the category labeling becomes a simple table look up with little extra computation and thus opening the possibility of it’s use in real-time interactive systems.


title: Field selection for job categorization and recommendation to social network users

abstract: Nowadays, in the Web 2.0 reality, one of the most challenging task for companies that aim to manage and recommend job offers is to convey this enormous amount of information in a succinct and intelligent manner such to increase the performances of matching operations against users profiles/curricula and optimize the time/space complexity of these processes. With this goal, this paper presents a novel method to formalize the textual content of job offers that aims at identifying the most relevant information and fields expressed by them and leverage this compact formalization for job recommendation and profile matching in social network environments. This method has been then developed and tested in the industrial environment represented by Multiposting and Work4, world leaders in digital solutions of e-recruitment problems. In this study three classes of documents are considered: job offers, job categories and social network user profiles (as potential job candidates); each class contains several fields with textual information. The proposed representation method permits to dynamically identify those text fields, for each class, that could help a cross-matching strategy in order to preserve, from one hand, the matching/recommendation performances and, on the other hand, reduce the cost of these operations (due to a straightforward dimensionality reduction mechanism). We then evaluated and compared the presented approach showing significant improvements on both categorization and recommendation tasks by also drastically reducing their computational costs.


title: Reducing Fragmentation in Incremental Author Name Disambiguation

abstract: Author name ambiguity is a hard problem that occurs when several authors publish articles with the same name or when a same author publishes their articles under different names. Traditionally, automatic disambiguation methods process the author names of all citation records in a repository. Aiming efficiency, incremental methods disambiguate author names only when new citation records are inserted into the repository. As a side effect, several citation records of a same author may be associated with different authors, aka, the fragmentation problem. To diminish this problem, we propose a new merge-oriented incremental method capable of reducing such side effect, without the need to apply a traditional disambiguation method on the whole repository. Our experimental evaluation shows that our method produces significant improvements when compared to an incremental baseline and is very competitive with batch-mode methods.


title: Reflective Practice in Healthcare Education: An Umbrella Review

abstract: Reflection in healthcare education is an emerging topic with many recently published studies and reviews. This current systematic review of reviews (umbrella review) of this field explores the following aspects: which definitions and models are currently in use; how reflection impacts design, evaluation, and assessment; and what future challenges must be addressed. Nineteen reviews satisfying the inclusion criteria were identified. Emerging themes include the following: reflection is currently regarded as self-reflection and critical reflection, and the epistemology-of-practice notion is less in tandem with the evidence-based medicine paradigm of modern science than expected. Reflective techniques that are recognised in multiple settings (e.g., summative, formative, group vs. individual) have been associated with learning, but assessment as a research topic, is associated with issues of validity, reliability, and reproducibility. Future challenges include the epistemology of reflection in healthcare education and the development of approaches for practising and assessing reflection without loss of theoretical background.


title: Transmission power control in mobile ad hoc network using network coding and Co-Operative Communication

abstract: MANET is a set of mobile devices with no fixed topology. Ad hoc means “for this purpose”. In MANET nodes are battery operated with dynamic network topology hence energy efficiency is an important design consideration to extend the lifetime of networks. Topology of network plays an important role for energy conservation. Topology control is to decide the transmission power of nodes and to increase network connectivity as well as increase the energy efficiency. In this paper, we present Cooperative Communication(CC) is to link disconnected networks in order to reduce transmission power of nodes with increased network connectivity.CC is a proficient technology to confess the quality of service (QoS) in MANETs. Thus, this paper proposes the energy efficient routing for MANET with the use of CC and network coding. Network coding used to reduce bandwidth consumption and hence power consumption also reduced. The proposed routing algorithm is efficient power aware routing with network coding (EPARN) identifies the capacity of a node not just by its residual battery power, but also by the expected energy spent in reliably forwarding data packets over a specific link. In simulation results, the performance of the proposed algorithms is compared with the other existing methods.


title: Combining segmentation and attention: a new foveal attention model

abstract: Artificial vision systems cannot process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. Inspired by biological perception systems, articial attention models pursuit to select only the relevant part of the scene. Besides, it is well established that the units of attention on human vision are not merely spatial but closely related to perceptual objects (proto-objects). This implies a strong bidirectional relationship between segmentation and attention processes. Therefore, while the segmentation process is the responsible to extract the proto-objects from the scene, attention can guide segmentation, arising the concept of foveal attention. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. In this paper, a bottom-up foveal attention model is presented. In this model the input image is a foveal image represented using a Cartesian Foveal Geometry (CFG), which encodes the field of view of the sensor as a fovea (placed in the focus of attention) surrounded by a set of concentric rings with decreasing resolution. Then multirresolution perceptual segmentation is performed by building a foveal polygon using the Bounded Irregular Pyramid (BIP). Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image proto-object. Saliency is computed as a linear combination of multiple low level features such us colour and intensity contrast, symmetry, orientation and roundness. Obtained results from natural images show that the performance of the combination of hierarchical foveal segmentation and saliency estimation is good in terms of accuracy and speed.


title: Cobalt bottom-up contact and via prefill enabling advanced logic and DRAM technologies

abstract: This work introduces two new metallization schemes using the electroless deposition (ELD) technique; one based on contact fill and one based on via prefill. One of the key features of the electroless process is its selective deposition, which can be used for bottom-up fill of high aspect ratio features. The feasibility of this Co ELD process is demonstrated on contacts landing on W and vias landing on Cu. Our simulation of the Co via resistance shows that it can serve as alternative to Cu with lower via resistance below 15nm dimension. The results from a planar capacitor study show that there is no degraded reliability in an organo-silicate glass low-k film when Co is in direct contact with this dielectric. Therefore, selective Co ELD process for contact and via prefill has the potential to enable future scaling of advanced logic and DRAM technologies.


title: Situation assessment algorithm for a collision prevention assistant

abstract: In this paper, we present the concept of a collision prevention assistant, a system that we believe can significantly contribute to road safety. We propose a new situation assessment algorithm which is tailored to the action of braking and that further accounts for the nonlinearities that arise when vehicles cut out or come to a standstill. The effect of sensor uncertainty on the performance of the proposed algorithm is modelled using a Markov chain and analyzed by means of a Monte Carlo simulation on a typical traffic situation.


title: An overview of topic modeling methods and tools

abstract: Topic modeling is a powerful technique for analysis of a huge collection of a document. Topic modeling is used for discovering hidden structure from the collection of a document. The topic is viewed as a recurring pattern of co-occurring words. A topic includes a group of words that often occurs together. Topic modeling can link words with the same context and differentiate across the uses of words with different meanings. In this paper, we discuss methods of Topic Modeling which includes Vector Space Model (VSM), Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) with their features and limitations. After that, we will discuss tools available for topic modeling such as Gensim, Standford topic modeling toolbox, MALLET, BigARTM. Then some of the applications of Topic Modeling covered. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction, information filtering and similarity search in the fields of text mining, information retrieval.


title: A review of influenza detection and prediction through social networking sites

abstract: Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.


title: Design of a Multifunctional Anthropomorphic Prosthetic Hand With Extrinsic Actuation

abstract: This paper presents an anthropomorphic prototype hand prosthesis that is intended for use with a multiple-channel myoelectric interface. The hand contains 16 joints, which are differentially driven by a set of five independent actuators. The hand prototype was designed with the minimum number of independent actuators required to provide a set of eight canonical hand postures. This paper describes the design of the prosthesis prototype, demonstrates the hand in the desired eight canonical postures, and experimentally characterizes the force and speed capability of the device. A video is included in the supplementary material that also illustrates the functionality and performance of the hand.


title: SPOCK: A Sensor Value Prediction and Online Control Algorithm for Building Resource Management

abstract: We present SPOCK, an algorithm for forecasting future sensor readings based on historical trends. Using sensor value forecasting, we can plan control decisions for building automation systems hours in advance, with the goal of improving resource consumption efficiency. We test SPOCK on a dataset gathered by our water softener optimization engine [13] on the UW-Madison campus. We use sensor value forecasts generated by SPOCK to plan water softener regenerations up to 24 hours in advance. We compare the performance of SPOCK to an oracle forecaster, which knows exactly what the sensor readings will be for all times in the future---this is possible because we are retroactively analyzing a dataset that has already been collected. For the dataset we analyze here, we demonstrate that SPOCK performs only about 2% worse than the oracle forecaster. Despite some errors in SPOCK's forecasts (compared to the oracle), it is still capable of making reasonable control decisions that are nearly optimal. SPOCK can reduce regeneration frequency---corresponding to reduction in wasted water--- by 10% compared to existing algorithms for scheduling regenerations.


A Multi-aspect Analysis of Automatic Essay Scoring for Brazilian Portuguese.


title: Large-scale validation and analysis of interleaved search evaluation

abstract: Interleaving is an increasingly popular technique for evaluating information retrieval systems based on implicit user feedback. While a number of isolated studies have analyzed how this technique agrees with conventional offline evaluation approaches and other online techniques, a complete picture of its efficiency and effectiveness is still lacking. In this paper we extend and combine the body of empirical evidence regarding interleaving, and provide a comprehensive analysis of interleaving using data from two major commercial search engines and a retrieval system for scientific literature. In particular, we analyze the agreement of interleaving with manual relevance judgments and observational implicit feedback measures, estimate the statistical efficiency of interleaving, and explore the relative performance of different interleaving variants. We also show how to learn improved credit-assignment functions for clicks that further increase the sensitivity of interleaving.


title: A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

abstract: As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.


title: User perception of media content association in olfaction-enhanced multimedia

abstract: Olfaction is an exciting challenge facing multimedia applications. In this article we have investigated user perception of the association between olfactory media content and video media content in olfactory-enhanced multimedia. Results show that the association between scent and content has a significant impact on the user-perceived experience of olfactory-enhanced multimedia.


title: Recognizing Subjectivity: A Case Study of Manual Tagging

abstract: In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and based on that analysis, each clause is given a final classification. To provide empirical support for the classifications, correlations are assessed in the data between the subjective category and a basic semantic class posited by Quirk, Greenbaum, Leech and Svartvik (1985).


title: Using Lexical Resources for Irony and Sarcasm Classification

abstract: The paper presents a language dependent model for classification of statements into ironic and non-ironic. The model uses various language resources: morphological dictionaries, sentiment lexicon, lexicon of markers and a WordNet based ontology. This approach uses various features: antonymous pairs obtained using the reasoning rules over the Serbian WordNet ontology (R), antonymous pairs in which one member has positive sentiment polarity (PPR), polarity of positive sentiment words (PSP), ordered sequence of sentiment tags (OSA), Part-of-Speech tags of words (POS) and irony markers (M). The evaluation was performed on two collections of tweets that had been manually annotated according to irony. These collections of tweets as well as the used language resources are in the Serbian language (or one of closely related languages --Bosnian/Croatian/Montenegrin). The best accuracy of the developed classifier was achieved for irony with a set of 5 features -- (PPR, PSP, POS, OSA, M) -- acc = 86.1%, while for sarcasm the best results were achieved with the set (R, PSP, POS, OSA, M) -- acc = 72.8.


title: A comparative study of arbitration algorithms for the Alpha 21364 pipelined router

abstract: Interconnection networks usually consist of a fabric of interconnected routers, which receive packets arriving at their input ports and forward them to appropriate output ports. Unfortunately, network packets moving through these routers are often delayed due to conflicting demand for resources, such as output ports or buffer space. Hence, routers typically employ arbiters that resolve conflicting resource demands to maximize the number of matches between packets waiting at input ports and free output ports. Efficient design and implementation of the algorithm running on these arbiters is critical to maximize network performance.This paper proposes a new arbitration algorithm called SPAA (Simple Pipelined Arbitration Algorithm), which is implemented in the Alpha 21364 processor's on-chip router pipeline. Simulation results show that SPAA significantly outperforms two earlier well-known arbitration algorithms: PIM (Parallel Iterative Matching) and WFA (Wave-Front Arbiter) implemented in the SGI Spider switch. SPAA outperforms PIM and WFA because SPAA exhibits matching capabilities similar to PIM and WFA under realistic conditions when many output ports are busy, incurs fewer clock cycles to perform the arbitration, and can be pipelined effectively. Additionally, we propose a new prioritization policy called the Rotary Rule, which prevents the network's adverse performance degradation from saturation at high network loads by prioritizing packets already in the network over new packets generated by caches or memory.


title: CELLULAR DNA REPLICASES: Components and Dynamics at the Replication Fork

abstract: ▪ Abstract Chromosomal DNA replicases are multicomponent machines that have evolved clever strategies to perform their function. Although the structure of DNA is elegant in its simplicity, the job of duplicating it is far from simple. At the heart of the replicase machinery is a heteropentameric AAA+ clamp-loading machine that couples ATP hydrolysis to load circular clamp proteins onto DNA. The clamps encircle DNA and hold polymerases to the template for processive action. Clamp-loader and sliding clamp structures have been solved in both prokaryotic and eukaryotic systems. The heteropentameric clamp loaders are circular oligomers, reflecting the circular shape of their respective clamp substrates. Clamps and clamp loaders also function in other DNA metabolic processes, including repair, checkpoint mechanisms, and cell cycle progression. Twin polymerases and clamps coordinate their actions with a clamp loader and yet other proteins to form a replisome machine that advances the replication fork.


title: Language classification from bilingual word embedding graphs

abstract: We study the role of the second language in bilingual word embeddings in monolingual semantic evaluation tasks. We find strongly and weakly positive correlations between down-stream task performance and second language similarity to the target language. Additionally, we show how bilingual word embeddings can be employed for the task of semantic language classification and that joint semantic spaces vary in meaningful ways across second languages. Our results support the hypothesis that semantic language similarity is influenced by both structural similarity as well as geography/contact.


title: EAACK—A Secure Intrusion-Detection System for MANETs

abstract: The migration to wireless network from wired network has been a global trend in the past few decades. The mobility and scalability brought by wireless network made it possible in many applications. Among all the contemporary wireless networks, Mobile Ad hoc NETwork (MANET) is one of the most important and unique applications. On the contrary to traditional network architecture, MANET does not require a fixed network infrastructure; every single node works as both a transmitter and a receiver. Nodes communicate directly with each other when they are both within the same communication range. Otherwise, they rely on their neighbors to relay messages. The self-configuring ability of nodes in MANET made it popular among critical mission applications like military use or emergency recovery. However, the open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. In this case, it is crucial to develop efficient intrusion-detection mechanisms to protect MANET from attacks. With the improvements of the technology and cut in hardware costs, we are witnessing a current trend of expanding MANETs into industrial applications. To adjust to such trend, we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new intrusion-detection system named Enhanced Adaptive ACKnowledgment (EAACK) specially designed for MANETs. Compared to contemporary approaches, EAACK demonstrates higher malicious-behavior-detection rates in certain circumstances while does not greatly affect the network performances.


title: Vocal Separation using Singer-Vowel Priors Obtained from Polyphonic Audio

abstract: Single-channel methods for the separation of the lead vocal from mixed audio have traditionally included harmonicsinusoidal modeling and matrix decomposition methods, each with its own strengths and shortcomings. In this work we use a hybrid framework to incorporate prior knowledge about singer and phone identity to achieve the superior separation of the lead vocal from the instrumental background. Singer specific dictionaries learned from available polyphonic recordings provide the soft mask that effectively attenuates the bleeding-through of accompanying melodic instruments typical of purely harmonic-sinusoidal model based separation. The dictionary learning uses NMF optimization across a training set of mixed signal utterances while keeping the vocal signal bases constant across the utterances. A soft mask is determined for each test mixed utterance frame by imposing sparseness constraints in the NMF partial co-factorization. We demonstrate significant improvements in reconstructed signal quality arising from the more accurate estimation of singer-vowel spectral envelope.


title: Using probabilistic generative models for ranking risks of Android apps

abstract: One of Android's main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a "stand-alone" fashion and in a way that requires too much technical knowledge and time to distill useful information. We introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scoring schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Experimental results conducted using real-world datasets show that probabilistic general models significantly outperform existing approaches, and that Naive Bayes models give a promising risk scor- ing approach.


title: Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching

abstract: To date, reinforcement learning has mostly been studied solving simple learning tasks. Reinforcement learning methods that have been studied so far typically converge slowly. The purpose of this work is thus two-fold: 1) to investigate the utility of reinforcement learning in solving much more complicated learning tasks than previously studied, and 2) to investigate methods that will speed up reinforcement learning.This paper compares eight reinforcement learning frameworks: adaptive heuristic critic (AHC) learning due to Sutton, Q-learning due to Watkins, and three extensions to both basic methods for speeding up learning. The three extensions are experience replay, learning action models for planning, and teaching. The frameworks were investigated using connectionism as an approach to generalization. To evaluate the performance of different frameworks, a dynamic environment was used as a testbed. The environment is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.


title: Learning Independent Causal Mechanisms

abstract: Independent causal mechanisms are a central concept in the study of causality with implications for machine learning tasks. In this work we develop an algorithm to recover a set of (inverse) independent mechanisms relating a distribution transformed by the mechanisms to a reference distribution. The approach is fully unsupervised and based on a set of experts that compete for data to specialize and extract the mechanisms. We test and analyze the proposed method on a series of experiments based on image transformations. Each expert successfully maps a subset of the transformed data to the original domain, and the learned mechanisms generalize to other domains. We discuss implications for domain transfer and links to recent trends in generative modeling.


title: Efficient Pattern Recognition Using a New Transformation Distance

abstract: Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product...), which are not particularly meaningful on pattern vectors. More complex, better suited distance measures are often expensive and rather ad-hoc (elastic matching, deformable templates). We propose a new distance measure which (a) can be made locally invariant to any set of transformations of the input and (b) can be computed efficiently. We tested the method on large handwritten character databases provided by the Post Office and the NIST. Using invariances with respect to translation, rotation, scaling, shearing and line thickness, the method consistently outperformed all other systems tested on the same databases.


title: Cognitive decision making in smart industry

abstract: A game theoretic model for automated decision making in smart industry is proposed.Smart industry is one which forms synergy of devices using Internet of Things (IoT).The model uses the data collected by IoT sensors to evaluate the performance of the employees.The results show a significant improvement in employee motivation achieved by removal of manual employee evaluation system. A smart industry integrates ubiquitous sensing capabilities of Internet of Things (IoT) with industrial infrastructure in order to automate various industrial operations. The data collected by IoT system in smart industry can be used to replace manual employee evaluation system where there are ample chances of biasness. This paper proposes a model for automated performance evaluation of employees in a smart industry. The model uses the data collected by embedded sensors in smart industrial system to identify various industrial activities of employees. The identified activities are then classified as positive, negative and neutral activities. In addition, an employee is said to be participating in an activity if employee and activity are co-located. Therefore, the model collects the location data of every employee using GPS devices and calculates the participation of each employee in each of the identified positive, negative and neutral activities based upon the location data. The information hence obtained is then used to draw cognitive decisions for employees using game theory. The experimental study compares the proposed model with manual employee evaluation system and the results depict performance improvement of proposed model over manual system. The impact of automated system on employees is then evaluated both experimentally and mathematically. The results show that the correct evaluation of employees by the model effectively motivates employees in the favor of industry. Thus, the proposed model effectively and efficiently automates cognitive employee evaluation system and decision making process in smart industry.


title: Automation of legal reasoning : a study on artificial intelligence and law

abstract: Part 1 Background and related work: jurisprudence and legal reasoning artificial intelligence integration. Part 2 The legal reasoning process: legal reasoning. Part 3 Potentialities of automation: automation of legal reasoning prospects.


title: Activity Recognition in Smart Homes Using Clustering Based Classification

abstract: Activity recognition in smart homes plays an important role in healthcare by maintaining the well being of elderly and patients through remote monitoring and assisted technologies. In this paper, we propose a two level classification approach for activity recognition by utilizing the information obtained from the sensors deployed in a smart home. In order to separates the similar activities from the non similar activities, we group the homogeneous activities using the Lloyd's clustering algorithm. For the classification of non-separated activities within each cluster, we apply a computationally less expensive learning algorithm Evidence Theoretic K-Nearest Neighbor, which performs better in uncertain conditions and noisy data. The approach enables us to achieve improved recognition accuracy particularly for overlapping activities. A comparison of the proposed approach with the existing activity recognition approaches is presented on two publicly available smart home datasets. The proposed approach demonstrates better recognition rate compared to the existing methods.


title: A wideband CMOS LNA design approach

abstract: A methodology for designing wideband CMOS LNAs is proposed. This is an extension to the existing narrowband power-constrained simultaneous noise-input matching (PCSNIM) LNA design technique. To demonstrate the application of the proposed method, two wideband single-ended cascode LNAs are designed in a 0.18 /spl mu/m CMOS technology to operate in the frequency bands of 1.5-2.5 GHz and 3.2-4.8 GHz, respectively. Based on simulation results, these LNAs achieve: maximum forward gain of 15.4 dB (17.9 dB) with 3 dB bandwidth of 1.6 GHz (2.7 GHz); maximum in-band noise figure of 0.9 dB (1.6 dB); worst case 1dB compression point of -13 dBm (-15 dBm); IIP3 of better than -2.5 dBm (-4.5 dBm) over the entire bandwidth. The LNAs draw 7.5 mA (8.8 mA) from a 1.5 V supply.


title: Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications

abstract: This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR models with stochastic volatility. As an example of empirical application, the TVP-VAR model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic variables.


title: Comparative studies of PID controller tuning methods on a DC-DC boost converter

abstract: This paper presents comparative studies of PID controller tuning methods on a DC-DC converter. A DC-DC boost converter was modelled using averaged state space method for controller design and dynamic analysis. PID controllers were designed using three different PID tuning methods; namely the Ziegler-Nichols frequency domain method, damped oscillation method and Good Gain method. The Matlab/Simulink was used to model and analyse the system transient response in terms of rise time, settling time, percentage overshoot and steady state error to step change input for their suitability in power supply design application. Among the three methods, the damped oscillation method demonstrated moderate fast rise and settling time with the advantage of lower overshoot to set point change.


title: Topic-Informed Neural Machine Translation

abstract: In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine ::: translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters ::: to maximize the conditional probability of target sentences given source sentences. In this paper, ::: we present a novel approach to improve the translation performance in NMT by conveying topic ::: knowledge during translation. The proposed topic-informed NMT can increase the likelihood of ::: selecting words from the same topic and domain for translation. Experimentally, we demonstrate ::: that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute ::: improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 ::: test sets, respectively, compared to NMT without topic information.


title: TripleProv: efficient processing of lineage queries in a native RDF store

abstract: Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads.


title: Deep Private-Feature Extraction

abstract: We present and evaluate Deep Private-Feature Extractor (DPFE) , a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user's device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to understand its complexity, resource demands, and efficiency trade-offs. Our results on benchmark image datasets demonstrate that under moderate resource utilization, DPFE can achieve high accuracy for primary tasks while preserving the privacy of sensitive information.


title: Augmented & Virtual Reality applications in the field of logistics

abstract: Changing basic conditions in the field of logistics requires intuitive planning methods as well as new ways of supporting and educating the operative staff. VR and AR show a great promise for that.


Machine Learning for Dialog State Tracking: A Review


title: Whispers in the hyperspace: high-speed covert channel attacks in the cloud

abstract: Information security and privacy in general are major concerns that impede enterprise adaptation of shared or public cloud computing. Specifically, the concern of virtual machine (VM) physical co-residency stems from the threat that hostile tenants can leverage various forms of side channels (such as cache covert channels) to exfiltrate sensitive information of victims on the same physical system. However, on virtualized ×86 systems, covert channel attacks have not yet proven to be practical, and thus the threat is widely considered a "potential risk". In this paper, we present a novel covert channel attack that is capable of high-bandwidth and reliable data transmission in the cloud. We first study the application of existing cache channel techniques in a virtualized environment, and uncover their major insufficiency and difficulties. We then overcome these obstacles by (1) redesigning a pure timing-based data transmission scheme, and (2) exploiting the memory bus as a high-bandwidth covert channel medium. We further design and implement a robust communication protocol, and demonstrate realistic covert channel attacks on various virtualized ×86 systems. Our experiments show that covert channels do pose serious threats to information security in the cloud. Finally, we discuss our insights on covert channel mitigation in virtualized environments.


title: Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

abstract: Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. ::: ::: We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. ::: ::: In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.


title: Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

abstract: Money laundering refers to activities that disguise money receive through illegal operations and make them become legitimate. It leaves serious consequence that may lead to economy corruption. Extensive research has been conducted to investigate proper solution for suspicious transactions detection. In the realm of clustering approaches, traditional research only concentrate on k-means as the best technique so far. On the other hand, although belongs to the same class, there is a lack of studies conducted in employing Expectation Maximization (EM) for Anti-Money Laundering (AML). The objective of this study is to exploit the advantages of EM for suspicious transaction detection. Data used in this study was obtained through a local bank in Malaysia. Subsets of crucial attributes were selected using genetic search and best first search algorithm. Results indicate that critical fields required for clustering phase include amount, number of credit & debit as well as its sum. The outcome of this study shows that EM overwhelmed traditional clustering method k-means for AML in terms of detecting correct suspicious and normal transactions. This lays the groundwork of employing EM in this field. However, further research is needed using different dataset of other banks in order to clarify the effectiveness of EM in AML.


title: Complementary Phase Power Divider Feed for Dipole Antenna Specific to GSM 900 Base Station Applications

abstract: Power Divider (PD) design intended for feeding dipole antenna meant for Global System for Mobile Communications (GSM) 900 applications with an antenna height of 22 mm, operating in the frequency range of 880~960 MHz is presented herein. The PD provides 3 dB power division along with complementary phase at its output. The out of phase division of power divider is obtained by utilizing the concept of defected ground structures. A slot line accompanied by T-junction makes up the defected ground region, while coupled microstrip lines form the feed positions. The simulated and the measured results are in good coherence. PD shows good return loss and low insertion loss. It is observed that the dual polarized base station antenna has VSWR below 2 in the required frequency range, 10 dB gain and port to port isolation is less than 22 dB. The antenna is dual polarized and a + or - 45 degrees polarization is maintained.


title: www.elsevier.com/locate/psyneuen Review The cognitive activation theory of stress

abstract: Abstract This paper presents a cognitive activation theory of stress (CATS), with a formal system of systematic definitions. The term ‘stress’ is used for four aspects of ‘stress’, stress stimuli, stress experience, the non-specific, general stress response, and experience of the stress response. These four meanings may be measured separately. The stress response is a general alarm in a homeostatic system, producing general and unspecific neurophysiological activation from one level of arousal to more arousal. The stress response occurs whenever there is something missing, for instance a homeostatic imbalance, or a threat to homeostasis and life of the organism. Formally, the alarm occurs when there is a discrepancy between what should be and what is—between the value a variable should have (set value ( SV )), and the real value (actual value ( AV )) of the same variable. The stress response, therefore, is an essential and necessary physiological response. The unpleasantness of the alarm is no health threat. However, if sustained, the response may lead to illness and disease through established pathophysiological processes (‘allostatic load’). The alarm elicits specific behaviors to cope with the situation. The level of alarm depends on expectancy of the outcome of stimuli and the specific responses available for coping. Psychological defense is defined as a distortion of stimulus expectancies. Response outcome expectancies are defined as positive, negative, or none, to the available responses. This offers formal definitions of coping, hopelessness, and helplessness that are easy to operationalize in man and in animals. It is an essential element of CATS that only when coping is defined as positive outcome expectancy does the concept predict relations to health and disease.


title: METEOR: An Automatic Metric For MT Evaluation With Improved Correlation With Human Judgments

abstract: We describe METEOR, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machineproduced translation and human-produced reference translations. Unigrams can be matched based on their surface forms, stemmed forms, and meanings; furthermore, METEOR can be easily extended to include more advanced matching strategies. Once all generalized unigram matches between the two strings have been found, METEOR computes a score for this matching using a combination of unigram-precision, unigram-recall, and a measure of fragmentation that is designed to directly capture how well-ordered the matched words in the machine translation are in relation to the reference. We evaluate METEOR by measuring the correlation between the metric scores and human judgments of translation quality. We compute the Pearson R correlation value between its scores and human quality assessments of the LDC TIDES 2003 Arabic-to-English and Chinese-to-English datasets. We perform segment-bysegment correlation, and show that METEOR gets an R correlation value of 0.347 on the Arabic data and 0.331 on the Chinese data. This is shown to be an improvement on using simply unigramprecision, unigram-recall and their harmonic F1 combination. We also perform experiments to show the relative contributions of the various mapping modules.


title: Research on Static Game Theory Based Secure Routing Algorithm in WSN

abstract: In deep research and analysis on WSN security routing protocols and according to the rational development trend of the sensor nodes, this paper proposes a new WSN security routing protocol based on the theory of the static game—GBR. GBR describes the interactions in a static game model using rational characteristics of nodes. When selecting the forwarding node, neighboring nodes decide whether to participate in forwarding data through game, and prevent the destruction of malicious nodes by calculating the utility function. They control and adjust the game process and results through incentive mechanism. GBR (Game theory Based Routing algorithm) reduces packet loss rate by the above mechanisms and strategies and effectively controls the self-ish behavior of nodes. In addition, GBR pursues security routing and makes design and improvement in the energy conservation, which considers node security and regards the energy of the nodes as an importing reference index, in order to balance the network energy consumption.


title: TAJ: effective taint analysis of web applications

abstract: Taint analysis, a form of information-flow analysis, establishes whether values from untrusted methods and parameters may flow into security-sensitive operations. Taint analysis can detect many common vulnerabilities in Web applications, and so has attracted much attention from both the research community and industry. However, most static taint-analysis tools do not address critical requirements for an industrial-strength tool. Specifically, an industrial-strength tool must scale to large industrial Web applications, model essential Web-application code artifacts, and generate consumable reports for a wide range of attack vectors. We have designed and implemented a static Taint Analysis for Java (TAJ) that meets the requirements of industry-level applications. TAJ can analyze applications of virtually any size, as it employs a set of techniques designed to produce useful answers given limited time and space. TAJ addresses a wide variety of attack vectors, with techniques to handle reflective calls, flow through containers, nested taint, and issues in generating useful reports. This paper provides a description of the algorithms comprising TAJ, evaluates TAJ against production-level benchmarks, and compares it with alternative solutions.


title: Towards Low Cost, High Speed Data Sampling Module for Multifunctional Real-Time UWB Radar

abstract: There is a need for the development of a low cost, low jitter, fast processing hardware for various ultrawideband (UWB) radar applications, such as dynamic real-time imaging and micro-Doppler human motion detection with high range resolution. Achieving such high range resolution is typically limited by the availability of low cost gigahertz sampling speed electronics. In order to achieve dynamic real-time imaging, a fast refresh rate and high-speed processing are essential, but they are typically very expensive and difficult to obtain. To circumvent the need for these very expensive processors, an equivalent time sampling method has been successfully applied to implement a compact data acquisition module with a low cost solution. The developed integrated compact module supports dual sampling channels with a maximal 100 giga-samples per second (GSPS) equivalent time sampling rate and relatively low jitter. The equivalent time sampling technique achieves a relatively high effective capture rate, however, this is at a cost of complicated timing control logic and sampled data reconstruction. The developed module includes a novel, robust, efficient, low-jitter data preprocessing scheme implemented in field-programmable gate arrays (FPGAs), and a faster back projection method based on look-up table for real-time 2D imaging. In the work presented here, the design details of the newly developed system as well as its performance are discussed. Various examples of utilizing the developed radar for real-time high resolution target imaging and micro-Doppler detection are also presented.


title: A data-oriented approach to integrate emotions in adaptive dialogue management

abstract: During the past years the involvement of emotions in dialogue design has attracted much interest in current research on intelligent human-computer interfaces. We focus on the implementation of a flexible and robust dialogue system which integrates emotions and other influencing parameters in the dialogue flow. In order to achieve a higher degree of adaptability we propose a simplified stochastic approach to model the dialogue manager's behavior based on the user's input and dialogue-influencing parameters like emotions.


title: Multiple facets for dynamic information flow

abstract: JavaScript has become a central technology of the web, but it is also the source of many security problems, including cross-site scripting attacks and malicious advertising code. Central to these problems is the fact that code from untrusted sources runs with full privileges. We implement information flow controls in Firefox to help prevent violations of data confidentiality and integrity. Most previous information flow techniques have primarily relied on either static type systems, which are a poor fit for JavaScript, or on dynamic analyses that sometimes get stuck due to problematic implicit flows, even in situations where the target web application correctly satisfies the desired security policy. We introduce faceted values, a new mechanism for providing information flow security in a dynamic manner that overcomes these limitations. Taking inspiration from secure multi-execution, we use faceted values to simultaneously and efficiently simulate multiple executions for different security levels, thus providing non-interference with minimal overhead, and without the reliance on the stuck executions of prior dynamic approaches.


title: Knowledge Based Real Time Monitoring System for Aquaculture Using IoT

abstract: Internet of things is one of the rapidly growing fields for delivering social and economic benefits for emerging and developing economy. The field of IOT is expanding its wings in all the domains like medical, industrial, transportation, education, mining etc. Now-a-days with the advancement in integrated on chip computers like Arduino, Raspberry pi the technology is reaching the ground level with its application in agriculture and aquaculture. Water quality is a critical factor while culturing aquatic organisms. It mainly depends on several parameters like dissolved oxygen, ammonia, pH, temperature, salt, nitrates, carbonates etc. The quality of water is monitored continuously with the help of sensors to ensure growth and survival of aquatic life. The sensed data is transferred to the aqua farmer mobile through cloud. As a result preventive measures can be taken in time to minimize the losses and increase the productivity.


title: Attributed network embedding via subspace discovery

abstract: Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at this https URL.


title: Region Description Using Extended Local Ternary Patterns

abstract: The local binary pattern (LBP) operator is a computationally efficient local texture descriptor and has found many useful applications. However, its sensitivity to noise and the high dimensionality of histogram associated with a mediocre size neighborhood have raised some concerns. In this paper, we attempt to improve the original LBP by proposing a novel extension named extended local ternary pattern (ELTP). We will investigate the characteristics of ELTP in terms of noise sensitivity, discriminability and computational efficiency. Preliminary experimental results have shown better efficacy of ELTP over the original LBP.


title: Automatic time series forecasting: The forecast package for R

abstract: Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.


title: Automated identification of copepods using digital image processing and artificial neural network

abstract: BackgroundCopepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique.ResultsWe developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus).ConclusionsThe methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.


title: A New Arbiter PUF for Enhancing Unpredictability on FPGA

abstract: In general, conventional Arbiter-based Physically Unclonable Functions (PUFs) generate responses with low unpredictability. The N-XOR Arbiter PUF, proposed in 2007, is a well-known technique for improving this unpredictability. In this paper, we propose a novel design for Arbiter PUF, called Double Arbiter PUF, to enhance the unpredictability on field programmable gate arrays (FPGAs), and we compare our design to conventional N-XOR Arbiter PUFs. One metric for judging the unpredictability of responses is to measure their tolerance to machine-learning attacks. Although our previous work showed the superiority of Double Arbiter PUFs regarding unpredictability, its details were not clarified. We evaluate the dependency on the number of training samples for machine learning, and we discuss the reason why Double Arbiter PUFs are more tolerant than the N-XOR Arbiter PUFs by evaluating intrachip variation. Further, the conventional Arbiter PUFs and proposed Double Arbiter PUFs are evaluated according to other metrics, namely, their uniqueness, randomness, and steadiness. We demonstrate that 3-1 Double Arbiter PUF archives the best performance overall.


title: Simulation study of large production network robustness in uncertain environment

abstract: Abstract Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems.


title: An annotation management system for relational databases

abstract: We present an annotation management system for relational databases. In this system, every piece of data in a relation is assumed to have zero or more annotations associated with it and annotations are propagated along, from the source to the output, as data is being transformed through a query. Such an annotation management system is important for understanding the provenance and quality of data, especially in applications that deal with integration of scientific and biological data. ::: ::: We present an extension, pSQL, of a fragment of SQL that has three different types of annotation propagation schemes, each useful for different purposes. The default scheme propagates annotations according to where data is copied from. The default-all scheme propagates annotations according to where data is copied from among all equivalent formulations of a given query. The custom scheme allows a user to specify how annotations should propagate. We present a storage scheme for the annotations and describe algorithms for translating a pSQL query under each propagation scheme into one or more SQL queries that would correctly retrieve the relevant annotations according to the specified propagation scheme. For the default-all scheme, we also show how we generate finitely many queries that can simulate the annotation propagation behavior of the set of all equivalent queries, which is possibly infinite. The algorithms are implemented and the feasibility of the system is demonstrated by a set of experiments that we have conducted.


title: A task-driven algorithm for configuration synthesis of modular robots

abstract: This paper presents a time-efficient, task-based configuration synthesis algorithm for modular robot manipulators. One of the main challenges in modular manipulators is to find possible combinations of modules that are able to complete given tasks while avoiding obstacles in the environment. Most studies on modular robots focus on obtaining combinations of modules to achieve a given task without considering the required path planning in an environment with obstacles. In contrast to previous works, we present a configuration synthesis method for modular manipulators, considering collision detection and path planning in task space. Our simulations show that our approach finds possible combinations with reduced computational time compared to previous techniques.