title: Body Cues, Not Facial Expressions, Discriminate Between Intense Positive and Negative Emotions

abstract: The distinction between positive and negative emotions is fundamental in emotion models. Intriguingly, neurobiological work suggests shared mechanisms across positive and negative emotions. We tested whether similar overlap occurs in real-life facial expressions. During peak intensities of emotion, positive and negative situations were successfully discriminated from isolated bodies but not faces. Nevertheless, viewers perceived illusory positivity or negativity in the nondiagnostic faces when seen with bodies. To reveal the underlying mechanisms, we created compounds of intense negative faces combined with positive bodies, and vice versa. Perceived affect and mimicry of the faces shifted systematically as a function of their contextual body emotion. These findings challenge standard models of emotion expression and highlight the role of the body in expressing and perceiving emotions.


Agricultural Crop Yield Prediction Using Artificial Neural Network Approach Miss .


title: Software Testing – Goals, Principles, and Limitations

abstract: testing is an activity which is aimed for evaluating quality of a program and also for improving it, by identifying defects and problems. Software testing strives for achieving its goals (both implicit and explicit) but it does have certain limitations, still testing can be done more effectively if certain established principles are be followed. In spite of having limitations, software testing continues to dominate other verification techniques like static analysis, model checking, and proofs. So it is indispensable to understand the goals, principles and limitations of software testing so that the effectiveness of software testing could be maximized.


title: Rhythm and Beat Perception in Motor Areas of the Brain

abstract: When we listen to rhythm, we often move spontaneously to the beat. This movement may result from processing of the beat by motor areas. Previous studies have shown that several motor areas respond when attending to rhythms. Here we investigate whether specific motor regions respond to beat in rhythm. We predicted that the basal ganglia and supplementary motor area (SMA) would respond in the presence of a regular beat. To establish what rhythm properties induce a beat, we asked subjects to reproduce different types of rhythmic sequences. Improved reproduction was observed for one rhythm type, which had integer ratio relationships between its intervals and regular perceptual accents. A subsequent functional magnetic resonance imaging study found that these rhythms also elicited higher activity in the basal ganglia and SMA. This finding was consistent across different levels of musical training, although musicians showed activation increases unrelated to rhythm type in the premotor cortex, cerebellum, and SMAs (pre-SMA and SMA). We conclude that, in addition to their role in movement production, the basal ganglia and SMAs may mediate beat perception.


title: Hybrid Reward Architecture for Reinforcement Learning

abstract: One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network. While this approach works well in many domains, in domains where the optimal value function cannot easily be reduced to a low-dimensional representation, learning can be very slow and unstable. This paper contributes towards tackling such challenging domains, by proposing a new method, called Hybrid Reward Architecture (HRA). HRA takes as input a decomposed reward function and learns a separate value function for each component reward function. Because each component typically only depends on a subset of all features, the overall value function is much smoother and can be easier approximated by a low-dimensional representation, enabling more effective learning. We demonstrate HRA on a toy-problem and the Atari game Ms. Pac-Man, where HRA achieves above-human performance.


title: Force Sensing Robot Fingers using Embedded Fiber Bragg Grating Sensors and Shape Deposition Manufacturing

abstract: Force sensing is an essential requirement for dexterous robot manipulation. Although strain gages have been widely used, a new sensing approach is desirable for applications that require greater robustness, design flexibility and immunity to electromagnetic noise. An exoskeletal force sensing robot finger was developed by embedding fiber Bragg grating (FBG) sensors into a polymer-based structure. Multiple FBG sensors were embedded into the structure to allow the manipulator to sense and measure both contact forces and grasping forces. In order to fabricate a three-dimensional structure, a new shape deposition manufacturing (SDM) process was explored. The sensorized SDM-fabricated finger was then characterized using an FBG interrogator. A force localization scheme is also described


title: Reconstruction of hidden 3D shapes using diffuse reflections

abstract: We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the hidden scene. We formulate the forward and inverse theory of secondary scattering using ideas from energy front propagation and tomography. We show that using Fresnel approximation greatly simplifies this problem and the inversion can be achieved via a backpropagation process. We study the invertibility, uniqueness and choices of space-time-angle dimensions using synthetic examples. We show that a 2D streak camera can be used to discover and reconstruct hidden geometry. Using a 1D high speed time of flight camera, we show that our method can be used recover 3D shapes of objects "around the corner".


title: Digital signal processors for a signal processing laboratory

abstract: An undergraduate level laboratory course on digital signal processors and their use in real-time digital signal processing (DSP) systems is described. Special emphasis is put on some practical issues such as execution time optimization, finite wordlength effects and a pipelined strategy as a suitable way to improve the performance of a real-time DSP system. The primary purpose of the laboratory is a better understanding of the theoretical concepts and a more active participation of the students in the course. The authors think that this paper will be of interest to those teaching related courses such as Introduction to DSP, Digital Signal Processing, Real-time DSP, Microprocessors and Hardware/Softmare Interaction. The well-known Texas Instruments' TMS320C2x and TMS320C5x fixed-point processors are used as the fundamental tool in the lab experiments and the TMS320C3x is suggested as a low-cost floating-point alternative. Enough detail is provided in the paper to duplicate certain parts of the experiments.


CONNOTATION ANALYSIS


title: Towards a Principled Approach for Engineering Privacy by Design

abstract: Privacy by Design has emerged as a proactive approach for embedding privacy into the early stages of the design of information and communication technologies, but it is no ‘silver bullet’. Challenges involved in engineering Privacy by Design include a lack of holistic and systematic methodologies that address the complexity and variability of privacy issues and support the translation of its principles into engineering activities. A consequence is that its principles are given at a high level of abstraction without accompanying tools and guidelines to address these challenges. We analyse three privacy requirements engineering methods from which we derive a set of criteria that aid in identifying data-processing activities that may lead to privacy violations and harms and also aid in specifying appropriate design decisions. We also present principles for engineering Privacy by Design that can be developed upon these criteria. Based on these, we outline some preliminary thoughts on the form of a principled framework that addresses the plurality and contextuality of privacy issues and supports the translation of the principles of Privacy by Design into engineering activities.


title: Optimizing some constructions with bars: new geometric knapsack problems

abstract: A set of vertical bars planted on given points of a horizontal line defines a fence composed of the quadrilaterals bounded by successive bars. A set of bars in the plane, each having one endpoint at the origin, defines an umbrella composed of the triangles bounded by successive bars. Given a collection of bars, we study how to use them to build the fence or the umbrella of maximum total area. We present optimal algorithms for these constructions. The problems introduced in this paper are related to the Geometric Knapsack problems (Arkin et al. in Algorithmica 10:399---427, 1993) and the Rearrangement Inequality (Wayne in Scripta Math 12(2):164---169, 1946).


title: A Self-Routing on-Chip Network

abstract: This paper introduces a new nonblocking self-routing network, called a multi-root binary tree , which may be used to interconnect the cores in multicore chips. The multi-root binary tree network differs from other binary tree-based networks in that the cores are placed at the roots rather than the leaves or the interior nodes of the trees. The self-routing property of a multi-root binary tree is built on the concept of replication and clustering. A new replication and clustering method, called the triangular shift pairing is given to connect the cores together over dedicated paths. It is shown that connecting cores in clusters requires at least $n/2$ columns of pairings for an $n$ -core network in a grid layout model and triangular shift wiring method matches this lower bound. The replication and clustering concept leads to a simple self-routing scheme that pairs cores by decoding both unicast and multicast connection requests using $\mathrm{lg} \;n$ -bit cluster address bits. In particular, it is established that cores can identify the cluster with which they pair with other cores using simple modulo addition of their own ids and addresses of the targeted cores in $2\mathrm{lg}\; n$ single bit-decoding steps. It is also shown that, if connection requests are uniformly distributed among the cores then blocking due to target conflicts can be avoided if cores serve connection requests at a small multiple of the frequencies with which they receive them.


title: A Nondominated Sorting Particle Swarm Optimizer for Multiobjective Optimization

abstract: This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles' personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle's personal best and its offspring, NSPSO compares all particles' personal bests and their offspring in the entire population. This proves to be effective in providing an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. By using the non-dominated sorting concept and two parameter-free niching methods, NSPSO and its variants have shown remarkable performance against a set of well-known difficult test functions (ZDT series). Our results and comparison with NSGA II show that NSPSO is highly competitive with existing evolutionary and PSO multiobjective algorithms.


title: An Energy-Efficient Adaptive Sensing Framework for Gait Monitoring Using Smart Insole

abstract: Gait analysis is an important process to gauge human motion. Recently, longitudinal gait analysis received much attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Due to the continuous sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we present an energy-efficient adaptive sensing framework to address this problem. Through presampling for content understanding, a selective sensing and sparsity-based signal reconstruction method is proposed. In particular, we develop and implement the new sensing scheme in a smart insole system to reduce the number of samples, while still preserving the information integrity of gait parameters. Experimental results show the effectiveness of our method in data point reduction. Our proposed method improves the battery life to 10.47 h, while normalized mean square error is within 10%.


An algorithm that finds truth even if most people are wrong


title: More is Better: Precise and Detailed Image Captioning Using Online Positive Recall and Missing Concepts Mining

abstract: Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-to-caption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts. The reasons are two-fold: 1) the extreme imbalance between the number of occurrence positive and negative samples of the concept and 2) the incomplete labeling in training captions caused by the biased annotation and usage of synonyms. In this paper, we propose a method, termed online positive recall and missing concepts mining , to overcome those problems. Our method adaptively re-weights the loss of different samples according to their predictions for online positive recall and uses a two-stage optimization strategy for missing concepts mining. In this way, more semantic concepts can be detected and a high accuracy will be expected. On the caption generation stage, we explore an element-wise selection process to automatically choose the most suitable concepts at each time step. Thus, our method can generate more precise and detailed caption to describe the image. We conduct extensive experiments on the MSCOCO image captioning data set and the MSCOCO online test server, which shows that our method achieves superior image captioning performance compared with other competitive methods.


title: Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

abstract: This paper describes an approach for improving semantic queries by utilizing Universal Words (UWs) and a graph database. Concept Description Language (CDL) is used for representing the semantic data, and Neo4j graph database is used as the storage back-end. Cypher graph query language is used as the basis for implementing the semantic queries. For improving the queries, query expansion is performed by utilizing semantic relationships between UWs, provided by UNL Ontology.


title: An Integrated Implementation of Simulative, Uncertain and Metaphorical Reasoning about Mental States

abstract: Abstract An unprecedented combination of simulative and metaphor based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs productively uses conceptual metaphors such as MIND AS CONTAINER and IDEAS AS INTERNAL UTTERANCES, and ATT-Meta's metaphor-based reasoning accordingly leads to crucial discourse comprehension decisions. ATT-Meta's non-metaphorical mode of belief reasoning includes simulative reasoning (SR). In ATT-Meta, metaphor-based reasoning can block and otherwise influence the course of SR.


title: Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?

abstract: This work was supported by the Center for Brains, Minds and Machines ::: (CBMM), funded by NSF STC award CCF - 1231216.


title: Trauma, change in strength of religious faith, and mental health service use among veterans treated for PTSD.

abstract: Abstract One of the most pervasive effects of traumatic exposure is the challenge that people experience to their existential beliefs concerning the meaning and purpose of life. Particularly at risk is the strength of their religious faith and the comfort that they derive from it. The purpose of this study is to examine a model of the interrelationships among veterans’ traumatic exposure, posttraumatic stress disorder (PTSD), guilt, social functioning, change in religious faith, and continued use of mental health services. Data are drawn from studies of outpatient (N = 554) and inpatient (N = 831) specialized treatment of PTSD in Department of Veterans Affairs programs. Structural equation modeling is used to estimate the parameters of the model and evaluate its goodness of fit to the data. The model achieved acceptable goodness of fit and suggested that veterans’ experiences of killing others and failing to prevent death weakened their religious faith, both directly and as mediated by feelings of guilt. Weakened religious faith and guilt each contributed independently to more extensive use of VA mental health services. Severity of PTSD symptoms and social functioning played no significant role in the continued use of mental health services. We conclude that veterans’ pursuit of mental health services appears to be driven more by their guilt and the weakening of their religious faith than by the severity of their PTSD symptoms or their deficits in social functioning. The specificity of these effects on service use suggests that a primary motivation of veterans’ continuing pursuit of treatment may be their search for a meaning and purpose to their traumatic experiences. This possibility raises the broader issue of whether spirituality should be more central to the treatment of PTSD, either in the form of a greater role for pastoral counseling or of a wider inclusion of spiritual issues in traditional psychotherapy for PTSD.


THE CAUSES AND PREVENTION OF CROWD DISASTERS


title: Interactive procedural street modeling

abstract: This paper addresses the problem of interactively modeling large street networks. We introduce an intuitive and flexible modeling framework in which a user can create a street network from scratch or modify an existing street network. This is achieved through designing an underlying tensor field and editing the graph representing the street network. The framework is intuitive because it uses tensor fields to guide the generation of a street network. The framework is flexible because it allows the user to combine various global and local modeling operations such as brush strokes, smoothing, constraints, noise and rotation fields. Our results will show street networks and three-dimensional urban geometry of high visual quality.


title: Low-Voltage Low-Power CMOS Oscillator with Low Temperature and Process Sensitivity

abstract: The design of an on-chip RC-based oscillator, implemented in a standard 0.35 mum CMOS process, without any external component, is presented. The proposed oscillator provides a clock signal at a frequency of about 80 KHz with a temperature coefficient smaller than 842 ppm/degC over a temperature range from 0 to 80 7degC and a standard deviation due to process variations smaller than 4%, without any external trimming. The proposed oscillator operates with a supply voltage of 1 V and has a power consumption of about 1 muW at room temperature. The chip area is 0.24 mm2.


title: Empirical research through design

abstract: This paper describes the empirical research through design method (ERDM), which differs from current approaches to research through design by enforcing the need for the designer, after a series of pilot prototype based studies, to a-priori develop a number of testable interaction design hypothesis which are then embedded in a working prototype and tested in context with target users. The approach builds on contextual enquiry methods such as context mapping leading to a verifiable working prototype while contributing to fundamental design knowledge. A case study is described to illustrate the application of the ERDM method.


title: Midlife Crisis: A Debate

abstract: Without doubt, the midlife crisis is the most popular concept describing middle adulthood. Facing the limitation of the time until death, men in particular are believed to pause from actively pursuing their goals and review their achievements, take stock of what they have and have not yet accomplished, at times taking drastic measures to fulfill their dreams. This paper critically discusses the concept of a midlife crisis and the relevant empirical evidence, presenting arguments for and against a strict, a moderate, and a lenient conceptualization of the midlife crisis. Although a strict and even moderate definition of the midlife crisis does not seem tenable on empirical and theoretical grounds, a lenient conceptualization has the potential to stimulate new research directions exemplifying processes of the interaction of social expectations on the one hand and personal goals on the other, and their importance for developmental regulation.


title: Big Data, Internet of Things and Cloud Convergence – An Architecture for Secure E-Health Applications

abstract: Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.


title: SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media

abstract: In this work, we present SAMAR, a system for Subjectivity and Sentiment Analysis (SSA) for Arabic social media genres. We investigate: how to best represent lexical information; whether standard features are useful; how to treat Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results suggest that we need individualized solutions for each domain and task, but that lemmatization is a feature in all the best approaches.


title: Time-Sensitive Networking: An Introduction

abstract: Ethernet is cheap and ubiquitous. As such, people want to use it to carry all sorts of traffic for which it was not originally intended. A popular current application area is the transport of multiple flows of data, each having different timing requirements. Such applications exist in professional audio, industrial and automotive networks, among others. This article briefly traces the history of the features of IEEE 802 Bridging intended to address those needs, and then describes recent advances in time-sensitive networking in more detail. As well as completed standards, some current projects are described. Areas for future standardization are identified. Encoding the priority of packets in the header allows high-priority packets to be scheduled for transmission ahead of lower-priority packets, providing a better quality of service for urgent traffic. Time-sensitive flows have varying requirements for maximum latency and latency variation. Audio-video bridging provides guaranteed quality of service in terms of those parameters, for booked traffic in a bridged network comprising only compliant bridges. Some types of flow (particularly in industrial networks) are very sensitive to packet loss. Time-sensitive networking can provide bounded latency and zero packet loss due to congestion. The most stringent guarantees and most efficient use of network resources is provided by cyclic queuing and forwarding, which combines time synchronization, transmission scheduling and per-stream filtering and policing to provide just-in-time delivery of time-sensitive streams. This requires careful planning and centralized control. Less stringent use cases allow use of distributed control techniques.


title: Building Deep Networks on Grassmann Manifolds

abstract: Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a deep network architecture by generalizing the Euclidean network paradigm to Grassmann manifolds. In particular, we design full rank mapping layers to transform input Grassmannian data to more desirable ones, exploit re-orthonormalization layers to normalize the resulting matrices, study projection pooling layers to reduce the model complexity in the Grassmannian context, and devise projection mapping layers to respect Grassmannian geometry and meanwhile achieve Euclidean forms for regular output layers. To train the Grassmann networks, we exploit a stochastic gradient descent setting on manifolds of the connection weights, and study a matrix generalization of backpropagation to update the structured data. The evaluations on three visual recognition tasks show that our Grassmann networks have clear advantages over existing Grassmann learning methods, and achieve results comparable with state-of-the-art approaches.


title: Mitochondrial ROS in cancer: initiators, amplifiers or an Achilles' heel?

abstract: Mitochondria cooperate with their host cells by contributing to bioenergetics, metabolism, biosynthesis, and cell death or survival functions. Reactive oxygen species (ROS) generated by mitochondria participate in stress signalling in normal cells but also contribute to the initiation of nuclear or mitochondrial DNA mutations that promote neoplastic transformation. In cancer cells, mitochondrial ROS amplify the tumorigenic phenotype and accelerate the accumulation of additional mutations that lead to metastatic behaviour. As mitochondria carry out important functions in normal cells, disabling their function is not a feasible therapy for cancer. However, ROS signalling contributes to proliferation and survival in many cancers, so the targeted disruption of mitochondria-to-cell redox communication represents a promising avenue for future therapy.


title: OPTIMIZING NUMBER OF HIDDEN NEURONS IN NEURAL NETWORKS

abstract: In this paper, a novel and effective criterion based on the estimation of the signal-to-noise-ratio figure (SNRF) is proposed to optimize the number of hidden neurons in neural networks to avoid overfitting in the function approximation. SNRF can quantitatively measure the useful information left unlearned so that overfitting can be automatically detected from the training error only without use of a separate validation set. It is illustrated by optimizing the number of hidden neurons in a multi-layer perceptron (MLP) using benchmark datasets. The criterion can be further utilized in the optimization of other parameters of neural networks when overfitting needs to be considered.


title: Intergenerational Advice and Matching: An Experimental Study

abstract: This paper is motivated by the idea that economic mechanisms should be tested in the environments in which they are used in the real world. For example, in school matching parents whose children are currently in the match receive advice from their predecessors. We investigate the impact of this intergenerational advice and demonstrate that it is dramatic. We nd that while subjects who repeatedly play the Gale-Shapley mechanism for 20 rounds wind up reporting truthfully 77% of the time, those who play the game once and get advice from their predecessors, wind up reporting truthfully only 44% of the time. We oer an explanation for this result that contrasts the experiential learning that takes place in our repeated treatments with the social learning that takes place in our intergenerational treatments.


title: New Concepts in Nasal Tip Contouring

abstract: The authors introduce the concept of favorable shadowing of the nasal tip surface. Contouring the nasal tip is an advanced concept in rhinoplasty. Several tip altering techniques exist, but proper selection of an appropriate technique or combination of techniques first requires understanding of the impact of manipulating underlying tip structure on nasal surface topography. Frequently, maneuvers that narrow the domes, inappropriately create a pinched or unnatural-appearing nasal tip. Many of these tip-narrowing techniques act to lower the caudal margin of the lateral crura below the cephalic margin and decrease support along the junction between the tip and alar lobule. The nasal tip skin can then collapse on this structure, creating a visible line of demarcation between the tip and alar lobule. Patients will describe their operated nasal tip as having the appearance of a round ball or bulbous tip, even thought their nasal tip may be narrow. This pinched appearance is due to the shadowing that isolates the nasal tip, creating a bulbous or pinched look to the nasal tip. Maneuvers such as dome sutures, lateral crural strut grafts, repositioning of the lateral crura, and alar rim grafts can create a favorable tip structure to support the underlying skin envelope. Using the methods described will enable the surgeon to focus less on narrowing the nasal tip and more on creating favorable shadowing of the nasal tip.


title: Cross-Level Sensor Network Simulation with COOJA

abstract: Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach.


title: LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates

abstract: State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures.


title: Trimidox-mediated morphological changes during erythroid differentiation is associated with the stimulation of hemoglobin and F-cell production in human K562 cells.

abstract: Trimidox (3,4,5-trihdroxybenzamidoxime) has been shown to reduce the activity of ribonucleotide reductase with accompanied growth inhibition and differentiation of mammalian cells. Hydroxyurea (HU) is the only ribonucleotide reductase inhibitor in clinical use for the treatment and management of sickle cell anemia, since this compound increases fetal hemoglobin (Hb F) production: a potent inhibitor of sickle hemoglobin (Hb SS) polymerization. However, the main limitations of HU is its lack of potency, myelosuppression and short half life. These studies investigated the effects of trimidox on the induction of hemoglobin and F-cells production in K562 erythroleukemia cells. Our study reveals that trimidox exhibits concentration dependent inhibitory effect on K562 cells with increase in benzidine positive normoblasts and F-cells production as well as morphological changes typical of erythroid differentiation. These findings provide the first evidence that the growth inhibitory differentiation of cells induced by trimidox enhance hemoglobin and F-cells production.


title: SPINE: SParse Interpretable Neural Embeddings

abstract: Prediction without justification has limited utility. Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations. While these representations capture the underlying complexity and latent trends in the data, they are far from being interpretable. We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec. Through large scale human evaluation, we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings. Moreover, our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks.


title: DUNet: A deformable network for retinal vessel segmentation

abstract: Abstract Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.


title: Asthmon: empowering asthmatic children's self-management with a virtual pet

abstract: Asthma is a common chronic childhood disease. Children spend a majority of their time in schools, and barriers to on-site asthma management have been reported. Previous forms of clinical intervention have regarded patients as passive subjects. However, self-management plays a significant role in caring for asthmatics. We consider asthmatic children and their parents, primary caregivers, as active participants in their treatment and care. To achieve this, we created Asthmon, a portable virtual pet that measures the lung capacity, and instructs appropriate actions to take.


title: Image Matching via Loopy RNN

abstract: Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.


title: The web impact factor: a critical review

abstract: Purpose – The purpose of this article is to analyze the link‐based web site impact measure known as the web impact factor (WIF). It is a quantitative tool for evaluating and ranking web sites, top‐level domains and sub‐domains. The paper also aims to discuss the WIF's advantages and disadvantages, data collection problems, and validity and reliability of WIF results.Design/methodology/approach – A key to webometric studies has been the use of large‐scale search engines, such as Yahoo! and AltaVista that allow measurements to be made of the total number of pages in a web site and the total number of back‐links to the web site. These search engines provide similar possibilities for the investigation of links between web sites/pages to those provided by the academic journals citation databases from the Institute of Scientific Information (ISI). But the content of the web is not of the same nature and quality as the databases maintained by the ISI.Findings – This paper reviews how the WIF has been developed a...


title: Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery

abstract: The Hyperion hyperspectral sensor has the highest spectral resolution, acquiring spectral information of Earth's surface objects in 242 spectral bands at a spatial resolution of 30m. In this study, we evaluate the performance of the Hyperion sensor in conjunction with the two different classification algorithms for delineating land use/cover in a typical Mediterranean setting. The algorithms include pixel-based support vector machines (SVMs) and the object-based classification algorithm. Validation of the derived land-use/cover maps from the above two algorithms was performed through error matrix statistics using the validation points from the very high resolution QuickBird imagery. Results suggested both classifiers as highly useful in mapping land use/cover in the study region with the object-based approach slightly outperforming the SVMs classification by overall higher classification accuracy and Kappa statistics. Results from the statistical significance testing using McNemar's chi-square test confirmed the superiority of the object-oriented approach compared to SVM. The relative strengths and weaknesses of the two classification algorithms for land-use/cover mapping studies are highlighted. Overall, our results underline the potential of hyperspectral remote sensing data together with an object-based classification approach for mapping land use/cover in the Mediterranean regions.


title: Growth and Institutions: A Review of the Evidence

abstract: Africa's disappointing economic performance, the East Asian financial crisis, and the weak record of the former Soviet Union have focused attention on the role of institutions in determining a country's economic growth. This article critically reviews the literature that tries to link quantitative measures of institutions, such as civil liberties and property rights, with growth of gross domestic product across countries and over time. An important distinction is made between indicators that measure the performance or quality of institutions and those that measure political and social characteristics and political instability. The evidence suggests a link between the quality of institutions and investment and growth, but the evidence is by no means robust. Copyright 2000 by Oxford University Press.


title: Action Recognition in the Presence of One Egocentric and Multiple Static Cameras

abstract: In this paper, we study the problem of recognizing human actions in the presence of a single egocentric camera and multiple static cameras. Some actions are better presented in static cameras, where the whole body of an actor and the context of actions are visible. Some other actions are better recognized in egocentric cameras, where subtle movements of hands and complex object interactions are visible. In this paper, we introduce a model that can benefit from the best of both worlds by learning to predict the importance of each camera in recognizing actions in each frame. By joint discriminative learning of latent camera importance variables and action classifiers, our model achieves successful results in the challenging CMU-MMAC dataset. Our experimental results show significant gain in learning to use the cameras according to their predicted importance. The learned latent variables provide a level of understanding of a scene that enables automatic cinematography by smoothly switching between cameras in order to maximize the amount of relevant information in each frame.


title: Quantitative analysis of gadoxetic acid-enhanced magnetic resonance imaging predicts histological grade of hepatocellular carcinoma

abstract: Abstract Objective To confirm the histological grade of hepatocellular carcinoma (HCC) by gadoxetic acid-enhanced MRI. Methods Ninety-five HCC patients underwent gadoxetic acid-enhanced MRI before surgical intervention. The correlations among the signal absolute enhancement, contrast enhancement ratio (CER) and tumor histological grade were analyzed. Results The correlation between CER of tumor-to-liver and the grades of tumor differentiation is the most significant negative. The k-value for the CER of tumor-to-liver and histopathologic analysis is 0.62, which gives evidence of good agreement. Conclusion The quantitative analysis of gadoxetic acid-enhanced MRI can predict the histological grades of small HCCs.


title: Second to fourth digit ratio and male ability in sport: implications for sexual selection in humans

abstract: Abstract Fetal and adult testosterone may be important in establishing and maintaining sex-dependent abilities associated with male physical competitiveness. There is evidence that the ratio of the length of the 2nd and 4th digits (2D:4D) is a negative correlate of prenatal and adult testosterone. We use ability in sports, and particularly ability in football, as a proxy for male physical competitiveness. Compared to males with high 2D:4D ratio, men with low ratio reported higher attainment in a range of sports and had higher mental rotation scores (a measure of visual–spatial ability). Professional football players had lower 2D:4D ratios than controls. Football players in 1st team squads had lower 2D:4D than reserves or youth team players. Men who had represented their country had lower ratios than those who had not, and there was a significant (one-tailed) negative association between 2D:4D and number of international appearances after the effect of country was removed. We suggest that prenatal and adult testosterone promotes the development and maintenance of traits which are useful in sports and athletics disciplines and in male:male fighting.


title: Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

abstract: Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.


title: Learning grasps for unknown objects in cluttered scenes

abstract: In this paper, we propose a method for grasping unknown objects from piles or cluttered scenes, given a point cloud from a single depth camera. We introduce a shape-based method - Symmetry Height Accumulated Features (SHAF) - that reduces the scene description complexity such that the use of machine learning techniques becomes feasible. We describe the basic Height Accumulated Features and the Symmetry Features and investigate their quality using an F-score metric. We discuss the gain from Symmetry Features for grasp classification and demonstrate the expressive power of Height Accumulated Features by comparing it to a simple height based learning method. In robotic experiments of grasping single objects, we test 10 novel objects in 150 trials and show significant improvement of 34% over a state-of-the-art method, achieving a success rate of 92%. An improvement of 29% over the competitive method was achieved for a task of clearing a table with 5 to 10 objects and overall 90 trials. Furthermore we show that our approach is easily adaptable for different manipulators by running our experiments on a second platform.


title: Teaching elements of English RP connected speech and CALL: Phonemic assimilation

abstract: Phonology represents an important part of the English language; however, in the course of English language acquisition, it is rarely treated with proper attention. Connected speech is one of the aspects essential for successful communication, which comprises effective auditory perception and speech production. In this paper I explored phonemic assimilation, which results in successive sounds at word boundaries influencing each other, as an element of connected speech, and studied how teaching it can be supported with computer-assisted language learning. The research conducted revealed that elements of phonemic assimilation are found frequently in all styles of speech and thus, the knowledge of it is necessary for developing proper listening and speaking skills. I advocate the use of the Internet as one of the best possible resources of listening materials for learning phonemic variations and show how various web tools and means of technology can be used for preparing, presenting and storing educational materials. I believe this article makes a contribution to the corpus of research and instruction on connected speech as part of the standard British accent and will have an impact in raising general awareness of its significant role in the process of English language learning.


title: When D2D meets cloud: Hybrid mobile task offloadings in fog computing

abstract: In this paper we propose HyFog, a novel hybrid task offloading framework in fog computing, where device users have the flexibility of choosing among multiple options for task executions, including local mobile execution, Device-to-Device (D2D) offloaded execution, and Cloud offloaded execution. We further develop a novel three-layer graph matching algorithm for efficient hybrid task offloading among the devices. Specifically, we first construct a three-layer graph to capture the choice space enabled by these three execution approaches, and then the problem of minimizing the total task execution cost is recast as a minimum weight matching problem over the constructed three-layer graph, which can be efficiently solved using the Edmonds's Blossom algorithm. Numerical results demonstrate that the proposed three-layer graph matching solution can achieve superior performance, with more than 50% cost reduction over the case of local task executions by all the devices.


title: Ground penetrating synthetic aperture radar imaging providing soil permittivity estimation

abstract: In this paper a combined Ground Penetrating Radar (GPR) and Synthetic Aperture Radar (SAR) technique is introduced, which considers the soil surface refraction and the wave propagation in the ground. By using Fermat's principle and the Sober operator, the SAR image of the GPR data is optimized, whereas the soil's permittivity is estimated. The theoretical approach is discussed thoroughly and measurements that were carried out on a test sand box verify the proposed technique.


title: A fast thumbnail extraction method in H.264/AVC video streams

abstract: In this paper, we propose a theoretical method for extracting a thumbnail directly from an H.264/AVC intra frame in the frequency domain. Furthermore, the proposed scheme can extract the thumbnail very fast since all operations are directly applied to transformed coefficients and a lookup table (LUT) for each intra mode is used for this scheme. Through performance evaluation, while the subjective quality difference between our scheme and a conventional method is negligible, the former can extract the thumbnail faster than the latter by up to 63%.


title: Propminer: A Workflow for Interactive Information Extraction and Exploration using Dependency Trees

abstract: The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction. Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this system demonstration, we present a tool and a workflow designed to enable initiate users to interactively explore the effect and expressivity of creating Information Extraction rules over dependency trees. We introduce the proposed five step workflow for creating information extractors, the graph query based rule language, as well as the core features of the PROPMINER tool.


Effi cacy and safety of non-immersive virtual reality exercising in stroke rehabilitation ( EVREST ) : a randomised , multicentre , single-blind , controlled trial


title: Automated reconstruction of ancient languages using probabilistic models of sound change

abstract: One of the oldest problems in linguistics is reconstructing the words that appeared in the protolanguages from which modern languages evolved. Identifying the forms of these ancient languages makes it possible to evaluate proposals about the nature of language change and to draw inferences about human history. Protolanguages are typically reconstructed using a painstaking manual process known as the comparative method. We present a family of probabilistic models of sound change as well as algorithms for performing inference in these models. The resulting system automatically and accurately reconstructs protolanguages from modern languages. We apply this system to 637 Austronesian languages, providing an accurate, large-scale automatic reconstruction of a set of protolanguages. Over 85% of the system’s reconstructions are within one character of the manual reconstruction provided by a linguist specializing in Austronesian languages. Being able to automatically reconstruct large numbers of languages provides a useful way to quantitatively explore hypotheses about the factors determining which sounds in a language are likely to change over time. We demonstrate this by showing that the reconstructed Austronesian protolanguages provide compelling support for a hypothesis about the relationship between the function of a sound and its probability of changing that was first proposed in 1955.


title: Optimization of IC encapsulation considering fluid/structure interaction using response surface methodology

abstract: Abstract Optimized design of the integrated circuit (IC) package gives better IC encapsulation process and minimizes the stress concentration and deformation of the IC structures. The physical and process parameters (i.e., pressure inlet, solder bump standoff height, chip thickness, gap-wise between chips, and mould and filled time) were optimized via response surface methodology using central composite design (CCD) to minimize the stress concentration of chip and solder bump, chip deformation, and void in package during the IC encapsulation process. The optimization of the moulded IC encapsulation is carried out by considering the fluid/structure interaction (FSI) aspects. The optimum empirical models were tested and well confirmed with the simulation results. The optimum design of the IC package (20 mm × 20 mm) with perimeter solder bump arrangement for both physical and process parameters was characterized by 150 μm of solder bump standoff height, 250 μm of chip thickness, and 50.43 μm of gap-wise at the inlet condition of 3.43 MPa.


A Novel Three-Phase Interleaved LLC Resonant Converter For High Efficiency Isolated AcDc Converter


title: On-line Random Forests

abstract: Random Forests (RFs) are frequently used in many computer vision and machine learning applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while achieving state-of-the-art results. However, in most applications RFs are used off-line. This limits their usability for many practical problems, for instance, when training data arrives sequentially or the underlying distribution is continuously changing. In this paper, we propose a novel on-line random forest algorithm. We combine ideas from on-line bagging, extremely randomized forests and propose an on-line decision tree growing procedure. Additionally, we add a temporal weighting scheme for adaptively discarding some trees based on their out-of-bag-error in given time intervals and consequently growing of new trees. The experiments on common machine learning data sets show that our algorithm converges to the performance of the off-line RF. Additionally, we conduct experiments for visual tracking, where we demonstrate real-time state-of-the-art performance on well-known scenarios and show good performance in case of occlusions and appearance changes where we outperform trackers based on on-line boosting. Finally, we demonstrate the usability of on-line RFs on the task of interactive real-time segmentation.


title: Leaf Rust of Cultivated Barley: Pathology and Control

abstract: Leaf rust of barley is caused by the macrocyclic, heteroecious rust pathogen Puccinia hordei, with aecia reported from selected species of the genera Ornithogalum, Leopoldia ,a ndDipcadi, and uredinia and telia occurring on Hordeum vulgare, H. vulgare ssp. spontaneum, Hordeum bulbosum ,a ndHordeum murinum, on which distinct parasitic specialization occurs. Although Puccinia hordei is sporadic in its occurrence, it is probably the most common and widely distributed rust disease of barley. Leaf rust has increased in importance in recent decades in temperate barley-growing regions, presumably because of more intensive agricultural practices. Although total crop loss does not occur, under epidemic conditions yield reductions of up to 62% have been reported in susceptible varieties. Leaf rust is primarily controlled by the use of resistant cultivars, and, to date, 21 seedling resistance genes and two adult plant resistance (APR) genes have been identified. Virulence has been detected for most seedling resistance genes but is unknown for the APR genes Rph20 and Rph23. Other potentially new sources of APR have been reported, and additivity has been described for some of these resistances. Approaches to achieving durable resistance to leaf rust in barley are discussed.


title: Blue Ocean Strategy: From Theory to Practice

abstract: By focusing on strategies of competition in established industries, companies and researchers have overlooked the value of creating new market spaces where there are no competitors.These market spaces are called “blue oceans.” Although established industries (“red oceans”) are growing increasingly cluttered, few companies are currently attempting to create new markets. The U.S. wine industry provides an example of how various tools and frameworks can be applied in the creation of blue oceans.One such framework, the strategy canvas, captures the current states of play in the known market space.The four actions framework reconstructs buyer value elements by posing four key questions that challenge an industry's strategic logic and business model.When a particular wine manufacturer applied the four actions framework to the strategy canvas, it created a wine whose strategic profile broke from the competition and created a blue ocean. A third tool, the eliminate-reduce-raise-create grid, pushes firms to act on all four questions posed by the four actions framework and to create a new value curve.When expressed through a value curve, an effective blue ocean strategy has three complementary qualities: focus, divergence, and a compelling tagline. Embedded in the value curves of an industry is a wealth of strategic knowledge on the current status and future of a business. For example, when a firm's value curve zigzags, it means that the firm does not have a coherent strategy.(SAA)


title: A comparative study of stemming algorithms for use with the Uzbek language

abstract: Stemming is one of the pipeline feature of Information Retrieval and commonly used in natural language processing and text mining. The main purpose of a stemming process is to reduce the inflectional or derivational word into its root form. The difficulties on developing stemming algorithm is to identify and remove affixes since each language in the world has unique characteristics and grammatical rules. This paper compares related study on existing stemmers to be used in Uzbek language. We discuss the type of stemming algorithms, an overview of available popular English stemmers and comparison between discussed stemmers as well as their evaluation and analysis of available stemmers on Uzbek language experiment. Based on the comparative study and experiment, we proposal our model of the Uzbek stemmer that enhances some of the features in Lovins stemmer to suit the requirements for the Uzbek language.


title: Bidirectional degeneration in the visual pathway in neuromyelitis optica spectrum disorder (NMOSD)

abstract: OBJECTIVE ::: This study aims to investigate whether bidirectional degeneration occurs within the visual pathway and, if so, the extent of such changes in neuromyelitis optica spectrum disorder (NMOSD). ::: ::: ::: METHODS ::: In total, 36 NMOSD and 24 healthy controls (HCs) were enrolled. Three-dimensional T1-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging were used to analyze damage to the posterior visual pathway. Damage to the anterior visual pathway was measured by optical coherence tomography. ::: ::: ::: RESULTS ::: In total, 24 NMOSD with prior optic neuritis (NMOON) patients showed significant reduction of peripapillary retinal nerve fiber layer, inner and outer retinal thickness, lateral geniculate nucleus volume, primary visual cortex volume, and decreased integrity of optic radiations, compared with 12 NMOSD without prior optic neuritis (NMONON) patients and 24 HCs. In NMONON, only the inner retinal thickness and the integrity of optic radiations were significantly reduced in comparison with HCs. Moreover, patients with optic neuritis showed severe bidirectional degeneration, the loss of the RNFL was greater than the atrophy of V1. ::: ::: ::: CONCLUSION ::: Our study indicated the presence of trans-synaptic degeneration in NMOSD. Damage to the inner retina and optic radiations can be observed even in NMONON. After an episode of optic neuritis, the anterior visual pathway damage is greater than the posterior visual pathway damage.


title: Bug characteristics in open source software

abstract: To design effective tools for detecting and recovering from software failures requires a deep understanding of software bug characteristics. We study software bug characteristics by sampling 2,060 real world bugs in three large, representative open-source projects--the Linux kernel, Mozilla, and Apache. We manually study these bugs in three dimensions--root causes, impacts, and components. We further study the correlation between categories in different dimensions, and the trend of different types of bugs. The findings include: (1) semantic bugs are the dominant root cause. As software evolves, semantic bugs increase, while memory-related bugs decrease, calling for more research effort to address semantic bugs; (2) the Linux kernel operating system (OS) has more concurrency bugs than its non-OS counterparts, suggesting more effort into detecting concurrency bugs in operating system code; and (3) reported security bugs are increasing, and the majority of them are caused by semantic bugs, suggesting more support to help developers diagnose and fix security bugs, especially semantic security bugs. In addition, to reduce the manual effort in building bug benchmarks for evaluating bug detection and diagnosis tools, we use machine learning techniques to classify 109,014 bugs automatically.


title: Automated Phrase Mining from Massive Text Corpora

abstract: As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, $\mathsf{AutoPhrase}$ , which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, $\mathsf{AutoPhrase}$ has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, $\mathsf{AutoPhrase}$ can be extended to model single-word quality phrases.


title: A 6.8GHz low-profile H-plane ridged SIW horn antenna

abstract: The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.


title: THREE OMNI-DIRECTIONAL WHEELS CONTROL ON A MOBILE ROBOT

abstract: Traditional two wheels differential drive normally used on mobile robots have manoeuvrability limitations and take time to sort out. Most teams use two driving wheels (with one or two cast wheels), four driving wheels and even three driving wheels. A three wheel drive with omni-directional wheel has been tried with success, and was implemented on fast moving autonomous mobile robots. This paper deals with the mathematical kinematics description of such mobile platform, it describes the advantages and also the type of control used.


title: Trajectory-Based Anomalous Event Detection

abstract: During the last years, the task of automatic event analysis in video sequences has gained an increasing attention among the research community. The application domains are disparate, ranging from video surveillance to automatic video annotation for sport videos or TV shots. Whatever the application field, most of the works in event analysis are based on two main approaches: the former based on explicit event recognition, focused on finding high-level, semantic interpretations of video sequences, and the latter based on anomaly detection. This paper deals with the second approach, where the final goal is not the explicit labeling of recognized events, but the detection of anomalous events differing from typical patterns. In particular, the proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring. The proposed approach is based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories. Particular attention is given to trajectory classification in absence of a priori information on the distribution of outliers. Experimental results prove the validity of the proposed approach.


title: A newly discovered Bordetella species carries a transcriptionally active CRISPR-Cas with a small Cas9 endonuclease

abstract: BackgroundClustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated genes (cas) are widely distributed among bacteria. These systems provide adaptive immunity against mobile genetic elements specified by the spacer sequences stored within the CRISPR.MethodsThe CRISPR-Cas system has been identified using Basic Local Alignment Search Tool (BLAST) against other sequenced and annotated genomes and confirmed via CRISPRfinder program. Using Polymerase Chain Reactions (PCR) and Sanger DNA sequencing, we discovered CRISPRs in additional bacterial isolates of the same species of Bordetella. Transcriptional activity and processing of the CRISPR have been assessed via RT-PCR.ResultsHere we describe a novel Type II-C CRISPR and its associated genes—cas1, cas2, and cas9—in several isolates of a newly discovered Bordetella species. The CRISPR-cas locus, which is absent in all other Bordetella species, has a significantly lower GC-content than the genome-wide average, suggesting acquisition of this locus via horizontal gene transfer from a currently unknown source. The CRISPR array is transcribed and processed into mature CRISPR RNAs (crRNA), some of which have homology to prophages found in closely related species B. hinzii.ConclusionsExpression of the CRISPR-Cas system and processing of crRNAs with perfect homology to prophages present in closely related species, but absent in that containing this CRISPR-Cas system, suggest it provides protection against phage predation. The 3,117-bp cas9 endonuclease gene from this novel CRISPR-Cas system is 990 bp smaller than that of Streptococcus pyogenes, the 4,017-bp allele currently used for genome editing, and which may make it a useful tool in various CRISPR-Cas technologies.


title: Using Twitter to engage with customers: a data mining approach

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to explore customer engagement in Twitter via data mining. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – This study’s intended contributions are twofold: to find a clear connection among customer engagement, presumption, and Web 2.0 in a context of service-dominant (S-D) logic; and to identify social networks created by prosumers. To this end, the study employed data mining techniques. Tweets about IKEA were used as a sample. The resulting algorithm based on 300 tweets was applied to 4,000 tweets to identify the patterns of electronic word-of-mouth (eWOM). ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – Social networks created in IKEA’s tweets consist of three forms of eWOM: objective statements, subjective statements, and knowledge sharing. Most objective statements are disseminated from satisfied or neutral customers, while subjective statements are disseminated from dissatisfied or neutral customers. Satisfied customers mainly carry out knowledge sharing, which seems to reflect presumption behavior. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: – This study provides partial evidence of customer engagement and presumption in IKEA’s tweets. The results indicate that there are three forms of eWOM in the networks: objective statements, subjective statements, and knowledge sharing. It seems that IKEA successfully engaged customers in knowledge sharing, while negative opinions were mainly disseminated in a limited circle. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: – Firms should make more of an effort to identify prosumers via data mining, since these networks are hidden behind “self-proclaimed” followers. Prosumers differ from opinion leaders, since they actively participate in product development. Thus, firms should seek prosumers in order to more closely fit their products to consumer needs. As a practical strategy, firms could employ celebrities for promotional purposes and use them as a platform to convert their followers to prosumers. In addition, firms are encouraged to make public how they resolve problematic customer complaints so that customers can feel they are a part of firms’ service development. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – Theoretically, the study makes unique contributions by offering a synergic framework of S-D logic and Web 2.0. The conceptual framework collectively relates customer engagement, presumption, and Web 2.0 to social networks. In addition, the idea of examining social networks based on different forms of eWOM has seldom been touched in the literature. Methodologically, the study employed seven algorithms to choose the most robust model, which was later applied to 4,000 tweets.


title: The Value of Purchase History Data in Target Marketing

abstract: An important aspect of marketing practice is the targeting of consumer segments for differential promotional activity. The premise of this activity is that there exist distinct segments of homogeneous consumers who can be identified by readily available demographic information. The increased availability of individual consumer panel data open the possibility of direct targeting of individual households. The goal of this paper is to assess the information content of various information sets available for direct marketing purposes. Information on the consumer is obtained from the current and past purchase history as well as demographic characteristics. We consider the situation in which the marketer may have access to a reasonably long purchase history which includes both the products purchased and information on the causal environment. Short of this complete purchase history, we also consider more limited information sets which consist of only the current purchase occasion or only information on past product choice without causal variables. ::: ::: Proper evaluation of this information requires a flexible model of heterogeneity which can accommodate observable and unobservable heterogeneity as well as produce household level inferences for targeting purposes. We develop new econometric methods to implement a random coefficient choice model in which the heterogeneity distribution is related to observable demographics. We couple this approach to modeling heterogeneity with a target couponing problem in which coupons are customized to specific households on the basis of various information sets. The couponing problem allows us to place a monetary value on the information sets. ::: ::: Our results indicate there exists a tremendous potential for improving the profitability of direct marketing efforts by more fully utilizing household purchase histories. Even rather short purchase histories can produce a net gain in revenue from target couponing which is 2.5 times the gain from blanket couponing. The most popular current electronic couponing trigger strategy uses only one observation to customize the delivery of coupons. Surprisingly, even the information contained in observing one purchase occasion boasts net couponing revenue by 50% more than that which would be gained by the blanket strategy. This result, coupled with increased competitive pressures, will force targeted marketing strategies to become much more prevalent in the future than they are today.


title: Modeling Compositionality with Multiplicative Recurrent Neural Networks

abstract: We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees.


title: Server-storage virtualization: Integration and load balancing in data centers

abstract: We describe the design of an agile data center with integrated server and storage virtualization technologies. Such data centers form a key building block for new cloud computing architectures.We also show how to leverage this integrated agility for non-disruptive load balancing in data centers across multiple resource layers - servers, switches, and storage. We propose a novel load balancing algorithm called VectorDot for handling the hierarchical and multi-dimensional resource constraints in such systems. The algorithm, inspired by the successful Toyoda method for multi-dimensional knapsacks, is the first of its kind. We evaluate our system on a range of synthetic and real data center testbeds comprising of VMware ESX servers, IBM SAN Volume Controller, Cisco and Brocade switches. Experiments under varied conditions demonstrate the end-to-end validity of our system and the ability of VectorDot to efficiently remove overloads on server, switch and storage nodes.


title: Web mining for cyber monitoring and filtering

abstract: Like any self-regulating environment, the Internet is fertile ground for all kinds of potential abuse ranging from get-rich-quick scams, touting of illegal or adult-oriented material, promotion of extremist/anarchist views, to online pimping, etc. Consequently, the ability to discreetly intercept and analyze Internet access has tremendous potential in shielding users, especially our youngsters, from inappropriate content. This paper proposes one such system, the Web access monitoring and filtering (WAMF) system. The WAMF system comprises two main decoupled components, one for online monitoring and filtering, and the other for offline Web classification and data analysis. The former tracks, tallies, and selectively blocks user Web access in real-time, whereas the latter employs Web mining techniques to classify Web pages into pre-defined user categories and analyze user Web access data for user behavior patterns. In this paper, we will discuss the WAMF system, and in particular, Web mining techniques for adaptive Web page categorization.


ON A THEOREM OF LYAPUNOV


title: REAL TIME TRACKING OF AN OMNIDIRECTIONAL ROBOT - An Extended Kalman Filter Approach

abstract: This paper describes a robust localization system, similar to the used by the teams participating in the Robocup Small size league (SLL). The system, developed in Object Pascal, allows real time localization and control of an autonomous omnidirectional mobile robot. The localization algorithm is done resorting to odometry and global vision data fusion, applying an extended Kalman filter, being this method a standard approach for reducing the error in a least squares sense, using measurements from different sources.


title: Compositional Imitation Learning: Explaining and executing one task at a time

abstract: We introduce a framework for Compositional Imitation Learning and Execution (CompILE) of hierarchically-structured behavior. CompILE learns reusable, variable-length segments of behavior from demonstration data using a novel unsupervised, fully-differentiable sequence segmentation module. These learned behaviors can then be re-composed and executed to perform new tasks. At training time, CompILE auto-encodes observed behavior into a sequence of latent codes, each corresponding to a variable-length segment in the input sequence. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate our model in a challenging 2D multi-task environment and show that CompILE can find correct task boundaries and event encodings in an unsupervised manner without requiring annotated demonstration data. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our agent could learn given only sparse rewards, where agents without task-specific policies struggle.


title: Treatment options for dermal filler complications.

abstract: The author contends that early adverse reactions to dermal fillers are caused by a superficial injection technique and that late adverse reactions result from unique patient responses. He has found all complications treatable with a correct dose of intralesional steroids or inconspicuous surgical entry. Here he provides guidelines for injection techniques and treatment for complications.


title: Computationally Efficient Truncated Nuclear Norm Minimization for High Dynamic Range Imaging

abstract: Matrix completion is a rank minimization problem to recover a low-rank data matrix from a small subset of its entries. Since the matrix rank is nonconvex and discrete, many existing approaches approximate the matrix rank as the nuclear norm. However, the truncated nuclear norm is known to be a better approximation to the matrix rank than the nuclear norm, exploiting a priori target rank information about the problem in rank minimization. In this paper, we propose a computationally efficient truncated nuclear norm minimization algorithm for matrix completion, which we call TNNM-ALM. We reformulate the original optimization problem by introducing slack variables and considering noise in the observation. The central contribution of this paper is to solve it efficiently via the augmented Lagrange multiplier (ALM) method, where the optimization variables are updated by closed-form solutions. We apply the proposed TNNM-ALM algorithm to ghost-free high dynamic range imaging by exploiting the low-rank structure of irradiance maps from low dynamic range images. Experimental results on both synthetic and real visual data show that the proposed algorithm achieves significantly lower reconstruction errors and superior robustness against noise than the conventional approaches, while providing substantial improvement in speed, thereby applicable to a wide range of imaging applications.


title: Vision-Based Positioning for Internet-of-Vehicles

abstract: This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.


title: Scalable lineage capture for debugging DISC analytics

abstract: A fundamental challenge for big-data analytics is how to efficiently tune and debug multi-step dataflows. This paper presents Newt, a scalable architecture for capturing and using record-level data lineage to discover and resolve errors in analytics. Newt's flexible instrumentation allows system developers to collect this fine-grain lineage from a range of data intensive scalable computing (DISC) architectures, actively recording the flow of data through multi-step, user-defined transformations. Newt pairs this API with a scale-out, fault-tolerant lineage store and query engine. We find that while active collection can be expensive, it incurs modest runtime overheads for real-world analytics (


title: The measurement and antecedents of affective, continuance and normative commitment to the organization.

abstract: Organizational commitment has been conceptualized and measured in various ways. The two studies reported here were conducted to test aspects of a three-component model of commitment which integrates these various conceptualizations. The affective component of organizational commitment, proposed by the model, refers to employees' emotional attachment to, identification with, and involvement in, the organization. The continuance component refers to commitment based on the costs that employees associate with leaving the organization. Finally, the normative component refers to employees' feelings of obligation to remain with the organization. In Study 1, scales were developed to measure these components. Relationships among the components of commitment and with variables considered their antecedents were examined in Study 2. Results of a canonical correlation analysis suggested that, as predicted by the model, the affective and continuance components of organizational commitment are empirically distinguishable constructs with different correlates. The affective and normative components, although distinguishable, appear to be somewhat related. The importance of differentiating the components of commitment, both in research and practice, is discussed.


title: Co-Evolution in the Successful Learning of Backgammon Strategy

abstract: Following Tesauro's work on TD-Gammon, we used a 4,000 parameter feedforward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of the dice, application of the network to all legal moves, and selection of the position with the highest evaluation. However, no backpropagation, reinforcement or temporal difference learning methods were employed. Instead we apply simple hillclimbing in a relative fitness environment. We start with an initial champion of all zero weights and proceed simply by playing the current champion network against a slightly mutated challenger and changing weights if the challenger wins. Surprisingly, this worked rather well. We investigate how the peculiar dynamics of this domain enabled a previously discarded weak method to succeed, by preventing suboptimal equilibria in a “meta-game” of self-learning.


title: Deep Residual Learning for Weakly-Supervised Relation Extraction

abstract: Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.


Asset pricing models and financial market anomalies


title: Overview of information flow tracking techniques based on taint analysis for Android

abstract: Smartphones today are ubiquitous source of sensitive information. Information leakage instances on the smartphones are on the rise because of exponential growth in smartphone market. Android is the most widely used operating system on smartphones. Many information flow tracking and information leakage detection techniques are developed on Android operating system. Taint analysis is commonly used data flow analysis technique which tracks the flow of sensitive information and its leakage. This paper provides an overview of existing Information flow tracking techniques based on the Taint analysis for android applications. It is observed that static analysis techniques look at the complete program code and all possible paths of execution before its run, whereas dynamic analysis looks at the instructions executed in the program-run in the real time. We provide in depth analysis of both static and dynamic taint analysis approaches.


title: Chisel: constructing hardware in a Scala embedded language

abstract: In this paper we introduce Chisel, a new hardware construction language that supports advanced hardware design using highly parameterized generators and layered domain-specific hardware languages. By embedding Chisel in the Scala programming language, we raise the level of hardware design abstraction by providing concepts including object orientation, functional programming, parameterized types, and type inference. Chisel can generate a high-speed C++-based cycle-accurate software simulator, or low-level Verilog designed to map to either FPGAs or to a standard ASIC flow for synthesis. This paper presents Chisel, its embedding in Scala, hardware examples, and results for C++ simulation, Verilog emulation and ASIC synthesis.


title: Alibi Routing

abstract: There are several mechanisms by which users can gain insight into where their packets have gone, but no mechanisms allow users undeniable proof that their packets did not traverse certain parts of the world while on their way to or from another host. This paper introduces the problem of finding "proofs of avoidance": evidence that the paths taken by a packet and its response avoided a user-specified set of "forbidden" geographic regions. Proving that something did not happen is often intractable, but we demonstrate a low-overhead proof structure built around the idea of what we call "alibis": relays with particular timing constraints that, when upheld, would make it impossible to traverse both the relay and the forbidden regions. We present Alibi Routing, a peer-to-peer overlay routing system for finding alibis securely and efficiently. One of the primary distinguishing characteristics of Alibi Routing is that it does not require knowledge of--or modifications to--the Internet's routing hardware or policies. Rather, Alibi Routing is able to derive its proofs of avoidance from user-provided GPS coordinates and speed of light propagation delays. Using a PlanetLab deployment and larger-scale simulations, we evaluate Alibi Routing to demonstrate that many source-destination pairs can avoid countries of their choosing with little latency inflation. We also identify when Alibi Routing does not work: it has difficulty avoiding regions that users are very close to (or, of course, inside of).


title: Sleep Quality as a Mediator Between Technology-Related Sleep Quality, Depression, and Anxiety

abstract: Abstract This study examines (a) relations among technology use during sleep time, sleep quality, and depression/anxiety and (b) time awake due to technology use. Two hundred thirty-six college students completed self-report questionnaires and week-long sleep diaries. Results revealed that 47 percent of students reported night-time waking to answer text messages and 40 percent to answer phone calls. Regression analyses indicated that higher levels of technology use after the onset of sleep predicted poorer sleep quality, and poorer sleep quality predicted symptoms of depression/anxiety. Finally, sleep quality is a mediator between technology use after the onset of sleep and depression/anxiety. College students who have difficulty setting boundaries around technology use may be at increased risk for psychological health concerns.


title: Hummel's Digital Transformation Toward Omnichannel Retailing: Key Lessons Learned

abstract: With the growing use of mobile and social media, many organizations are realizing they need an online presence to reach out to digitally savvy customers. But delivering a seamless customer experience across various online and offline channels is challenging. The full article describes how Hummel, a European sports fashion company, overcame the challenges and successfully transitioned toward omnichannel retailing. (Omnichannel capabilities integrate online and offline channels in engaging and serving customers.) Based on this case, the article provides insights to guide organizations with similar ambitions and the implications for their CIOs.


title: Learning by googling

abstract: The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.


title: Decision support for agri-food chains: A reverse engineering argumentation-based approach

abstract: Evaluating food quality is a complex process since it relies on numerous criteria historically grouped into four main types: nutritional, sensorial, practical and hygienic qualities. They may be completed by other emerging preoccupations such as the environmental impact, economic phenomena, etc. However, all these aspects of quality and their various components are not always compatible and their simultaneous improvement is a problem that sometimes has no obvious solution, which corresponds to a real issue for decision making. This paper proposes a decision support method guided by the objectives defined for the end products of an agrifood chain. It is materialised by a backward chaining approach based on argumentation.


title: Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints

abstract: This paper studies the use of received signal strength indicators (RSSI) applied to fingerprinting method in a Bluetooth network for indoor positioning. A Bayesian fusion (BF) method is proposed to combine the statistical information from the RSSI measurements and the prior information from a motion model. Indoor field tests are carried out to verify the effectiveness of the method. Test results show that the proposed BF algorithm achieves a horizontal positioning accuracy of about 4.7 m on the average, which is about 6 and 7 % improvement when compared with Bayesian static estimation and a point Kalman filter method, respectively.


Normative vs. Pragmatic: Two Perspectives on the Design of Explanations in Intelligent Systems


title: Alternative interest measures for mining associations in databases

abstract: Data mining is defined as the process of discovering significant and potentially useful patterns in large volumes of data. Discovering associations between items in a large database is one such data mining activity. In finding associations, support is used as an indicator as to whether an association is interesting. In this paper, we discuss three alternative interest measures for associations: any-confidence, all-confidence, and bond. We prove that the important downward closure property applies to both all-confidence and bond. We show that downward closure does not hold for any-confidence. We also prove that, if associations have a minimum all-confidence or minimum bond, then those associations will have a given lower bound on their minimum support and the rules produced from those associations will have a given lower bound on their minimum confidence as well. However, associations that have that minimum support (and likewise their rules that have minimum confidence) may not satisfy the minimum all-confidence or minimum bond constraint. We describe the algorithms that efficiently find all associations with a minimum all-confidence or minimum bond and present some experimental results.


title: Elastic Fragments for Dense Scene Reconstruction

abstract: We present an approach to reconstruction of detailed scene geometry from range video. Range data produced by commodity handheld cameras suffers from high-frequency errors and low-frequency distortion. Our approach deals with both sources of error by reconstructing locally smooth scene fragments and letting these fragments deform in order to align to each other. We develop a volumetric registration formulation that leverages the smoothness of the deformation to make optimization practical for large scenes. Experimental results demonstrate that our approach substantially increases the fidelity of complex scene geometry reconstructed with commodity handheld cameras.


title: SPFM: Scalable and Privacy-Preserving Friend Matching in Mobile Cloud

abstract: Profile (e.g., contact list, interest, and mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line, or WeChat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this paper, we propose a novel scalable and privacy-preserving friend matching (SPFM) protocol, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations. The result show that our scheme works even better when original data is large.


title: A Comparison of Implicit and Explicit Parallel Programming

abstract: The impact of the parallel programming model on scientific computing is examined. A comparison is made between Sisal, a functional language with implicit parallelism, and SR, an imperative language with explicit parallelism. Both languages are modern, high-level, concurrent programming languages. Five different scientific applications were programmed in each language, and evaluated for programmability and performance. The performance of these two concurrent languages on a shared-memory multiprocessor is compared to each other and to programs written in C with parallelism provided by library calls.


title: A specification-based intrusion detection engine for infrastructure-less networks

abstract: The proliferation of mobile computing devices has enabled the utilization of infrastructure-less networking as commercial solutions. However, the distributed and cooperative nature of routing in such networks makes them vulnerable to a variety of attacks. This paper proposes a host-based monitoring mechanism, called SIDE that safeguards the operation of the AODV routing protocol. SIDE encompasses two complementary functionalities: (i) a specification-based detection engine for the AODV routing protocol, and (ii) a remote attestation procedure that ensures the integrity of a running SIDE instance. The proposed mechanism operates on a trusted computing platform that provides hardware-based root of trust and cryptographic acceleration, used by the remote attestation procedure, as well as protection against runtime attacks. A key advantage of the proposed mechanism is its ability to effectively detect both known and unknown attacks, in real time. Performance analysis shows that attacks are resolved with high detection accuracy, even under conditions of high network volatility. Moreover, SIDE induces the least amount of control packet overhead in comparison with a number of other proposed IDS schemes.