title: A new mechanism of nervous system plasticity: activity-dependent myelination

abstract: The synapse is the focus of experimental research and theory on the cellular mechanisms of nervous system plasticity and learning, but recent research is expanding the consideration of plasticity into new mechanisms beyond the synapse, notably including the possibility that conduction velocity could be modifiable through changes in myelin to optimize the timing of information transmission through neural circuits. This concept emerges from a confluence of brain imaging that reveals changes in white matter in the human brain during learning, together with cellular studies showing that the process of myelination can be influenced by action potential firing in axons. This Opinion article summarizes the new research on activity-dependent myelination, explores the possible implications of these studies and outlines the potential for new research.


title: AODVSEC: A Novel Approach to Secure Ad Hoc on-Demand Distance Vector (AODV) Routing Protocol from Insider Attacks in MANETs

abstract: Mobile Ad hoc Network (MANET) is a collection of mobile nodes that can communicate with each other using multihop wireless links without requiring any fixed based-station infrastructure and centralized management. Each node in the network acts as both a host and a router. In such scenario, designing of an efficient, reliable and secure routing protocol has been a major challenging issue over the last many years. Numerous schemes have been proposed for secure routing protocols and most of the research work has so far focused on providing security for routing using cryptography. In this paper, we propose a novel approach to secure Ad hoc On-demand Distance Vector (AODV) routing protocol from the insider attacks launched through active forging of its Route Reply (RREP) control message. AODV routing protocol does not have any security provision that makes it less reliable in publicly open ad hoc network. To deal with the concerned security attacks, we have proposed AODV Security Extension (AODVSEC) which enhances the scope of AODV for the security provision. We have compared AODVSEC with AODV and Secure AODV (SAODV) in normal situation as well as in presence of the three concerned attacks viz. Resource Consumption (RC) attack, Route Disturb (RD) attack, Route Invasion (RI) attack and Blackhole (BH) attack. To evaluate the performances, we have considered Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), Average Throughput (AT), Normalized Routing Load (NRL) and Average Jitter and Accumulated Average Processing Time.


title: The MUG facial expression database

abstract: This paper presents a new extended collection of posed and induced facial expression image sequences. All sequences were captured in a controlled laboratory environment with high resolution and no occlusions. The collection consists of two parts: The first part depicts eighty six subjects performing the six basic expressions according to the “emotion prototypes” as defined in the Investigator's Guide in the FACS manual. The second part contains the same subjects recorded while they were watching an emotion inducing video. Most of the database recordings are available to the scientific community. Beyond the emotion related annotation the database contains also manual and automatic annotation of 80 facial landmark points for a significant number of frames. The database contains sufficient material for the development and the statistical evaluation of facial expression recognition systems using posed and induced expressions.


Lagrangian Support Vector Machines


title: Web usability inspection technique based on design perspectives

abstract: A usability inspection technique called Web design perspective based usability evaluation (WDP) as well as the experimentation methodology followed to support its development and improvement is described. Three experimental studies (one secondary and two primaries) were carried out and led to the current version of WDP. Results indicate the feasibility of the WDP technique and its possibility to be more effective than and as efficient as Nielsen's heuristics evaluation.


title: A case study of using HCI methods to improve tools for programmers

abstract: For more than five years, researchers at Carnegie Mellon University have been collaborating with several SAP teams to improve the usability of SAP's developer tools and programming APIs. Much research has shown that HCI techniques can improve the tools that developers use to write software. In a recent project, we applied HCI techniques to a SAP developer tool for the SAP NetWeaver Gateway product. The SAP team building this tool uses agile software development processes, which allowed them to quickly improve the tool's usability based upon the evaluations.


title: Cross-institutional data exchange using the clinical document architecture ( CDA )

abstract: applications; Computer communication networks; Computer security; Software design; Medical record linkage; Medical records systems Summary Problem: Although electronic communication of clinical data between various actors in the healthcare domain seems crucial for a cost-effective patient treatment, it is


title: Topic Modeling for Short Texts with Auxiliary Word Embeddings

abstract: For many applications that require semantic understanding of short texts, inferring discriminative and coherent latent topics from short texts is a critical and fundamental task. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of each document, short texts are much more sparse in terms of word co-occurrences. Data sparsity therefore becomes a bottleneck for conventional topic models to achieve good results on short texts. On the other hand, when a human being interprets a piece of short text, the understanding is not solely based on its content words, but also her background knowledge (e.g., semantically related words). The recent advances in word embedding offer effective learning of word semantic relations from a large corpus. Exploiting such auxiliary word embeddings to enrich topic modeling for short texts is the main focus of this paper. To this end, we propose a simple, fast, and effective topic model for short texts, named GPU-DMM. Based on the Dirichlet Multinomial Mixture (DMM) model, GPU-DMM promotes the semantically related words under the same topic during the sampling process by using the generalized Polya urn (GPU) model. In this sense, the background knowledge about word semantic relatedness learned from millions of external documents can be easily exploited to improve topic modeling for short texts. Through extensive experiments on two real-world short text collections in two languages, we show that GPU-DMM achieves comparable or better topic representations than state-of-the-art models, measured by topic coherence. The learned topic representation leads to the best accuracy in text classification task, which is used as an indirect evaluation.


title: Deep Directed Generative Models with Energy-Based Probability Estimation

abstract: Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately achieved by Markov chain Monte Carlo methods, but may still face a formidable obstacle that is the difficulty of mixing between modes with sharp concentrations of probability. Whereas an MCMC process is usually derived from a given energy function based on mathematical considerations and requires an arbitrarily long time to obtain good and varied samples, we propose to train a deep directed generative model (not a Markov chain) so that its sampling distribution approximately matches the energy function that is being trained. Inspired by generative adversarial networks, the proposed framework involves training of two models that represent dual views of the estimated probability distribution: the energy function (mapping an input configuration to a scalar energy value) and the generator (mapping a noise vector to a generated configuration), both represented by deep neural networks.


title: Recurrent Environment Simulators

abstract: Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.


title: Nonlinear dimension reduction for EEG-based epileptic seizure detection

abstract: Approximately 0.1 percent of epileptic patients die from unexpected deaths. In general, for intractable seizures, it is crucial to have an algorithm to accurately and automatically detect the seizures and notify care-givers to assist patients. EEG signals are known as definitive diagnosis of seizure events. In this work, we utilize the frequency domain features (normalized in-band power spectral density) for the EEG channels. We applied a nonlinear data-embedding technique based on stochastic neighbor distance metric to capture the relationships among data elements in high dimension and improve the accuracy of seizure detection. This proposed data embedding technique not only makes it possible to visualize data in two or three dimensions, but also tackles the inherent difficulties regarding high dimensional data classification such as time complexity and memory requirement. We also applied a patient specific KNN classification to detect seizure and non-seizure events. The results indicate that our nonlinear technique provides significantly better visualization and classification efficiency (F-measure greater than 87%) compared to conventional dimension reduction approaches.


title: the ottawa prenatal prospective study (OPPS): Methodological issues and findings — it's easy to throw the baby out with the bath water

abstract: Abstract In the OPPS we have been studying the effects of marihuana used during pregnancy since 1978. The subjects are primarily middle-class, low risk women who entered the study early in their pregnancy. Extensive demographic and life-style information was gathered several times during pregnancy and postnatally. The offspring have been assessed repeatedly during the neonatal period, at least annually until the age of 6 and less frequently thereafter. The outcome measures include a variety of age appropriate standardized global measures as well as a large battery of neuropsychological tests attempting to assess discrete functioning within particular domains including language development, memory, visual/perceptual functioning, components of reading and sustained attention. The results suggest that in the neonate, state alterations and altered visual responsiveness may be associated with in utero exposure to marihuana. Global measures, particularly between the ages of 1 and 3 years, did not reveal an association with prenatal marihuana exposure. However, this initial, apparent absence of effect during early childhood should not be interpreted as in utero marihuana exposure having only transient effects for, as the children became older, aspects of neuropsychological functioning did discriminate between marihuana and control children. Domains associated with prenatal marihuana exposure at four years of age and older included increased behavioral problems and decreased performance on visual perceptual tasks, language comprehension, sustained attention and memory. The nature and the timing of the appearance of these deficits is congruent with the notion of prenatal marihuana exposure affecting ‘executive functioning’ — goal directed behavior that includes planning, organized search, and impulse control. Such an interpretation would be consistent with the extant literature with animals and non-pregnant adult users suggesting that chronic marihuana use may impact upon prefrontal lobe functioning.


title: Deep Brain Stimulation of the Caudal Zona Incerta: Tremor Control in Relation to the Location of Stimulation Fields

abstract: BACKGROUND ::: The caudal zona incerta (cZi) and posterior subthalamic area (PSA) are an emerging deep brain stimulation (DBS) target for essential tremor (ET). ::: ::: ::: OBJECTIVES ::: To evaluate the efficacy of tremor control in relation to the anatomical locations of stimulation fields in 50 patients with ET and DBS of the cZi. ::: ::: ::: METHODS ::: A total of 240 contacts were evaluated separately with monopolar stimulation, and amplitudes were optimized for improvement of tremor and hand function. Stimulation fields, i.e., volumes of neural activation, were simulated for each optimized setting and assembled into probabilistic stimulation maps (PSMs). ::: ::: ::: RESULTS ::: There were differences in the anatomical distribution of PSMs associated with good versus poor tremor control. The location of PSMs which achieved good and excellent tremor control corresponded well with the PSM for the clinically used settings, and they were located within the superior part of the PSA. ::: ::: ::: CONCLUSIONS ::: PSMs may serve as a useful tool for defining the most efficacious anatomical location of stimulation. The best tremor control in this series of cZi DBS was achieved with stimulation of the superior part of the PSA, which corresponds to the final part of the cerebellothalamic projections before they reach the ventral lateral thalamus.


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Unseen Disadvantage: How American Universities ’ Focus on Independence Undermines the Academic Performance of First-Generation College Students

abstract: American universities increasingly admit first-generation college students whose parents do not have four-year degrees. Once admitted, these students tend to struggle academically, compared with continuing-generation students - students who have at least one parent with a four-year degree. [The authors] propose a cultural mismatch theory that identifies one important source of this social class achievement gap. Four studies test the hypothesis that first-generation students underperform because interdependent norms from their mostly working-class backgrounds constitute a mismatch with middle-class independent norms prevalent in universities. First, assessing university cultural norms, surveys of university administrators revealed that American universities focus primarily on norms of independence. Second, identifying the hypothesized cultural mismatch, a longitudinal survey revealed that universities' focus on independence does not match first-generation students' relatively interdependent motives for attending college and that this cultural mismatch is associated with lower grades. Finally, two experiments at both private and public universities created a match or mismatch for first-generation students and examined the performance consequences. Together these studies revealed that representing the university culture in terms of independence (i.e., paving one's own paths) rendered academic tasks difficult and, thereby, undermined first-generation students' performance. Conversely, representing the university culture in terms of interdependence (i.e., being part of a community) reduced this sense of difficulty and eliminated the performance gap without adverse consequences for continuing-generation students. These studies address the urgent need to recognize cultural obstacles that contribute to the social class achievement gap and to develop interventions to address them.


title: Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

abstract: Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.


title: Analysis of Translation Model Adaptation in Statistical Machine Translation

abstract: Numerous empirical results have shown that combining data from multiple domains often improve statistical machine translation (SMT) performance. For example, if we desire to build SMT for the medical domain, it may be beneficial to augment the training data with bitext from another domain, such as parliamentary proceedings. Despite the positive results, it is not clear exactly how and where additional outof-domain data helps in the SMT training pipeline. In this work, we analyze this problem in detail, considering the following hypotheses: out-of-domain data helps by either (a) improving word alignment or (b) improving phrase coverage. Using a multitude of datasets (IWSLT-TED, EMEA, Europarl, OpenSubtitles, KDE), we show that sometimes outof-domain data may help word alignment more than it helps phrase coverage, and more flexible combination of data along different parts of the training pipeline may lead to better results.


title: Topography of the greater palatine artery and the palatal vault for various types of periodontal plastic surgery

abstract: The purpose of the present study is to provide useful data that could be applied to various types of periodontal plastic surgery by detailing the topography of the greater palatine artery (GPA), looking in particular at its depth from the palatal masticatory mucosa (PMM) and conducting a morphometric analysis of the palatal vault. Forty-three hemisectioned hard palates from embalmed Korean adult cadavers were used in this study. The morphometry of the palatal vault was analyzed, and then the specimens were decalcified and sectioned. Six parameters were measured using an image-analysis system after performing a standard calibration. In one specimen, the PMM was separated from the hard palate and subjected to a partial Sihler's staining technique, allowing the branching pattern of the GPA to be observed in a new method. The distances between the GPA and the gingival margin, and between the GPA and the cementoenamel junction were greatest at the maxillary second premolar. The shortest vertical distance between the GPA and the PMM decreased gradually as it proceeded anteriorly. The GPA was located deeper in the high-vault group than in the low-vault group. The premolar region should be recommended as the optimal donor site for tissue grafting, and in particular the second premolar region. The maximum size and thickness of tissue that can be harvested from the region were 9.3 mm and 4.0 mm, respectively.


title: Learning to trade with incremental support vector regression experts

abstract: Support vector regression (SVR) is an established non-linear regression technique that has been applied successfully to a variety of predictive problems arising in computational finance, such as forecasting asset returns and volatilities. In real-time applications with streaming data two major issues that need particular care are the inefficiency of batch-mode learning, and the arduous task of training the learning machine in presence of non-stationary behavior. We tackle these issues in the context of algorithmic trading, where sequential decisions need to be made quickly as new data points arrive, and where the data generating process may change continuously with time. We propose a master algorithm that evolves a pool of on-line SVR experts and learns to trade by dynamically weighting the experts' opinions. We report on risk-adjusted returns generated by the hybrid algorithm for two large exchange-traded funds, the iShare S&P 500 and Dow Jones EuroStoxx 50.


title: Structural similarity based link prediction in social networks using firefly algorithm

abstract: Link prediction problem in social networks has received significant interest in the recent past from the researchers in diverse fields. Understanding and analyzing the present links in the social network or any complex networks either to understand their evolution or to predict the future possible links from the existing network (or links) forms the interesting link prediction problem. Link prediction based on Firefly optimization algorithm is proposed in this paper for social networks. The proposed algorithm is executed on a logical graph similar to social network and tested over real networks taking the benchmark data sets. Experimental values are compared with the other methods existing in the literature. From the comparison we can see that the proposed method performs better in terms of precision over the other methods.


title: Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations

abstract: Nodes with multiple GPUs are becoming the platform of choice for high-performance computing. However, most applications are written using bulk-synchronous programming models, which may not be optimal for irregular algorithms that benefit from low-latency, asynchronous communication. This paper proposes constructs for asynchronous multi-GPU programming, and describes their implementation in a thin runtime environment called Groute. Groute also implements common collective operations and distributed work-lists, enabling the development of irregular applications without substantial programming effort. We demonstrate that this approach achieves state-of-the-art performance and exhibits strong scaling for a suite of irregular applications on 8-GPU and heterogeneous systems, yielding over 7x speedup for some algorithms.


title: Turning analysis and its implementation of link-based dolphin-like robots

abstract: This paper addresses the turning issues of a multilink dolphin-like robot with a pair of mechanical flippers. The propulsion and maneuvering of the robot depend on the dorsoventral oscillations of the tail and the flapping movements of the artificial flippers. Taking into account both mechanical structure and functional characteristics of the dolphin-like robot, we present a practical approach to perform planar turning maneuvers as well as its dynamic analysis. The characteristic parameters affecting turning performance primarily include deflection angle, turning radius, and turning rate, which are discussed with a comparison between the simulation results and the experiments on circular motions. Preliminary tests in a robotics context partly demonstrate the effectiveness of the proposed turning scheme.


title: A Methodology for the Design of Robotic Hands with Multiple Fingers

abstract: This paper presents a methodology that has been applied for a design process of anthropomorphic hands with multiple fingers. Biomechanical characteristics of human hand have been analysed so that ergonomic and anthropometric aspects have been used as fundamental references for obtaining grasping mechanisms. A kinematic analysis has been proposed to define the requirements for designing grasping functions. Selection of materials and actuators has been discussed too. This topic has been based on previous experiences with prototypes that have been developed at the Laboratory of Robotics and Mechatronics (LARM) of the University of Cassino. An example of the application of the proposed method has been presented for the design of a first prototype of


title: 3D MEMS Simulation Modeling Using Modified Nodal Analysis

abstract: The modeling, simulation, and experimental verification of several MEMS devices are presented. Simulated results include 3D mode analysis, residual stress effects, thermal expansion, nonlinear deflections, time-varying electrostatic forces, process sensitivities, induced currents, and the transient performance in accelerated reference frames. To simulate the performance of these MEMS devices a modified nodal analysis approach is used to formulate a system of ODEs that is solved by static, steady state, and transient solvers. Index Terms SUGAR, modified nodal analysis, static, steady state, transient, accelerating frames, sensitivity.


title: Deceiving Google's Perspective API Built for Detecting Toxic Comments

abstract: Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently, Google and Jigsaw started a project called Perspective, which uses machine learning to automatically detect toxic language. A demonstration website has been also launched, which allows anyone to type a phrase in the interface and instantaneously see the toxicity score [1]. In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples. We show that an adversary can subtly modify a highly toxic phrase in a way that the system assigns significantly lower toxicity score to it. We apply the attack on the sample phrases provided in the Perspective website and show that we can consistently reduce the toxicity scores to the level of the non-toxic phrases. The existence of such adversarial examples is very harmful for toxic detection systems and seriously undermines their usability.


title: Exploring the Reasons for a Failure of Electronic Payment Systems: A Case Study of an Australian Company

abstract: This paper examines factors which lead to the failure of electronic payment systems (EPS). Inorder to understand these factors a single case study of a failed Australian EPS was conducted.The findings confirm the influence of EPS adoption factors identified from the literature, whichinclude cooperation with established entities, simplicity, trust, security and mutuality of stake-holder benefits. Furthermore, this study has specifically demonstrated the importance of largepartners in the adoption of EPS. These partners are able to provide EPS with access to a largeinstalled base of customers, association with their brand name and marketing resources in orderto achieve public awareness. This paper contributes to theory and practice through examining afailed case from the system provider’s perspective, which is rare in the study of EPS. ACM Classifications: H.m; K.4.4


title: Edlines: Real-time line segment detection by Edge Drawing (ed)

abstract: We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detection algorithm in the literature; namely, the LSD by Gioi et al. The proposed algorithm also includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. Our detector makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision applications.


title: Practical approach to childhood masturbation—a review

abstract: The aim of this article is to review the literature for information that could guide the clinical practitioner in the assessment and management of childhood masturbation. The boundary between normal and abnormal or deviant masturbation in children remains unclear. Besides the link with sexual abuse, other environmental factors and individual factors, as well as psychiatric disorders, are mentioned in relation to masturbation and sexual behaviour in general in children. However, evidence-based information is missing and, therefore, a safety management approach is advised when a clinician is confronted with childhood masturbation. We conclude that normal psychosexual development, as well as environmental and individual factors, should be considered in the assessment and management of childhood masturbation.


title: Can recursive neural tensor networks learn logical reasoning?

abstract: Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of "some animal walks" from "some dog walks" or "some cat walks," given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.


title: Fire and smoke detection in video with optimal mass transport based optical flow and neural networks

abstract: Detection of fire and smoke in video is of practical and theoretical interest. In this paper, we propose the use of optimal mass transport (OMT) optical flow as a low-dimensional descriptor of these complex processes. The detection process is posed as a supervised Bayesian classification problem with spatio-temporal neighborhoods of pixels;feature vectors are composed of OMT velocities and R,G,B color channels. The classifier is implemented as a single-hidden-layer neural network. Sample results show probability of pixels belonging to fire or smoke. In particular, the classifier successfully distinguishes between smoke and similarly colored white wall, as well as fire from a similarly colored background.


title: Efficiency Analysis of Drive Train Topologies Applied to Electric/Hybrid Vehicles

abstract: One of the most important research topics in drive train topologies applied to electric/hybrid vehicles is the efficiency analysis of the power train components, including the global drive efficiency. In this paper, two basic traction electric drive systems of electric/hybrid vehicles are presented and evaluated, with a special focus on the efficiency analysis. The first topology comprises a traditional pulsewidth-modulation (PWM) battery-powered inverter, whereas in the second topology, the battery is connected to a bidirectional dc-dc converter, which supplies the inverter. Furthermore, a variable-voltage control technique applied to this second topology is presented, which allows for the improvement of the drive overall performance. Some simulation results are presented, considering both topologies and a permanent-magnet synchronous motor (PMSM). An even more detailed analysis is performed through the experimental validation. Particular attention is given to the evaluation of the main drive components efficiency, including the global drive efficiency, presented in the form of efficiency maps. Other parameters such as motor voltage distortion and power factor are also considered. In addition, the comparison of the two topologies takes into account the drive operation under the motoring and regenerative-braking modes.


title: Ayahuasca, dimethyltryptamine, and psychosis: a systematic review of human studies

abstract: Ayahuasca is a hallucinogen brew traditionally used for ritual and therapeutic purposes in Northwestern Amazon. It is rich in the tryptamine hallucinogens dimethyltryptamine (DMT), which acts as a serotonin 5-HT2A agonist. This mechanism of action is similar to other compounds such as lysergic acid diethylamide (LSD) and psilocybin. The controlled use of LSD and psilocybin in experimental settings is associated with a low incidence of psychotic episodes, and population studies corroborate these findings. Both the controlled use of DMT in experimental settings and the use of ayahuasca in experimental and ritual settings are not usually associated with psychotic episodes, but little is known regarding ayahuasca or DMT use outside these controlled contexts. Thus, we performed a systematic review of the published case reports describing psychotic episodes associated with ayahuasca and DMT intake. We found three case series and two case reports describing psychotic episodes associated with ayahuasca intake, an...


title: Peripheral Cemento-Ossifying Fibroma in Child. A Follow-Up of 4 Years. Report of a Case

abstract: Peripheral cement-ossifying fibroma is a relatively common gingival growth of a reactive rather than neoplastic nature, whose pathogenesis is uncertain. It predominantly affects adolescents and young adults, with peak prevalence between 10 and 19 years. We report here the clinical case of a 5-year-old girl with disease duration of 3 years, who was followed up for 4 years, showing a gingival health and normal radiopacity of bone.


title: Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

abstract: Detection of brain tumor using a segmentation based approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. The automation of brain tumor segmentation remains a challenging problem mainly due to significant variations in its structure. An automated brain tumor segmentation algorithm using deep convolutional neural network (DCNN) is presented in this paper. A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images. Recent developments in deep neural networks such as drop-out, batch normalization, non-linear activation and inception module are used to build a new ILinear nexus architecture. The module overcomes the over-fitting problem arising due to scarcity of data using drop-out regularizer. Images are normalized and bias field corrected in the pre-processing step and then extracted patches are passed through a DCNN, which assigns an output label to the central pixel of each patch. Morphological operators are used for post-processing to remove small false positives around the edges. A two-phase weighted training method is introduced and evaluated using BRATS 2013 and BRATS 2015 datasets, where it improves the performance parameters of state-of-the-art techniques under similar settings.


title: Hierarchical control for parallel bidirectional power converters of a grid-connected DC microgrid

abstract: The DC microgrid is connected to the AC utility by parallel bidirectional power converters (BPCs) to import/export large power, whose control directly affects the performance of the grid-connected DC microgrid. Much work has focused on the hierarchical control of the DC, AC, and hybrid microgrids, but little has considered the hierarchical control of multiple parallel BPCs that directly connect the DC microgrid to the AC utility. In this paper, we propose a hierarchical control for parallel BPCs of a grid-connected DC microgrid. To suppress the potential zero-sequence circulating current in the AC side among the parallel BPCs and realize feedback linearization of the voltage control, a d-q-0 control scheme instead of a conventional d-q control scheme is proposed in the inner current loop, and the square of the DC voltage is adopted in the inner voltage loop. DC side droop control is applied to realize DC current sharing among multiple BPCs at the primary control level, and this induces DC bus voltage deviation. The quantified relationship between the current sharing error and DC voltage deviation is derived, indicating that there is a trade-off between the DC voltage deviation and current sharing error. To eliminate the current sharing error and DC voltage deviation simultaneously, slope-adjusting and voltage-shifting approaches are adopted at the secondary control level. The proposed tertiary control realizes precise active and reactive power exchange through parallel BPCs for economical operation. The proposed hierarchical control is applied for parallel BPCs of a grid-connected DC microgrid and can operate coordinately with the control for controllable/uncontrollable distributional generation. The effectiveness of the proposed control method is verified by corresponding simulation tests based on Matlab/Simulink, and the performance of the hierarchical control is evaluated for practical applications.


title: Identifying the semantic orientation of terms using S-HAL for sentiment analysis

abstract: Sentiment analysis continues to be a most important research problem due to its abundant applications. Identifying the semantic orientation of subjective terms (words or phrases) is a fundamental task for sentiment analysis. In this paper, we propose a new method for identifying the semantic orientation of subjective terms to perform sentiment analysis. The method takes a classification approach that is based on a novel semantic orientation representation model called S-HAL (Sentiment Hyperspace Analogue to Language). S-HAL basically produces a set of weighted features based on surrounding words, and characterizes the semantic orientation information of words via a specific feature space. Because the method incorporates the idea underlying HAL and the hypothesis verified by the method of semantic orientation inference from pointwise mutual information (SO-PMI), it can quickly and accurately identify the semantic orientation of terms without the use of an Internet search engine. The results of an empirical evaluation show that our method outperforms other known methods.


title: Deep Affinity Network for Multiple Object Tracking

abstract: Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis and computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modeling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact, yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.


title: DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining

abstract: In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and further mining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.


title: Flow-sensitive type qualifiers

abstract: We present a system for extending standard type systems with flow-sensitive type qualifiers. Users annotate their programs with type qualifiers, and inference checks that the annotations are correct. In our system only the type qualifiers are modeled flow-sensitively---the underlying standard types are unchanged, which allows us to obtain an efficient constraint-based inference algorithm that integrates flow-insensitive alias analysis, effect inference, and ideas from linear type systems to support strong updates. We demonstrate the usefulness of flow-sensitive type qualifiers by finding a number of new locking bugs in the Linux kernel.


title: Thales Alenia Space HTS/V-HTS Multiple Beam Antennas sub-systems on the right track

abstract: Thales Alenia Space present a synthesis of on-going developments of MBA (Multiple Beams Antennas) concepts for HTS/V-HTS satellites applications. The main topics of interest address: 1. Ka and Q/V SFPB MBA Tx/Rx feeds for HTS system • 3 Ka Tx/Rx reflectors of 3m5 class • 1 Q/V Tx/Rx reflector of 2m4 class 2. Ka MFPB Tx/Rx MBA for HTS system • 2 Ka Tx/Rx reflectors of 3m5 class (beams of 0.3°) • 1 Tx/Rx 3m5 class (beams of 0.42°)


title: Object Detection Combining Recognition and Segmentation

abstract: We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to generate a set of hypotheses of object locations and figure-ground masks, which have high recall and low precision rate. In the verification step, we first compute a set of feasible segmentations that are consistent with top-down object hypotheses, then we propose a False Positive Pruning (FPP) procedure to prune out false positives. We exploit the fact that false positive regions typically do not align with any feasible image segmentation. Experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many object classes.


title: Revenue Management with Strategic Customers: Last-Minute Selling and Opaque Selling

abstract: Companies in a variety of industries (e.g., airlines, hotels, theaters) often use last-minute sales to dispose of unsold capacity. Although this may generate incremental revenues in the short term, the long-term consequences of such a strategy are not immediately obvious: More discounted last-minute tickets may lead to more consumers anticipating the discount and delaying the purchase rather than buying at the regular (higher) prices, hence potentially reducing revenues for the company. To mitigate such behavior, many service providers have turned to opaque intermediaries, such as Hotwire.com, that hide many descriptive attributes of the service (e.g., departure times for airline tickets) so that the buyer cannot fully predict the ultimate service provider. Using a stylized economic model, this paper attempts to explain and compare the benefits of last-minute sales directly to consumers versus through an opaque intermediary. We utilize the notion of rational expectations to model consumer purchasing decisions: Consumers make early purchase decisions based on expectations regarding future availability, and these expectations are correct in equilibrium. We show that direct last-minute sales are preferred over selling through an opaque intermediary when consumer valuations for travel are high or there is little service differentiation between competing service providers, or both; otherwise, opaque selling dominates. Moreover, contrary to the usual belief that such sales are purely mechanisms for disposal of unused capacity, we show that opaque selling becomes more preferred over direct last-minute selling as the probability of having high demand increases. When firms randomize between opaque selling and last-minute selling strategies, they are increasingly likely to choose the opaque selling strategy as the probability of high demand increases. When firms with unequal capacities use the opaque selling strategy, consumers know more clearly where the opaque ticket is from and the efficacy of opaque selling decreases.


title: Two new low-power and high-performance full adders

abstract: Two new low-power, and high-performance 1-bit Full Adder cells are proposed in this paper. These cells are based on low-power XOR/XNOR circuit and Majority-not gate. Majority-not gate, which produces Cout (Output Carry), is implemented with an efficient method, using input capacitors and a static CMOS inverter. This kind of implementation benefits from low power consumption, a high degree of regularity and simplicity. Eight state-of-the-art 1-bit Full Adders and two proposed Full Adders are simulated with HSPICE using 0.18µm CMOS technology at several supply voltages ranging from 2.4v down to 0.8v. Although low power consumption is targeted in implementation of our designs, simulation results demonstrate great improvement in terms of power consumption and also PDP.


Experimental validation of a finite-element model updating procedure


title: Search based software engineering: Techniques, taxonomy, tutorial

abstract: The aim of Search Based Software Engineering (SBSE) research is to move software engineering problems from human-based search to machine-based search, using a variety of techniques from the metaheuristic search, operations research and evolutionary computation paradigms. The idea is to exploit humans' creativity and machines' tenacity and reliability, rather than requiring humans to perform the more tedious, error prone and thereby costly aspects of the engineering process. SBSE can also provide insights and decision support. This tutorial will present the reader with a step-by-step guide to the application of SBSE techniques to Software Engineering. It assumes neither previous knowledge nor experience with Search Based Optimisation. The intention is that the tutorial will cover sufficient material to allow the reader to become productive in successfully applying search based optimisation to a chosen Software Engineering problem of interest.


Blockstack : A New Decentralized Internet


An indirect Measure of Association in Spider Anxious Children; The Approach- Avoidance Task


title: Cluster-wise assessment of cluster stability

abstract: Stability in cluster analysis is strongly dependent on the data set, especially on how well separated and how homogeneous the clusters are. In the same clustering, some clusters may be very stable and others may be extremely unstable. The Jaccard coefficient, a similarity measure between sets, is used as a cluster-wise measure of cluster stability, which is assessed by the bootstrap distribution of the Jaccard coefficient for every single cluster of a clustering compared to the most similar cluster in the bootstrapped data sets. This can be applied to very general cluster analysis methods. Some alternative resampling methods are investigated as well, namely subsetting, jittering the data points and replacing some data points by artificial noise points. The different methods are compared by means of a simulation study. A data example illustrates the use of the cluster-wise stability assessment to distinguish between meaningful stable and spurious clusters, but it is also shown that clusters are sometimes only stable because of the inflexibility of certain clustering methods.


title: The Transport Disruption Ontology

abstract: This paper presents the Transport Disruption ontology, a formal framework for modelling travel and transport related events that have a disruptive impact on traveller’s journeys. We discuss related models, describe how transport events and their impacts are captured, and outline use of the ontology within an interlinked repository of the travel information to support intelligent transport systems.


title: Semantic Clustering of Pivot Paraphrases

abstract: Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications. In this study, we analyse the semantics of unigram pivot paraphrases and use a graph-based sense induction approach to unveil hidden sense distinctions in the paraphrase sets. The comparison of the acquired senses to gold data from the Lexical Substitution shared task (McCarthy and Navigli, 2007) demonstrates that sense distinctions exist in the paraphrase sets and highlights the need for a disambiguation step in applications using this resource.


Sudoku Solutions Using Logic Equations. in


title: MSC: A Dataset for Macro-Management in StarCraft II

abstract: Macro-management is an important problem in StarCraft, which has been studied for a long time. Various datasets together with assorted methods have been proposed in the last few years. But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, parsing and feature extraction procedures nor predefined training, validation and test set in some datasets. 2) Some datasets are only specified for certain tasks in macro-management. 3) Some datasets are either too small or don't have enough labeled data for modern machine learning algorithms such as deep neural networks. So most previous methods are trained with various features, evaluated on different test sets from the same or different datasets, making it difficult to be compared directly. To boost the research of macro-management in StarCraft, we release a new dataset MSC based on the platform SC2LE. MSC consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. We also split MSC into training, validation and test set for the convenience of evaluation and comparison. Besides the dataset, we propose a baseline model and present initial baseline results for global state evaluation and build order prediction, which are two of the key tasks in macro-management. Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II. Homepage: this https URL.


title: Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

abstract: Abstract Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.


title: Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates

abstract: We first introduce a surface normal estimation procedure for point clouds capable of handling geometric singularities in the data, such as edges and corners. Our formulation is based on recasting the popular Principal Component Analysis (PCA) method as a constrained nonlinear least squares (NLSQ) problem. In contrast to traditional PCA, the new formulation assigns appropriate weights to neighboring points automatically during the optimization process in order to minimize the contributions of points located across singularities. We extend this strategy to point cloud denoising by combining normal estimation, point projection, and declustering into one NLSQ formulation. Finally, we propose a point cloud segmentation technique based on surface normal estimates and local point connectivity. In addition to producing consistently oriented surface normals, the process segments the point cloud into disconnected components that can each be segmented further into piecewise smooth components as needed.


title: Implementing sponsored content in wireless data networks

abstract: Telecom service providers are increasingly looking to additional sources of revenue in order to cover the network infrastructure costs associated with the bandwidth explosion. One mechanism that has attracted attention is known as “sponsored content” and allows for content providers to sponsor their content and thereby make it free for end users. In this paper we examine some of the implementation and modeling issues associated with the design of a sponsored content system. In particular we present a general framework that assumes a set of users with an underlying desire to make a sequence of requests for certain content. Each content provider has the option to sponsor the content and therefore increase the chance that a potential view of the content will actually occur. A key feature of our model is that users pay for unsponsored content via the purchase of large monthly quotas.


Background subtraction techniques


title: Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

abstract: Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best.,,In this paper, we want to identify the trade-off between the average video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.


Multifocal melanoma of the glans penis.


title: Customer loyalty in e-commerce: an exploration of its antecedents and consequences

abstract: Abstract This paper investigates the antecedents and consequences of customer loyalty in an online business-to-consumer (B2C) context. We identify eight factors (the 8Cs—customization, contact interactivity, care, community, convenience, cultivation, choice, and character) that potentially impact e-loyalty and develop scales to measure these factors. Data collected from 1,211 online customers demonstrate that all these factors, except convenience, impact e-loyalty. The data also reveal that e-loyalty has an impact on two customer-related outcomes: word-of- mouth promotion and willingness to pay more.


title: Environmental Selection, Dispersal, and Organism Interactions Shape Community Assembly in High Throughput Enrichment Culturing

abstract: A central goal of microbial ecology is to identify and quantify the forces that lead to observed population distributions and dynamics. However, these forces, which include environmental selection, dispersal, and organism interactions, are often difficult to assess in natural environments. Here, we present a method that links microbial community structures with selective and stochastic forces through highly replicated subsampling and enrichment of a single environmental inoculum. Specifically, groundwater from a well-studied natural aquifer was serially diluted and inoculated into nearly 1,000 aerobic and anaerobic nitrate-reducing cultures, and the final community structures were evaluated with 16S rRNA gene amplicon sequencing. We analyzed the frequency and abundance of individual operational taxonomic units (OTUs) to understand how probabilistic immigration, relative fitness differences, environmental factors, and organismal interactions contributed to divergent distributions of community structures. We further used a most probable number (MPN) method to estimate the natural condition-dependent cultivable abundance of each of the nearly 400 OTU cultivated in our study and infer the relative fitness of each. Additionally, we infer condition-specific organism interactions and discuss how this high-replicate culturing approach is essential in dissecting the interplay between overlapping ecological forces and taxon-specific attributes that underpin microbial community assembly.IMPORTANCE Through highly replicated culturing, in which inocula are subsampled from a single environmental sample, we empirically determine how selective forces, interspecific interactions, relative fitness, and probabilistic dispersal shape bacterial communities. These methods offer a novel approach to untangle not only interspecific interactions but also taxon-specific fitness differences that manifest across different cultivation conditions and lead to the selection and enrichment of specific organisms. Additionally, we provide a method for estimating the number of cultivable units of each OTU in the original sample through the MPN approach.


title: Smart Tourism Destinations

abstract: The rapid development of technologies introduces smartness to all organisations and communities. The Smart Tourism Destinations (STD) concept emerges from the development of Smart Cities. With technology being embedded on all organisations and entities, destinations will exploit synergies between ubiquitous sensing technology and their social components to support the enrichment of tourist experiences. By applying smartness concept to address travellers’ needs before, during and after their trip, destinations could increase their competitiveness level. This paper aims to take advantage from the development of Smart Cities by conceptualising framework for Smart Tourism Destinations through exploring tourism applications in destination and addressing both opportunities and challenges it possessed.


title: Sharing Bandwidth by Allocating Switch Buffer in Data Center Networks

abstract: In today's data centers, the round trip propagation delay is quite small. Therefore, switch buffer sizes are much larger than the Bandwidth Delay Product (BDP). Based on this observation, in this paper we introduce a new transport protocol which provides bandwidth Sharing by Allocating switch Buffer (SAB) for data centers. SAB sets the congestion windows for flows based on the buffer size of the switches along the path. On one hand, as long as the total buffer allocated to all the flows is larger than the BDP, the network bandwidth can be fully utilized. On the other hand, since SAB only allocates the buffer space to flows, the totally injected traffic will not exceed the network capacity. Thus, SAB rarely loses packets. SAB also reduces flow completion time by allowing flows to reach their fair share of bandwidth quickly. The results of a series of experiments and simulations demonstrate that SAB has the features of fast convergence and rare packet loss. It reduces the latency of short flows and solves theTCP Incast and TCP Outcast problems.


title: TIMEX3 and event extraction using recurrent neural networks

abstract: This paper investigates the performance of Elman-type and Jordan-type recurrent neural networks (RNN) in extracting temporal information from textual data. The RNN architectures are applied to two tasks of TempEval-2 challenge: (1) extracting the extent of TIMEX3 tags and its TYPE, and (2) extracting the extent of EVENT tags and its CLASS attribute. For the first task, the performances of the RNN models are highly comparable to that of the wining entry for the challenge. For the second task, both models outperform the winning entry, attaining nearly full scores.


title: Optimal Pulsewidth Modulation for Current Source Inverters

abstract: Two different pulsewidth modulation (PWM) schemes for current source inverters (CSI) are described. The first one is based on off-line optimization of individual switching angles and requires a microprocessor for implementation and the second one uses a special subharmonic modulation and could be implemented with analog and medium-scale integration (MSI) digital circuits. When CSI's are used in ac motor drives, the optimal PWM pattern depends on the performance criteria being used, which in turn depend on the drive application. In this paper four different performance criteria are considered: 1) current or torque harmonic elimination, 2) current harmonic minimization, 3) speed ripple minimization, and 4) position error minimization. As an example a self-controlled synchronous motor (SCSM) supplied by the PWM CSI is considered. The performance of the CSI-SCSM with the optimal PWM schemes proposed herein are compared with that using a conventional 120° quasi-square wave current.


title: Design of adaptive hypermedia learning systems: A cognitive style approach

abstract: In the past decade, a number of adaptive hypermedia learning systems have been developed. However, most of these systems tailor presentation content and navigational support solely according to students' prior knowledge. On the other hand, previous research suggested that cognitive styles significantly affect student learning because they refer to how learners process and organize information. To this end, the study presented in this paper developed an adaptive hypermedia learning system tailored to students' cognitive styles, with an emphasis on Pask's Holist-Serialist dimension. How students react to this adaptive hypermedia learning system, including both learning performance and perceptions, was examined in this study. Forty-four undergraduate and postgraduate students participated in the study. The findings indicated that, in general, adapting to cognitive styles improves student learning. The results also showed that the adaptive hypermedia learning system have more effects on students' perceptions than performance. The implications of these results for the design of adaptive hypermedia learning systems are discussed.


title: Software defect prediction using Bayesian networks

abstract: There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.


title: A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

abstract: This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open.


title: Walking up and down stairs carrying a human by a biped locomotor with parallel mechanism

abstract: This paper describes the means of tuning-up method of the walking parameters to go up and down stairs for a biped robot with leg mechanisms using Stewart platforms. It has been confirmed that the stroke range of use could be reduced by tuning up the waist yaw trajectory and preset ZMP trajectories for motion pattern generation. By using the developed method, a walking experiment involving movement up and down a stair with the rise of 250 mm and certain walking experiments ascending and descending stairs carrying a human were successfully completed. Through these experiments, the effectiveness of the proposed method was confirmed.


title: Hand gesture recognition using depth data

abstract: A method is presented for recognizing hand gestures by using a sequence of real-time depth image data acquired by an active sensing hardware. Hand posture and motion information extracted from a video is represented in a gesture space which consists of a number of aspects including hand shape, location and motion information. In this space, it is shown to be possible to recognize many types of gestures. Experimental results are shown to validate our approach and characteristics of our approach are discussed.


title: DropBlock: A regularization method for convolutional networks

abstract: Deep neural networks often work well when they are over-parameterized and trained with a massive amount of noise and regularization, such as weight decay and dropout. Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutional layers. This lack of success of dropout for convolutional layers is perhaps due to the fact that activation units in convolutional layers are spatially correlated so information can still flow through convolutional networks despite dropout. Thus a structured form of dropout is needed to regularize convolutional networks. In this paper, we introduce DropBlock, a form of structured dropout, where units in a contiguous region of a feature map are dropped together. We found that applying DropbBlock in skip connections in addition to the convolution layers increases the accuracy. Also, gradually increasing number of dropped units during training leads to better accuracy and more robust to hyperparameter choices. Extensive experiments show that DropBlock works better than dropout in regularizing convolutional networks. On ImageNet classification, ResNet-50 architecture with DropBlock achieves $78.13\%$ accuracy, which is more than $1.6\%$ improvement on the baseline. On COCO detection, DropBlock improves Average Precision of RetinaNet from $36.8\%$ to $38.4\%$.


title: Deep Learning as Feature Encoding for Emotion Recognition

abstract: Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of low level descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.


title: Poincar\'e GloVe: Hyperbolic Word Embeddings

abstract: Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy.


title: Using an N-Gram-based document representation with a vector processing retrieval model

abstract: N-gram based representations for documents have several distinct advantages for various document processing tasks. First, they provide a more robust representation in the face of grammatical and typographical errors in the documents. Secondly, N-gram representations require no linguistic preparations such as word-stemming or stopword removal. Thus they are ideal in situations requiring multi-language operations. Vector processing retrieval models also have some unique advantages for information retrieval tasks. In particular, they provide a simple, uniform representation for documents and queries, and an intuitively appealing document similarity measure. Also, modern vector space models have good retrieval performance characteristics. In this work, we combine these two ideas by using a vector processing model for documents and queries, but using N-gram frequencies as the basis for the vector element values instead of more traditional term frequencies. The resulting system provides good retrieval performance on the TREC-1 and TREC-2 tests without the need for any kind of word stemming or stopword removal. We also have begun testing the system on Spanish language documents.


title: Depth driven people counting using deep region proposal network

abstract: People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.


title: Monitoring athlete training loads: Consensus statement

abstract: Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key find...


title: Trojan Detection using IC Fingerprinting

abstract: Hardware manufacturers are increasingly outsourcing their IC fabrication work overseas due to their much lower cost structure. This poses a significant security risk for ICs used for critical military and business applications. Attackers can exploit this loss of control to substitute Trojan ICs for genuine ones or insert a Trojan circuit into the design or mask used for fabrication. We show that a technique borrowed from side-channel cryptanalysis can be used to mitigate this problem. Our approach uses noise modeling to construct a set of fingerprints/or an IC family utilizing side- channel information such as power, temperature, and electromagnetic (EM) profiles. The set of fingerprints can be developed using a few ICs from a batch and only these ICs would have to be invasively tested to ensure that they were all authentic. The remaining ICs are verified using statistical tests against the fingerprints. We describe the theoretical framework and present preliminary experimental results to show that this approach is viable by presenting results obtained by using power simulations performed on representative circuits with several different Trojan circuitry. These results show that Trojans that are 3-4 orders of magnitude smaller than the main circuit can be detected by signal processing techniques. While scaling our technique to detect even smaller Trojans in complex ICs with tens or hundreds of millions of transistors would require certain modifications to the IC design process, our results provide a starting point to address this important problem.


On Measures of Entropy and Information


title: Modeling confirmation bias and polarization

abstract: Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.


Accelerometer Gesture Recognition


title: Total Synthesis and Biological Mode of Action of WAP-8294A2: A Menaquinone-Targeting Antibiotic

abstract: WAP-8294A2 (lotilibcin, 1) is a potent antibiotic with superior in vivo efficacy to vancomycin against methicillin-resistant Staphylococcus aureus (MRSA). Despite the great medical importance, its molecular mode of action remains unknown. Here we report the total synthesis of complex macrocyclic peptide 1 comprised of 12 amino acids with a β-hydroxy fatty-acid chain, and its deoxy analogue 2. A full solid-phase synthesis of 1 and 2 enabled their rapid assembly and the first detailed investigation of their functions. Compounds 1 and 2 were equipotent against various strains of Gram-positive bacteria including MRSA. We present evidence that the antimicrobial activities of 1 and 2 are due to lysis of the bacterial membrane, and their membrane-disrupting effects depend on the presence of menaquinone, an essential factor for the bacterial respiratory chain. The established synthetic routes and the menaquinone-targeting mechanisms provide valuable information for designing and developing new antibiotics based on their structures.


title: Weakly‐supervised convolutional neural networks for multimodal image registration

abstract: HighlightsA method to infer voxel‐level correspondence from higher‐level anatomical labels.Efficient and fully‐automated registration for MR and ultrasound prostate images.Validation experiments with 108 pairs of labelled interventional patient images.Open‐source implementation. ABSTRACT One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground‐truth for voxel‐level spatial correspondence. This work describes a method to infer voxel‐level transformation from higher‐level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel‐level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject‐specific ad hoc landmarks. The proposed end‐to‐end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real‐time and is fully‐automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2‐weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross‐validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high‐quality anatomical labels. Graphical abstract Figure. No caption available.


title: Distinct Representation and Distribution of Visual Information by Specific Cell Types in Mouse Superficial Superior Colliculus

abstract: The superficial superior colliculus (sSC) occupies a critical node in the mammalian visual system; it is one of two major retinorecipient areas, receives visual cortical input, and innervates visual thalamocortical circuits. Nonetheless, the contribution of sSC neurons to downstream neural activity and visually guided behavior is unknown and frequently neglected. Here we identified the visual stimuli to which specific classes of sSC neurons respond, the downstream regions they target, and transgenic mice enabling class-specific manipulations. One class responds to small, slowly moving stimuli and projects exclusively to lateral posterior thalamus; another, comprising GABAergic neurons, responds to the sudden appearance or rapid movement of large stimuli and projects to multiple areas, including the lateral geniculate nucleus. A third class exhibits direction-selective responses and targets deeper SC layers. Together, our results show how specific sSC neurons represent and distribute diverse information and enable direct tests of their functional role.


title: Remote fish aquaculture monitoring system based on wireless transmission technology

abstract: This article establishes an automated monitoring system for the fish farm aquaculture environment. The fish farm setting is usually in convenient places without common place traffic. The proposed system is network surveillance combined with mobile devices and a remote platform to collect real-time farm environmental information. This system permits real-time observation and control of fish farms with dissolved oxygen sensors, temperature sensing elements using A/D and 8051 module signal conversion. The real-time data is captured and displayed via ZigBee wireless transmission signal transmitter to remote computer terminals. Visual Basic 2010 software is used to design the interface functions and control sensing module. This system is low-cost, low power, easy operation with wireless transmission capability. A continuous, stable power supply is very important for the aquaculture industry. The proposed system will use municipal electricity coupled with a battery power source to provide power with battery intervention if municipal power is interrupted. The battery system is designed to avoid the self-discharge phenomenon which reduces the battery lifetime. Solar power is used to provide charging at any time.


title: SuperpowerGlass: A Wearable Aid for the At-Home Therapy of Children with Autism

abstract: We have developed a system for automatic facial expression recognition running on Google Glass, delivering real-time social cues to children with Autism Spectrum Disorder (ASD). The system includes multiple mechanisms to engage children and their parents, who administer this technology within the home. We completed an at-home design trial with 14 families that used the learning aid over a 3-month period. We found that children with ASD generally respond well to wearing the system at home and opt for the most expressive feedback choice. We further evaluated app usage, facial engagement, and model accuracy. We found that the device can act as a powerful training aid when used periodically in the home, that interactive video content from wearable therapy sessions should be augmented with sufficient context about the content to produce long-term engagement, and that the design of wearable systems for children with ASD should be heavily dependent on the functioning level of the child. We contribute general design implications for developing wearable aids used by children with ASD and other behavioral disorders as well as their parents during at-home parent-administered therapy sessions.


title: Influence of surface modification techniques on shear bond strength between different zirconia cores and veneering ceramics

abstract: PURPOSE. Veneering porcelain might be delaminated from underlying zirconia-based ceramics. The aim of this study was the evaluation ofthe effect of different surface treatments and type of zirconia (white or colored) on shear bond strength (SBS) of zirconia core and its veneer-ing porcelain. MATERIALS AND METHODS.Eighty zirconia disks (40 white and 40 colored; 10 mm in diameter and 4 mm thick) were treat-ed with three different mechanical surface conditioning methods (Sandblasting with 110 μm Al


title: Application of semi-supervised deep learning to lung sound analysis

abstract: The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.


“Multilingual” Deep Neural Network for Music Genre Classification


Face Detection : Histogram of Oriented Gradients and Bag of Feature Method


Teaching Kodu with physical manipulatives


title: Breaking Down Hadoop Distributed File Systems Data Analytics Tools: Apache Hive vs. Apache Pig vs. Pivotal HWAQ

abstract: Apache Hive, Apache Pig and Pivotal HWAQ are very popular open source cluster computing frameworks for large scale data analytics. These frameworks hide the complexity of task parallelism and fault-tolerance, by exposing a simple programming API to users. In this paper, we discuss the major architectural component differences in them and conduct detailed experiments to compare their performances with different inputs. Furthermore, we attribute these performance differences to different components which are architected differently in the three frameworks and we show the detailed execution overheads of Apache Hive, Apache Pig and Pivotal HAWQ, in which the CPU utilization, memory utilization, and disk read/write during their runtime are analyzed. Finally, a discussion and summary of our findings and suggestions are presented.


title: Targeting gut microbiota in obesity: effects of prebiotics and probiotics

abstract: At birth, the human colon is rapidly colonized by gut microbes. Owing to their vast number and their capacity to ferment nutrients and secrete bioactive compounds, these gastrointestinal microbes act as an environmental factor that affects the host's physiology and metabolism, particularly in the context of obesity and its related metabolic disorders. Experiments that compared germ‑free and colonized mice or analyzed the influence of nutrients that qualitatively change the composition of the gut microbiota (namely prebiotics) showed that gut microbes induce a wide variety of host responses within the intestinal mucosa and thereby control the gut's barrier and endocrine functions. Gut microbes also influence the metabolism of cells in tissues outside of the intestines (in the liver and adipose tissue) and thereby modulate lipid and glucose homeostasis, as well as systemic inflammation, in the host. A number of studies describe characteristic differences between the composition and/or activity of the gut microbiota of lean individuals and those with obesity. Although these data are controversial, they suggest that specific phyla, classes or species of bacteria, or bacterial metabolic activities could be beneficial or detrimental to patients with obesity. The gut microbiota is, therefore, a potential nutritional and pharmacological target in the management of obesity and obesity‑related disorders.


title: Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture

abstract: Deep neural networks have shown excellent performance for stereo matching. Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cost aggregation which is crucial for stereo matching. In this paper, we present a learning-based cost aggregation method for stereo matching by a novel sub-architecture in the end-to-end trainable pipeline. We reformulate the cost aggregation as a learning process of the generation and selection of cost aggregation proposals which indicate the possible cost aggregation results. The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals. The criterion for the selection is determined by the low-level structure information obtained from a light convolutional network. The two-stream network offers a global view guidance for the cost aggregation to rectify the mismatching value stemming from the limited view of the matching cost computation. The comprehensive experiments on challenge datasets such as KITTI and Scene Flow show that our method outperforms the state-of-the-art methods.


title: Discriminative Color Descriptors

abstract: Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.


title: [POSTER] 2D-3D Co-segmentation for AR-based Remote Collaboration

abstract: In Augmented Reality (AR) based remote collaboration, a remote user can draw a 2D annotation that emphasizes an object of interest to guide a local user accomplishing a task. This annotation is typically performed only once and then sticks to the selected object in the local user's view, independent of his or her camera movement. In this paper, we present an algorithm to segment the selected object, including its occluded surfaces, such that the 2D selection can be appropriately interpreted in 3D and rendered as a useful AR annotation even when the local user moves and significantly changes the viewpoint.


title: A Game Theoretic Approach to Autonomous Two-Player Drone Racing

abstract: To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also avoid collisions with the opponents. Since unveiling one's own strategy to the adversaries in not desirable, this requires each player to independently predict the other players future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a novel receding horizon planning algorithm that, exploiting sensitivity analysis within an iterated best response computational scheme, can approximate Nash equilibria in real time. The planner only requires that each player knows its own position (e.g. with GPS or SLAM), and can sense the other player's relative position (e.g. with on board vision). Our solution is demonstrated to effectively compete against alternative strategies in a large number of drone racing simulations. Hardware experiments with onboard vision sensing prove the practicality of our strategy.


title: C-band repeat-pass interferometric SAR observations of the forest

abstract: Properties of ERS-1 C-band repeat pass interferometric SAR information for a forested area are studied. The intensity information is rather limited but, including coherence and effective interferometric SAR (INSAR) height, more information about the forest parameters can be obtained via satellite. Such information is also important for correction of INSAR derived topographic maps. Coherence properties have been used to identify forested/nonforested areas and the interferometric effective height of the forest determined by comparison to a DEM of the area. The authors have developed a model to relate basic forest properties to INSAR observations. These show that the coherence and interferometric effective height of a forested area change between image pairs. The model demonstrates how these properties are related to the temporal decorrelation and the scattering from the vegetation canopy and the ground surface. Gaps in the vegetation are found to be important in the characterization of boreal forests.


title: Communicating Civic Engagement: Contrasting Models of Citizenship in the Youth Web Sphere

abstract: Communication aimed at promoting civic engagement may become problematic when citizenrolesundergohistoricchanges.Inthecurrentera,youngergenerationsareembracing more expressive styles of actualizing citizenship defined around peer content sharing and social media, in contrast to earlier models of dutiful citizenship based on one-way communication managed by authorities. An analysis of 90 youth Web sites operated by diverse civic and political organizations in the United States reveals uneven conceptions of citizenship and related civic skills, suggesting that many established organization are out of step with changing civic styles.


title: Why does the microbiome affect behaviour?

abstract: Growing evidence indicates that the mammalian microbiome can affect behaviour, and several symbionts even produce neurotransmitters. One common explanation for these observations is that symbionts have evolved to manipulate host behaviour for their benefit. Here, we evaluate the manipulation hypothesis by applying evolutionary theory to recent work on the gut–brain axis. Although the theory predicts manipulation by symbionts under certain conditions, these appear rarely satisfied by the genetically diverse communities of the mammalian microbiome. Specifically, any symbiont investing its resources to manipulate host behaviour is expected to be outcompeted within the microbiome by strains that do not manipulate and redirect their resources into growth and survival. Moreover, current data provide no clear evidence for manipulation. Instead, we show how behavioural effects can readily arise as a by-product of natural selection on microorganisms to grow within the host and natural selection on hosts to depend upon their symbionts. We argue that understanding why the microbiome influences behaviour requires a focus on microbial ecology and local effects within the host. The microbiota can influence host behaviour through the gut–brain axis. In this Opinion, Johnson and Foster explore the evolution of this relationship and propose that adaptations of competing gut microorganisms may affect behaviour as a by‑product, leading to host dependence.