title: CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces

abstract: In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples. To this end, we propose to learn a group of capsule subspaces onto which an input feature vector is projected. Then the lengths of resultant capsules are used to score the probability of belonging to different classes. We train such a Capsule Projection Network (CapProNet) by learning an orthogonal projection matrix for each capsule subspace, and show that each capsule subspace is updated until it contains input feature vectors corresponding to the associated class. We will also show that the capsule projection can be viewed as normalizing the multiple columns of the weight matrix simultaneously to form an orthogonal basis, which makes it more effective in incorporating novel components of input features to update capsule representations. In other words, the capsule projection can be viewed as a multi-dimensional weight normalization in capsule subspaces, where the conventional weight normalization is simply a special case of the capsule projection onto 1D lines. Only a small negligible computing overhead is incurred to train the network in low-dimensional capsule subspaces or through an alternative hyper-power iteration to estimate the normalization matrix. Experiment results on image datasets show the presented model can greatly improve the performance of the state-of-the-art ResNet backbones by $10-20\%$ and that of the Densenet by $5-7\%$ respectively at the same level of computing and memory expenses. The CapProNet establishes the competitive state-of-the-art performance for the family of capsule nets by significantly reducing test errors on the benchmark datasets.


title: Induction in Noisy Domains

abstract: This paper examines the induction of classification rules from examples using real-world data. Real-world data is almost always characterized by two features, which are important for the design of an induction algorithm. Firstly, there is often noise present, for example, due to imperfect measuring equipment used to collect the data. Secondly the description language is often incomplete, such that examples with identical descriptions in the language will not always be members of the same class. Many induction systems make the ‘noiseless domain’ assumption that the examples do not contain errors and the description language is complete, and consequently constrain their search for rules to those for which no counterexamples exist in the data used for induction. However, in real-world domains correlations between attributes and classes in a data set are rarely without exceptions. To locate such correlations and induce rules describing them it is also necessary to consider rules which may not classify all the training examples correctly. This paper firstly discusses some of the problems presented by noise and proposes a top-down induction algorithm for induction in real-world domains. Secondly, an experimental comparison of this algorithm with other induction systems is presented using three sets of real-world medical data.


title: A real-time peak discovering method for audio fingerprinting

abstract: Discovering peaks is a premise of audio fingerprinting algorithms for audio information retrieval, which focus on spectrogram peak pairs. In this paper, we discuss finding peaks in a two-direction scanning method which use a dynamic threshold vector to find local maximal point as peaks. And then an improved method using a slide window with two grids is proposed for discovering peaks at real-time. In this method, two key steps are executed alternately: scanning and sliding. In each scanning step, new peaks are found. When the slide window method is used, experiments on a database with 400,000 songs show that the average query time is shorten and the recall rate is slightly decreased.


title: ZABULIS et al.: 3D HEAD POSE ESTIMATION FROM MULTIPLE DISTANT VIEWS 1 3D head pose estimation from multiple distant views

abstract: 3D head pose estimation constitutes a special problem of human motion modeling. An accurate and robust solution to this problem is of particular interest, because the 3D head pose of a human conveys important information on his/her behavior. Significant advances have been achieved in human head pose estimation for relatively close-range images, but the related available methods are not directly applicable in wider-range imaging conditions. The proposed method is overviewed in Fig. 1. The visual hull of a person is obtained from images acquired synchronously from multiple viewpoints. While moving, the person’s head is tracked in 3D employing a variant of the Mean-Shift algorithm and a spherical kernel. The texture on the surface of the hull is collected from multiple views and projected on a hypothetical sphere S that is concentric to the person’s head. This gives rise to spherical image Is within which face detection is simplified, because exactly one frontal face is guaranteed to appear in it at a known spatial scale.


title: Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication

abstract: The paper studies distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and noisy inter-sensor communication. It introduces \emph{separably estimable} observation models that generalize the observability condition in linear centralized estimation to nonlinear distributed estimation. It studies two distributed estimation algorithms in separably estimable models, the $\mathcal{NU}$ (with its linear counterpart $\mathcal{LU}$) and the $\mathcal{NLU}$. Their update rule combines a \emph{consensus} step (where each sensor updates the state by weight averaging it with its neighbors' states) and an \emph{innovation} step (where each sensor processes its local current observation.) This makes the three algorithms of the \textit{consensus + innovations} type, very different from traditional consensus. The paper proves consistency (all sensors reach consensus almost surely and converge to the true parameter value,) efficiency, and asymptotic unbiasedness. For $\mathcal{LU}$ and $\mathcal{NU}$, it proves asymptotic normality and provides convergence rate guarantees. The three algorithms are characterized by appropriately chosen decaying weight sequences. Algorithms $\mathcal{LU}$ and $\mathcal{NU}$ are analyzed in the framework of stochastic approximation theory; algorithm $\mathcal{NLU}$ exhibits mixed time-scale behavior and biased perturbations, and its analysis requires a different approach that is developed in the paper.


title: Reinforcement Learning as a Framework for Ethical Decision Making

abstract: Emerging AI systems will be making more and more decisions that impact the lives of humans in a significant way. It is essential, then, that these AI systems make decisions that take into account the desires, goals, and preferences of other people, while simultaneously learning about what those preferences are. In this work, we argue that the reinforcement-learning framework achieves the appropriate generality required to theorize about an idealized ethical artificial agent, and offers the proper foundations for grounding specific questions about ethical learning and decision making that can promote further scientific investigation. We define an idealized formalism for an ethical learner, and conduct experiments on two toy ethical dilemmas, demonstrating the soundness and flexibility of our approach. Lastly, we identify several critical challenges for future advancement in the area that can leverage our proposed framework.


title: Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback

abstract: There has been great progress in understanding of anatomical and functional microcircuitry of the primate cortex. However, the fundamental principles of cortical computation - the principles that allow the visual cortex to bind retinal spikes into representations of objects, scenes and scenarios - have so far remained elusive. In an attempt to come closer to understanding the fundamental principles of cortical computation, here we present a functional, phenomenological model of the primate visual cortex. The core part of the model describes four hierarchical cortical areas with feedforward, lateral, and recurrent connections. The three main principles implemented in the model are information compression, unsupervised learning by prediction, and use of lateral and top-down context. We show that the model reproduces key aspects of the primate ventral stream of visual processing including Simple and Complex cells in V1, increasingly complicated feature encoding, and increased separability of object representations in higher cortical areas. The model learns representations of the visual environment that allow for accurate classification and state-of-the-art visual tracking performance on novel objects.


title: Design of LDO linear regulator with ultra low-output impedance buffer

abstract: This paper presents a low-dropout (LDO) linear regulator using ultra-low output resistance buffer for frequency compensation. The proposed buffer achieves ultra low output impedance with dual shunt feedback loops, which makes it possible to improve load and line regulations as well as the transient response for low voltage applications. A reference control scheme for programmable output voltage of the LDO is presented. The designed LDO linear regulator works under the input voltage of 2.5∼5.5V and provides up to 300mA load current for an output voltage range of 0.6∼3.3V.


title: Suicidal risk factors in major affective disorders

abstract: Background Rates and risk factors for suicidal behaviour require updating and comparisons among mood disorders. Aims To identify factors associated with suicidal risk in major mood disorders. Method We considered risk factors before, during and after intake assessments of 3284 adults with/without suicidal acts, overall and with bipolar disorder (BD) versus major depressive disorder (MDD), using bivariate comparisons, multivariable regression modelling and receiver operating characteristic (ROC) analysis. Results Suicidal prevalence was greater in BD versus MDD: ideation, 29.2 versus 17.3%; attempts, 18.8 versus 4.78%; suicide, 1.73 versus 0.48%; attempts/suicide ratio indicated similar lethality, 10.9 versus 9.96. Suicidal acts were associated with familial BD or suicide, being divorced/unmarried, fewer children, early abuse/trauma, unemployment, younger onset, longer illness, more dysthymic or cyclothymic temperament, attention-deficit hyperactivity disorder (ADHD), substance misuse, mixed features, hospital admission, percentage time unwell, less antidepressants and more antipsychotics and mood stabilisers. Logistic regression found five independent factors: hospital admission, more depression at intake, BD diagnosis, onset age ≤25 years and mixed features. These factors were more associated with suicidal acts in BD than MDD: percentage time depressed/ill, alcohol misuse, >4 pre-intake depressions, more dysthymic/cyclothymic temperament and prior abuse/trauma. ADHD and total years ill were similar in BD and MDD; other factors were more associated with MDD. By ROC analysis, area under the curve was 71.3%, with optimal sensitivity (76%) and specificity (55%) with any two factors. Conclusions Suicidal risks were high in mood disorders: ideation was highest with BD type II, attempts and suicides (especially violent) with BD type I. Several risk factors for suicidal acts differed between BD versus MDD patients. Declaration of interest No author or immediate family member has financial relationships with commercial entities that might appear to represent potential conflicts of interest with the information presented.


title: The Activity-Based Approach

abstract: Author(s): McNally, Michael G.; Rindt, Craig | Abstract: What is the activity-based approach (ABA) and how does it differ from the conventional trip-based model of travel behavior? From where has the activity approach evolved, what is its current status, and what are its potential applications in transportation forecasting and policy analysis. What have been the contributions of activity-based approaches to understanding travel behavior?The conventional trip-based model of travel demand forecasting (see Chapters 2 and 3) has always lacked a valid representation of underlying travel behavior. This model, commonly referred to as the four-step model (FSM), was developed to evaluate the impact of capital-intensive infrastructure investment projects during a period where rapid increases in transportation supply were arguably accommodating, if not directing, the growth in population and economic activity of the post-war boom. As long as the institutional environment and available resources supported this policy, trip-based models were sufficient to assess the relative performance of transportation alternatives. It was clear from the beginning, however, that the derived nature of the demand for transportation was understood and accepted, yet not reflected in the FSM. The 1970s, however, brought fundamental changes in urban, environmental, and energy policy, and with it the first re-consideration of travel forecasting. It was during this period that the ABA was first studied in depth.A wealth of behavioral theories, conceptual frameworks, analytical methodologies, and empirical studies of travel behavior emerged during this same period that the policy environment was evolving. These advances shared "a common philosophical perspective, whereby the conventional approach to the study of travel behavior ... is replaced by a richer, more holistic, framework in which travel is analyzed as daily or multi-day patterns of behavior, related to and derived from differences in lifestyles and activity participation among the population" (Jones et al., 1990). This common philosophy has become known as the “activity-based approach”. The motivation of the activity approach is that travel decisions are activity based, and that any understanding of travel behavior is secondary to a fundamental understanding of activity behavior. The activity approach explicitly recognizes and addresses the inability of trip-based models to reflect underlying behavior and, therefore, the inability of such models to be responsive to evolving policies oriented toward management versus expansion of transportation infrastructure and services.In the next section, a summary and critique of the convention trip-based approach is presented, followed by an overview of ABAs, focusing on how these approaches address the various limitations of the conventional model. This is followed by a review of representative examples of activity-based approaches, including several perhaps best considered as contributions to understanding travel behavior, and several oriented toward direct application in forecasting and policy analysis. Some summary comments are then provided including an assessment of the future of both trip-based and activity-based approaches.


title: Optimizing Paxos with batching and pipelining

abstract: Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput. We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these results with the prediction from the model. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latency WAN but also in many LAN scenarios.


title: The development of a model of dignity in illness based on qualitative interviews with seriously ill patients.

abstract: BACKGROUND ::: While knowledge on factors affecting personal dignity of patients nearing death is quite substantial, far less is known about how patients living with a serious disease understand dignity. ::: ::: ::: OBJECTIVE ::: To develop a conceptual model of dignity that illuminates the process by which serious illness can undermine patients' dignity, and that is applicable to a wide patient population. ::: ::: ::: DESIGN ::: Qualitative interview study. ::: ::: ::: PARTICIPANTS ::: 34 patients with either cancer, early stage dementia, or a severe chronic illness were selected from an extensive cohort study into advance directives. ::: ::: ::: METHOD ::: In-depth interviews were carried out exploring the experiences of seriously ill patients with regard to their personal dignity. The interview transcripts were analyzed using thematic analysis and a conceptual model was constructed based on the resulting themes. ::: ::: ::: RESULTS ::: We developed a two-step dignity model of illness. According to this model, illness related conditions do not affect patients' dignity directly but indirectly by affecting the way patients perceive themselves. We identified three components shaping self-perception: (a) the individual self: the subjective experiences and internally held qualities of the patient; (b) the relational self: the self within reciprocal interaction with others; and, (c) the societal self: the self as a social object in the eyes of others. ::: ::: ::: CONCLUSIONS ::: The merits of the model are two-folded. First, it offers an organizing framework for further research into patients' dignity. Secondly, the model can serve to facilitate care for seriously ill patients in practice by providing insight into illness and dignity at the level of the individual patient where intervention can be effectively targeted.


title: An Upper Bound on Trilaterating Simple Polygons

abstract: In this work, we introduce the Minimum Trilateration Problem, the problem of placing distance measuring guards in a polygon in order to locate points in the interior. We provide the first non-trivial bounds on trilaterating simple polygons, by showing that b 8N 9 c guards suffice for any non-degenerate polygon of N sides, and present an O(N logN) algorithm for the corresponding placement. We also show how this mapping can be efficiently inverted, in order to determine a point’s location given its distances to the guards which can see it.


title: A Low-Loss 50–70 GHz SPDT Switch in 90 nm CMOS

abstract: This paper presents an ultra-low-loss 50-70 GHz single-pole double-throw (SPDT) switch built using a standard 90 nm CMOS process. The switch is based on λ/4 transmission lines with shunt inductors at the output matching network. The SPDT switch results in a measured insertion loss of 1.5-1.6 dB at 53-60 GHz and 25 dB, and the output port-to-port isolation is > 27 dB at 50-70 GHz. The measured P1dB is 13.5 dBm with a corresponding IIP3 of 22.5 dBm at 60 GHz. The return loss is better than -8 dB at 50-70 GHz. The active chip area is 0.5 × 0.55 mm2 and can be reduced in future designs by folding the on λ/4 transmission lines. To our knowledge, this paper presents the lowest insertion loss 60 GHz SPDT in any CMOS technology.


title: YouTube-8M: A Large-Scale Video Classification Benchmark

abstract: Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. ::: In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities. To get the videos and their labels, we used a YouTube video annotation system, which labels videos with their main topics. While the labels are machine-generated, they have high-precision and are derived from a variety of human-based signals including metadata and query click signals. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. ::: We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow. We plan to release code for training a TensorFlow model and for computing metrics.


title: Tracking the World State with Recurrent Entity Networks

abstract: We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped ::: with a dynamic long-term memory which allows it to maintain and update a rep- ::: resentation of the state of the world as it receives new data. For language under- ::: standing tasks, it can reason on-the-fly as it reads text, not just when it is required ::: to answer a question or respond as is the case for a Memory Network (Sukhbaatar ::: et al., 2015). Like a Neural Turing Machine or Differentiable Neural Computer ::: (Graves et al., 2014; 2016) it maintains a fixed size memory and can learn to ::: perform location and content-based read and write operations. However, unlike ::: those models it has a simple parallel architecture in which several memory loca- ::: tions can be updated simultaneously. The EntNet sets a new state-of-the-art on ::: the bAbI tasks, and is the first method to solve all the tasks in the 10k training ::: examples setting. We also demonstrate that it can solve a reasoning task which ::: requires a large number of supporting facts, which other methods are not able to ::: solve, and can generalize past its training horizon. It can also be practically used ::: on large scale datasets such as Children’s Book Test, where it obtains competitive ::: performance, reading the story in a single pass.


title: On shape and the computability of emotions

abstract: We investigated how shape features in natural images influence emotions aroused in human beings. Shapes and their characteristics such as roundness, angularity, simplicity, and complexity have been postulated to affect the emotional responses of human beings in the field of visual arts and psychology. However, no prior research has modeled the dimensionality of emotions aroused by roundness and angularity. Our contributions include an in depth statistical analysis to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset we provide evidence for the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. We combine our shape features with other state-of-the-art features to show a gain in prediction and classification accuracy. We model emotions from a dimensional perspective in order to predict valence and arousal ratings which have advantages over modeling the traditional discrete emotional categories. Finally, we distinguish images with strong emotional content from emotionally neutral images with high accuracy.


title: Feature extraction based on the principal component analysis for pulsed magnetic flux leakage testing

abstract: Magnetic flux leakage testing is widely used in the defect inspection of ferromagnetic materials. Pulsed magnetic flux leakage (PMFL) testing technique is a new type of electromagnetic nondestructive testing technology. A new approach for defect classification by using magnetic flux leakage testing technology and principal component analysis based signal interpretation is presented. For defect classification, different features have been extracted from magnetic flux leakage signals using time-frequency analysis. The results from study show that the proposed technique can be used to classify the two types of defects: surface and subsurface discontinuities and the introduced technique are effective in the classification of defects.


title: A case study in a recommender system based on purchase data

abstract: Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.


title: Modeling and Application of Requirements Engineering Process Metamodel

abstract: With deeper researches on requirements engineering (RE), the researchers gradually realize that the requirements engineering processes are very complex and the related technologies are in a great variety. Thus, in order to analyze and compare different types of requirements engineering processes more deeply, a simple, clear, unified description method of requirements engineering process is needed. In this paper, the requirements engineering process itself is taken as the research object and a formal description method of requirements engineering process, requirements engineering process metamodel (REPM), is proposed. The REPM is a simple and unified method for describing different types of processes, in which different RE tools are applied. In this paper, the formal definition of REPM is given, and three typical requirements engineering processes are also described and analyzed using REPM. Furthermore, a new integrated RE process is defined using REPM as well.


title: Successor Features for Transfer in Reinforcement Learning

abstract: Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same. Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.


REVIEWARTICLE Two Traditions of Research on Gender Identity


title: Gamut Mapping to Preserve Spatial Luminance Variations

abstract: A spatial gamut mapping technique is proposed to overcome the shortcomings encountered with standard pointwise gamut mapping algorithms by preserving spatially local luminance variations in the original image. It does so by first processing the image through a standard pointwise gamut-mapping algorithm. The difference between the original image luminance Y and gamut mapped image luminance Y' is calculated. A spatial filter is then applied to this difference signal, whose output is added back to the gamut mapped signal Y'. The filtering operation can cause some pixel colors that lie near the gamut boundary to be moved outside of the gamut, hence a second gamut mapping step is required to move these pixel colors back into the gamut. Finally, all pixels are processed through a color correction function for the output device, and rendered for that device. The algorithm is designed to reduce many of the artifacts arising from standard pointwise techniques. Psychophysical experiments indicated an observer preference for the proposed algorithm.


title: Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus

abstract: The current study proposes a model to test whether online review valence and attributes have an effect on credibility, and whether regulatory focus and subjective knowledge have moderating effects. Three hundred nineteen university students participated in online experiments with a 2 (positive vs. negative review valence) by 2 (objective vs. subjective review attributes) between subject design. The experiment demonstrated that objective and negative online reviews have a significant positive and negative impact, respectively, on message credibility, which affects review adoption. The results also showed that the moderating effect produced by objective information and a consumer's subjective knowledge is supported. This study contributes to explaining the inconsistent results between review valence/attribute and credibility found in previous studies.


title: On the editing distance between unordered labeled trees

abstract: This paper considers the problem of computing the editing distance between unordered, labeled trees. We give efficient polynomial-time algorithms for the case when one tree is a string or has a bounded number of leaves. By contrast, we show that the problem is NP-complete even for binary trees having a label alphabet of size two.


title: Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases

abstract: Given a query graph q and a data graph g, the subgraph isomorphism search finds all occurrences of q in g and is considered one of the most fundamental query types for many real applications. While this problem belongs to NP-hard, many algorithms have been proposed to solve it in a reasonable time for real datasets. However, a recent study has shown, through an extensive benchmark with various real datasets, that all existing algorithms have serious problems in their matching order selection. Furthermore, all algorithms blindly permutate all possible mappings for query vertices, often leading to useless computations. In this paper, we present an efficient and robust subgraph search solution, called TurboISO, which is turbo-charged with two novel concepts, candidate region exploration and the combine and permute strategy (in short, Comb/Perm). The candidate region exploration identifies on-the-fly candidate subgraphs (i.e, candidate regions), which contain embeddings, and computes a robust matching order for each candidate region explored. The Comb/Perm strategy exploits the novel concept of the neighborhood equivalence class (NEC). Each query vertex in the same NEC has identically matching data vertices. During subgraph isomorphism search, Comb/Perm generates only combinations for each NEC instead of permutating all possible enumerations. Thus, if a chosen combination is determined to not contribute to a complete solution, all possible permutations for that combination will be safely pruned. Extensive experiments with many real datasets show that TurboISO consistently and significantly outperforms all competitors by up to several orders of magnitude.


title: Design of Secure ARP on MACsec(802.1AE)

abstract: MACsec provides authenticity and integrity for data frame on data link layer by implementing data encryption. For these advantages, MACsec is highlighted as a solution to protect and transmit data safely from the various security threats in the Wired and Wireless LAN. Until now, MACsec can not guarantee stable ARP management for itself [1]. This paper will propose a design of enhanced ARP to protect IP and MAC address from external threats and the enhanced ARP will encrypt ARP Packet by SAK (Secure Association Key) for the authentication. It will help to keep the address system safe from various security threats on data link layer.


title: Robust tracking using local sparse appearance model and K-selection

abstract: Online learned tracking is widely used for it's adaptive ability to handle appearance changes. However, it introduces potential drifting problems due to the accumulation of errors during the self-updating, especially for occluded scenarios. The recent literature demonstrates that appropriate combinations of trackers can help balance stability and flexibility requirements. We have developed a robust tracking algorithm using a local sparse appearance model (SPT). A static sparse dictionary and a dynamically online updated basis distribution model the target appearance. A novel sparse representation-based voting map and sparse constraint regularized mean-shift support the robust object tracking. Besides these contributions, we also introduce a new dictionary learning algorithm with a locally constrained sparse representation, called K-Selection. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than alternatives reported in the recent literature.


title: Nonparallel Support Vector Machines for Pattern Classification

abstract: We propose a novel nonparallel classifier, called nonparallel support vector machine (NPSVM), for binary classification. Our NPSVM that is fully different from the existing nonparallel classifiers, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM), has several incomparable advantages: 1) two primal problems are constructed implementing the structural risk minimization principle; 2) the dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly, while existing TWSVMs have to construct another two primal problems for nonlinear cases based on the approximate kernel-generated surfaces, furthermore, their nonlinear problems cannot degenerate to the linear case even the linear kernel is used; 3) the dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) it has the inherent sparseness as standard SVMs; 5) existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. In some sense, our NPSVM is a new starting point of nonparallel classifiers.


Techniques for Maximizing Efficiency of Solar Energy Harvesting Systems (Invited Paper)


title: MULTI-OBJECTIVE ARTIFICIAL BEE COLONY (MOABC) ALGORITHM TO IMPROVE CONTENT-BASED IMAGE RETRIEVAL PERFORMANCE

abstract: Multi-objective optimization has been a difficult a rea and focus for research in fields of image proce ssing. This paper presents anoptimization algorithm based on artificial bee colony (ABC) to deal with multiobjective optimization problems in CBIR. We have introduce to multi-object ABC algorithms is based on the intelligent scavenging behaviour for content ba se images. It uses less control parameters, and it can be efficiently used for solving for multi object optim ization problems. In the current work, MOABC for discrete variables hasbeen developed and implemented successfully for the multi-objective design optimization of composites. The proposed algorithm is corroborated using the standard test problems, a nd simulation results show that the proposed approach is highly competitive and can be considered a viabl e alternative to solve multi-objective optimization p roblems.Finally the performance is evaluated incomparison with other nature inspired techniques which includes Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Genetic Algorithm (MOGA). The performance of MOABC is better as par with thatof MOPSO,MOGA and ABC for all the loading configurations.


title: Rotor Temperature Detection in Permanent Magnet Synchronous Machine-Based Automotive Electric Traction Drives

abstract: The detection of the rotor temperature in permanent magnet synchronous machine-based electric traction drives is a challenging problem for the automotive business. The goal of this paper is to provide a simple detection method that can answer to this problem. To this end, first the rotor temperature dynamics is modeled as a first order parameter-varying system. In this way, the resulting model depends only on the rotor mechanical speed and the stator temperature. Second, a polynomial approach for model parameters is employed. Then, an effective solution based on the system inactivity time is used for computing the initial value of the model. It yields a low-computational solution which is suitable for real-time implementation. The experimental results obtained on a prototype hybrid electric vehicle show good accuracy over a wide operating range.


title: Using matched samples to estimate the effects of exercise on mental health from twitter

abstract: Recent work has demonstrated the value of social media monitoring for health surveillance (e.g., tracking influenza or depression rates). It is an open question whether such data can be used to make causal inferences (e.g., determining which activities lead to increased depression rates). Even in traditional, restricted domains, estimating causal effects from observational data is highly susceptible to confounding bias. In this work, we estimate the effect of exercise on mental health from Twitter, relying on statistical matching methods to reduce confounding bias. We train a text classifier to estimate the volume of a user's tweets expressing anxiety, depression, or anger, then compare two groups: those who exercise regularly (identified by their use of physical activity trackers like Nike+), and a matched control group. We find that those who exercise regularly have significantly fewer tweets expressing depression or anxiety; there is no significant difference in rates of tweets expressing anger. We additionally perform a sensitivity analysis to investigate how the many experimental design choices in such a study impact the final conclusions, including the quality of the classifier and the construction of the control group.


title: Building a Chatbot with Serverless Computing

abstract: Chatbots are emerging as the newest platform used by millions of consumers worldwide due in part to the commoditization of natural language services, which provide provide developers with many building blocks to create chatbots inexpensively. However, it is still difficult to build and deploy chatbots. Developers need to handle the coordination of the cognitive services to build the chatbot interface, integrate the chatbot with external services, and worry about extensibility, scalability, and maintenance. In this work, we present the architecture and prototype of a chatbot using a serverless platform, where developers compose stateless functions together to perform useful actions. We describe our serverless architecture based on function sequences, and how we used these functions to coordinate the cognitive microservices in the Watson Developer Cloud to allow the chatbot to interact with external services. The serverless model improves the extensibility of our chatbot, which currently supports 6 abilities: location based weather reports, jokes, date, reminders, and a simple music tutor.


title: The effect of CS unplugged on middle-school students' views of CS

abstract: Many students hold incorrect views of what computer science (CS) is, and they have negative attitudes towards the field. In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Bell et al. [3]. These activities expose young people to central concepts in CS in an entertaining way, without requiring a computer. Using questionnaires and interviews, we examined the effect of the activities on middle-school students' views of CS, specifically, on their views of: (a) the nature of CS; (b) the characteristics of computer scientists and work in CS; (c) the variety of employment in CS. The results indicate that 'although the students generally understood what CS is' they perceived the computer as the essence of CS and not primarily as a tool, contrary to the intention of the CS Unplugged activities. We suggest additions to the activities intended to increase the change in the views of CS that students have.


title: Design and Implementation of LPWA-Based Air Quality Monitoring System

abstract: Increasing attention has been paid to air quality monitoring with a rapid development in industry and transportation applications in the modern society. However, the existing air quality monitoring systems cannot provide satisfactory spatial and temporal resolutions of the air quality information with low costs in real time. In this paper, we propose a new method to implement the air quality monitoring system based on state-of-the-art Internet-of-Things (IoT) techniques. In this system, portable sensors collect the air quality information timely, which is transmitted through a low power wide area network. All air quality data are processed and analyzed in the IoT cloud. The completed air quality monitoring system, including both hardware and software, is developed and deployed successfully in urban environments. Experimental results show that the proposed system is reliable in sensing the air quality, which helps reveal the change patterns of air quality to some extent.


Early Pest Identification in i Greenhouse Crops using Image Processing Techniques


title: Design of human symbiotic robot TWENDY-ONE

abstract: In this paper, we propose a sophisticated design of human symbiotic robots that provide physical supports to the elderly such as attendant care with high-power and kitchen supports with dexterity while securing contact safety even if physical contact occurs with them. First of all, we made clear functional requirements for such a new generation robot, amounting to fifteen items to consolidate five significant functions such as “safety”, “friendliness”, “dexterity”, “high-power” and “mobility”. In addition, we set task scenes in daily life where support by robot is useful for old women living alone, in order to deduce specifications for the robot. Based on them, we successfully developed a new generation of human symbiotic robot, TWENDY-ONE that has a head, trunk, dual arms with a compact passive mechanism, anthropomorphic dual hands with mechanical softness in joints and skins and an omni-wheeled vehicle. Evaluation experiments focusing on attendant care and kitchen supports using TWENDY-ONE indicate that this new robot will be extremely useful to enhance quality of life for the elderly in the near future where human and robot co-exist.


title: Towards a soft pneumatic glove for hand rehabilitation

abstract: This paper presents preliminary results for the design, development and evaluation of a hand rehabilitation glove fabricated using soft robotic technology. Soft actuators comprised of elastomeric materials with integrated channels that function as pneumatic networks (PneuNets), are designed and geometrically analyzed to produce bending motions that can safely conform with the human finger motion. Bending curvature and force response of these actuators are investigated using geometrical analysis and a finite element model (FEM) prior to fabrication. The fabrication procedure of the chosen actuator is described followed by a series of experiments that mechanically characterize the actuators. The experimental data is compared to results obtained from FEM simulations showing good agreement. Finally, an open-palm glove design and the integration of the actuators to it are described, followed by a qualitative evaluation study.


title: Design of reciprocal recommendation systems for online dating

abstract: Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user’s interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious toward their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line.


title: Wall++: Room-Scale Interactive and Context-Aware Sensing

abstract: Human environments are typified by walls, homes, offices, schools, museums, hospitals and pretty much every indoor context one can imagine has walls. In many cases, they make up a majority of readily accessible indoor surface area, and yet they are static their primary function is to be a wall, separating spaces and hiding infrastructure. We present Wall++, a low-cost sensing approach that allows walls to become a smart infrastructure. Instead of merely separating spaces, walls can now enhance rooms with sensing and interactivity. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located. Through a series of evaluations, we demonstrate Wall++ can enable robust room-scale interactive and context-aware applications.


title: Performance comparison of speaker recognition systems using GMM and i-Vector methods with PNCC and RASTA PLP features

abstract: Biometrie recognition techniques using features such as iris, fingerprint, face etc. are nowadays being popularly used for the purpose of verification or authentication of individuals. Speech is one of the most natural forms of communication among humans and it has been proven that voice biometric also provides a unique identity measure of a person. Features extracted from speech represent speaker specific characteristics which help us to uniquely identify a person. Speaker recognition is now being employed in various applications such as transaction authentication, forensic purposes etc. In this paper, the performances of Text-Independent Speaker Recognition (SR) systems implemented using Gaussian Mixture Models (GMM) and i-Vector method with Probabilistic Linear Discriminant Analysis (PLDA) classifier are compared. Both systems have been realized with two types of features namely, Power Normalized Cepstral Coefficients (PNCC) and Relative Spectral Perceptual Linear Prediction (RASTA PLP) coefficients. It has been seen that PNCC features provide a closer approximation of human auditory system than RASTA PLP. The results also show that the performance of SR system is better with GMM for short utterances and is better for i-vector method for longer utterances.


title: Micro-connectomics: probing the organization of neuronal networks at the cellular scale

abstract: Micro-connectomics involves determining the principles of how neuronal networks are organized at the cellular level. In this Review, Schroter, Paulsen and Bullmore examine studies that have provided insight into the network organization of relatively small, as well as more complex, nervous systems.


title: Pricing fraud detection in online shopping malls using a finite mixture model

abstract: Abstract Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances.


title: Video Deblurring via Semantic Segmentation and Pixel-Wise Non-linear Kernel

abstract: Video deblurring is a challenging problem as the blur is complex and usually caused by the combination of camera shakes, object motions, and depth variations. Optical flow can be used for kernel estimation since it predicts motion trajectories. However, the estimates are often inaccurate in complex scenes at object boundaries, which are crucial in kernel estimation. In this paper, we exploit semantic segmentation in each blurry frame to understand the scene contents and use different motion models for image regions to guide optical flow estimation. While existing pixel-wise blur models assume that the blur kernel is the same as optical flow during the exposure time, this assumption does not hold when the motion blur trajectory at a pixel is different from the estimated linear optical flow. We analyze the relationship between motion blur trajectory and optical flow, and present a novel pixel-wise non-linear kernel model to account for motion blur. The proposed blur model is based on the non-linear optical flow, which describes complex motion blur more effectively. Extensive experiments on challenging blurry videos demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.


title: The worst-case time complexity for generating all maximal cliques and computational experiments

abstract: We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron-Kerbosch algorithm. All the maximal cliques generated are output in a tree-like form. Subsequently, we prove that its worst-case time complexity is O(3n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3n/3 maximal cliques in an n-vertex graph. The algorithm is also demonstrated to run very fast in practice by computational experiments.


title: Comparative experiments on learning information extractors for proteins and their interactions

abstract: Objective:: Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. Methods and Material:: We used a variety of machine learning methods to automatically develop information extraction systems for extracting information on gene/protein name, function and interactions from Medline abstracts. We present cross-validated results on identifying human proteins and their interactions by training and testing on a set of approximately 1000 manually-annotated Medline abstracts that discuss human genes/proteins. Results:: We demonstrate that machine learning approaches using support vector machines and maximum entropy are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules. Conclusion:: Our results show that it is promising to use machine learning to automatically build systems for extracting information from biomedical text. The results also give a broad picture of the relative strengths of a wide variety of methods when tested on a reasonably large human-annotated corpus.


title: Delay alignment of top- and sub loudspeaker systems; a survey of new and old methods.

abstract: A couple of popular methods are presented with their benefits and drawbacks. Commonly used methods are using wrapped phase and impulse response. With real time FFT analysis, magnitude and time domain can be analyzed simultaneously. Filtered impulse response and Cepstrum analysis are helpful tools when the spectral content differs and make it hard to analyse the impulse response. To make a successful time alignment the measurements must be anechoic. Methods such as multiple time windowing and averaging in frequency domain are presented. Group-delay and wavelets analysis are used to evaluate the measurements.


title: Technical errors in MR arthrography

abstract: This article discusses potential technical problems of MR arthrography. It starts with contraindications, followed by problems relating to injection technique, contrast material and MR imaging technique. For some of the aspects discussed, there is only little published evidence. Therefore, the article is based on the personal experience of the author and on local standards of procedures. Such standards, as well as medico-legal considerations, may vary from country to country. Contraindications for MR arthrography include pre-existing infection, reflex sympathetic dystrophy and possibly bleeding disorders, avascular necrosis and known allergy to contrast media. Errors in injection technique may lead to extra-articular collection of contrast agent or to contrast agent leaking from the joint space, which may cause diagnostic difficulties. Incorrect concentrations of contrast material influence image quality and may also lead to non-diagnostic examinations. Errors relating to MR imaging include delays between injection and imaging and inadequate choice of sequences. Potential solutions to the various possible errors are presented.


title: Network ties, reputation, and the financing of new ventures

abstract: Explaining how entrepreneurs overcome information asymmetry between themselves and potential investors to obtain financing is an important issue for entrepreneurship research. Our premise is that economic explanations for venture finance, which do not consider how social ties influence this process, are undersocialized and incomplete. However, we also argue that organization theoretic arguments, which draw on the concept of social obligation, are oversocialized. Drawing on the organizational theory literature, and in-depth fieldwork with 50 high-technology ventures, we examine the effects of direct and indirect ties between entrepreneurs and 202 seed-stage investors on venture finance decisions. We show that these ties influence the selection of ventures to fund through a process of information transfer.


title: A Novel Approach for Author Name Disambiguation Using Ranking Confidence

abstract: In digital libraries, ambiguous author names may occur because of the existence of multiple authors with the same name or different name variations for the same person. In recent years, name disambiguation has become a major challenge when integrating data from multiple sources in bibliographic digital libraries. Most of the previous works solve this issue by using many attributes, such as coauthors, title of articles/publications, topics of articles, and years of publications. However, in most cases, we can only get the coauthor and title attributes. In this paper, we propose an approach which is based on Hierarchical Agglomerative Clustering (HAC) and only use the coauthor and title attributes, but can more effectively identify the disambiguation authors. The whole algorithm can divide into two stages. In the first stage, we employ a pair-wise grouping algorithm which is based on coauthors’name to group records into clusters. Then, we merge two clusters if the similarity of the article titles from two clusters reach the threshold. Here, we use three kinds of similarity algorithms such as Jaccard Similarity, Cosine Similarity and Euclidean Distance to compare the similarity between the titles of two clusters. To minimize the risk of using only one similarity metric, we design the concept of ranking confidence to measure the confidence of different similarity meausrements. The ranking confidence decides which similarity measure to use when merging clusters. In the experiments, we use PairPresicion, PairRecall and PairF1 score to evaluate our method and compare with other methods. Experimental results indicate that our method significantly outperforms the baseline methods: HAC, K-means and SACluster when only use coauthor and title attributes.


title: Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

abstract: Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.


title: A Development Platform for Integrating Wireless Devices and Sensors into Ambient Intelligence Systems

abstract: The HYDRA project develops middleware for networked embedded systems that allow developers to create ambient intelligence (AmI) applications based on wireless devices and sensors. Through its unique combination of Service-oriented Architecture (SoA) and a semantic-based Model-Driven Architecture, HYDRA will enable the development of generic services based on open standards.


title: Type-2 Fuzzy Sets Made Simple

abstract: Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: (1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, (2) presenting a new representation for type-2 fuzzy sets, and (3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.


CSDSM: Cognitive switch-based DDoS sensing and mitigation in SDN-driven CDNi word


title: Snitches, Trolls, and Social Norms: Unpacking Perceptions of Social Media Use for Crime Prevention

abstract: In this paper, we describe how people perceive the use of social media to support crime prevention in their communities. Based on survey and interview data from residents in high- and low-crime neighborhoods in Chicago, we found that African Americans, people from high-crime neighborhoods, and people with low levels of trust in local police are less likely to view social media as an appropriate tool to support citizens or the police in local crime prevention efforts. Residents' concerns include information getting into the wrong hands, trolls, and being perceived as a snitch. Despite concerns with usage, citizens also viewed social media as a tool that can supplement in-person crime prevention efforts and facilitate relationship-building and information sharing. We discuss the complexities of hyper-local usage of social media to combat crime by describing the social and historical contexts in which these tools exist.


title: A justification-suppression model of the expression and experience of prejudice.

abstract: The authors propose a justification–suppression model (JSM), which characterizes the processes that lead to prejudice expression and the experience of one’s own prejudice. They suggest that “genuine” prejudices are not directly expressed but are restrained by beliefs, values, and norms that suppress them. Prejudices are expressed when justifications (e.g., attributions, ideologies, stereotypes) release suppressed prejudices. The same process accounts for which prejudices are accepted into the self-concept. The JSM is used to organize the prejudice literature, and many empirical findings are recharacterized as factors affecting suppression or justification, rather than directly affecting genuine prejudice. The authors discuss the implications of the JSM for several topics, including prejudice measurement, ambivalence, and the distinction between prejudice and its expression.


title: Blockchain Abstract Data Type

abstract: The presented work continues the line of recent distributed computing community efforts dedicated to the theoretical aspects of blockchains. This paper is the first to specify blockchains as a composition of abstract data types all together with a hierarchy of consistency criteria that formally characterizes the histories admissible for distributed programs that use them. Our work is based on an original oracle-based construction that, along with new consistency definitions, captures the eventual convergence process in blockchain systems. The paper presents as well some results on implementability of the presented abstractions and a mapping of representative existing blockchains from both academia and industry in our framework.


title: Image Registration for Placenta Reconstruction

abstract: In this paper we introduce a method to handle the challenges posed by image registration for placenta reconstruction from fetoscopic video as used in the treatment of Twinto-Twin Transfusion Syndrome (TTTS). Panorama reconstruction of the placenta greatly supports the surgeon in obtaining a complete view of the placenta to localize vascular anastomoses. The found shunts can subsequently be blocked by coagulation in the correct order. By using similarity learning in training a Convolutional Neural Network we created a novel feature extraction method, allowing robust matching of keypoints for image registration and therefore taking the most critical step in placenta reconstruction from fetoscopic video. The fetoscopic video we used for our experiments was acquired from a training simulator for TTTS surgery. We compared our method with state-of-the-art methods. The matching performance of our method is up to three times better while the mean projection error is reduced with 64% for the registered images. Our image registration method provides the ground work for a complete panorama reconstruction of the placenta.


title: DC/DC and DC/AC Converters Control for Hybrid Electric Vehicles Energy Management-Ultracapacitors and Fuel Cell

abstract: This paper presents the ultracapacitors and the fuel cell (FC) connection for hybrid electric vehicles (HEVs) applications. An original method for the embedded energy management is proposed. This method is used to share the energetic request of the HEV between the ultracapacitors and the FC. The ultracapacitors are linked to dc-bus through the buck-boost converter, and the FC is connected to dc-bus via a boost converter. An asynchronous machine is used like traction motor or generator, and it is connected to dc-bus through an inverter. A dc-motor is used to drive the asynchronous machine during the decelerations and the braking operations. The main contribution of this paper is focused on the embedded energy management based on the new European drive cycle (NEDC), using polynomial control technique. The performances of the proposed control method are evaluated through some simulations and the experimental tests dedicated to HEVs applications.


title: Segmentation of brain MRI structures with deep machine learning

abstract: Several studies on brain Magnetic Resonance Images (MRI) show relations between neuroanatomical abnormalities of brain structures and neurological disorders, ::: such as Attention De fficit Hyperactivity Disorder (ADHD) and Alzheimer. These ::: abnormalities seem to be correlated with the size and shape of these structures, and ::: there is an active fi eld of research trying to find accurate methods for automatic ::: MRI segmentation. In this project, we study the automatic segmentation of structures from the Basal Ganglia and we propose a new methodology based on Stacked ::: Sparse Autoencoders (SSAE). SSAE is a strategy that belongs to the family of ::: Deep Machine Learning and consists on a supervised learning method based on an ::: unsupervisely pretrained Feed-forward Neural Network. Moreover, we present two ::: approaches based on 2D and 3D features of the images. We compare the results ::: obtained on the di fferent regions of interest with those achieved by other machine ::: learning techniques such as Neural Networks and Support Vector Machines. We ::: observed that in most cases SSAE improves those other methods. We demonstrate ::: that the 3D features do not report better results than the 2D ones as could be ::: thought. Furthermore, we show that SSAE provides state-of-the-art Dice Coe fficient results (left, right): Caudate (90.6+-3 1.4, 90.31 +-1.7), Putamen (91.03 +-1.4, ::: 90.82+- 1.4), Pallidus (85.11+-1.8, 83.47 +-2.2), Accumbens (74.26+- 4.4, 74.46 +-4.6).


title: Flash Cookies and Privacy II: Now with HTML5 and ETag Respawning

abstract: In August 2009, we demonstrated that popular websites were using “Flash cookies” to track users. Some advertisers had adopted this technology because it allowed persistent tracking even where users had taken steps to avoid web profiling. We also demonstrated “respawning” on top sites with Flash technology. This allowed sites to reinstantiate HTTP cookies deleted by a user, making tracking more resistant to users’ privacy-seeking behaviors.In this followup study, we reassess the Flash cookies landscape and examine a new tracking vector, HTML5 local storage and Cache-Cookies via ETags. We found over 5,600 standard HTTP cookies on popular sites, over 4,900 were from third parties. Google-controlled cookies were present on 97 of the top 100 sites, including popular government websites. Seventeen sites were using HTML5, and seven of those sites had HTML5 local storage and HTTP cookies with matching values. Flash cookies were present on 37 of the top 100 sites. We found two sites that were respawning cookies, including one site – hulu.com – where both Flash and cache cookies were employed to make identifiers more persistent. The cache cookie method used ETags, and is capable of unique tracking even where all cookies are blocked by the user and “Private Browsing Mode” is enabled.Our 2009 study is also available at SSRN: http://ssrn.com/abstract=1446862.


title: A 25 Gb/s, 4.4 V-Swing, AC-Coupled Ring Modulator-Based WDM Transmitter with Wavelength Stabilization in 65 nm CMOS

abstract: Silicon photonics devices offer promising solution to meet the growing bandwidth demands of next-generation interconnects. This paper presents a 5 × 25 Gb/s carrier-depletion microring-based wavelength-division multiplexing (WDM) transmitter in 65 nm CMOS. An AC-coupled differential driver is proposed to realize 4 × VDD output swing as well as tunable DC-biasing. The proposed transmitter incorporates 2-tap asymmetric pre-emphasis to effectively cancel the optical nonlinearity of the ring modulator. An average-power-based dynamic wavelength stabilization loop is also demonstrated to compensate for thermal induced resonant wavelength drift. At 25 Gb/s operation, each transmitter channel consumes 113.5 mW and maintains 7 dB extinction ratio with a 4.4 V $_{\rm pp-diff}$ output swing in the presence of thermal fluctuations.


title: Mitosis detection in breast cancer histology images via deep cascaded networks

abstract: The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma. However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice. In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity. Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics. Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.


title: View-dependent displacement mapping

abstract: Significant visual effects arise from surface mesostructure, such as fine-scale shadowing, occlusion and silhouettes. To efficiently render its detailed appearance, we introduce a technique called view-dependent displacement mapping (VDM) that models surface displacements along the viewing direction. Unlike traditional displacement mapping, VDM allows for efficient rendering of self-shadows, occlusions and silhouettes without increasing the complexity of the underlying surface mesh. VDM is based on per-pixel processing, and with hardware acceleration it can render mesostructure with rich visual appearance in real time.


title: Speech enhancement based on deep denoising autoencoder.

abstract: We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, we further introduce an explicit denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a one hidden layer neural autoencoder (AE) using noisy-clean speech pairs as input and output (or transformed noisy-clean speech pairs by preceding AEs). Fine tuning was done by stacking all AEs with pretrained parameters for initialization. The trained DAE is used as a filter for speech estimation when noisy speech is given. Speech enhancement experiments were done to examine the performance of the trained denoising DAE. Noise reduction, speech distortion, and perceptual evaluation of speech quality (PESQ) criteria are used in the performance evaluations. Experimental results show that adding depth of the DAE consistently increase the performance when a large training data set is given. In addition, compared with a minimum mean square error based speech enhancement algorithm, our proposed denoising DAE provided superior performance on the three objective evaluations.


title: Wireless Physical Layer Security with Imperfect Channel State Information: A Survey

abstract: Physical layer security is an emerging technique to improve the wireless communication security, which is widely regarded as a complement to cryptographic technologies. To design physical layer security techniques under practical scenarios, the uncertainty and imperfections in the channel knowledge need to be taken into consideration. This paper provides a survey of recent research and development in physical layer security considering the imperfect channel state information (CSI) at communication nodes. We first present an overview of the main information-theoretic measures of the secrecy performance with imperfect CSI. Then, we describe several signal processing enhancements in secure transmission designs, such as secure on-off transmission, beamforming with artificial noise, and secure communication assisted by relay nodes or in cognitive radio systems. The recent studies of physical layer security in large-scale decentralized wireless networks are also summarized. Finally, the open problems for the on-going and future research are discussed.


title: Securing SMS Based One Time Password Technique from Man in the Middle Attack

abstract: Security of financial transactions in E-Commerce is difficult to implement and there is a risk that user's confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not all Internet users possess. For the last couple of year such naive users have been increasingly targeted by phishing attacks that are launched by miscreants who are aiming to make an easy profit by means of illegal financial transactions. In this paper, we have proposed an idea for securing e-commerce transaction from phishing attack. An approach already exists where phishing attack is prevented using one time password which is sent on user's registered mobile via SMS for authentication. But this method can be counter attacked by "Man in the Middle". In our paper, a new idea is proposed which is more secure compared to the existing online payment system using OTP. In this mechanism OTP is combined with the secure key and is then passed through RSA algorithm to generate the Transaction password. A Copy of this password is maintained at the server side and is being generated at the user side using a mobile application; so that it is not transferred over the insecure network leading to a fraudulent transaction. Keywords—Phishing, Replay attack, MITM attack, RSA, Random Generator.


title: Wide-residual-inception networks for real-time object detection

abstract: Since convolutional neural network(CNN)models emerged,several tasks in computer vision have actively deployed CNN models for feature extraction. However,the conventional CNN models have a high computational cost and require high memory capacity, which is impractical and unaffordable for commercial applications such as real-time on-road object detection on embedded boards or mobile platforms. To tackle this limitation of CNN models, this paper proposes a wide-residual-inception (WR-Inception) network, which constructs the architecture based on a residual inception unit that captures objects of various sizes on the same feature map, as well as shallower and wider layers, compared to state-of-the-art networks like ResNet. To verify the proposed networks, this paper conducted two experiments; one is a classification task on CIFAR-10/100 and the other is an on-road object detection task using a Single-Shot Multi-box Detector(SSD) on the KITTI dataset.


title: A feasibility study of mobile phone text messaging to support education and management of type 2 diabetes in Iraq.

abstract: BACKGROUND ::: We undertook a feasibility study to evaluate feasibility and utility of short message services (SMSs) to support Iraqi adults with newly diagnosed type 2 diabetes. ::: ::: ::: SUBJECTS AND METHODS ::: Fifty patients from a teaching hospital clinic in Basrah in the first year after diagnosis were recruited to receive weekly SMSs relating to diabetes self-management over 29 weeks. Numbers of messages received, acceptability, cost, effect on glycated hemoglobin (HbA1c), and diabetes knowledge were documented. ::: ::: ::: RESULTS ::: Forty-two patients completed the study, receiving an average 22 of 28 messages. Mean knowledge score rose from 8.6 (SD 1.5) at baseline to 9.9 (SD 1.4) 6 months after receipt of SMSs (P=0.002). Baseline and 6-month knowledge scores correlated (r=0.297, P=0.049). Mean baseline HbA1c was 79 mmol/mol (SD 14 mmol/mol) (9.3% [SD 1.3%]) and decreased to 70 mmol/mol (SD 13 mmol/mol) (8.6% [SD 1.2%]) (P=0.001) 6 months after the SMS intervention. Baseline and 6-month values were correlated (r=0.898, P=0.001). Age, gender, and educational level showed no association with changes in HbA1c or knowledge score. Changes in knowledge score were correlated with postintervention HbA1c (r=-0.341, P=0.027). All patients were satisfied with text messages and wished the service to be continued after the study. The cost of SMSs was €0.065 per message. ::: ::: ::: CONCLUSIONS ::: This study demonstrates SMSs are acceptable, cost-effective, and feasible in supporting diabetes care in the challenging, resource-poor environment of modern-day Iraq. This study is the first in Iraq to demonstrate similar benefits of this technology on diabetes education and management to those seen from its use in better-resourced parts of the world. A randomized controlled trial is needed to assess precise benefits on self-care and knowledge.


title: A signal analysis of network traffic anomalies

abstract: Identifying anomalies rapidly and accurately is critical to the efficient operation of large computer networks. Accurately characterizing important classes of anomalies greatly facilitates their identification; however, the subtleties and complexities of anomalous traffic can easily confound this process. In this paper we report results of signal analysis of four classes of network traffic anomalies: outages, flash crowds, attacks and measurement failures. Data for this study consists of IP flow and SNMP measurements collected over a six month period at the border router of a large university. Our results show that wavelet filters are quite effective at exposing the details of both ambient and anomalous traffic. Specifically, we show that a pseudo-spline filter tuned at specific aggregation levels will expose distinct characteristics of each class of anomaly. We show that an effective way of exposing anomalies is via the detection of a sharp increase in the local variance of the filtered data. We evaluate traffic anomaly signals at different points within a network based on topological distance from the anomaly source or destination. We show that anomalies can be exposed effectively even when aggregated with a large amount of additional traffic. We also compare the difference between the same traffic anomaly signals as seen in SNMP and IP flow data, and show that the more coarse-grained SNMP data can also be used to expose anomalies effectively.


Automated Provenance Analytics: A Regular Grammar Based Approach with Applications in Security


title: Data Mining Applications: A comparative Study for Predicting Student's performance

abstract: Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.


title: A modular manipulator for industrial applications: Design and implement

abstract: In order to solve the problem that the long cycle and the repetitive work in the process of designing the industrial robot, a modular manipulator system developed for general industrial applications is introduced in this paper. When the application scene is changed, the corresponding robotic modules can be selected to assemble a new robot configuration that meets the requirements. The modules can be divided into two categories: joint modules and link modules. Joint modules consist of three types of revolute joint modules with different torque, and link modules mainly contain T link module and L link module. By connection of different types of modules, various of configurations can be achieved. Considering the traditional 6-DoF manipulators are difficult to meet the needs of the unstructured industrial applications, a 7-DoF redundant manipulator prototype is designed on the basis of the robotic modules.


title: Implementing industry 4.0 — A technological readiness perspective

abstract: This paper identifies the relative importance of key enabling factors for implementing industry 4.0 from a technological readiness perspective. The research involves the identification of enabling factors, their categorization into technological readiness dimensions, followed by the determination of the relative importance of both technological readiness dimensions and key objective measures. The results show a strong relationship between technological readiness and design principles of Industry 4.0. The findings suggest that process-related objectives are more important than economic-related and environmental-related objectives when implementing industry 4.0. The results also show that “the knowledge of humans in technology and how to leverage it” and “improving the ability of machines and devices in connecting to the internet” are the most important factors for achieving all objective measures. Practitioners can use the apparent relationship between process related objectives and key technological dimensions for setting appropriate strategies and policies when moving towards Industry 4.0.


title: Building a Social Discount Rate to be Applied in US Afforestation Project Appraisal

abstract: This paper is focused on searching for the suitable discount rate to be applied to the valuation of a project related to forests in the USA, e.g., a recreational area inside a national park. To do this, we propose a new model based on hazard rate concepts, i.e., based on the risk that waiting time implies. More specifically, we derive the discount function whose instantaneous discount rate is the hazard rate of the system supporting the investment. We determine the rate of failure corresponding to different partition criteria of the whole system; in our case, we can use the information on forest fires caused in different ways, in different states or in different types of forest surfaces. After showing independence between the forest fires by states and causes, we derive a specific discount function for each cause which can be applied to every state or set of states which agree to fight against a concrete cause of forest fire. Additionally, we obtain a unique discount function by weighting the partial discount functions by type of forest surfaces. Our results are in line with the recommendations from several authors about using decreasing discount rates for projects with very long-term impacts.


title: Mining Location-based Social Networks: A Predictive Perspective

abstract: With the development of location-based social networks, an increasing amount of individual mobility data accumulate over time. The more mobility data are collected, the better we can understand the mobility patterns of users. At the same time, we know a great deal about online social relationships between users, providing new opportunities for mobility prediction. This paper introduces a noveltyseeking driven predictive framework for mining location-based social networks that embraces not only a bunch of Markov-based predictors but also a series of location recommendation algorithms. The core of this predictive framework is the cooperation mechanism between these two distinct models, determining the propensity of seeking novel and interesting locations.


title: A New Inverter for Improved Fuel Cell Performance in Grid-tied Application

abstract: The interconnection of a solid oxide fuel cell (SOFC) with the power conditioning units vis-a-vis a DC/DC converter and a DC/AC inverter for interfacing with the utility grid is presented. Fuel cells operate at low voltages and hence need to be boosted and inverted in order to be connected to the grid. The fuel cell and the DC/DC converter modeling are briefly explained. The methodology and the controller design for the control of power flow from the fuel cell to the utility grid are discussed. Power characteristics of the DC/AC inverter are compared with the characteristics of the DC/DC converter and the fuel cell. Fuel cells have slow response time which prevents it from grid-tie applications. Simulations validate the improvement in the response when the power conditioning unit is connected.


title: Grid tie inverter based interfacing model and its design

abstract: A solution to the incessant energy demand and heavy dependency on the mains grid for electricity is that the surplus energy generated from the non-conventional energy sources using microgrids be contributed back to the mains grid. This can be achieved by using a system for interfacing the microgrid to the mains grid with the help of a grid tie inverter. This inverter converts DC power from the microgrid into AC power with its phase and frequency locked with that of the grid itself. Such a system model is discussed in this paper along with the design of a 3kW single phase prototype grid tied inverter with 3-level PWM for the use at household level. The system also, consists of a switching unit between the microgrid and the mains grid to provide electricity to the load depending on the supply levels. Additionally, the net consumption and distribution of units are accounted for by a bi-directional meter connected in place of the conventional meter.


title: Prognostic value of obstetric Doppler ultrasound in fetuses with fetal growth restriction: an observational study in a tertiary care hospital

abstract: AbstractObjective: To determine the prognostic value of umbilical artery Doppler (UAD) with fetal growth restriction (FGR) and their perinatal outcomes.Methods: This was a retrospective cohort study of fetuses with growth ≤ 5th centile from 2001 to 2012. Pregnancy outcomes were compared according to UAD findings for 253 cases. Doppler findings were categorized as; Normal End Diastolic Flow (NEDF), Reduced End Diastolic Flow (REDF) and Absent/Reverse End Diastolic Flow (AREDF). Mean and proportion were calculated and odds of perinatal complications were compared by using logistic regression for REDF and AREDF with NEDF at 5% level of significance.Results: The perinatal morality rate was 3.2%. Neonates with abnormal Doppler were at increased risk of cesarean delivery, low birth weights and low Apgar scores. Among the perinatal morbidity, neonatal intensive care unit (NICU) admission was 4.2 and 15.3 times in neonates with REDF and AREDF and similarly the perinatal mortality of AREDF was 12.5 times higher as...


title: Lumped circuit model analysis of meander line antennas

abstract: The past decade has seen phenomenal advances in portable electronics technology like mobile phones, RFID tags and MP3 players. This has led to the development of System in Package (SiP) which combines all the necessary components into a single package. Miniaturization of RF circuit technology has resulted in the need for miniaturized antennas. Meander line antennas are widely used in applications where compactness and miniaturization are key objectives. Although this antenna has been widely used, yet no simple analytical model except those using complicated numerical techniques is available. In this paper, we present a simple analytical model to calculate the resonant frequency of the antenna employing a lumped equivalent circuit model. Circuit response of a typical meander line antenna using our model has been compared with simulated antenna response using Method of Moments based simulator IE3D with good agreement, thereby validating our approach.


title: Line search algorithms with guaranteed sufficient decrease

abstract: The development of software for minimization problems is often based on a line search method. We consider line search methods that satisfy sufficient decrease and curvature conditions, and formulate the problem of determining a point that satisfies these two conditions in terms of finding a point in a set T(μ) . We describe a search algorithm for this problem that produces a sequence of iterates that converge to a point in T(μ) and that, except for pathological cases, terminates in a finite number of steps. Numerical results for an implementation of the search algorithm on a set of test functions show that the algorithm terminates within a small number of iterations.


title: The Common-Sense Model of Self-Regulation (CSM): a dynamic framework for understanding illness self-management

abstract: The Common-Sense Model of Self-Regulation (the “Common-Sense Model”, CSM) is a widely used theoretical framework that explicates the processes by which patients become aware of a health threat, navigate affective responses to the threat, formulate perceptions of the threat and potential treatment actions, create action plans for addressing the threat, and integrate continuous feedback on action plan efficacy and threat-progression. A description of key aspects of the CSM’s history—over 50 years of research and theoretical development—makes clear the model’s dynamic underpinnings, characteristics, and assumptions. The current article provides this historical narrative and uses that narrative to highlight dynamic aspects of the model that are often not evaluated or utilized in contemporary CSM-based research. We provide suggestions for research advances that can more fully utilize these dynamic aspects of the CSM and have the potential to further advance the CSM’s contribution to medical practice and patients’ self-management of illness.


title: In-Memory Data Parallel Processor

abstract: Recent developments in Non-Volatile Memories (NVMs) have opened up a new horizon for in-memory computing. Despite the significant performance gain offered by computational NVMs, previous works have relied on manual mapping of specialized kernels to the memory arrays, making it infeasible to execute more general workloads. We combat this problem by proposing a programmable in-memory processor architecture and data-parallel programming framework. The efficiency of the proposed in-memory processor comes from two sources: massive parallelism and reduction in data movement. A compact instruction set provides generalized computation capabilities for the memory array. The proposed programming framework seeks to leverage the underlying parallelism in the hardware by merging the concepts of data-flow and vector processing. To facilitate in-memory programming, we develop a compilation framework that takes a TensorFlow input and generates code for our in-memory processor. Our results demonstrate 7.5x speedup over a multi-core CPU server for a set of applications from Parsec and 763x speedup over a server-class GPU for a set of Rodinia benchmarks.


title: Pedestrian Trajectory Prediction with Structured Memory Hierarchies

abstract: This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient information from multiple modalities to automatically store and retrieve important information for decision making without any supervision. We evaluate the effectiveness of the proposed models on a novel multimodal dataset that we introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from a radar system and a CCTV camera system installed in a public place. The performance is also evaluated on the publicly available New York Grand Central pedestrian database. In both settings, the proposed models demonstrate their capability to better anticipate future pedestrian motion compared to existing state of the art.


title: Semantic Interoperability via Category Theory

abstract: This paper aims to bring the benefits of the use of Category Theory to the field of Semantic Web, where the coexistence of intrinsically different models of local knowledge makes difficult the exchanging of information. The paper uses categorical limit and colimit to define operations of breaking and composing ontologies, formalizing usual concepts in ontologies (alignment, merge, integration, matching) and proposing a new operation (the hide operation). The presented set of operations form a useful framework that makes easier the manipulation and reuse of ontologies.


Enabling Transitivity for Lexical Inference on Chinese Verbs Using Probabilistic Soft Logic


title: Social acceptance of humanoid robots in Japan: A survey for development of the frankenstein syndorome questionnaire

abstract: This research explores the concept of “The Frankenstein Syndrome” in order to develop a psychological tool for measuring acceptance of humanoid robots, as well as expectations and anxieties toward these technologies. It also aims to explore factors that influences this acceptance, such as the effects of age and experiences with robots. The tool developed is tentatively named “The Frankenstein Syndrome Questionnaire”, a questionnaire to measure acceptance of humanoids in humans. A preliminary survey conducted in Japan suggested that: 1) the elder population has positive expectations for the specific applications of humanoid robots in their daily lives and view the developers of these technologies more favourably, in comparison with the younger population; 2) these expectations can be made more positive through having experiences of real humanoid robots or experiences of the robots via media information; 3) such experiences of humanoid robots do not seem to affect general anxiety toward the robots in the younger population, but they do decrease the anxiety in the elder population; 4) experiences of humanoid robots increase apprehension toward risks the robots entail in the society in the younger population, although they do not affect such apprehension in the elder population. The paper discusses about implications from the results.