title: TinMan: eliminating confidential mobile data exposure with security oriented offloading

abstract: The wide adoption of smart devices has stimulated a fast shift of security-critical data from desktop to mobile devices. However, recurrent device theft and loss expose mobile devices to various security threats and even physical attacks. This paper presents TinMan, a system that protects confidential data such as web site password and credit card number (we use the term cor to represent these data, which is short for Confidential Record) from being leaked or abused even under device theft. TinMan separates accesses of cor from the rest of the functionalities of an app, by introducing a trusted node to store cor and offloading any code from a mobile device to the trusted node to access cor. This completely eliminates the exposure of cor on the mobile devices. The key challenges to TinMan include deciding when and how to efficiently and transparently offload execution; TinMan addresses these challenges with security-oriented offloading with a low-overhead tainting scheme called asymmetric tainting to track accesses to cor to trigger offloading, as well as transparent SSL session injection and TCP pay-load replacement to offload accesses to cor. We have implemented a prototype of TinMan based on Android and demonstrated how TinMan protects the information of user's bank account and credit card number without modifying the apps. Evaluation results also show that TinMan incurs only a small amount of performance and power overhead.


title: LocationSpark: A Distributed In-Memory Data Management System for Big Spatial Data

abstract: We present LocationSpark, a spatial data processing system built on top of Apache Spark, a widely used distributed data processing system. LocationSpark offers a rich set of spatial query operators, e.g., range search, kNN, spatio-textual operation, spatial-join, and kNN-join. To achieve high performance, LocationSpark employs various spatial indexes for in-memory data, and guarantees that immutable spatial indexes have low overhead with fault tolerance. In addition, we build two new layers over Spark, namely a query scheduler and a query executor. The query scheduler is responsible for mitigating skew in spatial queries, while the query executor selects the best plan based on the indexes and the nature of the spatial queries. Furthermore, to avoid unnecessary network communication overhead when processing overlapped spatial data, We embed an efficient spatial Bloom filter into LocationSpark's indexes. Finally, LocationSpark tracks frequently accessed spatial data, and dynamically flushes less frequently accessed data into disk. We evaluate our system on real workloads and demonstrate that it achieves an order of magnitude performance gain over a baseline framework.


title: Closure-Tree: An Index Structure for Graph Queries

abstract: Graphs have become popular for modeling structured data. As a result, graph queries are becoming common and graph indexing has come to play an essential role in query processing. We introduce the concept of a graph closure, a generalized graph that represents a number of graphs. Our indexing technique, called Closure-tree, organizes graphs hierarchically where each node summarizes its descendants by a graph closure. Closure-tree can efficiently support both subgraph queries and similarity queries. Subgraph queries find graphs that contain a specific subgraph, whereas similarity queries find graphs that are similar to a query graph. For subgraph queries, we propose a technique called pseudo subgraph isomorphism which approximates subgraph isomorphism with high accuracy. For similarity queries, we measure graph similarity through edit distance using heuristic graph mapping methods. We implement two kinds of similarity queries: K-NN query and range query. Our experiments on chemical compounds and synthetic graphs show that for subgraph queries, Closuretree outperforms existing techniques by up to two orders of magnitude in terms of candidate answer set size and index size. For similarity queries, our experiments validate the quality and efficiency of the presented algorithms.


title: Experimental Measurements and Computer Simulations of FL and CFL for Harmonic Studies

abstract: Due to poor efficiencies of Incandescent Lamps (ILs), Fluorescent Lamps (FLs) and Compact Fluorescent Lamps (CFLs) are increasingly used in residential and commercial applications. This proliferation of FLs and CFLs increases the harmonics level in distribution systems that could affect power systems and end users. In order to quantify the harmonics produced by FLs and CFLs precisely, accurate modelling of these loads are required. Matlab Simulink is used to model and simulate the full models of FLs and CFLs to give close results to the experimental measurements. Moreover, a Constant Load Power (CLP) model is also modelled and its results are compared with the full models of FLs and CFLs. This CLP model is much faster to simulate and easier to model than the full model. Such models help engineers and researchers to evaluate the harmonics exist within households and commercial buildings.


title: The impact of shift work and organisational climate on nurse health: a cross-sectional study

abstract: The negative effects of shift work schedules, specifically night and rotating shifts, have been widely reported. However, little is understood whether particular aspects of the organisational environment, related to specific shifts, may influence the negative impact of shift work. This study investigated the variation in organisational climate and health outcomes across shift work schedules (day, night, rotating). This cross-sectional study involved nursing staff (n = 108) who were all registered nurses from two Melbourne health services. There were slightly more nursing staff that participated from one health service (n = 56) than the other health service (n = 52). Nursing staff completed a survey on either paper form or online which comprised of: demographic characteristics, organisational climate (work environment scale) and health outcomes (general health questionnaire). The study found that organisational climate factors and health outcomes differed across shift types. Rotating shift staff exhibited significantly higher coworker cohesion scores when compared to night staff. Night staff reported significantly greater levels of physical comfort within their work environment than rotating staff. Overall, supervisor support emerged as a significant predictor of health outcomes such as somatic complaints, social dysfunction and overall distress. Task orientation was also shown to significantly predict levels of social dysfunction. Findings suggest that interventions with a focus on enhancing the organisational climate, focused in increasing supervisor support, may mitigate the potential negative health outcomes experienced by shift workers. Not applicable to this study.


title: Dynamical systems and applications

abstract: Lie symmetries, hidden symmetries and time-dependent invariants, B. Abraham-Shrauner generalization of a theorem of Malta and Paris, V. Afraimovich and T. Young asymptotic distribution of entrance times for expanding maps of the interval, P. Collet and A. Galves conformal measures and s - unimodal maps, M. Denker et al holomorphic symplectomorphism in C2, J.E. Fornaess and N. Sibony on simplest Engel structures on 4-manifolds, V. Gershkovich polynomial-like mappings induced by real polynomials, J. Graczyk and G. Swiatek general method of Lyapunov functionals construction for stability investigation of stochastic difference equations, V. Kolmanovskii and L. Shaikhet continuity of entropy revisisted, M. Misiurewicz infinitesimal rigidity of group actions with hyperbolic generators, M. Pollicott and other papers.


title: Color guided thermal image super resolution

abstract: IR imaging is playing an important role in industry these days. However, the IR cameras only support low resolution for technical reasons. In this paper, we propose to use the visible camera as a guidance to super resolution the IR images. We setup a prototype of the IR-Color multi-sensor imaging system and construct a dataset including videos collected from different scenes for further research. Then we present our color guided algorithm which is suitable for this kind of super resolution problem. We test our algorithm on our dataset. The experiment results indicate that our algorithm performs well, and avoids the over-texture problems that traditional solutions suffer from. In further work, we will apply our multi-sensor system to more applications in computer vision.


title: Text/Non-Text Classification of Connected Components in Document Images

abstract: Text segmentation is an important problem in document analysis related applications. We address the problem of classifying connected components of a document image as text or non-text. Inspired from previous works in the literature, besides common size and shape related features extracted from the components, we also consider component images, without and with context information, as inputs of the classifiers. Muli-layer perceptrons and convolutional neural networks are used to classify the components. High precision and recall is obtained with respect to both text and non-text components.


title: Isolated myeloid sarcoma as the first manifestation of acute myeloid leukemia: a case study

abstract: Key Clinical Message ::: This case report brings awareness to the diverse extramedullary manifestations of isolated myeloid sarcoma, as well as the importance and difficulties that are associated with establishing a rapid diagnosis and initiating treatment.


title: Untreated Depression and Hippocampal Volume Loss

abstract: OBJECTIVE: The purpose of this study was to investigate the effect of antidepressant treatment on hippocampal volumes in patients with major depression. METHOD: For 38 female outpatients, the total time each had been in a depressive episode was divided into days during which the patient was receiving antidepressant medication and days during which no antidepressant treatment was received. Hippocampal gray matter volumes were determined by high resolution magnetic resonance imaging and unbiased stereological measurement. RESULTS: Longer durations during which depressive episodes went untreated with antidepressant medication were associated with reductions in hippocampal volume. There was no significant relationship between hippocampal volume loss and time depressed while taking antidepressant medication or with lifetime exposure to antidepressants. CONCLUSIONS: Antidepressants may have a neuroprotective effect during depression.


title: Disentangling Factors of Variation by Mixing Them

abstract: We propose an approach to learn image representations that consist of disentangled factors of variation without exploiting any manual labeling or data domain knowledge. A factor of variation corresponds to an image attribute that can be discerned consistently across a set of images, such as the pose or color of objects. Our disentangled representation consists of a concatenation of feature chunks, each chunk representing a factor of variation. It supports applications such as transferring attributes from one image to another, by simply mixing and unmixing feature chunks, and classification or retrieval based on one or several attributes, by considering a user-specified subset of feature chunks. We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored. We demonstrate the effectiveness of our approach on the MNIST, Sprites, and CelebA datasets.


title: Dominance Detection in Meetings Using Easily Obtainable Features

abstract: We show that, using a Support Vector Machine classifier, it is possible to determine with a 75% success rate who dominated a particular meeting on the basis of a few basic features. We discuss the corpus we have used, the way we had people judge dominance and the features that were used.


title: The space of human body shapes: reconstruction and parameterization from range scans

abstract: We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two resolutions to speed convergence. We demonstrate reconstruction and consistent parameterization of 250 human body models. With this parameterized set, we explore a variety of applications for human body modeling, including: morphing, texture transfer, statistical analysis of shape, model fitting from sparse markers, feature analysis to modify multiple correlated parameters (such as the weight and height of an individual), and transfer of surface detail and animation controls from a template to fitted models.


Theory-driven Learning Analytics and Open Learner Modelling: The Teacher's Toolbox of Tomorrow?


title: Emotional support and survival after myocardial infarction. A prospective, population-based study of the elderly.

abstract: OBJECTIVE ::: To compare the survival of elderly patients hospitalized for acute myocardial infarction who have emotional support with that of patients who lack such support, while controlling for severity of disease, comorbidity, and functional status. ::: ::: ::: DESIGN ::: A prospective, community-based cohort study. ::: ::: ::: SETTING ::: Two hospitals in New Haven, Connecticut. ::: ::: ::: PATIENTS ::: Men (n = 100) and women (n = 94) 65 years of age or more hospitalized for acute myocardial infarction between 1982 and 1988. ::: ::: ::: MEASUREMENTS ::: Social support, age, gender, race, education, marital status, living arrangements, presence of depression, smoking history, weight, and physical function were assessed prospectively using questionnaires. The presence of congestive heart failure, pulmonary edema, and cardiogenic shock; the position of infarction; in-hospital complications; and history of myocardial infarction were assessed using medical records. Comorbidity was defined using an index based on the presence of eight conditions. ::: ::: ::: RESULTS ::: Of 194 patients, 76 (39%) died in the first 6 months after myocardial infarction. In multiple logistic regression analyses, lack of emotional support was significantly associated with 6-month mortality (odds ratio, 2.9; 95% CI, 1.2 to 6.9) after controlling for severity of myocardial infarction, comorbidity, risk factors such as smoking and hypertension, and sociodemographic factors. ::: ::: ::: CONCLUSIONS ::: When emotional support was assessed before myocardial infarction, it was independently related to risk for death in the subsequent 6 months.


title: Input-parallel output-series connection of isolated quasi-Z-source DC-DC converters

abstract: This paper proposes an input-parallel-output-series (IPOS) power system based on the isolated qZS DC-DC converter. Reported as a solution for the integration of renewable energy sources into the medium voltage grid, such power systems can also be used to integrate low voltage energy sources into a 230V/400V grid. We examined the basic switching stage with three different rectifier topologies whereas the steady state analysis was made for each topology. Criteria for our comparison were implementation complexity and current and voltage stress of the components. An experimental prototype of the proposed IPOS power system composed of two converters was built at a rated output power of 400 W. DC gain characteristics and efficiency curves are presented. The experimental waveforms for the input voltage 80 V and the output voltage 400 V were measured.


title: A Dynamic Modularity Based Community Detection Algorithm for Large-scale Networks: DSLM

abstract: In this work, a new fast dynamic community detection algorithm for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by modifying well-known static modularity based community detection algorithm. The proposed framework is tested using several different datasets. According to our results, community detection algorithms in the proposed framework perform better than static algorithms when large scale dynamic networks are considered.


title: Abnormal behavior detection using hybrid agents in crowded scenes

abstract: We categorize the behaviors of people into individual and group interactive behavior.We propose a hybrid agent system that includes static and dynamic agents in a scene.We represent the behavior of a crowd as a bag of words to detect abnormal behavior. In this paper, we propose a hybrid agent method to detect abnormal behaviors in a crowded scene. In real-life situations, abnormal behavior occurs by violent movement which is apparent as sudden speeding up, or chaotic movement in a restricted area, or movement contrasting with that of one's neighbors such as in a panic situation. In our model, we categorize the behaviors of people into individual behavior and group interactive behavior. Individual behavior is defined only by native motion information such as speed and direction. By contrast, group interactive behavior is defined by information concerning interactive motion between neighbors. We propose a hybrid agent system that includes static and dynamic agents to observe efficiently the corresponding individual and interactive behaviors in a crowded scene. The static agent is assigned to a specific spot and analyzes motion information near that spot. Unlike the static agent, the dynamic agent is assigned to a moving object and analyzes motion information of neighbors as well as oneself by following the object's movement. We represent the behavior of a crowd as a bag of words through the integration of static and dynamic agent information to determine abnormalities in the crowd behavior. The experimental results show that our proposed method efficiently detects abnormal behaviors in crowded scenes.


title: Semi-Global Matching - Motivation, Developments and Applications

abstract: Since its original publication, the Semi-Global Matching (SGM) technique has been re-implemented by many researchers and companies. The method offers a very good trade off between runtime and accuracy, especially at object borders and fine structures. It is also robust against radiometric differences and not sensitive to the choice of parameters. Therefore, it is well suited for solving practical problems. The applications reach from remote sensing, like deriving digital surface models from aerial and satellite images, to robotics and driver assistance systems. This paper motivates and explains the method, shows current developments as well as examples from various applications.


title: Issues and challenges of data security in a cloud computing environment

abstract: Now customers can opt for software and information technology services according to his requirements and can get these services on a leased basis from the network service provider and this has the facility to scale its requirements to up or down. This service is known as cloud computing, provided by the infrastructure provider which is a third party provider. Cloud computing provides many advantages to the customer like scalability, better economics of scaling, its ability to recover from problems, its ability to outsource non-core activities and flexibility. Cloud computing is a better option for the organizations to take as their best option without any initial investment and day by day frequent and heavy use of cloud computing is increasing but despite all the benefits a cloud offers to an organization there are certain doubts and threats regarding the security issues associated with a cloud computing platform. The security issues primarily involve the external control over organizational structure and management and personal and private data of the organization can be compromised. Personal and private data in this computing environment has a very high risk of breach of confidentiality, integrity and availability. Growth of cloud computing is mainly hampered due to these security concerns and challenges. Proper security arrangements are need to be placed before selecting the service provider for any cloud computing service and customers need to be very careful about understanding the risks of security breaches and challenges of using this new computing environment. This detailed study discusses about some of the challenges associated with cloud computing services and security issues related to this platform.


title: Influential Node Tracking on Dynamic Social Network: An Interchange Greedy Approach

abstract: As both social network structure and strength of influence between individuals evolve constantly, it requires tracking the influential nodes under a dynamic setting. To address this problem, we explore the Influential Node Tracking (INT) problem as an extension to the traditional Influence Maximization problem (IM) under dynamic social networks. While the Influence Maximization problem aims at identifying a set of $k$ nodes to maximize the joint influence under one static network, the INT problem focuses on tracking a set of influential nodes that keeps maximizing the influence as the network evolves. Utilizing the smoothness of the evolution of the network structure, we propose an efficient algorithm, Upper Bound Interchange Greedy (UBI) and a variant, UBI+. Instead of constructing the seed set from the ground, we start from the influential seed set we found previously and implement node replacement to improve the influence coverage. Furthermore, by using a fast update method by calculating the marginal gain of nodes, our algorithm can scale to dynamic social networks with millions of nodes. Empirical experiments on three real large-scale dynamic social networks show that our UBI and its variants, UBI+ achieves better performance in terms of both influence coverage and running time.


title: The role of Internet addiction in online game loyalty: an exploratory study

abstract: Purpose – The paper's aim is to explore the factors that affect the online game addiction and the role that online game addiction plays in the relationship between online satisfaction and loyalty.Design/methodology/approach – A web survey of online game players was conducted, with 1,186 valid responses collected. Structure equation modeling – specifically partial least squares – was used to assess the relationships in the proposed research framework.Findings – The results indicate that perceived playfulness and descriptive norms influence online game addiction. Furthermore, descriptive norms indirectly affect online game addiction through perceived playfulness. Addiction also directly contributes to loyalty and attenuates the relationship between satisfaction and loyalty. This finding partially explains why people remain loyal to an online game despite being dissatisfied.Practical implications – Online gaming vendors should strive to create amusing game content and to maintain their online game communitie...


title: How to Effectively Train IBM Watson: Classroom Experience

abstract: Watson is a question answering system that uses natural language processing, information retrieval, knowledge interpretation, automated reasoning and machine learning techniques. It can analyze millions of documents and answer most of the questions accurately with varying level of confidence. However, training IBM Watson may be tedious and may not be efficient if certain set of guidelines are not followed. In this paper, we discuss an effective strategy to train IBM Watson question answering system. We experienced this strategy during the classroom teaching of IBM Watson at Ryerson University in Big Data Analytics certification program. We have observed that if documents are well segmented, contain relevant titles and have consistent formatting, then the recall of the answers can be as high as 95%.


title: App Miscategorization Detection: A Case Study on Google Play

abstract: An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+ : (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+ . Experiments on Google Play shows that FRAC+ ’s topics are more aligned with Google ’s new categories and 0.35-1.10 percent game apps are detected to be miscategorized.


title: Learning from Synthetic Humans

abstract: Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.


title: Sparse-Bayesian-Learning-Based Wideband Spectrum Sensing With Simplified Modulated Wideband Converter

abstract: Wideband spectrum sensing is an important aspect of cognitive radio systems. In current models of wide spectrum sensing, a discrete frequency denotes a continuous frequency band. This type of model is divorced from practice and cannot reflect the reality of spectrum occupation. In this paper, we propose a novel wideband spectrum sensing scheme in terms of a modulated wideband converter (MWC) and sparse Bayesian learning (SBL). By exploiting the MWC, a practical wideband signal denoted by a block-sparse model is sampled to acquire the compressed measurements. We then employ SBL to directly extract the relevant information from the compressed measurements for estimating the support set. The estimated support set is chosen as the test statistic to facilitate spectrum sensing and analyze the detection performance. For applications in actual situations of support sets, different matching criteria are presented according to the requirements imposed by wideband spectrum sensing. Finally, we analyze the detection performance when a frequency band’s location is given as well as when it is randomly generated by conducting simulations. These simulations show that the proposed method outperforms the MWC-based orthogonal matching pursuit method, and the best performance can be achieved under matching criterion 3.


title: Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

abstract: Abstract Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.


Emotional Attachment, Performance, and Viability in Teams Collaborating with Embodied Physical Action (EPA) Robots


title: Academic outcomes of flipped classroom learning: a meta‐analysis

abstract: CONTEXT ::: The flipped classroom (FC), reversing lecture and homework elements of a course, is popular in medical education. The FC uses technology-enhanced pre-class learning to transmit knowledge, incorporating in-class interaction to enhance higher cognitive learning. However, the FC model is expensive and research on its effectiveness remains inconclusive. The aim of this study was to compare the efficacy of the FC model over traditional lecture-based (LB) learning by meta-analysis. ::: ::: ::: METHODS ::: We systematically searched MEDLINE, PubMed, ERIC, CINAHL, EMBASE, reference lists and Association for Medical Education in Europe (AMEE) conference books. Controlled trials comparing academic outcomes between the FC and LB approaches in higher education were considered eligible. The main findings were pooled using a random-effects model when appropriate. ::: ::: ::: RESULTS ::: Forty-six studies (9026 participants) were included, comprising four randomised controlled trials (RCTs), 19 quasi-experimental studies and 23 cohort studies. Study populations were health science (n = 32) and non health science (n = 14) students. The risk of bias was high (36/37 articles). Meta-analyses revealed that the FC had significantly better outcomes than the LB method in examination scores (post-intervention and pre-post change) and course grades, but not in objective structured clinical examination scores. Subgroup analyses showed the advantage of the FC was not observed in RCTs, non-USA countries, nursing and other health science disciplines and earlier publication years (2013 and 2014). Cumulative analysis and meta-regression suggested a tendency for progressively better outcomes by year. Outcome assessments rarely focused on behaviour change. ::: ::: ::: CONCLUSIONS ::: The FC method is associated with greater academic achievement than the LB approach for higher-level learning outcomes, which has become more obvious in recent years. However, results should be interpreted with caution because of the high methodological diversity, statistical heterogeneity and risk of bias in the studies used. Future studies should have high methodological rigour, a standardised FC format and utilise assessment tools evaluating higher cognitive learning and behaviour change to further examine differences between FC and LB learning.


title: The algebra of entanglement and the geometry of composition

abstract: String diagrams turn algebraic equations into topological moves that have recurring shapes, involving the sliding of one diagram past another. We individuate, at the root of this fact, the dual nature of polygraphs as presentations of higher algebraic theories, and as combinatorial descriptions of"directed spaces". Operations of polygraphs modelled on operations of topological spaces are used as the foundation of a compositional universal algebra, where sliding moves arise from tensor products of polygraphs. We reconstruct several higher algebraic theories in this framework. In this regard, the standard formalism of polygraphs has some technical problems. We propose a notion of regular polygraph, barring cell boundaries that are not homeomorphic to a disk of the appropriate dimension. We define a category of non-degenerate shapes, and show how to calculate their tensor products. Then, we introduce a notion of weak unit to recover weakly degenerate boundaries in low dimensions, and prove that the existence of weak units is equivalent to a representability property. We then turn to applications of diagrammatic algebra to quantum theory. We re-evaluate the category of Hilbert spaces from the perspective of categorical universal algebra, which leads to a bicategorical refinement. Then, we focus on the axiomatics of fragments of quantum theory, and present the ZW calculus, the first complete diagrammatic axiomatisation of the theory of qubits. The ZW calculus has several advantages over ZX calculi, including a computationally meaningful normal form, and a fragment whose diagrams can be read as setups of fermionic oscillators. Moreover, its generators reflect an operational classification of entangled states of 3 qubits. We conclude with generalisations of the ZW calculus to higher-dimensional systems, including the definition of a universal set of generators in each dimension.


title: Steerable Higher Order Mode Dielectric Resonator Antenna With Parasitic Elements for 5G Applications

abstract: This paper presents the findings of a steerable higher order mode (TE $^{\mathrm {y}}_{1\delta 3}$ ) dielectric resonator antenna with parasitic elements. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element. In this light, all of the dielectric resonator antennas (DRAs) have the same dielectric permittivity similar to that of ten and excited by a $50\Omega $ microstrip with a narrow aperture. The effect of the mutual coupling on the radiation pattern and the reflection coefficient, as well as the array factor, was investigated clearly using MATLAB version 2014b and ANSYS HFSS version 16. As the result, the antenna beam of the proposed DRA array managed to steer from −32° to +32° at 15 GHz. Furthermore, the measured antenna array showed the maximum gain of 9.25 dBi and the reflection coefficients which are less than −10 dB with the bandwidth more than 1.3 GHz, which is viewed as desirable for device-to-device communication in 5G Internet of Things applications.


World Trade Organization and regional economic integrations: together in future or not?


title: Empirically grounded agent-based models of innovation diffusion: a critical review

abstract: Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.


title: MULTIAGENT SYSTEMS ENGINEERING

abstract: This paper describes the Multiagent Systems Engineering (MaSE) methodology. MaSE is a general purpose, methodology for developing heterogeneous multiagent systems. MaSE uses a number of graphically based models to describe system goals, behaviors, agent types, and agent communication interfaces. MaSE also provides a way to specify architecture-independent detailed definition of the internal agent design. An example of applying the MaSE methodology is also presented.


Fitness landscape analysis and design of metaheuristics for car sequencing


RAMP Gold : A High-Throughput FPGA-Based Manycore Simulator


title: Moral distress in health care professionals

abstract: Thousands of health care providers currently live and practice in Canada,1 and each day these providers are presented with new situations from their patients and clients. Many of these situations require much contemplation, and often both personal and professional judgment is used to come to a conclusion. In many cases, the decision-making process becomes difficult due to personal and professional beliefs, as well as institutional and legal requirements placed upon the health care provider. This phenomenon, known as moral distress, is “when one knows the right thing to do, but institutional constraints make it nearly impossible to pursue the right course of action”.2 This work provides a brief introduction to the topic of moral distress, the systemic factors that can lead to the development of moral distress, how it manifests in health care providers, and coping mechanisms used by health care providers to manage their moral distress.


title: A photometric approach to digitizing cultural artifacts

abstract: In this paper we present a photometry-based approach to the digital documentation of cultural artifacts. Rather than representing an artifact as a geometric model with spatially varying reflectance properties, we instead propose directly representing the artifact in terms of its reflectance field --- the manner in which it transforms light into images. The principal device employed in our technique is a computer-controlled lighting apparatus which quickly illuminates an artifact from an exhaustive set of incident illumination directions and a set of digital video cameras which record the artifact's appearance under these forms of illumination. From this database of recorded images, we compute linear combinations of the captured images to synthetically illuminate the object under arbitrary forms of complex incident illumination, correctly capturing the effects of specular reflection, subsurface scattering, self-shadowing, mutual illumination, and complex BRDF's often present in cultural artifacts. We also describe a computer application that allows users to realistically and interactively relight digitized artifacts.


title: Shannon Entropy based Randomness Measurement and Test for Image Encryption

abstract: The quality of image encryption is commonly measured by the Shannon entropy over the ciphertext image. However, this measurement does not consider to the randomness of local image blocks and is inappropriate for scrambling based image encryption methods. In this paper, a new information entropy-based randomness measurement for image encryption is introduced which, for the first time, answers the question of whether a given ciphertext image is sufficiently random-like. It measures the randomness over the ciphertext in a fairer way by calculating the averaged entropy of a series of small image blocks within the entire test image. In order to fulfill both quantitative and qualitative measurement, the expectation and the variance of this averaged block entropy for a true-random image are strictly derived and corresponding numerical reference tables are also provided. Moreover, a hypothesis test at significance-level is given to help accept or reject the hypothesis that the test image is ideally encrypted/random-like. Simulation results show that the proposed test is able to give both effectively quantitative and qualitative results for image encryption. The same idea can also be applied to measure other digital data, like audio and video.


title: Robust visual tracking using autoregressive hidden Markov Model

abstract: Recent studies on visual tracking have shown significant improvement in accuracy by handling the appearance variations of the target object. Whereas most studies present schemes to extract the time-invariant characteristics of the target and adaptively update the appearance model, the present paper concentrates on modeling the probabilistic dependency between sequential target appearances (Fig. 1-(a)). To actualize this interest, a new Bayesian tracking framework is formulated under the autoregressive Hidden Markov Model (AR-HMM), where the probabilistic dependency between sequential target appearances is implied. During the learning phase at each time step, the proposed tracker separates formerly seen target samples into several clusters based on their visual similarity, and learns cluster-specific classifiers as multiple appearance models, each of which represents a certain type of the target appearance. Then the dependency between these appearance models is learned. During the searching phase, the target state is estimated by inferring the most probable appearance model under the consideration of its dependency on formerly utilized appearance models. The proposed method is tested on 12 challenging video sequences containing targets with abrupt appearance variations, and demonstrates that it outperforms current state-of-the-art methods in accuracy.


title: A Framework for Optimization in Big Data: Privacy-Preserving Multi-agent Greedy Algorithm

abstract: Due to the variety of the data source and the veracity of their trustworthiness, it is challenging to solve the distributed optimization problems in the big data applications owing to the privacy concerns. We propose a framework for distributed multi-agent greedy algorithms whereby any greedy algorithm that fits our requirement can be converted to a privacy-preserving one. After the conversion, the private information associated with each agent will not be disclosed to anyone else but the owner, and the same output as the plain greedy algorithm is computed by the converted one. Our theoretic analysis shows the security of the framework, and the implementation also shows good performance.


title: Finger Detection for Sign Language Recognition

abstract: Computer recognition of sign language is an important research problem for enabling communication with hearing impaired people. This paper introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the American Sign Language. Finger Detection is accomplished based on the concept of Boundary Tracing and Finger Tip Detection. The system does not require the hand to be perfectly aligned to the camera or use any special markers or input gloves on the hand.


title: Preprocessing compressed 3D kinect skeletal joints in enhancing human motion classification

abstract: Human motion classification has been commonly analyzed from 2D and 3D temporal body postures. Previous analyses focused on exergaming, sport science, surveillance and rehabilitation for the betterment of living. A number of recent works had also applied Microsoft Kinect captured motions for its portability and capability convenience to detect RGB images, depth images and 3D skeleton joint coordinates. Nevertheless, Kinect captured data contains inaccuracies which necessitates preprocessing efforts to improve the motion recognition accuracy. To the best of our knowledge, works had rarely detailed the data preprocessing techniques in order to improve the qualities of motion recognition. Few had demonstrated the feasibilities of 2D motion data to represent 3D motions. The importance of data preprocessing role in enhancing the classification accuracy, thus remains a major research concern to date. Therefore, this paper presents the comparisons of human motion data preprocessing analyses on 2D and 3D skeletal joints. Uncertainties observed in raw 3D motion data are identified and filtered followed by the data compression tasks into 2D data. The preprocessing performances at each level are judged using four major classifiers: Bayes, Function, Lazy and Tree aided by the WEKA tool. The approaches are employed on the skeletal joints coordinates of data retrieved from UTKinect-Action Dataset. Our findings demonstrated that preprocessing efforts on the compressed 3D into 2D skeletal joints is comparable to the actual human motion by classification accuracy and at the same time reduces the execution time.


title: Multi-modal People Detection from Aerial Video

abstract: Automatic people detection is a highly desirable capability for autonomous Unmanned Aerial Vehicles (UAV) operating in search and rescue missions. Here we present an approach for automatic people detection from aerial video using a combination of infrared and visible imagery. The results presented show that existing state of the art part-based people detection algorithms can be used with considerable success in aerial imagery. In a cluttered urban environment, the detection rate is approximately 80% over an entire flight pattern. We show that, by fusing visible and infrared imagery and tracking detections over time, the false positive rate is reduced to a minimum.


title: SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity

abstract: We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize the entropy of that probability on labeled data and minimize it on unlabeled data following entropy regularization, which allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Furthermore, Seraph is regularized by encouraging a low-rank projection induced from the metric. The optimization of Seraph is solved efficiently and stably by an EM-like scheme with the analytical E-Step and convex M-Step. Experiments demonstrate that Seraph compares favorably with many well-known global and local metric learning methods.


title: One for All: Towards Language Independent Named Entity Linking

abstract: Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other languages, such as Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent Entity Linking system, which provides an EL framework which, once trained on one language, works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document, employing a discriminative reranking framework with many domain and language-independent feature functions. Experiments on numerous benchmark datasets, show that the proposed system, once trained on one language, English, outperforms several state-of-the-art systems in English (by 4 points) and the trained model also works very well on Spanish (14 points better than a competitor system), demonstrating the viability of the approach.


title: Multi-facet Category for Cultural Digital Resources

abstract: This paper points out a key issue related to the integration between cultural ontology based metadata management services and content management of digital resources. A multi-facet resource categorization approach is applied to metadata sets, allowing the metadata optimization of the description of cultural resources. It is part of the cultural ontology based metadata management system which supports interoperable semantic management.


title: The Sample Complexity of Exploration in the Multi-Armed Bandit Problem

abstract: We consider the multi-armed bandit problem under the PAC ("probably approximately correct") model. It was shown by Even-Dar et al. (2002) that given n arms, a total of O((n/e2)log(1/δ)) trials suffices in order to find an e-optimal arm with probability at least 1-δ. We establish a matching lower bound on the expected number of trials under any sampling policy. We furthermore generalize the lower bound, and show an explicit dependence on the (unknown) statistics of the arms. We also provide a similar bound within a Bayesian setting. The case where the statistics of the arms are known but the identities of the arms are not, is also discussed. For this case, we provide a lower bound of Θ((1/e2)(n+log(1/δ))) on the expected number of trials, as well as a sampling policy with a matching upper bound. If instead of the expected number of trials, we consider the maximum (over all sample paths) number of trials, we establish a matching upper and lower bound of the form Θ((n/e2)log(1/δ)). Finally, we derive lower bounds on the expected regret, in the spirit of Lai and Robbins.


title: Letter N-Gram-based Input Encoding for Continuous Space Language Models

abstract: We present a letter-based encoding for words in continuous space language models. We represent the words completely by letter n-grams instead of using the word index. This way, similar words will automatically have a similar representation. With this we hope to better generalize to unknown or rare words and to also capture morphological information. We show their influence in the task of machine translation using continuous space language models based on restricted Boltzmann machines. We evaluate the translation quality as well as the training time on a German-to-English translation task of TED and university lectures as well as on the news translation task translating from English to German. Using our new approach a gain in BLEU score by up to 0.4 points can be achieved.


title: ‘Clear as Mud’: Toward Greater Clarity in Generic Qualitative Research:

abstract: We have observed a growth in the number of qualitative studies that have no guiding set of philosophic assumptions in the form of one of the established qualitative methodologies. This lack of allegiance to an established qualitative approach presents many challenges for "generic qualitative" studies, one of which is that the literature lacks debate about how to do a generic study well. We encourage such debate and offer four basic requirements as a point of departure: noting the researchers’ position, distinguishing method and methodology, making explicit the approach to rigor, and identifying the researchers’ analytic lens.


title: Soft-Tissue Facial Characteristics of Attractive and Normal Adolescent Boys and Girls

abstract: OBJECTIVES ::: To identify possible esthetic canons in facial size and shape of Italian adolescent boys and girls. ::: ::: ::: MATERIALS AND METHODS ::: The three-dimensional coordinates of 50 facial landmarks (forehead, eyes, nose, cheeks, mouth, jaw, ears) were collected in 231 healthy, reference adolescents (10- 17 years old) and in 93 similar age group "attractive" adolescents selected by a commercial casting organization. Soft-tissue facial angles, distances, areas, and volumes were computed. Comparisons were made with analysis of variance. ::: ::: ::: RESULTS ::: Attractive adolescents had wider, shorter, and less deep faces than reference adolescents, with a relatively larger forehead and maxilla, and a reduced mandible relative to the maxilla. Lips were larger and more prominent, and the nasolabial angle was reduced, but in older boys the effect was reversed. The prominence of the soft-tissue profile, and of the maxilla relative to the mandible, were larger in attractive boys, but smaller in attractive girls than in their reference peers. In the horizontal plane, attractive "young" adolescents had a flatter face, while the opposite pattern was observed in the "old" adolescents, with a relatively more prominent chin. Attractive adolescents had smaller noses than reference subjects of the same age and sex. ::: ::: ::: CONCLUSIONS ::: Overall, all the measurements appeared sufficiently homogenous, and the quantitative characteristics of an "attractive" face well defined. Esthetic reference values can be used to determine optimal timing and goals in orthodontic treatment.


title: Modeling the Detection of Textual Cyberbullying

abstract: The scourge of cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Anonymity and the lack of meaningful supervision in the electronic medium are two factors that have exacerbated this social menace. Comments or posts involving sensitive topics that are personal to an individual are more likely to be internalized by a victim, often resulting in tragic outcomes. We decompose the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems. We experiment with a corpus of 4500 YouTube comments, applying a range of binary and multiclass classifiers. We find that binary classifiers for individual labels outperform multiclass classifiers. Our findings show that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.


title: Modeling and Control of a Flux-Modulated Compound-Structure Permanent-Magnet Synchronous Machine for Hybrid Electric Vehicles

abstract: The compound-structure permanent-magnet synchronous machine (CS-PMSM), comprising a double rotor machine (DRM) and a permanent-magnet (PM) motor, is a promising electronic-continuously variable transmission (e-CVT) concept for hybrid electric vehicles (HEVs). By CS-PMSM, independent speed and torque control of the vehicle engine is realized without a planetary gear unit. However, the slip rings and brushes of the conventional CS-PMSM are considered a major drawback for vehicle application. In this paper, a brushless flux-modulated CS-PMSM is investigated. The operating principle and basic working modes of the CS-PMSM are discussed. Mathematical models of the CS-PMSM system are given, and joint control of the two integrated machines is proposed. As one rotor of the DRM is mechanically connected with the rotor of the PM motor, special rotor position detection and torque allocation methods are required. Simulation is carried out by Matlab/Simulink, and the feasibility of the control system is proven. Considering the complexity of the controller, a single digital signal processor (DSP) is used to perform the interconnected control of dual machines instead of two separate ones, and a typical hardware implementation is proposed.


title: A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition

abstract: Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use Pyramid Histogram of Oriented Gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly available dataset demonstrated that the PHOG with a significantly shorter vector length could achieve as high a recognition rate as the Gabor features did. Furthermore, the feature selection conducted by an AdaBoost algorithm was not needed when using the PHOG features. To further improve the recognition performance, we combined these two feature extraction methods and achieved the best smile recognition rate, indicating a good value of the PHOG features for smile recognitions.


title: Two routes to emotional memory: Distinct neural processes for valence and arousal

abstract: Prior investigations have demonstrated that emotional information is often better remembered than neutral information, but they have not directly contrasted effects attributable to valence and those attributable to arousal. By using functional MRI and behavioral studies, we found that distinct cognitive and neural processes contribute to emotional memory enhancement for arousing information versus valenced, nonarousing information. The former depended on an amygdalar-hippocampal network, whereas the latter was supported by a prefrontal cortex-hippocampal network implicated in controlled encoding processes. A behavioral companion study, with a divided-attention paradigm, confirmed that memory enhancement for valenced, nonarousing words relied on controlled encoding processes: concurrent task performance reduced the enhancement effect. Enhancement for arousing words occurred automatically, even when encoding resources were diverted to the secondary task.


title: Semi-supervised learning with Ladder networks

abstract: We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on top of the Ladder network proposed by Valpola [1] which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification in addition to permutation-invariant MNIST classification with all labels.


title: The sector bound approach to quantized feedback control

abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.


title: FEM models to code non-rigid EKF monocular SLAM

abstract: Bayesian EKF (Extended Kalman Filter) is cross-fertilized with Navier's equations solid deformation modeling to compute 3D non-rigid structure from monocular camera motion. The method operates with a projective camera and autonomously computes -for every sequence frame- both the geometry and the matches. The combination results in an sequential efficient method to code the rich available physical priors, particularly relevant for medical monocular endoscope sequences such as those observing the abdominal cavity.


title: The Clinical Use of Ivabradine

abstract: The clinical use of ivabradine has and continues to evolve along channels that are predicated on its mechanism of action. It selectively inhibits the funny current (If) in sinoatrial nodal tissue, resulting in a decrease in the rate of diastolic depolarization and, consequently, the heart rate, a mechanism that is distinct from those of other negative chronotropic agents. Thus, it has been evaluated and is used in select patients with systolic heart failure and chronic stable angina without clinically significant adverse effects. Although not approved for other indications, ivabradine has also shown promise in the management of inappropriate sinus tachycardia. Here, the authors review the mechanism of action of ivabradine and salient studies that have led to its current clinical indications and use.


title: MUADDIB: A distributed recommender system supporting device adaptivity

abstract: Web recommender systems are Web applications capable of generating useful suggestions for visitors of Internet sites. However, in the case of large user communities and in presence of a high number of Web sites, these tasks are computationally onerous, even more if the client software runs on devices with limited resources. Moreover, the quality of the recommendations strictly depends on how the recommendation algorithm takes into account the currently used device. Some approaches proposed in the literature provide multidimensional recommendations considering, besides items and users, also the exploited device. However, these systems do not efficiently perform, since they assign to either the client or the server the arduous cost of computing recommendations. In this article, we argue that a fully distributed organization is a suitable solution to improve the efficiency of multidimensional recommender systems. In order to address these issues, we propose a novel distributed architecture, called MUADDIB, where each user's device is provided with a device assistant that autonomously retrieves information about the user's behavior. Moreover, a single profiler, associated with the user, periodically collects information coming from the different user's device assistants to construct a global user's profile. In order to generate recommendations, a recommender precomputes data provided by the profilers. This way, the site manager has only the task of suitably presenting the content of the site, while the computation of the recommendations is assigned to the other distributed components. Some experiments conducted on real data and using some well-known metrics show that the system works more effectively and efficiently than other device-based distributed recommenders.


title: Evaluating and Developing Theories in the Information Systems Discipline

abstract: This paper articulates a framework and criteria that can be used to evaluate the quality of theories. While the framework and criteria have general applicability, my focus is the evaluation of theories within the information systems discipline. To illustrate the usefulness of the framework and criteria, I show how they can be employed to pinpoint the strengths and weaknesses of a theory which, based upon citation evidence, has had a significant impact on other researchers within the information systems discipline. Because the evaluation of existing theories often provides the basis for refining existing theories or building new theories, I also show how the framework and criteria can be used to inform the development of high-quality theory.


title: Adaptive even mirror Fourier filtered error LMS algorithm for multichannel nonlinear active noise control

abstract: In this paper, the multichannel even mirror Fourier nonlinear (EMFN) filter based filtered-x least mean square (FFLMS) algorithm and filtered-error least mean square (FFELMS) algorithm are proposed for nonlinear active noise control (NANC) applications. The FFLMS algorithm is extended from a single-channel NANC system to a multi-channel NANC system, and the efficient filtered-error structure with the EMFN filter is explored in the developed FFELMS algorithm to reduce the computational complexity without sacrificing its control performance. The computational complexity analyses in comparison with the second-order Volterra filtered-x LMS (VFXLMS), first-order filtered-s LMS (FSLMS) and second-order Legendre filtered-x LMS (LFXLMS) algorithms are provided. The performance of the both FFLMS and FFELMS algorithms are validated through computer simulations. The computer simulations demonstrate that the developed multichannel FFLMS and FFELMS algorithms not only outperform the VFXLMS, FSLMS and LFXLMS algorithms in terms of the noise control performance but also converge faster than the second-order VFXLMS and the second-order LFXLMS algorithms.


title: ANOMALY DETECTION OF ATTACKS (ADA) ON DNN CLASSIFIERS AT TEST TIME

abstract: A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), for a host of application domains is adversarial learning (Adv-L) attacks. The main focus here is on exploits applied against (DNN-based) classifiers at test time. While much work has focused on devising attacks that make perturbations to a test pattern (e.g., an image) which are human-imperceptible and yet still induce a change in the classifier's decision, there is relative paucity of work in defending against such attacks. Moreover, our thesis is that most existing defense approaches"miss the mark", seeking to robustify the classifier to make"correct"decisions on perturbed patterns. While, unlike some prior works, we make explicit the motivation of such approaches, we argue that it is generally much more actionable to detect the attack, rather than to"correctly classify"in the face of it. We hypothesize that, even if human-imperceptible, adversarial perturbations are machine-detectable. We propose a purely unsupervised anomaly detector (AD), based on suitable (null hypothesis) density models for the different DNN layers and a novel Kullback-Leibler"distance"AD test statistic. Tested on MNIST and CIFAR10 image databases under the prominent attack strategy proposed by Goodfellow et al. [5], our approach achieves compelling ROC AUCs for attack detection of 0.992 on MNIST, 0.957 on noisy MNIST images, and 0.924 on CIFAR10. We also show that a simple detector that counts the number of white regions in the image achieves 0.97 AUC in detecting the attack on MNIST proposed by Papernot et al. [12].


title: Face occlusion detection by using B-spline active contour and skin color information

abstract: This paper proposes a face occlusion verification method for an automated teller machine (ATM) application. The proposed method mainly consists of three steps. Firstly, a head and shoulder shape is detected by applying B-spline active contour to motion edges. This motion edge is generated by a kurtosis-based frame selection and distance transformation-based motion edge detection. Secondly, a face area is estimated by fitting an ellipse to the detected head and shoulder shape. Finally, occlusion of the face area is determined by measuring skin color area ratio (SCAR) of whole face area and facial component areas. Experimental results show that the proposed head and shoulder detection method has 94.8% detection rate even though there are various types of severe occlusions in faces, and the proposed occlusion verifier has 86.7% verification rate.


title: Joint photo stream and blog post summarization and exploration

abstract: We propose an approach that utilizes large collections of photo streams and blog posts, two of the most prevalent sources of data on the Web, for joint story-based summarization and exploration. Blogs consist of sequences of images and associated text; they portray events and experiences with concise sentences and representative images. We leverage blogs to help achieve story-based semantic summarization of collections of photo streams. In the opposite direction, blog posts can be enhanced with sets of photo streams by showing interpolations between consecutive images in the blogs. We formulate the problem of joint alignment from blogs to photo streams and photo stream summarization in a unified latent ranking SVM framework. We alternate between solving the two coupled latent SVM problems, by first fixing the summarization and solving for the alignment from blog images to photo streams and vice versa. On a newly collected large-scale Disneyland dataset of 10K blogs (120K associated images) and 6K photo streams (540K images), we demonstrate that blog posts and photo streams are mutually beneficial for summarization, exploration, semantic knowledge transfer, and photo interpolation.


title: An In-Situ Study of Mobile App & Mobile Search Interactions

abstract: When trying to satisfy an information need, smartphone users frequently transition from mobile search engines to mobile apps and vice versa. However, little is known about the nature of these transitions nor how mobile search and mobile apps interact. We report on a 2-week, mixed-method study involving 18 Android users, where we collected real-world mobile search and mobile app usage data alongside subjective insights on why certain interactions between apps and mobile search occur. Our results show that when people engage with mobile search they tend to interact with more mobile apps and for longer durations. We found that certain categories of apps are used more intensely alongside mobile search. Furthermore we found differences in app usage before and after mobile search and show how mobile app interactions can both prompt mobile search and enable users to take action. We conclude with a discussion on what these patterns mean for mobile search and how we might design mobile search experiences that take these app interactions into account.


title: Robust Visual Knowledge Transfer via Extreme Learning Machine-Based Domain Adaptation

abstract: We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.


title: Pie Menus or Linear Menus, Which Is Better?

abstract: This paper is about a continuing investigation aiming to find o ut which menu type is more useable on a web site or application. This paper specifically compares pie menus with linear menus. Although other researchers have investigated similar themes it is felt that there is still not enough knowledge regarding the various effects of different menu types, including their positioning on a screen. An empirical approach using an experiment to test the effectiveness and user satisfaction of a pie menu and a linear menu is described in detail. Four tasks were administered to experimental participants under each menu design in a within users experimental design. Overall task time, number of errors and participants' subjective opinions were measured. The data were statistically analysed. There were no statistically significant results for task times and errors. However, subjective opinions were overall statistically significant, suggesting a preference for the pie menu by participants. These results are interesting for all web designers, user interface designers and developers. The issues being investigated are rather complex, because there is no clear pattern of results to suggest a categorical ‘best design’. These results add to the current knowledge in existence regarding menu types and to the wish of understanding better the issues involved with menu types.


RDF Graph Embeddings for Content-based Recommender Systems


title: Non-Rigid Registration Under Isometric Deformations

abstract: We present a robust and efficient algorithm for the pairwise non-rigid registration of partially overlapping 3D surfaces. Our approach treats non-rigid registration as an optimization problem and solves it by alternating between correspondence and deformation optimization. Assuming approximately isometric deformations, robust correspondences are generated using a pruning mechanism based on geodesic consistency. We iteratively learn an appropriate deformation discretization from the current set of correspondences and use it to update the correspondences in the next iteration. Our algorithm is able to register partially similar point clouds that undergo large deformations, in just a few seconds. We demonstrate the potential of our algorithm in various applications such as example based articulated segmentation, and shape interpolation.


title: Conditional Neural Fields

abstract: Conditional random fields (CRF) are widely used for sequence labeling such as natural language processing and biological sequence analysis. Most CRF models use a linear potential function to represent the relationship between input features and output. However, in many real-world applications such as protein structure prediction and handwriting recognition, the relationship between input features and output is highly complex and nonlinear, which cannot be accurately modeled by a linear function. To model the nonlinear relationship between input and output we propose a new conditional probabilistic graphical model, Conditional Neural Fields (CNF), for sequence labeling. CNF extends CRF by adding one (or possibly more) middle layer between input and output. The middle layer consists of a number of gate functions, each acting as a local neuron or feature extractor to capture the nonlinear relationship between input and output. Therefore, conceptually CNF is much more expressive than CRF. Experiments on two widely-used benchmarks indicate that CNF performs significantly better than a number of popular methods. In particular, CNF is the best among approximately 10 machine learning methods for protein secondary structure prediction and also among a few of the best methods for handwriting recognition.


title: Discriminative Feature Learning for Unsupervised Video Summarization

abstract: In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with longlength video inputs. To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss. The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance. For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features. Our CSNet gives better summarization results for long-length videos compared to the existing methods. In addition, we introduce an attention mechanism to handle the dynamic information in videos. We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.


title: Early post-mortem changes and stages of decomposition in exposed cadavers

abstract: Decomposition of an exposed cadaver is a continuous process, beginning at the moment of death and ending when the body is reduced to a dried skeleton. Traditional estimates of the period of time since death or post-mortem interval have been based on a series of grossly observable changes to the body, including livor mortis, algor mortis, rigor mortis and similar phenomena. These changes will be described briefly and their relative significance discussed. More recently, insects, mites and other arthropods have been increasingly used by law enforcement to provide an estimate of the post-mortem interval. Although the process of decomposition is continuous, it is useful to divide this into a series of five stages: Fresh, Bloated, Decay, Postdecay and Skeletal. Here these stages are characterized by physical parameters and related assemblages of arthropods, to provide a framework for consideration of the decomposition process and acarine relationships to the body.


title: The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies

abstract: The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.


title: A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

abstract: Conventionally, resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to obtain in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of a scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive MIMO systems shows that the proposed machine-learning-based resource


title: Building trust and collaboration in a virtual team

abstract: Organizations are increasing their reliance on virtual relationships in structuring operations for a global environment. Like all teams, virtual teams require a solid foundation of mutual trust and collaboration, if they are to function effectively. Identifying and applying appropriate team building strategies for a virtual environment will not only enhance organizational effectiveness but will also impact positively on the quality of working life for virtual team members.


title: Multimodal learning analytics

abstract: New high-frequency data collection technologies and machine learning analysis techniques could offer new insights into learning, especially in tasks in which students have ample space to generate unique, personalized artifacts, such as a computer program, a robot, or a solution to an engineering challenge. To date most of the work on learning analytics and educational data mining has focused on online courses or cognitive tutors, in which the tasks are more structured and the entirety of interaction happens in front of a computer. In this paper, I argue that multimodal learning analytics could offer new insights into students' learning trajectories, and present several examples of this work and its educational application.


title: System for authoring highly interactive, personality-rich interactive characters

abstract: We describe an innovative system for authoring expressive, fully autonomous interactive characters. The focus of our work is creating a system to allow rich authoring that captures as much of the artistic intent of the author in procedural form as we can, and that provides automatic support for expressive execution of that content. The system is composed of two parts: (1)a programming language with unusual language features including concurrency, reflection, backtracking, continuously monitored expressions, and a model of emotion, that was created for the expression of interactive self-animating characters; and (2) a motion synthesis system that combines hand-animated motion data with artistically authored procedures for generalizing the motion while preserving the artistic intent. This system has been used to create over a dozen interactive characters, which have been shown at juried venues, as well as being deployed commercially. We describe how artistic qualities important to interactive characters are encoded and supported using this system, and demonstrate the system with an implemented interactive character.


title: Using Fast Weights to Attend to the Recent Past

abstract: Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs. There is no good reason for this restriction. Synapses have dynamics at many different time-scales and this suggests that artificial neural networks might benefit from variables that change slower than activities but much faster than the standard weights. These ``fast weights'' can be used to store temporary memories of the recent past and they provide a neurally plausible way of implementing the type of attention to the past that has recently proven helpful in sequence-to-sequence models. By using fast weights we can avoid the need to store copies of neural activity patterns.


Proposal : Extreme Acceleration and Seamless Integration of Raw Data Analysis


title: The Sound of Pixels

abstract: We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel. Our approach capitalizes on the natural synchronization of the visual and audio modalities to learn models that jointly parse sounds and images, without requiring additional manual supervision. Experimental results on a newly collected MUSIC dataset show that our proposed Mix-and-Separate framework outperforms several baselines on source separation. Qualitative results suggest our model learns to ground sounds in vision, enabling applications such as independently adjusting the volume of sound sources.


title: Challenges and Limitations in Current Botnet Detection

abstract: Botnets are an emerging phenomenon that is becoming one of the most significant threats to security. Its danger lies less in the malicious codes themselves, but in the support they provide to implement a wide branch of very different criminal practices which are quite more compromising than harming an isolated computer, such as distributed denial of service attacks (DDoS), phishing, online fraud, dissemination of malware, building servers for exchange of illegal material or sending spam (bulk mail). Therefore, the scientific community together with the different business related corporations and public entities, should be aware of the need of developing mechanisms to improve their detection, analysis and deactivation. And these measures should be taken as soon as possible to stop the dissemination of a threat which impact factor and ?exibility in perpetrating attacks commanding an army of hijacked computers (bots), makes them a tool capable of compromising even the most complex information systems. Thus, this article sets out the main lines of current research in this field and proposing solutions to detect its existence through the analysis of the communication channels (via HTTP, P2P, IRC...) and the variations in the traffic detected, as well as their propagation mechanisms.


title: Red team vs. blue team hardware trojan analysis: detection of a hardware trojan on an actual ASIC

abstract: We infiltrate the ASIC development chain by inserting a small denial-of-service (DoS) hardware Trojan at the fabrication design phase into an existing VLSI circuit, thereby simulating an adversary at a semiconductor foundry. Both the genuine and the altered ASICs have been fabricated using a 180 nm CMOS process. The Trojan circuit adds an overhead of only 0.5% to the original design. In order to detect the hardware Trojan, we perform side-channel analyses and apply IC-fingerprinting techniques using templates, principal component analysis (PCA), and support vector machines (SVMs). As a result, we were able to successfully identify and classify all infected ASICs from non-infected ones. To the best of our knowledge, this is the first hardware Trojan manufactured as an ASIC and has successfully been analyzed using side channels.


title: Financial Aspect and Sentiment Predictions with Deep Neural Networks

abstract: In this paper, we describe our ensemble approach for sentiment and aspect predictions in the financial domain for a given text. This ensemble approach uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a ridge regression and a voting strategy for sentiment and aspect predictions, and therefore, does not rely on any handcrafted feature. Based on 5-cross validation on the released training set, the results show that CNNs overall perform better than RNNs on both tasks, and the ensemble approach can boost the performance further by leveraging different types of deep learning approaches.


title: A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

abstract: Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1) providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as 'originality' and 'impact'.


title: Profile-guided memory optimization for deep neural networks

abstract: Recent years have seen deep neural networks (DNNs) becoming wider and deeper to achieve better performance in many applications of AI. Such DNNs however require huge amounts of memory to store weights and intermediate results (e.g., activations, feature maps, etc.) in propagation. This requirement makes it difficult to run the DNNs on devices with limited, hard-to-extend memory, degrades the running time performance, and restricts the design of network models. We address this challenge by developing a novel profile-guided memory optimization to efficiently and quickly allocate memory blocks during the propagation in DNNs. The optimization utilizes a simple and fast heuristic algorithm based on the two-dimensional rectangle packing problem. Experimenting with well-known neural network models, we confirm that our method not only reduces the memory consumption by up to $49.5\%$ but also accelerates training and inference by up to a factor of four thanks to the rapidity of the memory allocation and the ability to use larger mini-batch sizes.


title: Genome analysis A faster circular binary segmentation algorithm for the analysis of array CGH data

abstract: Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number. The algorithm tests for change-points using a maximal t-statistic with a permutation reference distribution to obtain the corresponding P-value. The number of computations required for the maximal test statistic is O(N2), where N is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster algorithm. ::: ::: Results: We present a hybrid approach to obtain the P-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analyses of array CGH data from breast cancer cell lines to show the impact of the new approaches on the analysis of real data. ::: ::: Availability: An R version of the CBS algorithm has been implemented in the "DNAcopy" package of the Bioconductor project. The proposed hybrid method for the P-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher. ::: ::: Contact: venkatre@mskcc.org ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Explicit reference modeling methodology in parametric CAD system

abstract: Today parametric associative CAD systems must help companies to create more efficient virtual development processes. While dealing with complex parts (e.g. the number of surfaces of the solid) no CAD modeling methodology is existing. Based on the analysis of industrial designers' practices as well as student practices on CAD, we identified key factors that lead to better performance. Our objective in this article is to propose a practical method for complex parts modeling in parametric CAD system. An illustration of the performances and the results obtained by this method are presented comparing the traditional method with the proposed one while using an academic case and then an industrial case.


DETECTION OF ABNORMAL BLOOD CELLS USING IMAGE PROCESSING TECHNIQUE


title: Classifying malware using convolutional gated neural network

abstract: Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset.


What's a Brand Worth? Trademark Protection, Profits and Product Quality


A 2.4-GHz CMOS Receiver for IEEE 802.11 Wireless LAN’s


title: Satire Detection from Web Documents Using Machine Learning Methods

abstract: Satire exposes humanity's vices and foibles through the use of irony, wit, and sometimes sarcasm too. It is also frequently used in online communities. Recognition of satire can help in many NLP applications like dialogue system and review summarization. In this paper we filter online news articles as satirical or true news documents using SVM (Support Vector Machine) classification method combined with machine learning techniques. With ample training documents SVM tends to give good classification results. For obtaining promising results with SVM an understanding of its working and ways to influence its accuracy is required. We also use various feature extraction strategies and conclude that TF-IDF-BNS feature extraction gives maximum accuracy for detection of satire in web content.


title: Common-near-neighbor analysis for person re-identification

abstract: Person re-identification tackles the problem whether an observed person of interest reappears in a network of cameras. The difficulty primarily originates from few samples per class but large amounts of intra-class variations in real scenarios: illumination, pose and viewpoint changes across cameras. So far, proposals in the literature have treated this either as a matching problem focusing on feature representation or as a classification/ranking problem relying on metric optimization. This paper presents a new way called Common-Near-Neighbor Analysis, which to some extent combines the strengths of these two methodologies. It analyzes the commonness of the near neighbors of each pair of samples in a learned metric space, measured by a novel rank-order based dissimilarity. Our method, using only color cue, has been tested on widely-used benchmark datasets, showing significant performance improvement over the state-of-the-art.


title: Read-copy update: using execution history to solve concurrency problems, Parallel and Distributed Computing and Systems

abstract: Synchronization overhead, contention, and deadlock can pose severe challenges to designers and implementers of parallel programs. Therefore, many researchers have proposed update disciplines that solve these problems in restricted but commonly occurring situations. However, these proposals rely either on garbage collectors [7, 8], termination of all processes currently using the data structure [10], or expensive explicit tracking of all processes accessing the data structure [5, 15]. These mechanisms are inappropriate in many cases, such as within many operating-system kernels and server applications. This paper proposes a novel and extremely efficient mechanism, called read-copy update, and compares its performance to that of conventional locking primitives.


title: On flat-rate and usage-based pricing for tiered commodity internet services

abstract: In this note, we discuss issues pertaining to end-to-end quality- of-service management of commodity Internet applications and associated pricing incentive mechanisms. The issue of service differentiation is first studied using a simple two- class model including delay and throughput sensitive traffic. We show that by introducing service differentiation one can make more efficient use of resources, however this depends on the differences in required QoS as well as the typical capacities of the systems involved. As such, service differentiation may be more beneficial in lower capacity access networks than in high capacity core networks. We then focus on delay-sensitive and study flat-rate versus usage- based pricing under overload conditions. Our results suggest that in overload scenarios usage-based pricing is advantageous both from the system perspective, i.e., reduces degree of overload, and individual users' perspective, increases their perceived utilization.


Experiments with a Stemming Algorithm for Malay Words