title: Burst time prediction in cascades

abstract: Studying the bursty nature of cascades in social media is practically important in many applications such as product sales prediction, disaster relief, and stock market prediction. Although the cascade volume prediction has been extensively studied, how to predict when a burst will come remains an open problem. It is challenging to predict the time of the burst due to the "quick rise and fall" pattern and the diverse time spans of the cascades. To this end, this paper proposes a classification based approach for burst time prediction by utilizing and modeling rich knowledge in information diffusion. Particularly, we first propose a time window based approach to predict in which time window the burst will appear. This paves the way to transform the time prediction task to a classification problem. To address the challenge that the original time series data of the cascade popularity only are not sufficient for predicting cascades with diverse magnitudes and time spans, we explore rich information diffusion related knowledge and model them in a scale-independent manner. Extensive experiments on a Sina Weibo reposting dataset demonstrate the superior performance of the proposed approach in accurately predicting the burst time of posts.


title: A Graph Search Heuristic for Shortest Distance Paths

abstract: This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.


title: Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature

abstract: Background ::: Information about drug–drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs.


title: Can you see what i hear?: the design and evaluation of a peripheral sound display for the deaf

abstract: We developed two visual displays for providing awareness of environmental audio to deaf individuals. Based on fieldwork with deaf and hearing participants, we focused on supporting awareness of non-speech audio sounds such as ringing phones and knocking in a work environment. Unlike past work, our designs support both monitoring and notification of sounds, support discovery of new sounds, and do not require a priori knowledge of sounds to be detected. Our Spectrograph design shows pitch and amplitude, while our Positional Ripples design shows amplitude and location of sounds. A controlled experiment involving deaf participants found neither display to be significantly distracting. However, users preferred the Positional Ripples display and found that display easier to monitor (notification sounds were detected with 90% success in a laboratory setting). The Spectrograph display also supported successful detection in most cases, and was well received when deployed in the field.


Cascaded Switched-Capacitor Multilevel Inverter


title: Analysis and Design of Average Current Mode Control Using a Describing-Function-Based Equivalent Circuit Model

abstract: This paper proposes a small-signal model for average current mode control based on an equivalent circuit. The model uses a three-terminal equivalent circuit model based on a linearized describing function method to include the feedback effect of the sideband frequency components of the inductor current. The model extends the results obtained in peak current mode control to average current mode control. The proposed small-signal model is accurate up to half switching frequency, predicting the subharmonic instability. The proposed model is verified using SIMPLIS simulation and hardware experiments, which show good agreement with the measurement results. Based on the proposed model, new feedback design guidelines are presented. The proposed design guidelines are compared with several conventional, widely used design criteria. By designing the external ramp following the proposed design guidelines, the quality factor of the double poles at half of the switching frequency in the control-to-output transfer function can be precisely controlled. This helps the feedback loop design to achieve wide control bandwidth and proper damping.


title: A Printed Monopole Antenna With Two Steps and a Circular Slot for UWB Applications

abstract: This letter presents a printed monopole antenna with two steps and a circular slot for ultrawide band (UWB) applications. The proposed antenna is fabricated and tested. The proposed antenna has a wide frequency bandwidth of 8.4 GHz starting from 3 GHz up to 11.4 GHz for a return loss (S_11) of less than - 10dB and gain flatness over the frequency range. Measured results show also that the proposed antenna features satisfactory radiation characteristics within the achieved impedance bandwidth. By introducing a simple and proper narrow slot in the radiating element, frequency-notched characteristics can be obtained and a good band-notched performance in the 56 GHz band can be achieved.


title: Fast unsupervised ego-action learning for first-person sports videos

abstract: Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering first-person action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster video content. Our approach assumes a completely unsupervised scenario, where labeled training videos are not available, videos are not pre-segmented and the number of ego-action categories are unknown. In our proposed framework we show that a stacked Dirichlet process mixture model can be used to automatically learn a motion histogram codebook and the set of ego-action categories. We quantitatively evaluate our approach on both in-house and public YouTube videos and demonstrate robust ego-action categorization across several sports genres. Comparative analysis shows that our approach outperforms other state-of-the-art topic models with respect to both classification accuracy and computational speed. Preliminary results indicate that on average, the categorical content of a 10 minute video sequence can be indexed in under 5 seconds.


WIDEBAND BALANCED ANTIPODAL VIVALDI ANTENNA WITH ENHANCED RADIATION PARAMETERS


title: Analytical Calculation of Breakdown Voltage for Dielectric RESURF Power Devices

abstract: Dielectric REduced SURface Field (RESURF) is a promising concept to enhance the breakdown voltage of power semiconductor devices. This letter reports a set of simple and unified analytical equations to calculate the breakdown voltage, critical electric field, and depletion widths for dielectric RESURF p-n junctions. These analytical models are derived from the basic power law relationship between the p-n junction breakdown voltage and doping concentration, and use the material and structural information of the dielectric RESURF p-n junction as variables to accurately predict reverse bias performance. The analytically calculated results are compared with 2D TCAD simulation results for Si, GaN, and SiC in combination with dielectrics, such as SiO2 and Si3N4, and show reasonable agreement well within 10% of error.


title: Image segmentation by data driven Markov chain Monte Carlo

abstract: This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian, statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to explore the solution space and makes the split and merge process reversible at a middle level vision formulation. Thus it achieves globally optimal solution independent of initial segmentations. Secondly, instead of computing a single maximum a posteriori solution, it proposes a mathematical principle for computing multiple distinct solutions to incorporates intrinsic ambiguities in image segmentation. A k-adventurers algorithm is proposed for extracting distinct multiple solutions from the Markov chain sequence. Thirdly, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which effectively drive the Markov chain dynamics and achieve tremendous speedup in comparison to traditional jump-diffusion method. Thus DDM-CMC paradigm provides a unifying framework where the role of existing segmentation algorithms, such as; edge detection, clustering, region growing, split-merge, SNAKEs, region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. We report some results on color and grey level image segmentation in this paper and refer to a detailed report and a web site for extensive discussion.


Competition in Banking


title: Batched High-dimensional Bayesian Optimization via Structural Kernel Learning

abstract: Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high-dimensional settings. In this paper, we propose to tackle these challenges by (1) assuming a latent additive structure in the function and inferring it properly for more efficient and effective BO, and (2) performing multiple evaluations in parallel to reduce the number of iterations required by the method. Our novel approach learns the latent structure with Gibbs sampling and constructs batched queries using determinantal point processes. Experimental validations on both synthetic and real-world functions demonstrate that the proposed method outperforms the existing state-of-the-art approaches.


title: Overexpression of cytosolic malate dehydrogenase (MDH2) causes overproduction of specific organic acids in Saccharomyces cerevisiae

abstract: Saccharomyces cerevisiae accumulates l-malic acid through a cytosolic pathway starting from pyruvic acid and involving the enzymes pyruvate carboxylase and malate dehydrogenase. In the present study, the role of malate dehydrogenase in the cytosolic pathway was studied. Overexpression of cytosolic malate dehydrogenase (MDH2) under either the strong inducible GAL10 or the constitutive PGK promoter causes a 6- to 16-fold increase in cytosolic MDH activity in growth and production media and up to 3.7-fold increase in l-malic acid accumulation in the production medium. The high apparent Km of MDH2 for l-malic acid (11.8 mM) indicates a low affinity of the enzyme for this acid, which is consistent with the cytosolic function of the enzyme and differs from the previously published Km of the mitochondrial enzyme (MDH1, 0.28 mM). Under conditions of MDH2 overexpression, pyruvate carboxylase appears to be a limiting factor, thus providing a system for further metabolic engineering of l-malic acid production. The overexpression of MDH2 activity also causes an elevation in the accumulation of fumaric acid and citric acid. Accumulation of fumaric acid is presumably caused by high intracellular l-malic acid concentrations and the activity of the cytosolic fumarase. The accumulation of citric acid may suggest the intriguing possibility that cytosolic l-malic acid is a direct precursor of citric acid in yeast.


title: Show Me: Automatic Presentation for Visual Analysis

abstract: This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.


title: Process Mining on Databases: Unearthing Historical Data from Redo Logs

abstract: Process Mining techniques rely on the existence of event data. However, in many cases it is far from trivial to obtain such event data. Considerable efforts may need to be spent on making IT systems record historic data at all. But even if such records are available, it may not be possible to derive an event log for the case notion one is interested in, i.e., correlating events to form process instances may be challenging. This paper proposes an approach that exploits a commonly available and versatile source of data, i.e. database redo logs. Such logs record the writing operations performed in a general-purpose database for a range of objects, which constitute a collection of events. By using the relations between objects as specified in the associated data model, it is possible to turn such events into an event log for a wide range of case types. The resulting logs can be analyzed using existing process mining techniques.


title: CapsGAN: Using Dynamic Routing for Generative Adversarial Networks

abstract: In this paper, we propose a novel technique for generating images in the 3D domain from images with high degree of geometrical transformations. By coalescing two popular concurrent methods that have seen rapid ascension to the machine learning zeitgeist in recent years: GANs (Goodfellow et al.) and Capsule networks (Sabour, Hinton et al.) - we present: CapsGAN. We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST. In the process, we also show the efficacy of using capsules architecture in the GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the performance control and training stability by experimenting with using Wasserstein distance (gradient clipping, penalty) and Spectral Normalization. The experimental findings of this paper should propel the application of capsules and GANs in the still exciting and nascent domain of 3D image generation, and plausibly video (frame) generation.


title: Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise

abstract: Subjective judgments in software engineering tasks are of critical importance but can be difficult to study with conventional means. Medical imaging techniques hold the promise of relating cognition to physical activities and brain structures. In a controlled experiment involving 29 participants, we examine code comprehension, code review and prose review using functional magnetic resonance imaging. We find that the neural representations of programming languages vs. natural languages are distinct. We can classify which task a participant is under-taking based solely on brain activity (balanced accuracy 79%, p


title: Task-Based Language Learning and Student Motivation in Vocabulary Acquisition

abstract: This article explores the impact of task-based language learning on motivating non-English majors to acquire vocabulary at a community college in Vietnam. An experimental study was used to investigate the effectiveness of the use of text-based tasks to enhance students’ vocabulary. The quantitative analysis used data from a questionnaire and vocabulary tests to examine students’ motivation in vocabulary learning over twelve weeks. The qualitative analysis from follow-up interviews with students examined their attitudes towards the use of text-based tasks in terms of task-based language learning. The findings indicated that the participants were motivated to learn vocabulary and their vocabulary achievement improved after the experiment. Suggestions for language teachers to make better use of this approach are also discussed.


title: Wireless sensor networks with RF energy harvesting: Energy models and analysis

abstract: This paper formulates the location-dependent power harvesting rates in generalized 2D and 3D placement of multiple Radio Frequency (RF) Energy Transmitters (ETs) for recharging the nodes of a wireless sensor network (WSN). In particular, we study the distributions of total available and harvested power over the entire WSN. We provide closed matrix forms of harvestable power at any given point in space due to the action of concurrent energy transfer from multiple ETs, explicitly considering constructive and destructive interference of the transmitted energy signals. We also analyze the performance of energy transfer in the WSN through power outage probability, interference, and harvested voltage as a function of the wireless power received from the ETs. Our results reveal that the network wide received power and interference power from concurrent energy transfers exhibit Log-Normal distributions, and the harvested voltage over the network follows a Rayleigh distribution.


title: Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

abstract: The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.


title: Comparing Mining Algorithms for Predicting the Severity of a Reported Bug

abstract: A critical item of a bug report is the so-called "severity", i.e. the impact the bug has on the successful execution of the software system. Consequently, tool support for the person reporting the bug in the form of a recommender or verification system is desirable. In previous work we made a first step towards such a tool: we demonstrated that text mining can predict the severity of a given bug report with a reasonable accuracy given a training set of sufficient size. In this paper we report on a follow-up study where we compare four well-known text mining algorithms (namely, Naive Bayes, Naive Bayes Multinomial, K-Nearest Neighbor and Support Vector Machines) with respect to accuracy and training set size. We discovered that for the cases under investigation (two open source systems: Eclipse and GNOME) Naive Bayes Multinomial performs superior compared to the other proposed algorithms.


title: Face detection using convolutional neural networks and gabor filters

abstract: This paper proposes a method for detecting facial regions by combining a Gabor filter and a convolutional neural network. The first stage uses the Gabor filter which extracts intrinsic facial features. As a result of this transformation we obtain four subimages. The second stage of the method concerns the application of the convolutional neural network to these four images. The approach presented in this paper yields better classification performance in comparison to the results obtained by the convolutional neural network alone.


CLPsych 2016 Shared Task: Triaging content in online peer-support forums


title: Knowledge-Based Data Mining Using Semantic Web

abstract: Semantic web offers a smarter web service which synchronizes and arranges all the data over web in a disciplined manner. In data mining over web, the accuracy of selecting necessary data according to user demand and pick them for output is considered as a major challenging task over the years. This paper proposes an approach to mapping data over the web 3.0 through ontology and access the required data via an intelligent agent. The agent provides all the searched data related to user query from which user can find desired information. When the user does not have sufficient search parameter, knowledge can be perceived from the information provided by the agent. The derivation of such unknown knowledge from the existing can be achieved by semantic web mining. We present an intelligent agent-based web mining model where users’ query is being searched by following existing traditional way, e.g. by Google. The intelligent agent checks the searched data and derives only those are the semantically related to users search parameter. A work-in-progress case study of University Faculty Information presented to examine the effectiveness of the proposed model.


title: FixCaffe: Training CNN with Low Precision Arithmetic Operations by Fixed Point Caffe

abstract: The convolutional neural networks are widely used in deep learning model because of its advantages in image classification, speech recognition and natural language processing. However, training large-scale networks is very time and resource consuming, because it is both compute-intensive and memory-intensive. In this paper, we proposed to use the fixed point arithmetic to train CNN with popular deep learning framework Caffe. We propose our framework FixCaffe (Fixed Point Caffe), where fixed point matrix multiply function is substitute for part of the original floating point matrix multiply function in Caffe. We analyze the range of the operands during the training process, and choose the proper scaling factor for transform floating point operands to fixed point operands. Training LeNet-S model, obtained by modifying LeNet-5, on the MNIST benchmark, the result shows that after training 1000 iterations, FixCaffe with 8-bit fixed point multiplications only leads to about 0.5% loss in the classification accuracy compared to the single-precision floating point Caffe baseline. Using Xilinx V7 690T to implement the multiplier, the cost of computing resource can save up to 83.3%, and the on-chip storage overhead for the LeNet-S model’s parameters can save 75%.


title: Dual polarization antennas for UHF RFID readers

abstract: This paper presents various concepts of switching polarization in patch antenna dedicated for UHF RFID readers. Proposed designs allow for switching between linear and circular polarization. The first design does not require electronic switching as the polarization can be changed by choosing one of two available feeding terminals. Two remaining designs use PIN diode or FET SPDT switch.


Extending a CRF-based named entity recognition model for Turkish well formed text and user generated content1


title: Multiview Convolutional Neural Networks for Multidocument Extractive Summarization

abstract: Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features for sentence representation. In this paper, we leverage on word embedding to represent sentences so as to avoid the intensive labor in feature engineering. An enhanced convolutional neural networks (CNNs) termed multiview CNNs is successfully developed to obtain the features of sentences and rank sentences jointly. Multiview learning is incorporated into the model to greatly enhance the learning capability of original CNN. We evaluate the generic summarization performance of our proposed method on five Document Understanding Conference datasets. The proposed system outperforms the state-of-the-art approaches and the improvement is statistically significant shown by paired $t$ -test.


title: Monaural Audio Speaker Separation with Source Contrastive Estimation

abstract: We propose an algorithm to separate simultaneously speaking persons from each other, the "cocktail party problem", using a single microphone. Our approach involves a deep recurrent neural networks regression to a vector space that is descriptive of independent speakers. Such a vector space can embed empirically determined speaker characteristics and is optimized by distinguishing between speaker masks. We call this technique source-contrastive estimation. The methodology is inspired by negative sampling, which has seen success in natural language processing, where an embedding is learned by correlating and de-correlating a given input vector with output weights. Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference. Doing so will ensure that source separation is explicitly speaker-independent. Our approach is similar to recent deep neural network clustering and permutation-invariant training research; we use weighted spectral features and masks to augment individual speaker frequencies while filtering out other speakers. We avoid, however, the severe computational burden of other approaches with our technique. Furthermore, by training a vector space rather than combinations of different speakers or differences thereof, we avoid the so-called permutation problem during training. Our algorithm offers an intuitive, computationally efficient response to the cocktail party problem, and most importantly boasts better empirical performance than other current techniques.


title: Breaking mifare DESFire MF3ICD40: power analysis and templates in the real world

abstract: With the advent of side-channel analysis, implementations of mathematically secure ciphers face a new threat: by exploiting the physical characteristics of a device, adversaries are able to break algorithms such as AES or Triple-DES (3DES), for which no efficient analytical or brute-force attacks exist. In this paper, we demonstrate practical, noninvasive side-channel attacks on the Mifare DESFire MF3ICD40 contactless smartcard, a 3DES-based alternative to the cryptanalytically weak Mifare Classic [9,25]. We detail on how to recover the complete 112-bit secret key of the employed 3DES algorithm, using non-invasive power analysis and template attacks. Our methods can be put into practice at a low cost with standard equipment, thus posing a severe threat to many real-world applications that employ the DESFire MF3ICD40 smartcard.


title: Termite: visualization techniques for assessing textual topic models

abstract: Topic models aid analysis of text corpora by identifying latent topics based on co-occurring words. Real-world deployments of topic models, however, often require intensive expert verification and model refinement. In this paper we present Termite, a visual analysis tool for assessing topic model quality. Termite uses a tabular layout to promote comparison of terms both within and across latent topics. We contribute a novel saliency measure for selecting relevant terms and a seriation algorithm that both reveals clustering structure and promotes the legibility of related terms. In a series of examples, we demonstrate how Termite allows analysts to identify coherent and significant themes.


title: DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

abstract: Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI). In this work, we focus on directly estimating each individual's self-reported visual-analog scale (VAS) pain metric, as this is considered the gold standard for pain measurement. The VAS pain score is highly subjective and context-dependent, and its range can vary significantly among different persons. To tackle these issues, we propose a novel two-stage personalized model, named DeepFaceLIFT, for automatic estimation of VAS. This model is based on (1) Neural Network and (2) Gaussian process regression models, and is used to personalize the estimation of self-reported pain via a set of hand-crafted personal features and multi-task learning. We show on the benchmark dataset for pain analysis (The UNBC-McMaster Shoulder Pain Expression Archive) that the proposed personalized model largely outperforms the traditional, unpersonalized models: the intra-class correlation improves from a baseline performance of 19\% to a personalized performance of 35\% while also providing confidence in the model\textquotesingle s estimates -- in contrast to existing models for the target task. Additionally, DeepFaceLIFT automatically discovers the pain-relevant facial regions for each person, allowing for an easy interpretation of the pain-related facial cues.


Experiments in Idiom Recognition


title: Acoustic SLAM

abstract: An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a three-dimensional (3D) representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.


title: Food waste reduction practices in German food retail

abstract: Purpose ::: The purpose of this paper is to investigate food retailers food waste reduction practices in Germany. The focus is on selling and redistributing agricultural produce with visual impairments and other surplus food items. In addition, drivers and barriers regarding the implementation of both waste reduction practices are explored. ::: ::: ::: Design/methodology/approach ::: In total, 12 in-depth interviews with managerial actors in the food retail sector and a food bank spokesperson were recorded, transcribed and analyzed through a qualitative content analysis. ::: ::: ::: Findings ::: In contrast to organic retailers, conventional retailers were reluctant to include agricultural produce with visual impairments in their product assortments, due to fears of negative consumer reactions. Another obstacle was EU marketing standards for specific produce. All retailers interviewed engaged in redistribution of surplus food. Logistics and the regulatory framework were the main barriers to food redistribution. ::: ::: ::: Originality/value ::: The present study adds to the existing body of literature on food waste reduction practices as it explores selling produce with visual impairments and elaborates on the legal background of food redistribution in German retail. The results are the foundation for providing recommendations to policy makers and charitable food organizations.


title: High-Quality Real-Time Video Stabilization Using Trajectory Smoothing and Mesh-Based Warping

abstract: Some state-of-the-art video stabilization methods can achieve quite good visual effect, but they always cost a lot of time. On the other hand, current real-time video stabilization methods cannot generate satisfactory results. In this paper, we propose a novel trajectory-based video stabilization method which can generate high-quality results in real time. Our method runs very fast, because many techniques are proposed for acceleration. In the trajectory smoothing step, trajectories are extracted, pre-processed, and smoothed. A video splitting algorithm is proposed for pre-processing, and binomial filtering is used for smoothing. Both of them are simple and fast. In the frame warping step, we calculate a spatially varying warp that is directed by a grid mesh for each frame. Instead of solving time consuming global optimization problems, the transformation matrix of each grid is calculated using nearby trajectories in our method, leading to very high speed. We implement our method and run it on a variety of videos. Experiments show that while the stabilization effect is comparable with state-of-the-art methods, our algorithm can run in real time.


title: Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images

abstract: Constructing 3D structures from serial section data is a long standing problem in microscopy. The structure of a fiber reinforced composite material can be reconstructed using a tracking-by-detection model. Tracking-by-detection algorithms rely heavily on detection accuracy, especially the recall performance. The state-of-the-art fiber detection algorithms perform well under ideal conditions, but are not accurate where there are local degradations of image quality, due to contaminants on the material surface and/or defocus blur. Convolutional Neural Networks (CNN) could be used for this problem, but would require a large number of manual annotated fibers, which are not available. We propose an unsupervised learning method to accurately detect fibers on the large scale, that is robust against local degradations of image quality. The proposed method does not require manual annotations, but uses fiber shape/size priors and spatio-temporal consistency in tracking to simulate the supervision in the training of the CNN. Experiments show significant improvements over state-of-the-art fiber detection algorithms together with advanced tracking performance.


title: Technical Note: Q-Learning

abstract: Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.


title: Analysis of Safety-Critical Computer Failures in Medical Devices

abstract: Malfunctioning medical devices are one of the leading causes of serious injury and death in the US. Between 2006 and 2011, 5,294 recalls and approximately 1.2 million adverse events were reported to the US Food and Drug Administration (FDA). Almost 23 percent of these recalls were due to computer-related failures, of which approximately 94 percent presented medium to high risk of severe health consequences (such as serious injury or death) to patients. This article investigates the causes of failures in computer-based medical devices and their impact on patients by analyzing human-written descriptions of recalls and adverse event reports obtained from public FDA databases. The authors characterize computer-related failures by deriving fault classes, failure modes, recovery actions, and number of devices affected by the recalls. This analysis is used as a basis for identifying safety issues in life-critical medical devices and providing insights on the future challenges in the design of safety-critical medical devices.


title: Genes CEP55, FOXD3, FOXF2, GNAO1, GRIA4, and KCNA5 as potential diagnostic biomarkers in colorectal cancer

abstract: Background ::: Colorectal cancer (CRC) is one of the leading causes of death by cancer worldwide and in need of novel potential diagnostic biomarkers for early discovery.


title: The Relationship of Academic Stress with Aggression, Depression and Academic Performance of College Students in Iran.

abstract: This paper investigates the relationship of academic stress with aggression, depression and academic performance of college students. Using a random sampling technique, 60 students consist of boys and girls were selected as students having academic stress. The scale for assessing academic stress (Sinha, Sharma and Mahendra, 2001); the Buss-Perry aggression questionnaire (1992); and Beck depression inventory were used for collecting the data. The Pearson's coefficient of correlation showed that academic stress has a simple significant correlation with aggression (r=.54), depression (r=.49) and academic performance (r=-44) (p<. /..1). Further, the analysis of the study shows that academic stress, aggression and depression are able to explain 68% of the variance of academic performance. Also, results show that boys and girls are different in terms of aggression and depression.


title: Active AU Based Patch Weighting for Facial Expression Recognition

abstract: Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the speciﬁcity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the speciﬁcity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed.


title: A Vehicle Routing Problem with Flexible Time Windows

abstract: In this paper, we introduce the Vehicle Routing Problem with Flexible Time Windows (VRPFlexTW), in which vehicles are allowed to deviate from customer time windows by a given tolerance. This flexibility enables savings in the operational costs of carriers, since customers may be served before and after the earliest and latest time window bounds, respectively. However, as time window deviations are undesired from a customer service perspective, a penalty proportional to these deviations is accounted for in the objective function. We develop a solution procedure, in which feasible vehicle routes are constructed via a tabu search algorithm. Furthermore, we propose a linear programming model to handle the detailed scheduling of customer visits for given routes. We validate our solution procedure by a number of Vehicle Routing Problem with Time Windows (VRPTW) benchmark instances. We highlight the costs involved in integrating flexibility in time windows and underline the advantages of the VRPFlexTW, when compared to the VRPTW.


title: COMA-boost: co-operative multi agent AdaBoost

abstract: Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. This is made possible by periodically sharing of response states by our learner agents in the boosting framework. Theoretically, such soft weight update policy allows infinite combinations of weight updates on training space compared to only two possibilities in AdaBoost. This opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more confident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stacked autoencoder network. Our work opens a new avenue of research for combining a popular statistical machine learning paradigm with deep network architectures.


title: DeepFood: Automatic Multi-Class Classification of Food Ingredients Using Deep Learning

abstract: Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features from a dataset of food ingredient images using deep learning but also improves the average accuracy of multi-class classification by applying advanced machine learning techniques. First, a set of transfer learning algorithms based on Convolutional Neural Networks (CNNs) are leveraged for deep feature extraction. Then, a multi-class classification algorithm is exploited based on the performance of the classifiers on each deep feature set. The DeepFood framework is evaluated on a multi-class dataset that includes 41 classes of food ingredients and 100 images for each class. Experimental results illustrate the effectiveness of the DeepFood framework for multi-class classification of food ingredients. This model that integrates ResNet deep feature sets, Information Gain (IG) feature selection, and the SMO classifier has shown its supremacy for foodingredients recognition compared to several existing work in this area.


title: Learning the MMSE Channel Estimator

abstract: We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invariance structures, the complexity of the minimum mean squared error (MMSE) channel estimator can be reduced to ${\mathcal O}(M\log M)$ floating point operations, where $M$ is the channel dimension. While in the absence of structure the complexity is much higher, we obtain a similarly efficient (but suboptimal) estimator by using the MMSE estimator of the structured model as a blueprint for the architecture of a neural network. This network learns the MMSE estimator for the unstructured model, but only within the given class of estimators that contains the MMSE estimator for the structured model. Numerical simulations with typical spatial channel models demonstrate the generalization properties of the chosen class of estimators to realistic channel models.


title: Pattern recognition of human arm movement using deep reinforcement learning

abstract: Hand gesture recognition is one of the major research areas in the field of Human computer interaction (HCl). This paper proposes a deep reinforcement learning algorithm to recognize the human arm movement patterns using an IoT sensor device. Recent studies have explored supervised learning based methods, such as CNN and RNN to implement the HCl device. On the other hand, the deep reinforcement learning approach has also been investigated. Algorithms using this approach, learn the patterns from sensors using only the reward feedback with no class labels. This allows users to control the IoT device and produce the desired arm movement patterns without creating any labels. In this paper, the performance of convolutional neural network (CNN) with the DQN model was compared with that of long short-term memory (LSTM) models with DQN. Results show that the CNN based DQN model was more stable compared to the LSTM based model, and yielded a high classification accuracy of 98.33% to predict the arm movement patterns.


title: Non-rigid image registration using fully convolutional networks with deep self-supervision

abstract: We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. At the same time, our method also learns FCNs for encoding the spatial transformations at the same spatial resolution of images to be registered, rather than learning coarse-grained spatial transformation information. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different resolutions with deep self-supervision through typical feedforward and backpropagation computation. Since our method simultaneously optimizes and learns spatial transformations for the image registration, our method can be directly used to register a pair of images, and the registration of a set of images is also a training procedure for FCNs so that the trained FCNs can be directly adopted to register new images by feedforward computation of the learned FCNs without any optimization. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.


title: What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?

abstract: Over the past few years, a number of new "fringe" communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting "people and free speech first", welcoming users banned or suspended from other social networks. In this paper, we provide, to the best of our knowledge, the first characterization of Gab. We collect and analyze 22M posts produced by 336K users between August 2016 and January 2018, finding that Gab is predominantly used for the dissemination and discussion of news and world events, and that it attracts alt-right users, conspiracy theorists, and other trolls. We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan's Politically Incorrect board.


title: Feeling and believing: The influence of emotion on trust

abstract: The authors report results from 5 experiments that describe the influence of emotional states on trust. They found that incidental emotions significantly influence trust in unrelated settings. Happiness and gratitude—emotions with positive valence—increase trust, and anger—an emotion with negative valence—decreases trust. Specifically, they found that emotions characterized by other-person control (anger and gratitude) and weak control appraisals (happiness) influence trust significantly more than emotions characterized by personal control (pride and guilt) or situational control (sadness). These findings suggest that emotions are more likely to be misattributed when the appraisals of the emotion are consistent with the judgment task than when the appraisals of the emotion are inconsistent with the judgment task. Emotions do not influence trust when individuals are aware of the source of their emotions or when individuals are very familiar with the trustee.


title: The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge

abstract: This paper presents the University of Passau’s approaches for the Multimodal Emotion Recognition Challenge 2016. For audio signals, we exploit Bag-of-Audio-Words techniques combining Extreme Learning Machines and Hierarchical Extreme Learning Machines. For video signals, we use not only the information from the cropped face of a video frame, but also the broader contextual information from the entire frame. This information is extracted via two Convolutional Neural Networks pre-trained for face detection and object classification. Moreover, we extract facial action units, which reflect facial muscle movements and are known to be important for emotion recognition. Long Short-Term Memory Recurrent Neural Networks are deployed to exploit temporal information in the video representation. Average late fusion of audio and video systems is applied to make prediction for multimodal emotion recognition. Experimental results on the challenge database demonstrate the effectiveness of our proposed systems when compared to the baseline.


title: Lheidli intervocalic consonants: phonetic and morphological effects

abstract: Several researchers have noted that intervocalic consonants are unusually long in Navajo (Sapir & Hoijer 1967, Young & Morgan 1987, McDonough & Ladefoged 1993). This paper explores intervocalic consonant duration in the Lheidli dialect of Dakelh (Carrier) in order to determine whether the long intervocalic consonants found in Navajo are characteristic of other Athabaskan languages as well. It is shown that Lheidli intervocalic consonants are substantially longer overall than (a) consonants in other positions within Lheidli, (b) vowels within Lheidli, and (c) singletons and geminates in other non-Athabaskan languages. Furthermore, intervocalic consonant duration is at least in part a function of morphological structure, with intervocalic consonants lengthening to signal a stem morpheme boundary. The data presented here provide new evidence for the existence of a language-specific phonetic component of grammar as well as for the interaction between phonetics and morphology.


title: A Multiharvested Self-Powered System in a Low-Voltage Low-Power Technology

abstract: A multiharvesting system conception focused on low-voltage (up to 2.5 V) and low-power applications is presented and validated as a point-of-view system. Using just this application-specified integrated circuit, with a total quiescent power consumption of 160 μW, the harvesting system is able to collect and manage energy from different power sources, such as solar light (indoor environment), vibration (low-voltage piezoelectric generators), and electromagnetic induction (operating with a carrier frequency of 13.56-MHz regulated band). The maximum total power harvested with the addition of the three harvesting sources is around 6.4 mW, for the operating conditions defined by a PZT at 7 m/s2 at 80 Hz, 1500 lx for a laboratory illumination, and 200 mW emitted by a base transmitter at 25-mm distance between coils. A broad and detailed description of all low-power-consumption circuits involved in the multiharvesting system is described, emphasizing their design for low-voltage and low-power applications.


title: Technology-assisted title and abstract screening for systematic reviews: a retrospective evaluation of the Abstrackr machine learning tool

abstract: BACKGROUND ::: Machine learning tools can expedite systematic review (SR) processes by semi-automating citation screening. Abstrackr semi-automates citation screening by predicting relevant records. We evaluated its performance for four screening projects. ::: ::: ::: METHODS ::: We used a convenience sample of screening projects completed at the Alberta Research Centre for Health Evidence, Edmonton, Canada: three SRs and one descriptive analysis for which we had used SR screening methods. The projects were heterogeneous with respect to search yield (median 9328; range 5243 to 47,385 records; interquartile range (IQR) 15,688 records), topic (Antipsychotics, Bronchiolitis, Diabetes, Child Health SRs), and screening complexity. We uploaded the records to Abstrackr and screened until it made predictions about the relevance of the remaining records. Across three trials for each project, we compared the predictions to human reviewer decisions and calculated the sensitivity, specificity, precision, false negative rate, proportion missed, and workload savings. ::: ::: ::: RESULTS ::: Abstrackr's sensitivity was > 0.75 for all projects and the mean specificity ranged from 0.69 to 0.90 with the exception of Child Health SRs, for which it was 0.19. The precision (proportion of records correctly predicted as relevant) varied by screening task (median 26.6%; range 14.8 to 64.7%; IQR 29.7%). The median false negative rate (proportion of records incorrectly predicted as irrelevant) was 12.6% (range 3.5 to 21.2%; IQR 12.3%). The workload savings were often large (median 67.2%, range 9.5 to 88.4%; IQR 23.9%). The proportion missed (proportion of records predicted as irrelevant that were included in the final report, out of the total number predicted as irrelevant) was 0.1% for all SRs and 6.4% for the descriptive analysis. This equated to 4.2% (range 0 to 12.2%; IQR 7.8%) of the records in the final reports. ::: ::: ::: CONCLUSIONS ::: Abstrackr's reliability and the workload savings varied by screening task. Workload savings came at the expense of potentially missing relevant records. How this might affect the results and conclusions of SRs needs to be evaluated. Studies evaluating Abstrackr as the second reviewer in a pair would be of interest to determine if concerns for reliability would diminish. Further evaluations of Abstrackr's performance and usability will inform its refinement and practical utility.


title: Geofencing 2.0: taking location-based notifications to the next level

abstract: Recently, Location-based Services (LBS) became proactive by supporting smart notifications in case the user enters or leaves a specific geographical area, well-known as Geofencing. However, different geofences cannot be temporally related to each other. Therefore, we introduce a novel method to formalize sophisticated Geofencing scenarios as state and transition-based geofence models. Such a model considers temporal relations between geofences as well as duration constraints for the time being within a geofence or in transition between geofences. These are two highly important aspects in order to cover sophisticated scenarios in which a notification should be triggered only in case the user crosses multiple geofences in a defined temporal order or leaves a geofence after a certain amount of time. As a proof of concept, we introduce a prototype of a suitable user interface for designing complex geofence models in conjunction with the corresponding proactive LBS.


title: A proposed reliable and power efficient 14T full adder circuit design

abstract: This paper presents design of a new stable 14T full power efficient adder circuit. The proposed circuit is designed based on Pass Transistor Logic (PTL) network using NMOS transistor only. The proposed circuit is simulated at layout level using Microwind EDA tools for 45nm technology in terms of power and voltage level at the sum and carry nodes. The proposed circuit performance is compared with a similar 14T adder circuits and found the proposed adder circuit consumes lower power due to smaller load capacitance and parasitic resistance. The logic level at the sum and carry nodes maintains at strong 1 or strong 0 due to proposed circuit's design architecture.


Hafez: an Interactive Poetry Generation System.


title: 3DMatch: Learning the Matching of Local 3D Geometry in Range Scans.

abstract: Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of real-world scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-the-art approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction.


title: Online Human-Bot Interactions: Detection, Estimation, and Characterization

abstract: Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9% and 15% of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.


title: A Penalty Method for American Options with Jump Diffusion Processes

abstract: The fair price for an American option where the underlying asset follows a jump diffusion process can be formulated as a partial integral differential linear complementarity problem. We develop an implicit discretization method for pricing such American options. The jump diffusion correlation integral term is computed using an iterative method coupled with an FFT while the American constraint is imposed by using a penalty method. We derive sufficient conditions for global convergence of the discrete penalized equations at each timestep. Finally, we present numerical tests which illustrate such convergence.


title: Rice crop yield prediction in India using support vector machines

abstract: Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.


title: Technologies for Continuous Glucose Monitoring: Current Problems and Future Promises

abstract: Devices for continuous glucose monitoring (CGM) are currently a major focus of research in the area of diabetes management. It is envisioned that such devices will have the ability to alert a diabetes patient (or the parent or medical care giver of a diabetes patient) of impending hypoglycemic/hyperglycemic events and thereby enable the patient to avoid extreme hypoglycemic/hyperglycemic excursions as well as minimize deviations outside the normal glucose range, thus preventing both life-threatening events and the debilitating complications associated with diabetes. It is anticipated that CGM devices will utilize constant feedback of analytical information from a glucose sensor to activate an insulin delivery pump, thereby ultimately realizing the concept of an artificial pancreas. Depending on whether the CGM device penetrates/breaks the skin and/or the sample is measured extracorporeally, these devices can be categorized as totally invasive, minimally invasive, and noninvasive. In addition, CGM devices are further classified according to the transduction mechanisms used for glucose sensing (i.e., electrochemical, optical, and piezoelectric). However, at present, most of these technologies are plagued by a variety of issues that affect their accuracy and long-term performance. This article presents a critical comparison of existing CGM technologies, highlighting critical issues of device accuracy, foreign body response, calibration, and miniaturization. An outlook on future developments with an emphasis on long-term reliability and performance is also presented.


title: The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study

abstract: Background ::: Few research in multiple sclerosis (MS) has focused on physical rehabilitation of upper limb dysfunction, though the latter strongly influences independent performance of activities of daily living. Upper limb rehabilitation technology could hold promise for complementing traditional MS therapy. Consequently, this pilot study aimed to examine the feasibility of an 8-week mechanical-assisted training program for improving upper limb muscle strength and functional capacity in MS patients with evident paresis.


title: Marijuana Use in the Elderly: Implications and Considerations.

abstract: OBJECTIVE ::: This article reviews the literature on the use of marijuana in the elderly. Pharmacists play an important role in the management of medications including drug use of potentially illegal drugs, including marijuana. The use of both recreational and medical marijuana has grown exponentially in the general population, including in older adults. As of 2017, marijuana for medical use is legal in 26 states and the District of Columbia. ::: ::: ::: DATA SOURCES ::: PubMed and Internet search using the following terms: marijuana, cannabis, delta-9-tetrhydrocannabinol (THC), cannabidiol, cannabinoid, elderly, geriatric, and pharmacology. Findings are based on data collected from older adults (65 years of age and older) through August 2016. ::: ::: ::: STUDY SELECTION ::: Because of the lack of research and funding, reputable literature on the impact of marijuana on older adults is scarce. The available evidence suggests that elderly individuals should be cautious when consuming marijuana, especially those who have certain comorbid conditions. ::: ::: ::: DATA EXTRACTION ::: The geriatric population has a higher likelihood of having multiple comorbidities and is subject to polypharmacy. Marijuana use, medicinal or recreational, complicates the picture with additive central nervous system side effects. ::: ::: ::: DATA SYNTHESIS ::: This article reviews the growing information on marijuana use and discusses issues to consider and cautions in usage that can apply to day-to-day clinical practice and geriatric care. The role of the pharmacist in educating patients, caregivers, and health care providers is expanding with the growing number of states that have legalized medical marijuana (26 states and the District of Columbia, as of 2017). Important education points including drug-drug interactions, drug-disease interactions, and signs and symptoms of acute overdose should be considered. ::: ::: ::: CONCLUSION ::: With this review, pharmacists will be informed on recommendations on the use of marijuana in the older adult. Monitoring of therapy, as well as adverse effects, will be reviewed, including some legal issues and challenges.


title: RDBMS vs NoSQL: Performance and Scaling Comparison

abstract: The massive amounts of data collected today by software in fields varying from academia to business and many other fields, is increasingly becoming a huge problem due to storage technologies not advancing fast enough to provide the performance scalability needed. This is even more true for data which are highly organised and require analysis while being stored in databases and being accessed by various applications simultaneously. Databases also have the added advantage of providing failover mechanism in case of disruptions or one node failing. As database vendors struggle to gain more market share new technologies emerge attempting to overcome the disadvantages of previous designs while providing more features. Two popular database types, the Relational Database Management Systems and NoSQL databases are examined. The aim of this project was to examine and compare two databases from these two database models and answer the question of whether one performs and scales better than the other. From the comparison of the results it was found that MongoDB can perform much better for complicated queries at the cost of data duplication which in turn results to a larger database. Also the database size did not appear to be a deciding factor as performance was not crippled significantly for larger database sizes. Writing benchmarks were also run which showed that MySQL performs best at deletion whereas MongoDB excels at inserting documents. The last comparison showed that using sharding to split up the database in MongoDB did not provide a performance advantage which may be related to the routing done by the MongoDB system.


title: A comparison of data serialization formats for optimal efficiency on a mobile platform

abstract: Because of the increase in easily obtainable internet-connected mobile devices and their unique characteristics, choosing the proper data serialization format has become increasingly difficult. These devices are resource scarce and bandwidth limited. In this paper, we compare four different data serialization formats with an emphasis on serialization speed, data size, and usability. The selected serialization formats include XML, JSON, Thrift, and ProtoBuf. XML and JSON are the most well known text-based data formats, while ProtoBuf and Thrift are relatively new binary serialization formats. These data serialization formats are tested on an Android device using both text-heavy and number-heavy objects.


title: Raw meat based diet influences faecal microbiome and end products of fermentation in healthy dogs

abstract: BACKGROUND ::: Dietary intervention studies are required to deeper understand the variability of gut microbial ecosystem in healthy dogs under different feeding conditions and to improve diet formulations. The aim of the study was to investigate in dogs the influence of a raw based diet supplemented with vegetable foods on faecal microbiome in comparison with extruded food. ::: ::: ::: METHODS ::: Eight healthy adult Boxer dogs were recruited and randomly divided in two experimental blocks of 4 individuals. Dogs were regularly fed a commercial extruded diet (RD) and starting from the beginning of the trial, one group received the raw based diet (MD) and the other group continued to be fed with the RD diet (CD) for a fortnight. After 14 days, the two groups were inverted, the CD group shifted to the MD and the MD shifted to the CD, for the next 14 days. Faeces were collected at the beginning of the study (T0), after 14 days (T14) before the change of diet and at the end of experimental period (T28) for DNA extraction and analysis of metagenome by sequencing 16SrRNA V3 and V4 regions, short chain fatty acids (SCFA), lactate and faecal score. ::: ::: ::: RESULTS ::: A decreased proportion of Lactobacillus, Paralactobacillus (P < 0.01) and Prevotella (P < 0.05) genera was observed in the MD group while Shannon biodiversity Index significantly increased (3.31 ± 0.15) in comparison to the RD group (2.92 ± 0.31; P < 0.05). The MD diet significantly (P < 0.05) decreased the Faecal Score and increased the lactic acid concentration in the feces in comparison to the RD treatment (P < 0.01). Faecal acetate was negatively correlated with Escherichia/Shigella and Megamonas (P < 0.01), whilst butyrate was positively correlated with Blautia and Peptococcus (P < 0.05). Positive correlations were found between lactate and Megamonas (P < 0.05), Escherichia/Shigella (P < 0.01) and Lactococcus (P < 0.01). ::: ::: ::: CONCLUSION ::: These results suggest that the diet composition modifies faecal microbial composition and end products of fermentation. The administration of MD diet promoted a more balanced growth of bacterial communities and a positive change in the readouts of healthy gut functions in comparison to RD diet.


title: UNDERSTANDING THE ACCEPTANCE OF MOBILE HEALTH SERVICES: A COMPARISON AND INTEGRATION OF ALTERNATIVE MODELS

abstract: The advancement of mobile technology and the increasing importance of health promote the boom in mobile health services (MHS) around the world. Although there have been several studies investigating the health technology acceptance behavior from a variety of theoretical perspectives, they have not provided a unified understanding. To fill this research gap, this paper: (1) reviews the health technology acceptance literature and discusses three prominent models (e.g., the technology acceptance model, the theory of planned behavior or the unified theory of use and acceptance of technology, and the protection motivation theory), (2) empirically compares the three models, and (3) formulates and empirically validates the unified model in the context of mobile health services. In the unified model of health technology acceptance, we propose that users’ intention to use mobile health services is determined by five key factors: performance expectancy, effort expectancy, social influence, facilitating conditions, and threat appraisals. The results show that the unified model outperforms the three alternative models by significantly improving the R-squares. Finally, the implications for theory and practice are put forward.


CellSDN: Software-Defined Cellular Networks


Extractivism: Extracting Activists Events from News Articles Using Existing NLP Tools and Services


Improving Stealth Assessment in Game-based Learning with LSTM-based Analytics


title: Automatic spatial coding of perceived gaze direction is revealed by the Simon effect

abstract: In a typical Simon task, the (irrelevant) spatial position of the stimulus interferes with the processing of the salient characteristic (e.g., color). We used the Simon effect to investigate the automatic processing of gaze cues. We show that a simple drawing of schematic eyes automatically generates a spatially defined code of gaze direction. Although completely irrelevant to the task, direction of gaze influenced reaction times in a spatially selective two-choice discrimination based on eye color. Moreover, in one experiment employing an orthogonal manipulation of stimulus position and gaze direction, we found that coding of gaze direction is independent of stimulus spatial coding. Our finding of a “gazedirection Simon effect” is congruent with the hypothesis that gaze direction is coded by a specialized mechanism.


title: Deep EHR: A Survey of Recent Advances on Deep Learning Techniques for Electronic Health Record (EHR) Analysis

abstract: The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research.


title: Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning.

abstract: An evolved module for fear elicitation and fear learning with 4 characteristics is proposed. (a) The fear module is preferentially activated in aversive contexts by stimuli that are fear relevant in an evolutionary perspective. (b) Its activation to such stimuli is automatic. (c) It is relatively impenetrable to cognitive control. (d) It originates in a dedicated neural circuitry, centered on the amygdala. Evidence supporting these propositions is reviewed from conditioning studies, both in humans and in monkeys; illusory correlation studies; studies using unreportable stimuli; and studies from animal neuroscience. The fear module is assumed to mediate an emotional level of fear learning that is relatively independent and dissociable from cognitive learning of stimulus relationships.


title: Improving Reinforcement Learning Based Image Captioning with Natural Language Prior

abstract: Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow. To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.


title: Introducing UN Salient360! Benchmark: A platform for evaluating visual attention models for 360° contents

abstract: Virtual Reality (VR) provides the users with new immersive media experiences, offering the possibility to freely explore 360° content. Understanding these new exploration behaviors is crucial for the development of efficient techniques for processing, coding, delivering and rendering omnidirectional content to offer the highest possible Quality of Experience (QoE). Progress has already been made on visual attention (VA) modeling for 360° content. In this paper we briefly review the current status of research on this topic that led us to propose a benchmarking platform for evaluating and comparing the performance of models for saliency and scanpath prediction for 360° content. This paper introduces the ‘UN Salient360! benchmark” platform featuring a dataset, a toolbox and a framework for evaluation of different class of models. This online platform can be found in httns://salient360.ls2n.fr/.


title: Cloud/Fog Computing Resource Management and Pricing for Blockchain Networks

abstract: Public blockchain networks using proof of work (PoW)-based consensus protocols are considered as a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, PoW-based consensus protocols financially incentivize the nodes in the network to compete for the privilege of block generation through cryptographic puzzle solving. For rational consensus nodes, i.e., miners with limited local computational resources, offloading the computation load for PoW to the cloud/fog providers (CFPs) becomes a viable option. In this paper, we study the interaction between the CFPs and the miners in a PoW-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the PoW-based blockchains, where the computation-intensive part of the consensus process is offloaded to the cloud/fog. We formulate the computation resource management in the blockchain consensus process as a two-stage Stackelberg game, where the profit of the CFP and the utilities of the individual miners are jointly optimized. In the first stage of the game, the CFP sets the price of offered computing resource. In the second stage, the miners decide on the amount of service to purchase accordingly. We apply backward induction to analyze the subgame perfect equilibria in each stage for both uniform and discriminatory pricing schemes. For uniform pricing where the same price applies to all miners, the uniqueness of the Stackelberg equilibrium is validated by identifying the best response strategies of the miners. For discriminatory pricing where the different prices are applied, the uniqueness of the Stackelberg equilibrium is proved by capitalizing on the variational inequality theory. Further, the real experimental results are employed to justify our proposed model.


title: Location-Aware Image Classification

abstract: Currently, the most popular image classification methods are based on global image representations. They face an obvious contradiction between the uncertainty of object position and the global image representation. In this paper, we propose a novel location-aware image classification framework to address this problem. In our framework, an image is classified based on local image representation, and the classifier is learned using an iterative multi-instance learning with a latent SVM, i.e., we infer object location using latent SVM to improve image classification. Our method is very efficient and outperforms the popular spatial pyramid matching SPM method and the Region Based Latent SVM RBLSVM method [1] on the challenging PASCAL VOC dataset.


title: Building an Arabic Stemmer for Information Retrieval

abstract: In TREC 2002 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and three English-Arabic cross-language runs were submitted. Our approach to the crosslanguage retrieval was to translate the English topics into Arabic using online English-Arabic machine translation systems. The four official runs are named as BKYMON, BKYCL1, BKYCL2, and BKYCL3. The BKYMON is the Arabic monolingual run, and the other three runs are English-to-Arabic cross-language runs. This paper reports on the construction of an Arabic stoplist and two Arabic stemmers, and the experiments on Arabic monolingual retrieval, English-to-Arabic cross-language retrieval.


title: A First Step Towards Leveraging Commodity Trusted Execution Environments for Network Applications

abstract: Network applications and protocols are increasingly adopting security and privacy features, as they are becoming one of the primary requirements. The wide-spread use of transport layer security (TLS) and the growing popularity of anonymity networks, such as Tor, exemplify this trend. Motivated by the recent movement towards commoditization of trusted execution environments (TEEs), this paper explores alternative design choices that application and protocol designers should consider. In particular, we explore the possibility of using Intel SGX to provide security and privacy in a wide range of network applications. We show that leveraging hardware protection of TEEs opens up new possibilities, often at the benefit of a much simplified application/protocol design. We demonstrate its practical implications by exploring the design space for SGX-enabled software-defined inter-domain routing, peer-to-peer anonymity networks (Tor), and middleboxes. Finally, we quantify the potential overheads of the SGX-enabled design by implementing it on top of OpenSGX, an open source SGX emulator.


title: Fault diagnosis using dynamic trend analysis: A review and recent developments

abstract: Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method for trend extraction and a fuzzy-matching-based method for similarity estimation and inferencing are also presented. The effectiveness of the interval halving and trend matching is shown through simulation studies on the fault diagnosis of the Tennessee Eastman process. Industrial experiences on the application of trend analysis technique for fault detection and diagnosis is also presented followed by a discussion on outstanding issues and solution approaches.


title: MultiPoint : Comparing laser and manual pointing as remote input in large display interactions

abstract: We present MultiPoint, a set of perspective-based remote pointing techniques that allows users to perform bimanual and multi-finger remote manipulation of graphical objects on large displays. We conducted two empirical studies that compared remote pointing techniques performed using fingers and laser pointers, in single and multi-finger pointing interactions. We explored three types of manual selection gestures: squeeze, breach and trigger. The fastest and most preferred technique was the trigger gesture in the single point experiment and the unimanual breach gesture in the multi-finger pointing study. The laser pointer obtained mixed results: it is fast, but inaccurate in single point, and it obtained the lowest ranking and performance in the multipoint experiment. Our results suggest MultiPoint interaction techniques are superior in performance and accuracy to traditional laser pointers for interacting with graphical objects on a large display from a distance.


title: Polymorphisms near EXOC4 and LRGUK on chromosome 7q32 are associated with Type 2 Diabetes and fasting glucose; The NHLBI Family Heart Study

abstract: Background ::: The chromosome 7q32 region is linked to metabolic syndrome and obesity related traits in the Family Heart Study. As part of a fine mapping study of the region, we evaluated the relationship of polymorphisms to fasting glucose levels and Type 2 diabetes.


title: Design and Characterization of Novel Paper-based Inkjet-Printed RFID and Microwave Structures for Telecommunication and Sensing Applications

abstract: In this paper, inkjet-printed microwave circuits fabricated on paper-based substrates were investigated, as a system-level solution for ultra-low-cost mass production. The RF characteristics of the paper-based substrate were studied by using the cavity resonator method and the Transmission Line method in order to characterize the dielectric constant (epsivr) and loss tangent (tandelta) of the substrate. A UHF RFID tag module was then developed with the inkjet-printing technology which could function as a technology for much simpler and faster fabrication on/in paper. Simulation and well-agreed measurement results verify a good performance of the tag module. In addition, for the first time, the possibility of paper-based substrate for multilayer microwave structures was explored, and a 2.4 GHz multilayer patch resonator bandpass filter with insertion loss < 0.6dB was demonstrated. These results show that the paper material can serve for the purpose of economical multilayer structures for telecommunication and sensing applications.


title: Interest-Driven Discovery of Local Process Models

abstract: Abstract Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers to questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constraints can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on three real-life data sets.


title: Automatic Generation and Ranking of Questions for Critical Review

abstract: Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based system which automatically generates specific trigger questions from citations for critical review support. The performance of the proposed question ranking models was evaluated and the quality of generated questions is reported. Experimental results showed an accuracy of 75.8% on the top 25% ranked questions. These top ranked questions are as useful for selfreflection as questions generated by human tutors and supervisors. A qualitative analysis was also conducted using an information seeking question taxonomy in order to further analyze the questions generated by humans. The analysis revealed that explanation and association questions are the most frequent question types and that the explanation questions are considered the most valuables by student writers.


title: Generating News Headlines with Recurrent Neural Networks

abstract: We describe an application of an encoder-decoder recurrent neural network with LSTM units and attention to generating headlines from the text of news articles. We find that the model is quite effective at concisely paraphrasing news articles. Furthermore, we study how the neural network decides which input words to pay attention to, and specifically we identify the function of the different neurons in a simplified attention mechanism. Interestingly, our simplified attention mechanism performs better that the more complex attention mechanism on a held out set of articles.


title: Augmented Blendshapes for Real-Time Simultaneous 3D Head Modeling and Facial Motion Capture

abstract: We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera. Our framework is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head. Anyone's head can be instantly reconstructed and his facial motion captured without requiring any training or pre-scanning. The user starts facing the camera with a neutral expression in the first frame, but is free to move, talk and change his face expression as he wills otherwise. The facial motion is tracked using a blendshape representation while the fine geometric details are captured using a Bump image mapped over the template mesh. We propose an efficient algorithm to grow and refine the 3D model of the head on-the-fly and in real-time. We demonstrate robust and high-fidelity simultaneous facial motion tracking and 3D head modeling results on a wide range of subjects with various head poses and facial expressions. Our proposed method offers interesting possibilities for animation production and 3D video telecommunications.


Failure of carbon fiber yacht mast in heavy weather


title: New architecture of the object-oriented functional coverage mechanism for digital verification

abstract: Functional Coverage is a mechanism used in digital integrated circuits functional verification to measure whether the executed test set covered the declared functionality. It helps to examine test scenarios, by providing metrics that give information about the testcase or design-under-test (DUT) reached states (coverage points). SystemVerilog language provides dedicated syntax to make it possible, such as covergroup and coverpoint objects. However, these features have very limited functionality. It is difficult to manipulate functional coverage data on the testbench level, e.g. to make the test scenario dependent on the real-time coverage metrics. It is also not clear how to define coverage objects for complex design features. The architecture of a new, object-oriented functional coverage mechanism for digital verification, implemented in Python, is proposed in this paper. The testbench is based on the Cocotb open verification framework. The implemented solution gives more flexibility than standard SystemVerilog syntax and enables more agile creation of verification environments.


title: Design and Implementation of Phalanges Based on Iteration of a Prosthesis

abstract: In this work an electromechanical hand is implemented based on the study and implementation of the phalanges of the five fingers of the hand, a control system based on a microcontroller was implemented that takes as feedback signals coming from flexosensors and strain gauges. The movement of the fingers was made based on myoelectric signals in real time, these signals were acquired through an innovative method of acquisition based on patterns. The tests performed were measurements with extended hand, closed hand and pressing an object that validates the present proposal.


title: Sharing with caution: Managing parking spaces in vehicular networks

abstract: By exchanging events in a vehicular ad hoc network VANET, drivers can receive interesting information while driving. For example, they can be informed of available parking spaces in their vicinity. A suitable protocol is needed to disseminate the events efficiently within the area where they are relevant. Moreover, in such a competitive context where each vehicle may be interested in a resource, it is crucial not to communicate that resource to each driver in the vicinity. Otherwise, those drivers would waste time trying to reach a parking space and only one of them would be fulfilled, which would lead to a poor satisfaction in the system. To solve this problem, we detail in this paper a reservation protocol that efficiently allocates parking spaces in vehicular ad hoc networks and avoids the competition among the vehicles. We have integrated our protocol within VESPA, a system that we have designed for vehicles to share information in VANETs. An experimental evaluation is provided, which proves the usefulness and benefits of our reservation protocol in both parking lots and urban scenarios. Besides, we present an in-depth study of the state of the art on this topic, that shows the interest and the originality of our approach.


title: The design of an adoption and acceptance framework for mobile digital library services

abstract: Along with the rapid increase of smartphone ownership, mobile applications have increased and mobile digital library services have evolved. Yet, mobile digital library services are not among the frequently used services. Apparently, there is still a lack of adoption and acceptance of mobile digital library services among libraries and their users. It is critical to study the factors that influence the adoption and acceptance of such services. The proposed framework in this research will help in the process of determining factors affecting users' acceptance of mobile digital library services. An extended TAM model with 21 factors derived from five selected models was the base for designing the proposed framework. The newly designed framework is classified into three categories: interface characteristics, personal characteristics, and system characteristics.


title: Neuroimaging and Anxiety: the Neural Substrates of Pathological and Non-pathological Anxiety

abstract: Advances in the use of noninvasive neuroimaging to study the neural correlates of pathological and non-pathological anxiety have shone new light on the underlying neural bases for both the development and manifestation of anxiety. This review summarizes the most commonly observed neural substrates of the phenotype of anxiety. We focus on the neuroimaging paradigms that have shown promise in exposing this relevant brain circuitry. In this way, we offer a broad overview of how anxiety is studied in the neuroimaging laboratory and the key findings that offer promise for future research and a clearer understanding of anxiety.


Scalable Visual Navigation for Micro Aerial Vehicles using Geometric Prior Knowledge


title: Cultural Similarities and Differences in the Design of University Web sites

abstract: This study examines cultural differences and similarities in design of university Web sites using Hofstede’s model of cultural dimensions. Graphical elements on a sample of university home pages from Malaysia, Austria, the United States, Ecuador, Japan, Sweden, Greece and Denmark are compared using content analysis methods. The home pages were analyzed on the basis of two criteria: organization and graphical design. Element frequency scores were correlated with Hofstede’s indexes and interpreted on the basis of the existing literature. The results suggest that similarities and differences in Web site design can be brought out through Hofstede’s cultural model. Computed correlations between Hofstede’s scores and frequency counts of interface elements were weaker than anticipated, but in most cases occurred in the hypothesized direction.