title: Mobile Learning as 'Microlearning': Conceptual Considerations towards Enhancements of Didactic Thinking

abstract: This paper presents microlearning as a relational cross-over concept in the context of technological, societal, and cultural transformation. It maps the changing but ultimately convergent meanings of the term 'microlearning' as they have emerged and developed over the last few years. The author reveals microlearning to be not simply one approach among many, but instead as a perspective that applies to many aspects of education and learning including mobile learning. Furthermore, the question of a need for enhancement of didactic thinking is being presented. The answer is given in terms of four models such as the aggregation model, conglomerate model, emergence model, and the medium/form distinction, which are considered to be useful for the enhancement of didactic thinking.


title: Performance analysis of 3 DOF Delta parallel robot

abstract: Parallel robots have inherent advantages for many applications in the fields of robotics. They offer high dynamic capabilities combined with high accuracy and stiffness. There are a lot of performance criteria which have to be taken into account and which are pose dependent. The main idea of this paper is to present the fundamentals for a performance evaluation of the 3 DOF Delta parallel robot. Therefore we discuss a large number of performance criteria dealing with workspace, quality transmission, manipulability, dexterity and stiffness.


title: High performance air-filled substrate integrated waveguide filter post-process tuning using capacitive post

abstract: This paper presents an air-filled substrate integrated waveguide (AFSIW) filter post-process tuning technique. The emerging high-performance AFSIW technology is of high interest for the design of microwave and millimeter-wave substrate integrated systems based on low-cost multilayer printed circuit board (PCB) process. However, to comply with stringent specifications, especially for space, aeronautical and safety applications, a filter post-process tuning technique is desired. AFSIW single pole filter post-process tuning using a capacitive post is theoretically analyzed. It is demonstrated that a tuning of more than 3% of the resonant frequency is achieved at 21 GHz using a 0.3 mm radius post with a 40% insertion ratio. For experimental demonstration, a fourth-order AFSIW band pass filter operating in the 20.88 to 21.11 GHz band is designed and fabricated. Due to fabrication tolerances, it is shown that its performances are not in line with expected results. Using capacitive post tuning, characteristics are improved and agree with optimized results. This post-process tuning can be used for other types of substrate integrated devices.


title: Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics

abstract: UNLABELLED ::: Regionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroir ::: ::: ::: IMPORTANCE ::: Wine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity.


title: A Novel Graph-Based Fisher Kernel Method for Semi-supervised Learning

abstract: Graph-based semi-supervised learning methods play a key role in machine learning applications, particularly when no parametric information or other prior knowledge is available. Given a graph whose nodes represent the points and the weighted edges the relations between them, the goal is to predict the values of all unlabeled nodes exploiting the information provided by both label and unlabeled nodes. In this paper, we propose a novel graph-based approach for semi-supervised binary classification. The algorithm extends the Fisher Subspace estimation approaches by adopting a kernel graph covariance measure. This similarity measure defines a relation between nodes generalizing both the shortest path and the commute time distance. This quantity is called the sum-over-paths covariance. Experiments on synthetic and real-world datasets highlight that the proposed algorithm achieves better results with respect to those obtained by state-of-the-art competitors.


title: The mechanism study of ABS hydraulic control system brake pressure change rate

abstract: The change rate of brake pressure controlled precisely can realize the stable control of wheel state. Therefore, it is necessary to study the pressure change rate mechanism of hydraulic control system. In this paper, the joint simulation platform of Matlab and AMESim has been established, from which the relationship curves between component parameters of ABS hydraulic control unit and the change rate of brake pressure are got, and from which the relationship curves between control signals and the physical Characteristics of high speed switching valve are also got. The research results are provided the basis for the precise control of brake pressure change rate.


title: An In-depth Comparison of Subgraph Isomorphism Algorithms in Graph Databases

abstract: Finding subgraph isomorphisms is an important problem in many applications which deal with data modeled as graphs. While this problem is NP-hard, in recent years, many algorithms have been proposed to solve it in a reasonable time for real datasets using different join orders, pruning rules, and auxiliary neighborhood information. However, since they have not been empirically compared one another in most research work, it is not clear whether the later work outperforms the earlier work. Another problem is that reported comparisons were often done using the original authors' binaries which were written in different programming environments. In this paper, we address these serious problems by re-implementing five state-of-the-art subgraph isomorphism algorithms in a common code base and by comparing them using many real-world datasets and their query loads. Through our in-depth analysis of experimental results, we report surprising empirical findings.


title: Using Dynamic Time Warping to Find Patterns in Time Series

abstract: Knowledge discovery in databases presents many interesting challenges within the context of providing computer tools for exploring large data archives. Electronic data repositories are growing quickly and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detecting patterns in such data streams or time series is an important knowledge discovery task. This paper describes some preliminary experiments with a dynamic programming approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.


title: Antenna Selection in Spatial Modulation Systems

abstract: Novel transmit antenna selection techniques are conceived for Spatial Modulation (SM) systems and their symbol error rate (SER) performance is investigated. Specifically, low-complexity Euclidean Distance optimized Antenna Selection (EDAS) and Capacity Optimized Antenna Selection (COAS) are studied. It is observed that the COAS scheme gives a better SER performance than the EDAS scheme. We show that the proposed antenna selection based SM systems are capable of attaining a significant gain in signal-to-noise ratio (SNR) compared to conventional SM systems, and also outperform the conventional MIMO systems employing antenna selection at both low and medium SNRs.


title: RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

abstract: Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a “fast” reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data. In our proposed method, RL^2, the algorithm is encoded in the weights of the RNN, which are learned slowly through a general-purpose (“slow”) RL algorithm. The RNN receives all information a typical RL algorithm would receive, including observations, actions, rewards, and termination flags; and it retains its state across episodes in a given Markov Decision Process (MDP). The activations of the RNN store the state of the “fast” RL algorithm on the current (previously unseen) MDP. We evaluate RL^2 experimentally on both small-scale and large-scale problems. On the small-scale side, we train it to solve randomly generated multi-arm bandit problems and finite MDPs. After RL^2 is trained, its performance on new MDPs is close to human-designed algorithms with optimality guarantees. On the large-scale side, we test RL^2 on a vision-based navigation task and show that it scales up to high-dimensional problems.


title: Self-Mutilation of Genitalia Masquerading as Suicide: A Rare Case Report

abstract: Genital self-mutilation is an uncommon, but severe form of self-injury. The causes of self-mutilation of genitalia are multiple that is, from command hallucination in psychotic disorder, drug intoxication, mental retardation, gender dysphasia to personality disorders. Self-mutilation of genitalia as a method of suicide is very rare in depression. Here we report a case of 28 year old male, who presented to emergency department with self-mutilation of genitalia as an attempt of suicide. Thus the clinician should always be watchful while dealing with self-mutilation in psychiatric patients, as sometimes self-mutilation of genitalia in psychiatric patients can present as suicide.


title: Issues and Opinions - Information Technologies in Business: A Blueprint for Education and Research

abstract: How are business schools thinking about developing leaders for the emerging digital economy? Is there a set of core principles we can apply to thinking about the enabling potential of information technologies and their consequences for business and society? We present a business-centric framework and a technology-centric framework that together form a blueprint for answering these questions. The business-centric framework articulates three compelling reasons why information technology (IT) matters in business: (1) IT continually transform industry and society, (2) executive decisions about IT investments, governance, and strategy are critical to organizational success, and (3) deriving value from increasingly available data trails defines effective decision making in the digital economy. However, our conversations with the leadership of 45 business schools and our subsequent data indicate that business schools are challenged by effectively training future executives to think about these reasons and act on them as part of a forward-looking program of business education that is grounded in stable concepts. In response, the technology-centric framework provides a set of grounding concepts and stable principles about IT that have emerged over the last four decades, and leads to a natural set of consequences that can inform thinking about IT in business. We illustrate how these complementary frameworks---business and technology---can be combined to frame an educational program by outlining a set of key questions, by placing these questions in the context suggested by our frameworks, and by providing guidelines toward answering them. These questions also define a natural path for future research about IT in business and society that will lead to stronger intellectual foundations for the field and define future education that is better grounded in concepts and theories that emerge from academic research.


title: Depth map denoising using graph-based transform and group sparsity

abstract: Depth maps, characterizing per-pixel physical distance between objects in a 3D scene and a capturing camera, can now be readily acquired using inexpensive active sensors such as Microsoft Kinect. However, the acquired depth maps are often corrupted due to surface reflection or sensor noise. In this paper, we build on two previously developed works in the image denoising literature to restore single depth maps-i.e., to jointly exploit local smoothness and nonlocal self-similarity of a depth map. Specifically, we propose to first cluster similar patches in a depth image and compute an average patch, from which we deduce a graph describing correlations among adjacent pixels. Then we transform similar patches to the same graph-based transform (GBT) domain, where the GBT basis vectors are learned from the derived correlation graph. Finally, we perform an iterative thresholding procedure in the GBT domain to enforce group sparsity. Experimental results show that for single depth maps corrupted with additive white Gaussian noise (AWGN), our proposed NLGBT denoising algorithm can outperform state-of-the-art image denoising methods such as BM3D by up to 2.37dB in terms of PSNR.


title: Homography estimation using one ellipse correspondence and minimal additional information

abstract: In sport scenarios like football or basketball, we often deal with central views where only the central circle and some additional primitives like the central line and the central point or a touch line are visible. In this paper we first characterize, from a mathematical point of view, the set of homographies that project a given ellipse into the unit circle, next, using some extra minimal additional information like the knowledge of the position in the image of the central line and central point or a touch line we show a method to fully determine the plane homography. We present some experiments in sport scenarios to show the ability of the proposed method to properly recover the plane homography.


title: Conflict detection and resolution in QoS policy based management

abstract: In this paper, we present our policy based rule representation for a Diffserv network and show how they are translated and downloaded into the network element. One common problem in specifying rules for policy-based networks is the conflict detection problem, where rules may contradict or duplicate each other. We explore the algorithmic issues related to conflict detection problem and present a conflict detection algorithm. Performance results are also presented. An interactive Java-based policy management tool editor was designed for specifying the rules and detecting the conflicts among rules


title: Pencil-like sketch rendering of 3D scenes using trajectory planning and dynamic tracking

abstract: Objective: We present a new non-photorealistic rendering method to render 3D scenes in the form of pencil-like sketches. Methods: This work is based on the observation that the dynamic feedback mechanism involving the human visual system and the motor control of the hand collectively generates the visual characteristics unique to hand-drawn sketches. At the heart of our approach is a trajectory planning and tracking algorithm that generate the sketch in multiple layers using a dynamic pen model. On each layer, a set of target strokes are generated from the silhouette lines, edges, and shaded regions which serve as the target trajectory for a closed-loop dynamic pen model. The pen model then produces the rendered sketch, whose characteristics can be adjusted with a set of trajectory and tracking parameters. This process continues in several layers until the tonal difference between the sketch and the original 3D render is minimized. Results: We demonstrate our approach with examples that are created by controlling the parameters of our sketch rendering algorithms. Conclusion: The examples not only show typical sketching artifacts that are common to human-drawn sketches but also demonstrate that it is capable of producing multiple sketching styles.


title: Fabrication of MOF-5 membranes using microwave-induced rapid seeding and solvothermal secondary growth

abstract: Here we report the fabrication of continuous MOF-5 membranes via a secondary growth method. Densely packed MOF-5 seed layers on porous α-alumina substrates were rapidly prepared using microwave-induced thermal deposition (MITD) method. The MOF-5 seed layers were then solvothermally treated to grow into continuous MOF-5 membranes. The presence of a proton scavenging amine in precursor solutions during the secondary growth step was found to be critical to prevent the seed crystals from dissolving, yet enabling the growth of the MOF seed crystals into a continuous film. For the first time, preferentially oriented MOF-5 membranes were prepared by growing from oriented MOF-5 seed layers. Permeation results of MOF-5 membranes indicate Knudsen transport of small gas molecules, consistent with a previously reported result.


title: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems

abstract: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and one- and two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.


title: Auditing subtype inconsistencies among gene ontology concepts

abstract: Gene Ontology (GO) provides a controlled vocabulary for describing genes and related gene products. Quality assurance of Gene ontology (GO) is a vital aspect of the terminology management lifecycle. In this paper, we introduce a lexical-based inference approach to detecting subtype (or isa) inconsistencies among GO terms (i.e., biological concepts). We first model the name of each concept as a set of words. Then, we generate hierarchically linked and unlinked pairs of concepts (A, B), where A and B have the same number of words, and contain common words as well as a single different word. Each linked concept-pair infers a linked term-pair, and each unlinked concept-pair infers an unlinked term-pair. A term-pair appearing as both linked and unlinked is considered a potential inconsistency, which may represent a subtype inconsistency between the original linked and unlinked concept-pair. Applying this approach to the 03/28/2017 release of GO, a total of 3,715 potential subtype inconsistencies were obtained. Evaluation of a random sample of potential inconsistencies revealed two types of potential errors: missing subtype relations and incorrect subtype relations in GO, and achieved an accuracy of 56.33% for detecting such errors. This indicates that this lexical-based inference approach using the set-of-words model is a promising way to facilitate quality improvement of GO.


title: Multiple Instance Learning: A Survey of Problem Characteristics and Applications

abstract: Abstract Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at https://github.com/macarbonneau/MILSurvey .


title: KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

abstract: We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.


A Hierarchical Model for Text Autosummarization


title: Enumeration of Extractive Oracle Summaries

abstract: To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.


title: Asynchronous consensus and broadcast protocols

abstract: A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes that can also send false messages. The class of asynchronous systems with fair schedulers is defined, and consensus protocols that terminate with probability 1 for these systems are investigated. With fail-stop processes, it is shown that ⌈( n + 1)/2⌉ correct processes are necessary and sufficient to reach agreement. In the malicious case, it is shown that ⌈(2 n + 1)/3⌉ correct processes are necessary and sufficient to reach agreement. This is contrasted with an earlier result, stating that there is no consensus protocol for the fail-stop case that always terminates within a bounded number of steps, even if only one process can fail. The possibility of reliable broadcast (Byzantine Agreement) in asynchronous systems is also investigated. Asynchronous Byzantine Agreement is defined, and it is shown that ⌈(2 n + 1)/3⌉ correct processes are necessary and sufficient to achieve it.


title: permission. Digital Pulse–Width Modulation Control in Power Electronic Circuits: Theory and Applications

abstract: Digital Pulse-Width Modulation Control in Power Electronic Circuits: Theory and Applications


Shuchi Grover and Roy Pea 12 : A Review of the State of the Field − Computational Thinking in


title: General duality between optimal control and estimation

abstract: Optimal control and estimation are dual in the LQG setting, as Kalman discovered, however this duality has proven difficult to extend beyond LQG. Here we obtain a more natural form of LQG duality by replacing the Kalman-Bucy filter with the information filter. We then generalize this result to non-linear stochastic systems, discrete stochastic systems, and deterministic systems. All forms of duality are established by relating exponentiated costs to probabilities. Unlike the LQG setting where control and estimation are in one-to-one correspondence, in the general case control turns out to be a larger problem class than estimation and only a sub-class of control problems have estimation duals. These are problems where the Bellman equation is intrinsically linear. Apart from their theoretical significance, our results make it possible to apply estimation algorithms to control problems and vice versa.


title: A Probabilistic Generative Model for Mining Cybercriminal Networks from Online Social Media

abstract: There has been a rapid growth in the number of cybercrimes that cause tremendous financial loss to organizations. Recent studies reveal that cybercriminals tend to collaborate or even transact cyber-attack tools via the "dark markets" established in online social media. Accordingly, it presents unprecedented opportunities for researchers to tap into these underground cybercriminal communities to develop better insights about collaborative cybercrime activities so as to combat the ever increasing number of cybercrimes. The main contribution of this paper is the development of a novel weakly supervised cybercriminal network mining method to facilitate cybercrime forensics. In particular, the proposed method is underpinned by a probabilistic generative model enhanced by a novel context-sensitive Gibbs sampling algorithm. Evaluated based on two social media corpora, our experimental results reveal that the proposed method significantly outperforms the Latent Dirichlet Allocation (LDA) based method and the Support Vector Machine (SVM) based method by 5.23% and 16.62% in terms of Area Under the ROC Curve (AUC), respectively. It also achieves comparable performance as the state-of-the-art Partially Labeled Dirichlet Allocation (PLDA) method. To the best of our knowledge, this is the first successful research of applying a probabilistic generative model to mine cybercriminal networks from online social media.


title: 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

abstract: Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks. We demonstrate the strong performance of the resulting models, called submanifold sparse convolutional networks (SSCNs), on two tasks involving semantic segmentation of 3D point clouds. In particular, our models outperform all prior state-of-the-art on the test set of a recent semantic segmentation competition.


title: Competitive coding scheme for palmprint verification

abstract: There is increasing interest in the development of reliable, rapid and non-intrusive security control systems. Among the many approaches, biometrics such as palmprints provide highly effective automatic mechanisms for use in personal identification. This paper presents a new method for extracting features from palmprints using the competitive coding scheme and angular matching. The competitive coding scheme uses multiple 2-D Gabor filters to extract orientation information from palm lines. This information is then stored in a feature vector called the competitive code. The angular matching with an effective implementation is then defined for comparing the proposed codes, which can make over 9,000 comparisons within 1s. In our testing database of 7,752 palmprint samples from 386 palms, we can achieve a high genuine acceptance rate of 98.4% and a low false acceptance rate of 3/spl times/10/sup -6/%. The execution time for the whole process of verification, including preprocessing, feature extraction and final matching, is 1s.


title: Immuno-Inspired Autonomic System for Cyber Defense. Information Security

abstract: The biological immune system is an autonomic system for self-protection, which has evolved over millions of years probably through extensive redesigning, testing, tuning and optimization process. The powerful information processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors for its artificial counterpart. Our study focuses on building an autonomic defense system, using some immunological metaphors for information gathering, analyzing, decision making and launching threat and attack responses. This on-going research effort is not to mimic the nature but to explore and learn valuable lessons useful for self-adaptive cyber defense systems.


title: A mobile interface for navigating hierarchical information space

abstract: This paper presents ERELT (Enhanced Radial Edgeless Tree), a tree visualization approach on modern mobile devices. ERELT is designed to offer a clear visualization of any tree structure with intuitive interaction. Such visualization can assist users in interacting with a hierarchical structure such as a media collection, file system, etc.In the ERELT visualization, a subset of the tree is displayed at a time. The displayed tree size depends on the maximum number of tree elements that can be put on the screen while maintaining clarity. Users can quickly navigate to the hidden parts of the tree through touch-based gestures. We have conducted a user study to evaluate this visualization for a music collection. The study results show that this approach reduces the time and effort in navigating tree structures for exploration and search tasks. We implemented a tree visualization as a UI for hierarchical structures for phones.We compared our UI against traditional list layout found in modern smartphones.We displayed media library in a music app as tree and list UI for user tests.The tree UI requires lesser number of touches than list UI for most of the tasks.The tree UI takes lesser time for exploration and search in hierarchical structure.


title: Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement

abstract: Improved traffic camera calibration method based on vanishing points detection.Automatic scene scale inference by alignment of 3D model bounding box.Extensive experiments on a recent speed measurement dataset BrnoCompSpeed.Results show that our method outpeforms manual camera calibration.Mean speed measurement error of the method is 1.10km/h. Display Omitted In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86% 7.98km/h to 1.10km/h) and manual calibration (error reduction by 19% 1.35km/h to 1.10km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day).


Neural Tensor Networks with Diagonal Slice Matrices


title: Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

abstract: As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles aggregated from multiple sources. Such a hand-pick process is labor intensive and time-consuming. In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool. Our data analysis shows that (i) editors' selection criteria are non-explicit, which are less based only on the keywords or topics, but more depend on the quality and attractiveness of the writing from the candidate article, which is hard to capture based on traditional bag-of-words article representation. And (ii) editors' article selection behaviors are dynamic: articles with different data distribution come into the pool everyday and the editors' preference varies, which are driven by some underlying periodic or occasional patterns. To address such problems, we propose a meta-attention model across multiple deep neural nets to (i) automatically catch the editors' underlying selection criteria via the automatic representation learning of each article and its interaction with the meta data and (ii) adaptively capture the change of such criteria via a hybrid attention model. The attention model strategically incorporates multiple prediction models, which are trained in previous days. The system has been deployed in a commercial article feed platform. A 9-day A/B testing has demonstrated the consistent superiority of our proposed model over several strong baselines.


title: Anomaly Detection using One-Class Neural Networks

abstract: We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is suboptimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods.


title: Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning

abstract: Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn. Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence. In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient. Experimental results on a simulated robot locomotion task show that APDO achieves better sample efficiency and faster convergence than state-of-the-art approaches for CMDPs.


title: Capacity of Generalized UTRA FDD Closed-Loop Transmit Diversity Modes

abstract: Transmit diversity techniques have received a lot of attention recently, and open-loop and closed-loop downlink transmit diversity modes for two transmit antennae have been included into universal terrestrial radio access (UTRA) frequency division duplex (FDD) specification. Closed-loop modes provide larger system capacity than open-loop modes, but they need additional side information of the downlink channel in the transmitter. In FDD systems this requires a separate feedback channel. Quantization of channel state information (CSI) in closed-loop transmit diversity schemes decreases the performance when compared to a closed-loop system where the transmitter has access to complete CSI. In this paper, we analyze the effect of quantization of CSI and deduce approximate capacity formulae for closed-loop transmit diversity schemes that are generalizations of the closed-loop schemes included in UTRA FDD specification. Moreover, we calculate approximation error and show by simulations that our approximation is tight for flat Rayleigh fading environments with and without fast transmit power control.


title: A +22dBm IIP3 and 3.5dB NF wideband receiver with RF and baseband blocker filtering techniques

abstract: The real challenge in designing wide-band receivers is the ability to tolerate out of band blockers. In this paper, different blocker rejection techniques are proposed to significantly improve the linearity. The blockers are first rejected prior to the LNA, then, a novel base-band blocker filtering technique further rejects the blockers at the TIA input. A dual mixer architecture is also employed to further attenuate blockers. Finally, a very low impedance TIA is designed to improve the linearity of the entire receiver chain. The receiver has an IIP3 of +22 dBm and a NF of 3.5 dB in 0.18 μm CMOS technology.


title: BUNDLE ADJUSTMENT FOR MULTI-CAMERA SYSTEMS WITH POINTS AT INFINITY

abstract: Abstract. We present a novel approach for a rigorous bundle adjustment for omnidirectional and multi-view cameras, which enables an efficient maximum-likelihood estimation with image and scene points at infinity. Multi-camera systems are used to increase the resolution, to combine cameras with different spectral sensitivities (Z/I DMC, Vexcel Ultracam) or – like omnidirectional cameras – to augment the effective aperture angle (Blom Pictometry, Rollei Panoscan Mark III). Additionally multi-camera systems gain in importance for the acquisition of complex 3D structures. For stabilizing camera orientations – especially rotations – one should generally use points at the horizon over long periods of time within the bundle adjustment that classical bundle adjustment programs are not capable of. We use a minimal representation of homogeneous coordinates for image and scene points. Instead of eliminating the scale factor of the homogeneous vectors by Euclidean normalization, we normalize the homogeneous coordinates spherically. This way we can use images of omnidirectional cameras with single-view point like fisheye cameras and scene points, which are far away or at infinity. We demonstrate the feasibility and the potential of our approach on real data taken with a single camera, the stereo camera FinePix Real 3D W3 from Fujifilm and the multi-camera system Ladybug 3 from Point Grey.


title: A survey on vision-based fall detection

abstract: Falls are a major cause of fatal injury for the elderly population. To improve the quality of living for seniors, a wide range of monitoring systems with fall detection functionality have been proposed over recent years. This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured. Existing fall detection methods can be categorized as using sensors, or being exclusively vision-based. This literature review focuses on vision-based methods.


title: Development of Embedded STT-MRAM for Mobile System-on-Chips

abstract: Spin-transfer-torque magnetoresistive random access memory (STT-MRAM) has been considered a promising technology for future mobile system-on-chip memories due to negligible static leakage power, high-speed read/write operations, and unlimited read/write endurance. In this paper, we address key challenges and recent advances for enabling embedded STT-MRAM on a deeply scaled CMOS logic platform.


title: Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

abstract: In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tasks on a composite performance model.


title: A Joint Framework for Coreference Resolution and Mention Head Detection

abstract: In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred mentions. However, there are significant gaps between the performance on gold mentions and the performance on the real problem, when mentions are predicted from raw text via an imperfect Mention Detection (MD) module. Motivated by the goal of reducing such gaps, we develop an ILP-based joint coreference resolution and mention head formulation that is shown to yield significant improvements on coreference from raw text, outperforming existing state-ofart systems on both the ACE-2004 and the CoNLL-2012 datasets. At the same time, our joint approach is shown to improve mention detection by close to 15% F1. One key insight underlying our approach is that identifying and co-referring mention heads is not only sufficient but is more robust than working with complete mentions.


title: Laws of order: expensive synchronization in concurrent algorithms cannot be eliminated

abstract: Building correct and efficient concurrent algorithms is known to be a difficult problem of fundamental importance. To achieve efficiency, designers try to remove unnecessary and costly synchronization. However, not only is this manual trial-and-error process ad-hoc, time consuming and error-prone, but it often leaves designers pondering the question of: is it inherently impossible to eliminate certain synchronization, or is it that I was unable to eliminate it on this attempt and I should keep trying? In this paper we respond to this question. We prove that it is impossible to build concurrent implementations of classic and ubiquitous specifications such as sets, queues, stacks, mutual exclusion and read-modify-write operations, that completely eliminate the use of expensive synchronization. We prove that one cannot avoid the use of either: i) read-after-write (RAW), where a write to shared variable A is followed by a read to a different shared variable B without a write to B in between, or ii) atomic write-after-read (AWAR), where an atomic operation reads and then writes to shared locations. Unfortunately, enforcing RAW or AWAR is expensive on all current mainstream processors. To enforce RAW, memory ordering--also called fence or barrier--instructions must be used. To enforce AWAR, atomic instructions such as compare-and-swap are required. However, these instructions are typically substantially slower than regular instructions. Although algorithm designers frequently struggle to avoid RAW and AWAR, their attempts are often futile. Our result characterizes the cases where avoiding RAW and AWAR is impossible. On the flip side, our result can be used to guide designers towards new algorithms where RAW and AWAR can be eliminated.


title: A One-Hour Curriculum to Engage Middle School Students in Robotics and Computer Science Using Cubelets

abstract: Robotics has become a standard K-12 outreach tool and for attracting students to the STEM disciplines. Performing these activities in the class room usually requires substantial time commitment by the teacher and integration into the curriculum requires major effort, which makes spontaneous and short-term engagements difficult. This paper studies using “Cubelets”, a modular robotic construction kit, which requires virtually no setup time and allows substantial engagement and change of perception of STEM in as little as a 1-hour session. This paper describes the constructivist curriculum and provides qualitative and quantitative results on perception changes with respect to STEM and computer science in particular as a field of study.


title: Estimating heart rate using wrist-type Photoplethysmography and acceleration sensor while running

abstract: This study provides Heart Rate (HR) Estimation using wrist-type Photoplethysmogpraphy (PPG) sensor while the subject is running. We propose the algorithm to estimate heart rate for the wrist-type PPG sensor. Since body motion artifacts easily affect the arm portion, our method in this study also uses accelerometer built in the wrist-type sensor to improve the accuracy of heart rate estimation. Our method has two components. One is rejecting artifacts with the power spectrum's difference between PPG and acceleration obtained by frequency analysis. The other is the reliability of heart rate estimation, defined by the acceleration. Experimental results while our test subjects were running came closer to the holter Electrocardiogram (ECG) in high accuracy (r= 0.98, SD= 8.7 bpm). We, therefore, report the heart rate estimation method which has a higher degree of usability compared to existing methods using ECG.


title: Optical proximity correction (OPC): friendly maze routing

abstract: As the technology migrates into the deep submicron manufacturing(DSM) era, the critical dimension of the circuits is getting smaller than the lithographic wavelength. The unavoidable light diffraction phenomena in the sub-wavelength technologies have become one of the major factors in the yield rate. Optical proximity correction (OPC) is one of the methods adopted to compensate for the light diffraction effect as a post layout process.However, the process is time-consuming and the results are still limited by the original layout quality. In this paper, we propose a maze routing method that considers the optical effect in the routing algorithm. By utilizing the symmetrical property of the optical system, the light diffraction is efficiently calculated and stored in tables. The costs that guide the router to minimize the optical interferences are obtained from these look-up tables. The problem is first formulated as a constrained maze routing problem, then it is shown to be a multiple constrained shortest path problem. Based on the Lagrangian relaxation method, an effective algorithm is designed to solve the problem.


title: A 1 mW 71.5 dB SNDR 50 MS/s 13 bit Fully Differential Ring Amplifier Based SAR-Assisted Pipeline ADC

abstract: This paper presents a 13 bit 50 MS/s fully differential ring amplifier based SAR-assisted pipeline ADC, implemented in 65 nm CMOS. We introduce a new fully differential ring amplifier, which solves the problems of single-ended ring amplifiers while maintaining the benefits of high gain, fast slew based charging and an almost rail-to-rail output swing. We implement a switched-capacitor (SC) inter-stage residue amplifier that uses this new fully differential ring amplifier to give accurate amplification without calibration. In addition, a new floated detect-and-skip (FDAS) capacitive DAC (CDAC) switching method reduces the switching energy and improves linearity of first-stage CDAC. With these techniques, the prototype ADC achieves measured SNDR, SNR, and SFDR of 70.9 dB (11.5b), 71.3 dB and 84.6 dB, respectively, with a Nyquist frequency input. The prototype achieves 13 bit linearity without calibration and consumes 1 mW. This measured performance is equivalent to Walden and Schreier FoMs of 6.9 fJ/conversion $\cdot$ step and 174.9 dB, respectively.


title: A Study in Machine Learning from Imbalanced Data for Sentence Boundary Detection in Speech

abstract: Abstract Enriching speech recognition output with sentence boundaries improves its human readability and enables further processing by downstream language processing modules. We have constructed a hidden Markov model (HMM) system to detect sentence boundaries that uses both prosodic and textual information. Since there are more nonsentence boundaries than sentence boundaries in the data, the prosody model, which is implemented as a decision tree classifier, must be constructed to effectively learn from the imbalanced data distribution. To address this problem, we investigate a variety of sampling approaches and a bagging scheme. A pilot study was carried out to select methods to apply to the full NIST sentence boundary evaluation task across two corpora (conversational telephone speech and broadcast news speech), using both human transcriptions and recognition output. In the pilot study, when classification error rate is the performance measure, using the original training set achieves the best performance among the sampling methods, and an ensemble of multiple classifiers from different downsampled training sets achieves slightly poorer performance, but has the potential to reduce computational effort. However, when performance is measured using receiver operating characteristics (ROC) or area under the curve (AUC), then the sampling approaches outperform the original training set. This observation is important if the sentence boundary detection output is used by downstream language processing modules. Bagging was found to significantly improve system performance for each of the sampling methods. The gain from these methods may be diminished when the prosody model is combined with the language model, which is a strong knowledge source for the sentence detection task. The patterns found in the pilot study were replicated in the full NIST evaluation task. The conclusions may be dependent on the task, the classifiers, and the knowledge combination approach.


title: Audience effects: what can they tell us about social neuroscience, theory of mind and autism?

abstract: An audience effect arises when a person’s behaviour changes because they believe someone else is watching them. Though these effects have been known about for over 110 years, the cognitive mechanisms of the audience effect and how it might vary across different populations and cultures remains unclear. In this review, we examine the hypothesis that the audience effect draws on implicit mentalising abilities. Behavioural and neuroimaging data from a number of tasks are consistent with this hypothesis. We further review data suggest that how people respond to audiences may vary over development, personality factors, cultural background and clinical diagnosis including autism and anxiety disorder. Overall, understanding and exploring the audience effect may contribute to our models of social interaction, including reputation management and mentalising.


title: CookiExt: Patching the browser against session hijacking attacks

abstract: Session cookies constitute one of the main attack targets against client authentication on the Web. To counter these attacks, modern web browsers implement native cookie protection mechanisms based on the HttpOnly and Secure ags. While there is a general understanding about the eectiveness of these defenses, no formal result has so far been proved about the security guarantees they convey. With the present paper we provide the rst such result, by presenting a mechanized proof of noninterference assessing the robustness of the HttpOnly and Secure cookie ags against both web and network attackers with the ability to perform arbitrary XSS code injection. We then develop CookiExt, a browser extension that provides client-side protection against session hijacking, based on appropriate agging of session cookies and automatic redirection over HTTPS for HTTP requests carrying these cookies. Our solution improves over existing client-side defenses by combining protection against both web and network attacks, while at the same time being designed so as to minimise its eects on the user's browsing experience. Finally, we report on the experiments we carried out to practically evaluate the eectiveness of our approach.


title: Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers

abstract: This paper presents a machine vision system for automatic recognition of plant leaves diseases from images. The proposed system is based on serial combination technique of two SVM classifiers. The first classifier uses the color to classify the images; it considers, at this phase, that the diseases with similar or nearest color belonging to the same class. Then, the second classifier is used to differentiate between the classes with similar color according to the shape and texture features. The tests of this study are carried out on six classes of diseases including three types of pest insects damages (Leaf miners, Thrips and Tuta absoluta) and three forms of pathogens symptoms (Early blight, Late blight and Powdery mildew). The results of the study show the advantages of the proposed method compared to the other existing methods.


title: A joint model for IEEE 802.15.4 physical and medium access control layers

abstract: Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.


title: Voice Activity Detection: Merging Source and Filter-based Information

abstract: Voice Activity Detection (VAD) refers to the problem of distinguishing speech segments from background noise. Numerous approaches have been proposed for this purpose. Some are based on features derived from the power spectral density, others exploit the periodicity of the signal. The goal of this letter is to investigate the joint use of source and filter-based features. Interestingly, a mutual information-based assessment shows superior discrimination power for the source-related features, especially the proposed ones. The features are further the input of an artificial neural network-based classifier trained on a multi-condition database. Two strategies are proposed to merge source and filter information: feature and decision fusion. Our experiments indicate an absolute reduction of 3% of the equal error rate when using decision fusion. The final proposed system is compared to four state-of-the-art methods on 150 minutes of data recorded in real environments. Thanks to the robustness of its source-related features, its multi-condition training and its efficient information fusion, the proposed system yields over the best state-of-the-art VAD a substantial increase of accuracy across all conditions (24% absolute on average).


title: A 28-GHz 32-Element TRX Phased-Array IC With Concurrent Dual-Polarized Operation and Orthogonal Phase and Gain Control for 5G Communications

abstract: This paper presents the first reported 28-GHz phased-array IC for 5G communications. Implemented in 130-nm SiGe BiCMOS, the IC includes 32 TRX elements and features concurrent independent beams in two polarizations in either TX or RX operation. Circuit techniques to enable precise beam steering, orthogonal phase and amplitude control at each front end, and independent tapering and beam steering at the array level are presented. A TX/RX switch design is introduced which minimizes TX path loss resulting in 13.5 dBm/16 dBm Op1dB/Psat per front end with >20% peak power added efficiency of the power amplifier (including switch and off-mode LNA) while maintaining a 6 dB noise figure in the low noise amplifier (including switch and off-mode PA). Comprehensive on-wafer measurement results for the IC across multiple samples and temperature variation are presented. A package with four ICs and 64 dual-polarized antennas provides eight 16-element or two 64-element concurrent beams with 1.4°/step beam steering (<0.6° rms error) across a ±50° steering range without requiring calibration. A maximum saturated effective isotropic radiated power of 54 dBm is measured in the broadside direction for each polarization. Tapering control without requiring calibration achieves up to 20-dB sidelobe rejection without affecting the main lobe direction.


title: Requirement for Mdm2 in the survival effects of Bcr-Abl and interleukin 3 in hematopoietic cells.

abstract: The p53/Mdm2 pathway plays an important role in the induction of cell cycle arrest or apoptosis in response to genotoxic stress. Both the oncogene Bcr-Abl and physiological growth factors such as interleukin (IL)-3 can modulate the outcome of cellular exposure to DNA damage. To determine whether Bcr-Abl and growth factors can affect the p53/Mdm2 pathway, we studied the expression of Mdm2 in the IL-3-dependent pre-B cell line BaF3 and its bcr-abl-transfected derivative BaF3p185 after IL-3 deprivation or treatment with the c-Abl tyrosine kinase inhibitor STI571. We found that both growth factor withdrawal and inhibition of Bcr-Abl kinase lead to a down-regulation of Mdm2 preceding the induction of apoptosis. Apoptotic cell death induced by STI571 is partially dependent on p53. The early decrease of Mdm2 protein was not attributable to transcriptional regulation or to caspase-mediated cleavage. On the other hand, it could be completely blocked by the proteasomal inhibitor lactacystin. Targeted down-regulation of Mdm2 protein by antisense oligodeoxynucleotides overcame the survival effects of IL-3 and Bcr-Abl and resulted in accelerated apoptosis. Taken together, survival signals provided either by physiological growth factors or by oncogenic Bcr-Abl can positively regulate Mdm2, whereas Mdm2 ablation can reduce cell survival. These findings imply that, similarly to physiological growth factors such as IL-3, Bcr-Abl can promote cell survival through modulating the p53-Mdm2 pathway.


title: Voltage Pulse Profile Characteristics with Space Charge of a Loaded Pulsed Ionization Chamber

abstract: An analytical model describing the voltage pulse profile of a pulsed ionization chamber and its relationship to the electron density in a field drift dominated plasma is formulated. The differential equation derived from the equations of motion and conservation of electron density combined with Poisson's equation for the electric space‐charge field in the system is solved analytically for the cylindrical‐electrode geometry with an external RC circuit. The numerical analysis for the given initial and boundary conditions yields the anode voltage‐signal pulse profiles for the period of electron collection as a function of the initial electron density, the gas pressure, and ion‐chamber radii and length. Thus, the theory permits the determination of the electron density from the observed voltage signal.


title: Smart traffic light for congestion monitoring using LoRaWAN

abstract: Traffic light congestion normally occurs in urban areas where the number of vehicles is too many on the road. This problem drives the need for innovation and provide efficient solutions regardless this problem. Smart system that will monitor the congestion level at the traffic light will be a new option to replace the old system which is not practical anymore. Implementing internet of thinking (IoT) technology will provide the full advantage for monitoring and creating a congestion model based on sensor readings. Multiple sensor placements for each lane will give a huge advantage in detecting vehicle and increasing the accuracy in collecting data. To gather data from each sensor, the LoRaWAN technology is utilized where it features low power wide area network, low cost of implementation and the communication is secure bi-directional for the internet of thinking. The radio frequency used between end nodes to gateways range is estimated around 15-kilometer radius. A series of test is carried out to estimate the range of signal and it gives a positive result. The level of congestion for each lane will be displayed on Grafana dashboard and the algorithm can be calculated. This provides huge advantages to the implementation of this project, especially the scope of the project will be focus in urban areas where the level of congestion is bad.


title: Characterizing Thermal Energy Consumption through Exploratory Data Mining Algorithms

abstract: Nowadays large volumes of energy data are continuously collected through a variety of meters from dierent smart-city environments. Such data have a great potential to influence the overall energy balance of our communities by optimizing building energy consumption and by enhancing people's awareness of energy wasting. This paper presents FARTEC, a data mining engine based on exploratory and unsupervised data mining algorithms to characterize building energy consumption together with meteorological conditions. FARTEC exploits a joint approach coupling cluster analysis and association rules. First, a partitional clustering algorithm is applied to weather conditions to discover groups of thermal energy consumption that occurred in similar weather conditions. Each computed cluster is then locally characterized through a set of association rules to ease the manual inspection of the most interesting correlations between thermal consumption and weather conditions. FARTEC also includes a categorization of the rules into a few groups according to their meaning. Each group is determined by the data features appearing in the rule. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering interesting knowledge items to raise people's awareness of their energy consumption


title: Exfiltration of information from air-gapped machines using monitor's LED indicator

abstract: In this paper we present a new approach for data exfiltration by leaking data from monitor's LED to Smartphone's camera. The new approach may be used by attackers to leak valuable information from the organization as part of an Advanced Persistent Threat (APT). The proof of concept that was developed is described in the paper followed by a description of an experiment that demonstrates that practically people are not aware of the attack. We propose ways that will facilitate the detection of such threats and some possible countermeasures.


title: Transparent ROP Exploit Mitigation using Indirect Branch Tracing

abstract: Return-oriented programming (ROP) has become the primary exploitation technique for system compromise in the presence of non-executable page protections. ROP exploits are facilitated mainly by the lack of complete address space randomization coverage or the presence of memory disclosure vulnerabilities, necessitating additional ROP-specific mitigations. ::: ::: In this paper we present a practical runtime ROP exploit prevention technique for the protection of third-party applications. Our approach is based on the detection of abnormal control transfers that take place during ROP code execution. This is achieved using hardware features of commodity processors, which incur negligible runtime overhead and allow for completely transparent operation without requiring any modifications to the protected applications. Our implementation for Windows 7, named kBouncer, can be selectively enabled for installed programs in the same fashion as user-friendly mitigation toolkits like Microsoft's EMET. The results of our evaluation demonstrate that kBouncer has low runtime overhead of up to 4%, when stressed with specially crafted workloads that continuously trigger its core detection component, while it has negligible overhead for actual user applications. In our experiments with in-the-wild ROP exploits, kBouncer successfully protected all tested applications, including Internet Explorer, Adobe Flash Player, and Adobe Reader.


title: Advanced Modulation Formats for High-Capacity Optical Transport Networks

abstract: Advanced optical modulation formats have become a key ingredient to the design of modern wavelength-division-multiplexed (WDM) optically routed networks. In this paper, we review the generation and detection of multigigabit/second intensity- and phase-modulated formats and highlight their resilience to key impairments found in optical networking, such as optical amplifier noise, chromatic dispersion, polarization-mode dispersion, WDM crosstalk, concatenated optical filtering, and fiber nonlinearity


title: Segmentation and recognition of text written in 3D using Leap motion interface

abstract: In this paper, we present a word extraction and recognition methodology from online cursive handwritten text-lines recorded by Leap motion controller The online text, drawn by 3D gesture in air, is distinct from usual online pen-based strokes. The 3D gestures are recorded in air, hence they produce often non-uniform text style and jitter-effect while writing. Also, due to the constraint of writing in air, the pause of stroke-flow between words is missing. Instead all words and lines are connected by a continuous stroke. In this paper, we have used a simple but effective heuristic to segment words written in air. Here, we propose a segmentation methodology of continuous 3D strokes into text-lines and words. Separation of text lines is achieved by heuristically finding the large gap-information between end and start-positions of successive text lines. Word segmentation is characterized in our system as a two class problem. In the next phase, we have used Hidden Markov Model-based approach to recognize these segmented words. Our experimental validation with a large dataset consisting with 320 sentences reveals that the proposed heuristic based word segmentation algorithm performs with accuracy as high as 80.3%c and an accuracy of 77.6% has been recorded by HMM-based word recognition when these segmented words are fed to HMM. The results show that the framework is efficient even with cluttered gestures.


title: DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy

abstract: We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7 million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.


title: The Internet of Things: a security point of view

abstract: Purpose – The purpose of this paper is to provide an in-depth overview of the security requirements and challenges for Internet of Things (IoT) and discuss security solutions for various enabling technologies and implications to various applications. Design/methodology/approach – Security requirements and solutions are analysed based on a four-layer framework of IoT on sensing layer, network layer, service layer, and application layer. The cross-layer threats are analysed followed by the security discussion for the enabling technologies including identification and tracking technologies, WSN and RFID, communication, networks, and service management. Findings – IoT calls for new security infrastructure based on the new technical standards. As a consequence, new security design for IoT shall pay attention to these new standards. Security at both the physical devices and service-applications is critical to the operation of IoT, which is indispensable for the success of IoT. Open problems remain in a number o...


title: One millisecond face alignment with an ensemble of regression trees

abstract: This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.


title: The research of development capability and application of schedule estimation method

abstract: The most rapid development often stems from a exact schedule plan, the schedule problems of each project should be found answers from the schedule realizable. In a actual project management, schedule estimating and establishing are difficulties for a long time, these difficulties not only for we do not have historical data and also for we do not have realizable and easy estimating methods. Therefore it is necessary for us to study methods of schedule estimation and how to practice it in project planning. Therefore, the article focus on development capability analysis, research the methods and quantitative analysis model in software product project schedule estimate and quality evaluation filed, and apply those methods to practice in project management.


title: Anatomy of the medial femoral circumflex artery with respect to the vascularity of the femoral head

abstract: We performed a series of 16 anatomical dissections on Caucasian cadaver material to determine the surgical anatomy of the medial femoral circumflex artery (MFCA) and its anastomoses. These confirmed that the femoral head receives its blood supply primarily from the MFCA via a group of posterior superior nutrient arteries and the posterior inferior nutrient artery. In terms of anastomoses that may also contribute to the blood supply, the anastomosis with the inferior gluteal artery, via the piriformis branch, is the most important. These dissections provide a base of knowledge for further radiological studies on the vascularity of the normal femoral head and its vascularity after dislocation of the hip.


title: Response procedure, memory, and dichotic emotion recognition

abstract: Abstract Three experiments investigated the role of memory and rehearsal in a dichotic emotion recognition task by manipulating the response procedure as well as the interval between encoding and retrieval while taking into account order of report. For all experiments, right-handed undergraduates were presented with dichotic pairs of the words bower, dower, power, and tower pronounced in a sad, angry, happy, or neutral tone of voice. Participants were asked to report the two emotions presented on each trial by clicking on the corresponding drawings or words on a computer screen, either following no delay or a five second delay. Experiment 1 applied the delay conditions as a between-subjects factor whereas it was a within-subject factor in Experiment 2. In Experiments 1 and 2, more correct responses occurred for the left than the right ear, reflecting a left ear advantage (LEA) that was slightly larger with a nonverbal than a verbal response. The LEA was also found to be larger with no delay than with the 5 s delay. In addition, participants typically responded first to the left ear stimulus. In fact, the first response produced a LEA whereas the second response produced a right ear advantage. Experiment 3 involved a concurrent task during the delay to prevent rehearsal. In Experiment 3, the pattern of results supported the claim that rehearsal could account for the findings of the first two experiments. The findings are interpreted in the context of the role of rehearsal and memory in models of dichotic listening.


title: Adopt , Adapt , Enact or Use ? – A Framework and Methodology for Extracting and Integrating Conceptual Mechanisms of IT Adoption and Use

abstract: Information Systems (IS) are omnipresent in today’s organizations. While much research has been performed on adoption, implementation, and use of IS, still many practitioners are faced with IS change endeavors in organizations that equal “death march” projects and fail before or directly after go-live. Research with a positivist stance has thoroughly studied factors that describe individuals’ intentions to adopt or use technology, while largely ignoring social and organizational contexts. Researchers with a constructivist view, on the other hand, have studied how social processes and structures change or emerge in the light of the new IS. We suggest that there is a need to combine what we know from these two streams in an attempt to clarify terminological bafflement that seems to be caused by the different philosophical stances. Our paper contributes by suggesting a framework and methodology for collecting and re-assembling scattered conceptual pieces of organizational and individual IT adoption and integrating them into a coherent understanding.


Can Hardware Performance Counters Produce Expected , Deterministic Results ?


title: Poisson Inverse Problems by the Plug-and-Play scheme

abstract: The Anscombe transform offers an approximate conversion of a Poisson random variable into a Gaussian one. This transform is important and appealing, as it is easy to compute, and becomes handy in various inverse problems with Poisson noise contamination. Solution to such problems can be done by first applying the Anscombe transform, then applying a Gaussian-noise-oriented restoration algorithm of choice, and finally applying an inverse Anscombe transform. The appeal in this approach is due to the abundance of high-performance restoration algorithms designed for white additive Gaussian noise (we will refer to these hereafter as "Gaussian-solvers"). This process is known to work well for high SNR images, where the Anscombe transform provides a rather accurate approximation. When the noise level is high, the above path loses much of its effectiveness, and the common practice is to replace it with a direct treatment of the Poisson distribution. Naturally, with this we lose the ability to leverage on vastly available Gaussian-solvers. ::: In this work we suggest a novel method for coupling Gaussian denoising algorithms to Poisson noisy inverse problems, which is based on a general approach termed "Plug-and-Play". Deploying the Plug-and-Play approach to such problems leads to an iterative scheme that repeats several key steps: 1) A convex programming task of simple form that can be easily treated; 2) A powerful Gaussian denoising algorithm of choice; and 3) A simple update step. ::: Such a modular method, just like the Anscombe transform, enables other developers to plug their own Gaussian denoising algorithms to our scheme in an easy way. While the proposed method bares some similarity to the Anscombe operation, it is in fact based on a different mathematical basis, which holds true for all SNR ranges.


title: Malware Coordination using the Blockchain: An Analysis of the Cerber Ransomware

abstract: In order for malicious software to receive configuration information or commands, malware needs to be able to locate and connect to its owner. As hard-coded addresses are easy to block and thus render the malware installation inoperable, malware writers have turned to dynamically generated addresses. Domain generation algorithms (DGA) generate a list of candidate domain names, each valid for only a short time, at which the malware installation searches for its command & control (C&C) server. As DGAs generate a large list of potential domains - out of which one or a few is actually in use –, they leave a characteristic trace of many failed DNS lookups (NXDomain) in the network, and in result most DGAs can be efficiently detected. In this paper we describe an entirely new principle of domain generation, actively deployed in the Cerber ransomware, which finds and coordinates with its owner based on transaction information in the bitcoin blockchain. This allows the malware author to dynamically update the location of the server in realtime, and as the malware directly goes to the right location no longer generates a sequence of NXDomain responses. We describe the concept of coordination via the blockchain, and report results on a year-long observation of the assets used in the Cerber campaign.


title: The functional neuroanatomy of working memory: Contributions of human brain lesion studies

abstract: Studies of patients with focal brain lesions remain critical components of research programs attempting to understand human brain function. Whereas functional imaging typically reveals activity in distributed brain regions that are involved in a task, lesion studies can define which of these brain regions are necessary for a cognitive process. Further, lesion studies are less critical regarding the selection of baseline conditions needed in functional brain imaging research. ::: ::: Lesion studies suggest a functional subdivision of the visuospatial sketchpad of working memory with a ventral stream reaching from occipital to temporal cortex supporting object recognition and a dorsal stream connecting the occipital with parietal cortex enabling spatial operations. The phonological loop can be divided into a phonological short-term store in inferior parietal cortex and an articulatory subvocal rehearsal process relying on brain areas necessary for speech production, i.e. Broca’s area, the supplementary motor association area and possibly the cerebellum. ::: ::: More uncertainty exists regarding the role of the prefrontal cortex in working memory. Whereas single cell studies in non-human primates and functional imaging studies in humans have suggested an extension of the ventral and dorsal path into different subregions of the prefrontal cortex, lesion studies together with recent single-cell and imaging studies point to a non-mnemonic role of the prefrontal cortex, including attentional control of sensory processing, integration of information from different domains, stimulus selection and monitoring of information held in memory. Our own data argue against a modulatory view of the prefrontal cortex and suggest that processes supporting working memory are distributed along ventral and dorsal lateral prefrontal cortex.


title: Domain-general mechanisms of complex working memory span

abstract: Abstract A new fMRI complex working memory span paradigm was used to identify brain regions making domain-general contributions to working memory task performance. For both verbal and spatial versions of the task, complex working memory span performance increased the activity in lateral prefrontal, anterior cingulate, and parietal cortices during the Encoding, Maintenance, and Coordination phase of task performance. Meanwhile, overlapping activity in anterior prefrontal and medial temporal lobe regions was associated with both verbal and spatial recall from working memory. These findings help to adjudicate several contested issues regarding the executive mechanisms of working memory, the separability of short-term and working memory in the verbal and spatial domains, and the relative contribution of short-term and long-term memory mechanisms to working memory capacity. The study also provides a vital bridge between psychometric and neuroimaging approaches to working memory, and constrains our understanding of how working memory may contribute to the broader landscape of cognitive performance.


SigBox: Automatic Signature Generation Method for Fine-grained Traffic Identification.


title: Make It Big!: The Effect of Font Size and Line Spacing on Online Readability

abstract: We report from an eye-tracking experiment with 104 participants who performed reading tasks on the most popular text-heavy website of the Web: Wikipedia. Using a hybrid-measures design, we compared objective and subjective readability and comprehension of the articles for font sizes ranging from 10 to 26 points, and line spacings ranging from 0.8 to 1.8 (font: Arial). Our findings provide evidence that readability, measured via mean fixation duration, increased significantly with font size. Further, comprehension questions had significantly more correct responses for font sizes 18 and 26. For line spacing, we found marginal effects, suggesting that the two tested extremes (0.8 and 1.8) impair readability. These findings provide evidence that text-heavy websites should use fonts of size 18 or larger and use default line spacing when the goal is to make a web page easy to read and comprehend. Our results significantly differ from previous recommendations, presumably, because this is the first work to cover font sizes beyond 14 points.


title: Classification and Numbering of Dental Radiographs for an Automated Human Identification System

abstract: Dental based human identification is commonly used in forensic. In a case of large scale investigation, manual identification needs a large amount of time. In this paper, we developed an automated human identification system based on dental radiographs. The system developed has two main stages. The first stage is to arrange a database consisting of labeled dental radiographs. The second stage is the searching process in the database in order to retrieve the identification result. Both sta ges use a number of image processing techniques, classification methods, and a numbering system in order to generate dental radiograph’s features and patterns. The first technique is preprocessing which includes image enhancement and binarization, single tooth extraction, and feature extraction. Next, we performed dental classification process which aims to classify the extracted tooth into molar or premolar using the binary support vector machine method. After that, a numbering process is executed in accordance with molar and premolar pattern obtained in the previous process. Our experiments using 16 dental radiographs that consist of 6 bitewing radiographs and 10 panoramic radiographs, 119 teeth objects in total, has shown good performance of classification. The accuracy value of dental pattern classification and dental numbering system are 91.6 % and 81.5% respectively.


title: Semantic memory and the brain: structure and processes

abstract: Abstract Recent functional brain imaging studies suggest that object concepts may be represented, in part, by distributed networks of discrete cortical regions that parallel the organization of sensory and motor systems. In addition, different regions of the left lateral prefrontal cortex, and perhaps anterior temporal cortex, may have distinct roles in retrieving, maintaining and selecting semantic information.


title: An improved Sobel edge detection

abstract: This paper proposes a method which combines Sobel edge detection operator and soft-threshold wavelet de-noising to do edge detection on images which include White Gaussian noises. In recent years, a lot of edge detection methods are proposed. The commonly used methods which combine mean de-noising and Sobel operator or median filtering and Sobel operator can not remove salt and pepper noise very well. In this paper, we firstly use soft-threshold wavelet to remove noise, then use Sobel edge detection operator to do edge detection on the image. This method is mainly used on the images which includes White Gaussian noises. Through the pictures obtained by the experiment, we can see very clearly that, compared to the traditional edge detection methods, the method proposed in this paper has a more obvious effect on edge detection.


title: DeltaEcore - A Model-Based Delta Language Generation Framework.

abstract: Software product lines (SPLs) and software ecosystems (SECOs) represent families of closely related software systems in terms of configurable variable assets. Delta modeling is an approach for capturing variability resulting from different configurations and for deriving concrete software products of an SPL or SECO through transformation. Even though the general concepts of delta modeling are languageindependent, custom delta languages are required for all source languages, which are tedious to create and lack interoperability due to different implementation technologies. In this paper, we present a framework to automatically derive delta languages for textual or graphical languages given as EMOF-based meta models. We further illustrate how to automatically generate the syntax and large parts of the semantics of the derived delta language by inspecting the source language’s meta model. We demonstrate our approach by applying our implementation DeltaEcore to four selected source languages.


title: Closing the loop in scene interpretation

abstract: Image understanding involves analyzing many different aspects of the scene. In this paper, we are concerned with how these tasks can be combined in a way that improves the performance of each of them. Inspired by Barrow and Tenenbaum, we present a flexible framework for interfacing scene analysis processes using intrinsic images. Each intrinsic image is a registered map describing one characteristic of the scene. We apply this framework to develop an integrated 3D scene understanding system with estimates of surface orientations, occlusion boundaries, objects, camera viewpoint, and relative depth. Our experiments on a set of 300 outdoor images demonstrate that these tasks reinforce each other, and we illustrate a coherent scene understanding with automatically reconstructed 3D models.


title: Learning Concepts and Categories Is Spacing the “Enemy of Induction”?

abstract: Inductive learning—that is, learning a new concept or category by observing exemplars—happens constantly, for example, when a baby learns a new word or a doctor classifies x-rays. What influence does the spacing of exemplars have on induction? Compared with massing, spacing enhances long-term recall, but we expected spac- ing to hamper induction by making the commonalities that define a concept or category less apparent. We asked participants to study multiple paintings by different ar- tists, with a given artist's paintings presented consecutive- ly (massed) or interleaved with other artists' paintings (spaced). We then tested induction by asking participants to indicate which studied artist (Experiments 1a and 1b) or whether any studied artist (Experiment 2) painted each of a series of new paintings. Surprisingly, induction profited from spacing, even though massing apparently created a sense of fluent learning: Participants rated massing as more effective than spacing, even after their own test performance had demonstrated the opposite.


title: Reading between the lines: linguistic cues to deception in online dating profiles

abstract: This study investigates whether deception in online dating profiles is detectable through a linguistic approach, which assumes that liars nonconsciously produce different word patterns than truth-tellers. We objectively measure deception in online dating profiles and analyze the linguistic composition of the open-ended component of the profile (i.e., "about me" section) using computerized text analysis. Results show that profile deceptions correlate with fewer self-references, increased negations, fewer negative emotion words and fewer overall words used in the textual self-description. Results are discussed in terms of (1) practical implications for detecting deception in online profiles; and (2) theoretical implications regarding the impact of media affordances (i.e., asynchronicity and editability) on the occurrence of linguistic cues to deception.


title: Active refocusing of images and videos

abstract: We present a system for refocusing images and videos of dynamic scenes using a novel, single-view depth estimation method. Our method for obtaining depth is based on the defocus of a sparse set of dots projected onto the scene. In contrast to other active illumination techniques, the projected pattern of dots can be removed from each captured image and its brightness easily controlled in order to avoid under- or over-exposure. The depths corresponding to the projected dots and a color segmentation of the image are used to compute an approximate depth map of the scene with clean region boundaries. The depth map is used to refocus the acquired image after the dots are removed, simulating realistic depth of field effects. Experiments on a wide variety of scenes, including close-ups and live action, demonstrate the effectiveness of our method.


The Physicians Committee for Responsible Medicine


title: Information system security threats and vulnerabilities: evaluating the human factor in data protection

abstract: in information security have all these while been concerned only with technical problems and efforts to improve information security have been software-centered or hardware-oriented. There have been limited attempts in addressing the people who use the computers though they are the greatest loophole in information systems security. This paper examines and addresses the threats end-users pose to systems security. Regardless of the countlessly introduced technological solutions aimed at addressing system vulnerabilities, the human factor is still of greater threat to systems security. The study draws its data from a survey conducted on people who frequently use information systems. Professional and technical inputs were also solicited from IT personnel through interviews. Four experiments were conducted to test the accuracy of the survey. A phony phish system was developed to test respondents" information security consciousness. The goal of the phony phish system was to send phishing emails that can be used to measure the accuracy of the survey. The rest of the experiments were SQL injection, cross site scripting and brute force attack. The results from the study revealed that, the numerous technical advances in information technology do not always produce more secure environments. Thus, information security cannot be described as solely a technical problem. Computers are operated by people and this means that information security is also a human factor issue. It is therefore suggested, for information and data breaches to be curbed, organizations must adopt a holistic security framework, incorporating the human factor.


title: Linking theory and practice: Changing the pedagogy of teacher education

abstract: The pressure towards more school-based teacher education programs, visible in many countries, creates a need to rethink the relationship between theory and practice. The traditional application-of-theory model appears to be rather ineffective and is currently being replaced by other, more reflective approaches. However, until now the variety of different notions and assumptions underlying these new approaches have not provided a sound basis for further development. Two related theoretical bases are presented for a new paradigm in teacher education. The first uses the concepts of episteme and phronesis to introduce a new way of framing relevant knowledge. The second is a more holistic way of describing the relationship between teacher cognition and teacher behavior, leading to a model of three levels in learning about teaching, the Gestalt level, the schema level and the theory level, which are illustrated by interview data. Building on these two theoretical, frameworks, a so-called “realistic approach” to...


title: Co-word Maps and Topic Modeling: A Comparison Using Small and Medium-Sized Corpora (n<1000)

abstract: Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument "The Leiden Manifesto" and then upscale to a sample of moderate size n=687 to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping using two different routines versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones e.g., linguistic ones. In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.