title: Development and electrical investigation of novel fine-pitch Cu/Sn pad bumping using ultra- thin buffer layer technique in 3D integration

abstract: A high yielding fine-pitch submicron Cu/Sn bonding scheme has been successfully demonstrated. With inserting the ultra-thin buffer layer, near sub-micron thickness Cu/Sn pad bonding can be achieved. The fine pitch Cu/Sn interconnects can be also further extended. The modified Kelvin feature in chip level and tens of thousands series interconnects per chip with a density of 3.4 × 105/cm2 in wafer level are fabricated and completely investigated on electrical characteristics. Several critical reliability assessments, such as TCT and un-bias HAST, are also investigated the variation and standard error of the fine-pitch pad bonding scheme. With excellent mechanical properties, bonding quality, electrical and reliability results, the approach is suitable for future 3D vertical interconnects.


title: Geometry-Aware Similarity Learning on SPD Manifolds for Visual Recognition

abstract: Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, which has a well-established Riemannian structure. Under this solution, we exploit a new supervised SPDSL technique to learn the manifold–manifold transformation by regressing the similarities of selected SPD data pairs to their ground-truth similarities on the target SPD manifold. To optimize the proposed objective function, we further derive an optimization algorithm on the PSD manifold. Evaluations on three visual classification tasks show the advantages of the proposed approach over the existing SPD-based discriminant learning methods.


title: An efficient image-mosaicing method based on multifeature matching

abstract: Mosaicing is connecting two or more images and making a new wide area image with no visible seam-lines. Several algorithms have been proposed to construct mosaics from image sequence where the camera motion is more or less complex. Most of these methods are based either on the interest points matching or on theoretical corner models. This paper describes a fully automated image-mosaicing method based on the regions and the Harris points primitives. Indeed, in order to limit the search window of potential homologous points, for each point of interest, regions segmentation and matching steps are being performed. This enables us to improve the reliability and the robustness of the Harris points matching process by estimating the camera motion. The main originality of the proposed system resides in the preliminary manipulation of regions matching, thus making it possible to estimate the rotation, the translation and the scale factor between two successive images of the input sequence. This estimation allows an initial alignment of the images along with the framing of the interest points search window, and therefore reducing considerably the complexity of the interest points matching algorithm. Then, the resolution of a minimization problem, altogether considering the couples of matched-points, permits us to perform the homography. In order to improve the mosaic continuity around junctions, radiometric corrections are applied. The validity of the herewith described method is illustrated by being tested on several sequences of complex and challenging images captured from real-world indoor and outdoor scenes. These simulations proved the validity of the proposed method against camera motions, illumination variations, acquirement conditions, moving objects and image noise. To determine the importance of the regions matching stage in motion estimation, as well as for the framing of the search window associated to a point of interest, we compared the matching points results of this described method with those produced using the zero-mean normalized cross correlation score (without regions matching). We made this comparison in the case of a simple motion (without the presence of a rotation around optical axis and/or a scale factor), in the case of a rotation and in the general case of an homothety. For justifying the effectiveness of this method, we proposed an objective assessment by defining a reconstruction error.


title: Feasibility and Power Control for Linear Multiuser Receivers in CDMA Networks

abstract: This paper is concerned with linear multiuser receivers for code division multiple access (CDMA). Mainly three are of practical relevance: the matched filter (MF), the successive interference cancellation (SIC) and the minimum mean squared error (MMSE) receiver. For the first time, an explicit representation of the signal-to-interference-plus-noise (SINR) ratio for the linear SIC receiver under general random spreading is given. For the MMSE receiver the SINR is obtained by using the asymptotic large system regime as an accurate approximation. Once the SINR for each receiver type is known, the question arises what quality-of-service (QoS) requirements can be supported by an adequate power assignment. A vector of QoS requirements (gammai) is called feasible, if there exists some power assignment such that the SINR of each user i does not fall short of threshold gammai. It is shown that for each receiver type there exists a componentwise minimal power assignment such that the SINR of each user equals this threshold. This minimal vector may be determined for the MF and SIC receiver by solving a system of linear equations with coefficients explicitly known. For the MMSE receiver in the large system regime an iterative algorithm is derived, which converges to the optimum power allocation.


title: A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine

abstract: A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of arbitrarily many and arbitrarily complex binary input patterns. Top-down attentional and matching mechanisms are critical in self-stabilizing the code learning process. The architecture embodies a parallel search scheme which updates itself adaptively as the learning process unfolds. After learning self-stabilizes, the search process is automatically disengaged. Thereafter input patterns directly access their recognition codes without any search. Thus recognition time does not grow as a function of code complexity. A novel input pattern can directly access a category if it shares invariant properties with the set of familiar exemplars of that category. These invariant properties emerge in the form of learned critical feature patterns, or prototypes. The architecture possesses a context-sensitive self-scaling property which enables its emergent critical feature patterns to form. They detect and remember statistically predictive configurations of featural elements which are derived from the set of all input patterns that are ever experienced. Four types of attentional process—priming, gain control, vigilance, and intermodal competition—are mechanistically characterized. Top—down priming and gain control are needed for code matching and self-stabilization. Attentional vigilance determines how fine the learned categories will be. If vigilance increases due to an environmental disconfirmation, then the system automatically searches for and learns finer recognition categories. A new nonlinear matching law (the ⅔ Rule) and new nonlinear associative laws (the Weber Law Rule, the Associative Decay Rule, and the Template Learning Rule) are needed to achieve these properties. All the rules describe emergent properties of parallel network interactions. The architecture circumvents the noise, saturation, capacity, orthogonality, and linear predictability constraints that limit the codes which can be stably learned by alternative recognition models.


title: A Survey of Lightweight-Cryptography Implementations

abstract: The tight cost and implementation constraints of high-volume products, including secure RFID tags and smart cards, require specialized cryptographic implementations. The authors review recent developments in this area for symmetric and asymmetric ciphers, targeting embedded hardware and software. In this article, we present a selection of recently published lightweight-cryptography implementations and compare them to state-of-the-art results in their field.


title: Policy shaping with human teachers

abstract: In this work we evaluate the performance of a policy shaping algorithm using 26 human teachers. We examine if the algorithm is suitable for human-generated data on two different boards in a pac-man domain, comparing performance to an oracle that provides critique based on one known winning policy. Perhaps surprisingly, we show that the data generated by our 26 participants yields even better performance for the agent than data generated by the oracle. This might be because humans do not discourage exploring multiple winning policies. Additionally, we evaluate the impact of different verbal instructions, and different interpretations of silence, finding that the usefulness of data is affected both by what instructions is given to teachers, and how the data is interpreted.


title: Falcon: Scaling Up Hands-Off Crowdsourced Entity Matching to Build Cloud Services

abstract: Many works have applied crowdsourcing to entity matching (EM). While promising, these approaches are limited in that they often require a developer to be in the loop. As such, it is difficult for an organization to deploy multiple crowdsourced EM solutions, because there are simply not enough developers. To address this problem, a recent work has proposed Corleone, a solution that crowdsources the entire EM workflow, requiring no developers. While promising, Corleone is severely limited in that it does not scale to large tables. We propose Falcon, a solution that scales up the hands-off crowdsourced EM approach of Corleone, using RDBMS-style query execution and optimization over a Hadoop cluster. Specifically, we define a set of operators and develop efficient implementations. We translate a hands-off crowdsourced EM workflow into a plan consisting of these operators, optimize, then execute the plan. These plans involve both machine and crowd activities, giving rise to novel optimization techniques such as using crowd time to mask machine time. Extensive experiments show that Falcon can scale up to tables of millions of tuples, thus providing a practical solution for hands-off crowdsourced EM, to build cloud-based EM services.


title: A CMOS Rectifier With a Cross-Coupled Latched Comparator for Wireless Power Transfer in Biomedical Applications

abstract: A highly efficient rectifier for wireless power transfer in biomedical implant applications is implemented using 0.18-m CMOS technology. The proposed rectifier with active nMOS and pMOS diodes employs a four-input common-gate-type capacitively cross-coupled latched comparator to control the reverse leakage current in order to maximize the power conversion efficiency (PCE) of the rectifier. The designed rectifier achieves a maximum measured PCE of 81.9% at 13.56 MHz under conditions of a low 1.5-Vpp RF input signal with a 1- k output load resistance and occupies 0.009 mm2 of core die area.


title: What is where: inferring containment relations from videos

abstract: In this paper, we present a probabilistic approach to explicitly infer containment relations between objects in 3D scenes. Given an input RGB-D video, our algorithm quantizes the perceptual space of a 3D scene by reasoning about containment relations over time. At each frame, we represent the containment relations in space by a containment graph, where each vertex represents an object and each edge represents a containment relation. We assume that human actions are the only cause that leads to containment relation changes over time, and classify human actions into four types of events: move-in, move-out, no-change and paranormal-change. Here, paranomal-change refers to the events that are physically infeasible, and thus are ruled out through reasoning. A dynamic programming algorithm is adopted to finding both the optimal sequence of containment relations across the video, and the containment relation changes between adjacent frames. We evaluate the proposed method on our dataset with 1326 video clips taken in 9 indoor scenes, including some challenging cases, such as heavy occlusions and diverse changes of containment relations. The experimental results demonstrate good performance on the dataset.


title: Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

abstract: We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption oflikelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence. We show that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen—aprior network—and a single measure of confidence for that network. Second, using these priors, we show how to compute the relative posterior probabilities of network structures given data. Third, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highest-scoring network structures in the special case where every node has at mostk=1 parent. For the general case (k>1), which is NP-hard, we review heuristic search algorithms including local search, iterative local search, and simulated annealing. Finally, we describe a methodology for evaluating Bayesian-network learning algorithms, and apply this approach to a comparison of various approaches.


title: Analysis of Inductance According to the Applied Current in Spoke-Type PMSM and Suggestion of Driving Mode

abstract: The spoke-type permanent magnet synchronous motor (PMSM) is a motor built using nonrare earth elements, which feature low flux density. It has a flux concentrated structure of permanent magnet arrangement to improve power density. Further, the torque in the spoke-type PMSM is composed of a magnetic torque component from the permanent magnets and a reluctance torque arising from the inductance difference between the d - and q -axis. This paper analyzed the d - and q -axes inductances that affect the reluctance torque depending on the magnitude of the applied current and the current phase angle of the spoke-type PMSM at base speed. We also derived the variation of the d - and q - axis inductances, which is shown at the operation speed region. Accordingly, the variation of the current phase angle for generating the maximum output power is analyzed. Based on the above results, we predicted the variation in the driving mode of the spoke-type PMSM, and subsequently verified the prediction experimentally.


Forensic reconstruction of severely degraded license plates


Deanonymization in the Bitcoin P 2 P Network


title: Towards the utilization of EEG as a brain imaging tool

abstract: Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.


title: Improving Automated Controversy Detection on the Web

abstract: Automatically detecting controversy on the Web is a useful capability for a search engine to help users review web content with a more balanced and critical view. The current state-of-the art approach is to find K-Nearest-Neighbors in Wikipedia to the document query, and to aggregate their controversy scores that are automatically computed from the Wikipedia edit-history features. In this paper, we discover two major weakness in the prior work and propose modifications. First, the generated single query from document to find KNN Wikipages easily becomes ambiguous. Thus, we propose to generate multiple queries from smaller but more topically coherent paragraph of the document. Second, the automatically computed controversy scores of Wikipedia articles that depend on "edit war" features have a drawback that without an edit history, there can be no edit wars. To infer more reliable controversy scores for articles with little edit history, we smooth the original score from the scores of the neighbors with more established edit history. We show that the modified framework is improved by up to 5% for binary controversy classification in a publicly available dataset.


title: DDoS detection and prevention based on artificial intelligence techniques

abstract: DDoS attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of things, artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks. Because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.


title: Metaphors are Embodied, and so are Their Literal Counterparts

abstract: This study investigates whether understanding up/down metaphors as well as semantically homologous literal sentences activates embodied representations online. Participants read orientational literal sentences (e.g., she climbed up the hill), metaphors (e.g., she climbed up in the company), and abstract sentences with similar meaning to the metaphors (e.g., she succeeded in the company). In Experiments 1 and 2, participants were asked to perform a speeded upward or downward hand motion while they were reading the sentence verb. The hand motion either matched or mismatched the direction connoted by the sentence. The results showed a meaning-action effect for metaphors and literals, that is, faster hand motion responses in the matching conditions. Notably, the matching advantage was also found for homologous abstract sentences, indicating that some abstract ideas are conceptually organized in the vertical dimension, even when they are expressed by means of literal sentences. In Experiment 3, participants responded to an upward or downward visual motion associated with the sentence verb by pressing a single key. In this case, the facilitation effect for matching visual motion-sentence meaning faded, indicating that the visual motion component is less important than the action component in conceptual metaphors. Most up and down metaphors convey emotionally positive and negative information, respectively. We suggest that metaphorical meaning elicits upward/downward movements because they are grounded on the bodily expression of the corresponding emotions.


title: EXPLORING THE CUSTOMER PERSPECTIVE OF AGILE DEVELOPMENT : ACCEPTANCE FACTORS AND ON-SITE CUSTOMER PERCEPTIONS IN SCRUM PROJECTS

abstract: In recent years, agile development methodologies have attracted great attention. Although the success of agile development projects depends considerably on the willingness of customers to actively participate, little research has examined which factors of such methodologies customers perceive as benefits or drawbacks. Employing an exploratory, primarily qualitative study design and the Diffusion of Innovations Theory as theoretical lens, we identify several acceptance factors of Scrum as a specific methodology and describe how customers perceive them. As basis for our examination, we use empirical data that was collected at a world-wide leading insurance company and a mixed-method approach that combines qualitative with quantitative data analyses. The results suggest that customers found Scrum to deliver relative advantages. Furthermore, they indicate that Scrum is perceived as more compatible to the way customers prefer to work in development projects. Factors that characterize the perceived complexity of Scrum were viewed as potential acceptance barriers, however.


title: Model-Independent Online Learning for Influence Maximization

abstract: We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users to expose the product to. While prior work assumes a known model of information diffusion, we propose a novel parametrization that not only makes our framework agnostic to the underlying diffusion model, but also statistically efficient to learn from data. We give a corresponding monotone, submodular surrogate function, and show that it is a good approximation to the original IM objective. We also consider the case of a new marketer looking to exploit an existing social network, while simultaneously learning the factors governing information propagation. For this, we propose a pairwise-influence semi-bandit feedback model and develop a LinUCB-based bandit algorithm. Our model-independent analysis shows that our regret bound has a better (as compared to previous work) dependence on the size of the network. Experimental evaluation suggests that our framework is robust to the underlying diffusion model and can efficiently learn a near-optimal solution.


title: Choice-based preference elicitation for collaborative filtering recommender systems

abstract: We present an approach to interactive recommending that combines the advantages of algorithmic techniques with the benefits of user-controlled, interactive exploration in a novel manner. The method extracts latent factors from a matrix of user rating data as commonly used in Collaborative Filtering, and generates dialogs in which the user iteratively chooses between two sets of sample items. Samples are chosen by the system for low and high values of each latent factor considered. The method positions the user in the latent factor space with few interaction steps, and finally selects items near the user position as recommendations. In a user study, we compare the system with three alternative approaches including manual search and automatic recommending. The results show significant advantages of our approach over the three competing alternatives in 15 out of 24 possible parameter comparisons, in particular with respect to item fit, interaction effort and user control. The findings corroborate our assumption that the proposed method achieves a good trade-off between automated and interactive functions in recommender systems.


Security Analysis of Zigbee


title: CONTEMPORARY PERSPECTIVES ON IMPROVING EFFECTIVE TEACHING AT FIRST YEAR STUDENTS IN ENGINEERING HIGHER EDUCATION- A STUDENTS’ PERSPECTIVE

abstract: The ultimate criterion of effective teaching is effective and successful learning. The paper focuses on an ascertaining research that investigates the students’ opinion about the effective teaching characteristics through a survey based on a questionnaire that was sent to 233 first year students from Technical University of Cluj-Napoca, Romania, attending classes to prepare for a teaching career. The revised set of characteristics of effective teaching according to the Griffith University’s PRO-Teaching project (Klopper, C., Drew, S., 2015) presented originally as ‘eight dimensions of good teaching’ (Nulty, 2001) provided a structured framework that has been the basis for our study in students’ evaluation of effective teaching. The data collection procedure was based on a survey with a 5-point Likert scale and an open-ended question with comment box. The students in the contemporary educational system prefer interactive teaching strategies, suitable personal characteristics of teachers and a good teacher-student relationship .


title: Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories

abstract: Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.


title: An AI Framework to Teach English as a Foreign Language: CSIEC

abstract: CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.


DBSCAN & SNN Density-based clustering algorithms – DBSCAN and SNN


title: Using linked data to mine RDF from wikipedia's tables

abstract: The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedia's tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.


title: Design of a Compact GPS and SDARS Integrated Antenna for Automotive Applications

abstract: This letter describes a novel compact solution for integrating a Global Positioning System (GPS) and a Satellite Digital Audio Radio Service (SDARS) antenna in a very small volume to satisfy the requirements of the automotive market. The GPS antenna is a classic small commercial ceramic patch, and the SDARS antenna is designed in order to fit in the reduced space without affecting radiation and band performance of the GPS antenna. The SDARS basic geometry is a square-ring microstrip antenna operating in the frequency range 2.320-2.345 GHz with left-hand circular polarization (LHCP), and it is wrapped around the GPS patch, which receives a right-hand circular polarization (RHCP) signal at 1.575 GHz. The overall volume of the presented integrated antenna solution is only 30 x 30 x 7.6 mm3, rendering it attractive for the automotive market.


title: Stixel based scene understanding for autonomous vehicles

abstract: We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors.


title: EasyMKL: A scalable multiple kernel learning algorithm

abstract: Abstract The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel machine. State-of-the-art methods of MKL have the drawback that the time required to solve the associated optimization problem grows (typically more than linearly) with the number of kernels to combine. Moreover, it has been empirically observed that even sophisticated methods often do not significantly outperform the simple average of kernels. In this paper, we propose a time and space efficient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. The proposed method has been compared with other baselines (random, average, etc.) and three state-of-the-art MKL methods showing that our approach is often superior. We show empirically that the advantage of using the method proposed in this paper is even clearer when noise features are added. Finally, we have analyzed how our algorithm changes its performance with respect to the number of examples in the training set and the number of kernels combined.


title: Management approaches for Industry 4.0: A human resource management perspective

abstract: Industry 4.0 is characterized by smart manufacturing, implementation of Cyber Physical Systems (CPS) for production, i.e., embedded actuators and sensors, networks of microcomputers, and linking the machines to the value chain. It further considers the digital enhancement and reengineering of products. It is also characterized by highly differentiated customized products, and well-coordinated combination of products and services, and also the value added services with the actual product or service, and efficient supply chain. All these challenges require continuous innovation and learning, which is dependent on people and enterprise's capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and effective learning and innovation climate. This paper aims at offering a viewpoint on best suitable management practices which can promote the climate of innovation and learning in the organization, and hence facilitate the business to match the pace of industry 4.0. This paper is one of the initial attempts to draw the attention towards the important role of management practices in industry 4.0, as most of the recent studies are discussing the technological aspect. This paper also suggests empirical and quantitative investigation on these management approaches in the context of industry 4.0.


title: Protective Effects of Dihydrocaffeic Acid, a Coffee Component Metabolite, on a Focal Cerebral Ischemia Rat Model

abstract: We recently reported the protective effects of chlorogenic acid (CGA) in a transient middle cerebral artery occlusion (tMCAo) rat model. The current study further investigated the protective effects of the metabolites of CGA and dihydrocaffeic acid (DHCA) was selected for further study after screening using the same tMCAo rat model. In the current study, tMCAo rats (2 h of MCAo followed by 22 h of reperfusion) were injected with various doses of DHCA at 0 and 2 h after onset of ischemia. We assessed brain damage, functional deficits, brain edema, and blood-brain barrier damage at 24 h after ischemia. For investigating the mechanism, in vitro zymography and western blotting analysis were performed to determine the expression and activation of matrix metalloproteinase (MMP)-2 and -9. DHCA (3, 10, and 30 mg/kg, i.p.) dose-dependently reduced brain infarct volume, behavioral deficits, brain water content, and Evans Blue (EB) leakage. DHCA inhibited expression and activation of MMP-2 and MMP-9. Therefore, DHCA might be one of the important metabolites of CGA and of natural products, including coffee, with protective effects on ischemia-induced neuronal damage and brain edema.


title: Online Multi-Task Learning Framework for Ensemble Forecasting

abstract: Ensemble forecasting is a widely-used numerical prediction method for modeling the evolution of nonlinear dynamic systems. To predict the future state of such systems, a set of ensemble member forecasts is generated from multiple runs of computer models, where each run is obtained by perturbing the starting condition or using a different model representation of the system. The ensemble mean or median is typically chosen as a point estimate for the ensemble member forecasts. These approaches are limited in that they assume each ensemble member is equally skillful and may not preserve the temporal autocorrelation of the predicted time series. To overcome these limitations, we present an online multi-task learning framework called ORION to estimate the optimal weights for combining the ensemble member forecasts. Unlike other existing formulations, the proposed framework is novel in that its learning algorithm must backtrack and revise its previous forecasts before making future predictions if the earlier forecasts were incorrect when verified against new observation data. We termed this strategy as online learning with restart . Our proposed framework employs a graph Laplacian regularizer to ensure consistency of the predicted time series. It can also accommodate different types of loss functions, including $\epsilon$ -insensitive and quantile loss functions, the latter of which is particularly useful for extreme value prediction. A theoretical proof demonstrating the convergence of our algorithm is also given. Experimental results on seasonal soil moisture forecasts from 12 major river basins in North America demonstrate the superiority of ORION compared to other baseline algorithms.


title: Feature Enriched Nonparametric Bayesian Co-clustering

abstract: Co-clustering has emerged as an important technique for mining relational data, especially when data are sparse and high-dimensional. Co-clustering simultaneously groups the different kinds of objects involved in a relation. Most co-clustering techniques typically only leverage the entries of the given contingency matrix to perform the two-way clustering. As a consequence, they cannot predict the interaction values for new objects. In many applications, though, additional features associated to the objects of interest are available. The Infinite Hidden Relational Model (IHRM) has been proposed to make use of these features. As such, IHRM has the capability to forecast relationships among previously unseen data. The work on IHRM lacks an evaluation of the improvement that can be achieved when leveraging features to make predictions for unseen objects. In this work, we fill this gap and re-interpret IHRM from a co-clustering point of view. We focus on the empirical evaluation of forecasting relationships between previously unseen objects by leveraging object features. The empirical evaluation demonstrates the effectiveness of the feature-enriched approach and identifies the conditions under which the use of features is most useful, i.e., with sparse data.


title: Multi-aspect Sentiment Analysis with Topic Models

abstract: We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge -- in the form of seed words -- to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that overall ratings can be used in conjunction with our sentence labelings to achieve reasonable performance compared to a fully supervised baseline. When gold-standard aspect-ratings are available, we find that topic model based features can be used to improve unsophisticated supervised baseline performance, in agreement with previous multi-aspect rating prediction work. This improvement is diminished, however, when topic model features are paired with a more competitive supervised baseline -- a finding not acknowledged in previous work.


title: Knowledge transfer in SVM and neural networks

abstract: The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.


title: To Activate English Learning: Listen and Speak in Real Life Context with an AR Featured U-Learning System

abstract: Introduction Technological advancements in wireless applications and the popularity of mobile devices have created great advantages and opportunities for learning (Huang & Chiu, 2015; Hsieh et al., 2016). As a natural complement to ubiquitous computing, AR provides an intuitive and interactive interface to a three-dimensional information space embedded within physical reality. The application of Augmented Reality (AR) with context-aware ubiquitous learning (u-learning) systems offers considerable advantages for English as a foreign language (EFL) learning. EFL learners often confront difficulties and challenges when they need to speak English in real life settings. Contextual learning environments have been proven to improve performance in EFL learning (Sandberg et al., 2011). Context-aware ubiquitous learning (u-learning) refers to the computer systems that seamlessly integrate into our everyday life. It is a version of our future computing lifestyle in which providing services and information occurs anywhere and anytime. Researchers have found positive outcomes in the use of mobile devices for English language learners. Kolb (1984) emphasized learning is constructed and contextual which relies on an engagement with social interactions and experience from the real world (De Freitas & Neumann, 2009). Based on Kolb's arguments, we intend to design and develop a U-Learning Instruction System with Augment Reality Features (UL-IAR) to help users to learn EFL in real life contexts. Our UL-IAR allows users to learn EFL in a constructed and contextual environment that involves social interactions and experience. Despite prior studies have considered the technical affordances of u-learning, there is an ongoing need to examine pedagogies that are suitable for u-learning to inform teacher practice, policy makers, curriculum developers, and teacher education (Pegrum et al., 2013). To implement a context-aware u-learning system for EFL learners, we further design learning strategies for different individuals. We believe that individual difference also leads to different learning performance in using u-learning systems. The individual differentiation can be discerned through cognitive styles, learning styles, cognitive control, and intelligence (Jonassen & Grabowski, 1993). The design of learning systems must consider individuals' cognitive styles which would increase the efficacy and satisfaction of the learning experience. However, the performance outcomes of distinct cognitive styles that applied to new learning technologies remain inconclusive (Dillon & Gabbard, 1998). This knowledge gap highlights the need to examine the difference between cognitive styles in u-learning systems with AR. Therefore, our research objective is to empirically examine the learning performance of u-learning system with different learning strategies and cognitive styles. Overall, this paper intends to answer the following research questions. * What kind of learning strategy leads to better EFL learning performance when using UL-IAR? * Do the users with different cognitive styles have different learning performance when using UL-IAR? * Is there best alignment between learning strategies and cognitive styles when using UL-IAR? This paper is organized as follows. We first review the definitions and studies of u-learning, augmented reality, and cognitive styles. We then show our research method which includes system design and development, experimental design and experiment participants. Last, we discuss our research results and findings and conclude theoretical and practical findings, limitations, and future research. Literature review This section reviews the definitions and studies of ubiquitous learning, the applications of augmented reality in education, and cognitive styles. Ubiquitous learning Ubiquitous learning (u-learning) refers to a ubiquitous learning environment that enables individuals to conduct learning activities in the right place and right time. …


title: Flea inspired catapult mechanism with active energy storage and release for small scale jumping robot

abstract: Fleas have a unique catapult mechanism with a special muscle configuration. Energy is stored in an elastic material, resilin, and the extensor muscle. Force is applied by the extensor muscle to generate a torque. Energy is released as a small triggering muscle reverses the direction of the aforementioned torque. A flea can jump 150 times its body length using this elastic catapult mechanism. In this paper, a flea-inspired catapult mechanism is presented. This mechanism can be categorized as an active storage and active release elastic catapult. Owing to its unique stiffness change characteristic, a shape-memory-alloy coil spring actuator enables the mimicking of the flea's catapult mechanism. Two types of flea-inspired jumping mechanisms were developed for verifying the feasibility of applying the concept to an efficient jumping robot. The first prototype has a flea-like appearance and the second is simplified to contain just the essential components of the flea-inspired catapult mechanism. The two prototypes are 20-mm- and 30-mm-long and can jump 64 cm and 120 cm, respectively. This unique catapult mechanism can be used not only for jumping robots but also for other small-sized robots to generate fast-releasing motion.


title: User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario

abstract: In this report, we provide a comparative analysis of different techniques for user intent classification towards the task of app recommendation. We analyse the performance of different models and architectures for multi-label classification over a dataset with a relative large number of classes and only a handful examples of each class. We focus, in particular, on memory network architectures, and compare how well the different versions perform under the task constraints. Since the classifier is meant to serve as a module in a practical dialog system, it needs to be able to work with limited training data and incorporate new data on the fly. We devise a 1-shot learning task to test the models under the above constraint. We conclude that relatively simple versions of memory networks perform better than other approaches. Although, for tasks with very limited data, simple non-parametric methods perform comparably, without needing the extra training data.


title: Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

abstract: Problems at the intersection of vision and language are of significant importance both as challenging research questions and for the rich set of applications they enable. However, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in models that ignore visual information, leading to an inflated sense of their capability. ::: We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter! Specifically, we balance the popular VQA dataset by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset is publicly available at this http URL as part of the 2nd iteration of the Visual Question Answering Dataset and Challenge (VQA v2.0). ::: We further benchmark a number of state-of-art VQA models on our balanced dataset. All models perform significantly worse on our balanced dataset, suggesting that these models have indeed learned to exploit language priors. This finding provides the first concrete empirical evidence for what seems to be a qualitative sense among practitioners. ::: Finally, our data collection protocol for identifying complementary images enables us to develop a novel interpretable model, which in addition to providing an answer to the given (image, question) pair, also provides a counter-example based explanation. Specifically, it identifies an image that is similar to the original image, but it believes has a different answer to the same question. This can help in building trust for machines among their users.


title: Differentially Private Regression with Gaussian Processes

abstract: A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the provable privacy guarantees of the differential privacy framework with the flexibility of Gaussian processes (GPs). We propose a method using GPs to provide differentially private (DP) regression. We then improve this method by crafting the DP noise covariance structure to efficiently protect the training data, while minimising the scale of the added noise. We find that this cloaking method achieves the greatest accuracy, while still providing privacy guarantees, and offers practical DP for regression over multi-dimensional inputs. Together these methods provide a starter toolkit for combining differential privacy and GPs.


title: Generative adversarial networks for specular highlight removal in endoscopic images

abstract: Providing the surgeon with the right assistance at the right time during minimally-invasive surgery requires computer-assisted surgery systems to perceive and understand the current surgical scene. This can be achieved by analyzing the endoscopic image stream. However, endoscopic images often contain artifacts, such as specular highlights, which can hinder further processing steps, e.g., stereo reconstruction, image segmentation, and visual instrument tracking. Hence, correcting them is a necessary preprocessing step. In this paper, we propose a machine learning approach for automatic specular highlight removal from a single endoscopic image. We train a residual convolutional neural network (CNN) to localize and remove specular highlights in endoscopic images using weakly labeled data. The labels merely indicate whether an image does or does not contain a specular highlight. To train the CNN, we employ a generative adversarial network (GAN), which introduces an adversary to judge the performance of the CNN during training. We extend this approach by (1) adding a self-regularization loss to reduce image modification in non-specular areas and by (2) including a further network to automatically generate paired training data from which the CNN can learn. A comparative evaluation shows that our approach outperforms model-based methods for specular highlight removal in endoscopic images.


title: Potential Uses of Probiotics in Clinical Practice

abstract: Probiotics are defined as live microorganisms that, when administered in adequate amounts, confer a health benefit on the host. There is now mounting evidence that selected probiotic strains can provide health benefits to their human hosts. Numerous clinical trials show that certain strains can improve the outcome of intestinal infections by reducing the duration of diarrhea. Further investigations have shown benefits in reducing the recurrence of urogenital infections in women, while promising studies in cancer and allergies require research into the mechanisms of activity for particular strains and better-designed trials. At present, only a small percentage of physicians either know of probiotics or understand their potential applicability to patient care. Thus, probiotics are not yet part of the clinical arsenal for prevention and treatment of disease or maintenance of health. The establishment of accepted standards and guidelines, proposed by the Food and Agriculture Organization of the United Nations and the World Health Organization, represents a key step in ensuring that reliable products with suitable, informative health claims become available. Based upon the evidence to date, future advances with single- and multiple-strain therapies are on the horizon for the management of a number of debilitating and even fatal conditions.


Petri Nets approach to simulate and control of Flexible Manufacturing Systems


title: Cooperative driving: an ant colony system for autonomous intersection management

abstract: Autonomous intersection management (AIM) is an innovative concept for directing vehicles through the intersections. AIM assumes that the vehicles negotiate the right-of-way. This assumption makes the problem of the intersection management significantly different from the usually studied ones such as the optimization of the cycle time, splits, and offsets. The main difficulty is to define a strategy that improves the traffic efficiency. Indeed, due to the fact that each vehicle is considered individually, AIM faces a combinatorial optimization problem that needs quick and efficient solutions for a real time application. This paper proposes a strategy that evacuates vehicles as soon as possible for each sequence of vehicle arrivals. The dynamic programming (DP) that gives the optimal solution is shown to be greedy. A combinatorial explosion is observed if the number of lanes rises. After evaluating the time complexity of the DP, the paper proposes an ant colony system (ACS) to solve the control problem for large number of vehicles and lanes. The complete investigation shows that the proposed ACS algorithm is robust and efficient. Experimental results obtained by the simulation of different traffic scenarios show that the AIM based on ACS outperforms the traditional traffic lights and other recent traffic control strategies.


title: High-Speed Generator and Multilevel Converter for Energy Recovery in Automotive Systems

abstract: This paper deals with the design solution adopted for a high-speed axial-flux permanent-magnet generator devoted to supplementing power in automotive 42 V electrical systems through a low-voltage multilevel neutral-point-clamped converter. The axial-flux permanent-magnet generator is suitably designed to be directly coupled with a radial turbo-expander which provides recovery of kinetic energy available from the exhaust gases of an internal-combustion engine. The multilevel converter is properly sized for systems having low voltage and high first-order harmonic frequency. The paper describes the proposed 4-kW power rating generating system and discusses various issues resulting from electromagnetic, thermal, and mechanical design of the high-speed axial-flux permanent-magnet generator. As well, investigations on modulation strategy, total harmonic distortion, and power losses are presented for the low-voltage neutral-point-clamped power electronic converter.


Nowcasting Precipitation Using Weather Radar Data for Lithuania : the First Results


title: Security event analysis in XBee-based wireless mesh networks

abstract: In modern cyber-physical systems and wireless sensor networks the complexity of crisis management processes is caused by a variety of software/hardware assets and communication protocols, the necessity of their collaborative function, possible inconsistency of data flows between particular devices and increased requirements to cyber-physical security. A crisis management oriented model of a communicational mobile network is constructed. A general architecture of network nodes by the use of XBee circuits, Arduino microcontrollers and connecting equipment are developed. An analysis of possible cyber-physical security events on the base of existing intruder models is performed. A series of experiments on modeling attacks on network nodes is conducted. Possible ways for attack revelations by means of components for security event collection and data correlation is discussed.


title: Spam filtering email classification (SFECM) using gain and graph mining algorithm

abstract: This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context -based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.


title: From Part to Whole: Who is Behind the Painting?

abstract: Compared with normal modalities, the representations of paintings are much more complex due to its large intra-class and small inter-class variation. This poses more difficulties in the task of authorship identification. In this paper, we propose a multi-task multi-range (MTMR) representation framework and try to resolve this issue in two ways. First, we investigate how to improve the representation through multi-task learning. Specifically, we attempt to optimize authorship identification with subtly correlated identification tasks such as style, genre and date. Second, in order to make the representation more comprehensive and reduce the information loss from image scaling, we propose a multi-range structure which is composed of local, regional and global representations. Experiments on the two most representative large-scale painting datasets, Rijksmuseum Challenge and Wikiart, have shown that our method significantly outperforms the existing methods. To give better understanding and provide more effective predictions, we utilize random forest as the feature ranking method to analyze the importance of different features and apply external knowledge matching to further examine the predictions. Moreover, the framework's effects of identifying the authorship are visualized on the paintings' artist-characteristic regions and t-SNE is further applied to perform artist-based cluster analysis. Extensive validation has demonstrated that the proposed framework yields superior performance in the chanllenging task of painting authorship identification.


title: End-to-End Speech Recognition From the Raw Waveform

abstract: State-of-the-art speech recognition systems rely on fixed, hand-crafted features such as mel-filterbanks to preprocess the waveform before the training pipeline. In this paper, we study end-to-end systems trained directly from the raw waveform, building on two alternatives for trainable replacements of mel-filterbanks that use a convolutional architecture. The first one is inspired by gammatone filterbanks (Hoshen et al., 2015; Sainath et al, 2015), and the second one by the scattering transform (Zeghidour et al., 2017). We propose two modifications to these architectures and systematically compare them to mel-filterbanks, on the Wall Street Journal dataset. The first modification is the addition of an instance normalization layer, which greatly improves on the gammatone-based trainable filterbanks and speeds up the training of the scattering-based filterbanks. The second one relates to the low-pass filter used in these approaches. These modifications consistently improve performances for both approaches, and remove the need for a careful initialization in scattering-based trainable filterbanks. In particular, we show a consistent improvement in word error rate of the trainable filterbanks relatively to comparable mel-filterbanks. It is the first time end-to-end models trained from the raw signal significantly outperform mel-filterbanks on a large vocabulary task under clean recording conditions.


title: Failure to Respond Autonomically to Anticipated Future Outcomes Following Damage to Prefrontal Cortex

abstract: Following damage to specific sectors of the prefrontal cortex, humans develop a defect in real-life decision making, in spite of otherwise normal intellectual performance. The patients so affected may even realize the consequences of their actions but fail to act accordingly, thus appearing oblivious to the future. The neural basis of this defect has resisted explanation. Here we identify a physiological correlate for the defect and discuss its possible significance. We measured the skin conductance responses (SCRs) of 7 patients with prefrontal damage, and 12 normal controls, during the performance of a novel task, a card game that simulates real-life decision making in the way it factors uncertainty, rewards, and penalties. Both patients and controls generated SCRs after selecting cards that were followed by penalties or by reward. However, after a number of trials, controls also began to generate SCRs prior to their selection of a card, while they pondered from which deck to choose, but no patients showed such anticipatory SCRs. The absence of anticipatory SCRs in patients with prefrontal damage is a correlate of their insensitivity to future outcomes. It is compatible with the idea that these patients fail to activate biasing signals that would serve as value markers in the distinction between choices with good or bad future outcomes; that these signals also participate in the enhancement of attention and working memory relative to representations pertinent to the decision process; and that the signals hail from the bioregulatory machinery that sustains somatic homeostasis and can be expressed in emotion and feeling. Language: en


title: Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems

abstract: The bag-of-words model is a standard representation of text for many linear classifier learners. In many problem domains, linear classifiers are preferred over more complex models due to their efficiency, robustness and interpretability, and the bag-of-words text representation can capture sufficient information for linear classifiers to make highly accurate predictions. However in settings where there is a large vocabulary, large variance in the frequency of terms in the training corpus, many classes and very short text (e.g., single sentences or document titles) the bag-of-words representation becomes extremely sparse, and this can reduce the accuracy of classifiers. A particular issue in such settings is that short texts tend to contain infrequently occurring or rare terms which lack class-conditional evidence. In this work we introduce a method for enriching the bag-of-words model by complementing such rare term information with related terms from both general and domain-specific Word Vector models. By reducing sparseness in the bag-of-words models, our enrichment approach achieves improved classification over several baseline classifiers in a variety of text classification problems. Our approach is also efficient because it requires no change to the linear classifier before or during training, since bag-of-words enrichment applies only to text being classified.


title: Glide-Symmetric Fully Metallic Luneburg Lens for 5G Communications at Ka-Band

abstract: Here, we propose a fully metallic implementation of a Luneburg lens operating at Ka-band with potential use for 5G communications. The lens is implemented with a parallel plate that is loaded with glide-symmetric holes. These holes are employed to produce the required equivalent refractive index profile of a Lune-burg lens. Glide symmetry and inner metallic pins are employed to increase the equivalent refractive index. The lens is fed with rectangular waveguides designed to match the height of the parallel plate, and it is ended with a flare to minimize the reflections.


Deep unsupervised learning from speech


title: Trending Chic: Analyzing the Influence of Social Media on Fashion Brands

abstract: Social media platforms are popular venues for fashion brand marketing and advertising. With the introduction of native advertising, users don't have to endure banner ads that hold very little saliency and are unattractive. Using images and subtle text overlays, even in a world of ever-depreciating attention span, brands can retain their audience and have a capacious creative potential. While an assortment of marketing strategies are conjectured, the subtle distinctions between various types of marketing strategies remain under-explored. This paper presents a qualitative analysis on the influence of social media platforms on different behaviors of fashion brand marketing. We employ both linguistic and computer vision techniques while comparing and contrasting strategic idiosyncrasies. We also analyze brand audience retention and social engagement hence providing suggestions in adapting advertising and marketing strategies over Twitter and Instagram.


title: Generating Natural Language Inference Chains

abstract: The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.


title: Artificial intelligence, machine learning and deep learning

abstract: It is increasingly recognized that artificial intelligence has been touted as a new mobile. Because of the high volume of data that being generated by devices, sensors and social media users, the machine can learn to distinguish the pattern and makes a reasonably good prediction. This article will explore the use of machine learning and its methodologies. Furthermore, the field of deep learning which is being exploited in many leading IT providers will be clarified and discussed.


title: Efficient super-resolution via image warping

abstract: This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visually by comparing the resulting images and those using bi-linear resampling and back-projection and quantitatively using OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing.


title: A Deep Predictive Model in Healthcare for Inpatients

abstract: With the exponential growth of clinical data which are longitudinal, sparse and heterogeneous, deep learning methods are receiving increasingly attention for predictive tasks in healthcare. These methods have strong abilities to extract low-dimensional representations for prediction from patient’s historical information without human intervention. However, most of existing deep learning approaches focus on predictive tasks of outpatients, such as disease progression, readmission risk and so on. Considering the differences about data characteristics and prediction goals between outpatients and inpatients, these outpatient-oriented methods are not suitable for inpatients. In this study, we propose an end-to-end predictive model for inpatients called DPMI to address the challenges about fixed diagnosis and time irregularity. DPMI is a modified Long-Short Term Memory network with three kinds of representations and an attention mechanism. For an inpatient visit that consist of several days, the guidance role of diagnosis for the days and the temporal relation among the days are utilized by DPMI to learn the representation of the visit. Our experiments on large real-world data demonstrate that DPMI achieves significant improvement in prediction accuracy of two typical inpatient predictive tasks, and the prediction outputs are easy to interpret.


title: Logical Aspects of Massively Parallel and Distributed Systems

abstract: Database research has witnessed a renewed interest for data processing in distributed and parallel settings. While distributed and parallel data management systems have been around for quite some time, it is the rise of cloud computing and the advent of Big Data that presents the community with new challenges. This paper highlights recent research concerning the logical foundations of massively parallel and distributed systems. The first part of the paper concerns massively parallel systems where computation proceeds in a number of synchronized rounds. Here, the focus is on evaluation algorithms for conjunctive queries as well as on reasoning about correctness and optimization of such algorithms. The second part of the paper addresses a distributed asynchronous setting where eventual consistency comes into play. Here, the focus is on coordination-free computation and its relationship to logical monotonicity and Datalog programs.


title: Interactive 3D exploration of a virtual sculpture collection: an analysis of user behavior in museum settings

abstract: We present a usage analysis of an interactive system for the exploration of highly detailed 3D models of a collection of protostoric mediterranean sculptures. In this system, after selecting the object of interest inside the collection, its detailed 3D model and associated information are presented at high resolution on a large vertical display controlled by a touch-enabled horizontal surface placed at a suitable distance in front of it. The indirect user interface combines an object-aware interactive camera controller with an interactive point-of-interest selector and is implemented within a scalable implementation based on multiresolution structures shared between the rendering and user interaction subsystems. The system has been installed in several temporary and permanent exhibitions, and has been extensively used by tens of thousands of visitors. We provide here a data-driven analysis of usage experience based on logs gathered during a 24 months period in four exhibitions in Archeological museums, for a total of over 75K exploration sessions. The results highlight the main trends in visitor behavior during the interactive sessions, which can provide useful insights for the design of 3D exploration user interfaces in future digital installations.


title: Complex PTSD – a better description for borderline personality disorder?

abstract: Objective:To consider the use of the diagnostic category ‘complex posttraumatic stress disorder’ (c-PTSD) as detailed in the forthcoming ICD-11 classification system as a less stigmatising, more clinically useful term, instead of the current DSM-5 defined condition of ‘borderline personality disorder’ (BPD).Conclusions:Trauma, in its broadest definition, plays a key role in the development of both c-PTSD and BPD. Given this current lack of differentiation between these conditions, and the high stigma faced by people with BPD, it seems reasonable to consider using the diagnostic term ‘complex posttraumatic stress disorder’ to decrease stigma and provide a trauma-informed approach for BPD patients.


title: AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings

abstract: Visual analytics techniques help users explore high-dimensional data. However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. The proposed interaction is performed through directly sketching lines over the visualization. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge; 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent; and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.


title: Movie piracy tracking using temporal psychovisual modulation

abstract: Nowadays, camcorder piracy has great impact on the motion picture industry. Although some watermarking technologies can track the movie pirate, the video content viewed in the theater may be affected and they cannot obstruct the need of pirated movie because the watermarks in pirated moves are invisible. This paper presents a new method to defeat camcorder piracy and realize content protection in the theater using a new paradigm of information display technology, called Temporal Psychovisual Modulation (TPVM), which utilizes the differences between the human-eye perception and digital camera imageforming to stack an invisible pattern on digital screen and projector. The images formed in human vision are continuous integration of the light field, while discrete sampling is used in digital video acquisition which has “blackout” period in each sampling cycle. Based on this difference, we can decompose a movie into a set of display frames with specific patterns and broadcast them out at high speed so that the audience cannot notice any disturbance, while the video frames captured by camcorder will contain highly objectionable artifacts (i.e., the patterns). The pattern embedded in the movies can also serves as tracking information to reveal the one responsibility for the camcorder piracy.


title: Multicollisions in Iterated Hash Functions. Application to Cascaded Constructions

abstract: In this paper, we study the existence of multicollisions in iterated hash functions. We show that finding multicollisions, i.e. r-tuples of messages that all hash to the same value, is not much harder than finding ordinary collisions, i.e. pairs of messages, even for extremely large values of r. More precisely, the ratio of the complexities of the attacks is approximately equal to the logarithm of r. Then, using large multicollisions as a tool, we solve a long standing open problem and prove that concatenating the results of several iterated hash functions in order to build a larger one does not yield a secure construction. We also discuss the potential impact of our attack on several published schemes. Quite surprisingly, for subtle reasons, the schemes we study happen to be immune to our attack.


title: Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills

abstract: The goal of our research is to investigate whether a Cognitive Tutor can be made more effective by extending it to help students acquire help-seeking skills. We present a preliminary model of help-seeking behavior that will provide the basis for a Help-Seeking Tutor Agent. The model, implemented by 57 production rules, captures both productive and unproductive help-seeking behavior. As a first test of the model’s efficacy, we used it off-line to evaluate students’ help-seeking behavior in an existing data set of student-tutor interactions, We found that 72% of all student actions represented unproductive help-seeking behavior. Consistent with some of our earlier work (Aleven & Koedinger, 2000) we found a proliferation of hint abuse (e.g., using hints to find answers rather than trying to understand). We also found that students frequently avoided using help when it was likely to be of benefit and often acted in a quick, possibly undeliberate manner. Students’ help-seeking behavior accounted for as much variance in their learning gains as their performance at the cognitive level (i.e., the errors that they made with the tutor). These findings indicate that the help-seeking model needs to be adjusted, but they also underscore the importance of the educational need that the Help-Seeking Tutor Agent aims to address.


Gradual Typing for Functional Languages


title: Mobile Government Applications: Challenges and Needs for a Comprehensive Design Approach

abstract: The ubiquity of mobile devices has drawn new attention to the field of electronic government. Literature studies report on the significance of m-government, including its motivation, success, and failure in developed and developing countries. However, research on the design of m-government applications is still scarce. Design approaches in the literature lack a comprehensive way of addressing m-government challenges. This paper aims to (1) identify challenges of m-government in developed and developing countries and (2) investigate approaches used for designing m-government applications. The challenges are categorised based on the factors of PESTELMO and are further examined to identify requirements for suitable m-government design. Design approaches are analysed by the Content, Context and Process (CCP) framework and are examined to identify requirements, methods and guidelines addressed. The paper finally outlines research needs for a comprehensive design framework for m-government solutions and presents initial requirements for the framework.


title: DeepSkeleton: Skeleton Map for 3D Human Pose Regression

abstract: Despite recent success on 2D human pose estimation, 3D human pose estimation still remains an open problem. A key challenge is the ill-posed depth ambiguity nature. This paper presents a novel intermediate feature representation named skeleton map for regression. It distills structural context from irrelavant properties of RGB image e.g. illumination and texture. It is simple, clean and can be easily generated via deconvolution network. For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works. We further exploit the power of multiple 3D hypothesis generation to obtain reasonbale 3D pose in consistent with 2D pose detection. The effectiveness of our approach is validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M.


title: A Monolithic Current-Mode Buck Converter With Advanced Control and Protection Circuits

abstract: A monolithic current-mode pulse width modulation (PWM) step-down dc-dc converter with 96.7% peak efficiency and advanced control and protection circuits is presented in this paper. The high efficiency is achieved by "dynamic partial shutdown strategy" which enhances circuit speed with less power consumption. Automatic PWM and "pulse frequency modulation" switching boosts conversion efficiency during light load operation. The modified current sensing circuit and slope compensation circuit simplify the current-mode control circuit and enhance the response speed. A simple high-speed over-current protection circuit is proposed with the modified current sensing circuit. The new on-chip soft-start circuit prevents the power on inrush current without additional off-chip components. The dc-dc converter has been fabricated with a 0.6 mum CMOS process and measured 1.35 mm2 with the controller measured 0.27 mm2. Experimental results show that the novel on-chip soft-start circuit with longer than 1.5 ms soft-start time suppresses the power-on inrush current. This converter can operate at 1.1 MHz with supply voltage from 2.2 to 6.0 V. Measured power efficiency is 88.5-96.7% for 0.9 to 800 mA output current and over 85.5% for 1000 mA output current.


title: Statistical compression-based models for text classification

abstract: Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers.


title: TextLogger: inferring longer inputs on touch screen using motion sensors

abstract: Today's smartphones are equipped with precise motion sensors like accelerometer and gyroscope, which can measure tiny motion and rotation of devices. While they make mobile applications more functional, they also bring risks of leaking users' privacy. Researchers have found that tap locations on screen can be roughly inferred from motion data of the device. They mostly utilized this side-channel for inferring short input like PIN numbers and passwords, with repeated attempts to boost accuracy. In this work, we study further for longer input inference, such as chat record and e-mail content, anything a user ever typed on a soft keyboard. Since people increasingly rely on smartphones for daily activities, their inputs directly or indirectly expose privacy about them. Thus, it is a serious threat if their input text is leaked. To make our attack practical, we utilize the shared memory side-channel for detecting window events and tap events of a soft keyboard. The up or down state of the keyboard helps triggering our Trojan service for collecting accelerometer and gyroscope data. Machine learning algorithms are used to roughly predict the input text from the raw data and language models are used to further correct the wrong predictions. We performed experiments on two real-life scenarios, which were writing emails and posting Twitter messages, both through mobile clients. Based on the experiments, we show the feasibility of inferring long user inputs to readable sentences from motion sensor data. By applying text mining technology on the inferred text, more sensitive information about the device owners can be exposed.


title: Interactive swept surface modeling in virtual reality with motion-tracked controllers

abstract: In this work we describe an interactive technique which enables gestural curve and surface design in an immersive virtual environment. We use a pair of motion tracked controllers to allow the user to intuitively control a Hermite spline curve which can be swept through space to create surfaces, or to define the location and orientation of cloned meshes. A head mounted display and tracked controllers replace the traditional keyboard and mouse for view selection and object interaction. Natural and expressive body motions allow the user to specify the parameters which define the curves and surfaces. Results are demonstrated using the HTC Vive VR system.


title: Improving network connection locality on multicore systems

abstract: Incoming and outgoing processing for a given TCP connection often execute on different cores: an incoming packet is typically processed on the core that receives the interrupt, while outgoing data processing occurs on the core running the relevant user code. As a result, accesses to read/write connection state (such as TCP control blocks) often involve cache invalidations and data movement between cores' caches. These can take hundreds of processor cycles, enough to significantly reduce performance. We present a new design, called Affinity-Accept, that causes all processing for a given TCP connection to occur on the same core. Affinity-Accept arranges for the network interface to determine the core on which application processing for each new connection occurs, in a lightweight way; it adjusts the card's choices only in response to imbalances in CPU scheduling. Measurements show that for the Apache web server serving static files on a 48-core AMD system, Affinity-Accept reduces time spent in the TCP stack by 30% and improves overall throughput by 24%.


title: An additional antecedent of empathic concern: Valuing the welfare of the person in need.

abstract: Two experiments examined the role of valuing the welfare of a person in need as an antecedent of empathic concern. Specifically, these experiments explored the relation of such valuing to a well-kn ...


Asset Pricing with Liquidity Risk


title: Gender differences in time perception and its relation with academic performance: non-linear dynamics in the formation of cognitive systems

abstract: Non-linear dynamics is probably much more common in the epigenetic dynamics of living beings than hitherto recognized. Here we report a case of global bifurcation triggered by gender that affects higher cognitive functions in humans. We report a cross-cultural study showing deviations in time perception, as assessed by estimating the duration of brief sounds, according to their durations and to the gender of the perciver. Results show that the duration of sounds lasting less than 10 s were on average overestimated, whereas those lasting longer were underestimated; estimates of sounds shorter than 1 s were extremely inaccurate. Females consistently gave longer estimates than males. Accuracy in time estimation was correlated to academic performance in disciplines requiring mathematical or scientific skills in male, but not in female students. This difference in correlation however had nothing to do with overall skills in mathematics. Both sexes scored similarly in scientific and technical disciplines, but females had higher grades than males in languages and lower ones in physical education. Our results confirm existing evidence for gender differences in cognitive processing, hinting to the existence of different "mathematical intelligences" with different non-linear relationships between natural or biological mathematical intuition and time perception.


title: Reference programme: Diagnosis and treatment of headache disorders and facial pain. Danish Headache Society, 2nd Edition, 2012

abstract: Headache and facial pain are among the most common, disabling and costly disorders in Europe. Correct diagnosis and treatment is important for achieving a high quality of care. As a national organisation whose role is to educate and advocate for the needs of patients with primary headaches, the Danish Headache Society has set up a task force to develop a set of guidelines for the diagnosis, organisation and treatment of the most common types of headaches and for trigeminal neuralgia in Denmark. The guideline was published in Danish in 2010 and has been a great success. The Danish Headache Society decided to translate and publish our guideline in English to stimulate the discussion on optimal organisation and treatment of headache disorders and to encourage other national headache authorities to produce their own guidelines. The recommendations regarding the most common primary headaches and trigeminal neuralgia are largely in accordance with the European guidelines produced by the European Federation of Neurological Societies. The guideline provides a practical tool for use in daily clinical practice for primary care physicians, neurologists with a common interest in headache, as well as other health-care professionals treating headache patients. The guideline first describes how to examine and diagnose the headache patient and how headache treatment is organised in Denmark. This description is followed by individual sections on the characteristics, diagnosis, differential diagnosis and treatment of each of the major headache disorders and trigeminal neuralgia. The guideline includes many tables to facilitate a quick overview. Finally, the particular problems regarding headache in children and headache in relation to female hormones and pregnancy are described.


title: Contextual Moderation of Racial Bias: The Impact of Social Roles on Controlled and Automatically Activated Attitudes.

abstract: Three experiments tested the hypothesis that the social roles implied by specific contexts can attenuate or reverse the typical pattern of racial bias obtained on both controlled and automatic evaluation measures. Study 1 assessed evaluations of Black and Asian faces in contexts related to athlete or student roles. Study 2 compared evaluations of Black and White faces in 3 role-related contexts (prisoner, churchgoer, and factory worker). Study 3 manipulated role cues (lawyer or prisoner) within the same prison context. All 3 studies produced significant reversals of racial bias as a function of implied role on measures of both controlled and automatic evaluation. These results support the interpretation that differential evaluations based on Race Role interactions provide one way that context can moderate both controlled and automatic racial bias. Over the past decade and a half, researchers in social psychology have focused a great deal of effort on investigating automatic or implicit forms of racial bias. In general, differential racial attitudes (bias) are indicated when cues of one racial category elicit more positive and/or less negative automatically activated evaluations than cues from other racial categories. For White respondents, such measures generally reveal a pattern of in-group bias—more positive and/or less negative evaluations of White names or faces compared with Black names or faces. Because there is individual variance around this overall pattern of bias, results from these measures can also be used as measures of individual differences in automatically activated racial bias (Fazio, Jackson, Dunton, & Williams, 1995). When researchers first reported methods to assess automatically activated attitudes, these procedures appeared to provide relatively pure measures of automatic racial bias (Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997; Fazio et al., 1995; Greenwald, McGhee, & Schwartz, 1998). In particular, these measures have been viewed as an improvement over self-report measures, be


title: A Ground-Plane-Truncated, Broadly Steerable Yagi–Uda Patch Array Antenna

abstract: A low-profile, broadly steerable, and reconfigurable array antenna with parasitic patches is proposed and investigated in this work. For patch-type antenna arrays, improvement of the beam-scanning range using parasitic patches is very limited. The typically achieved scanning range is between $-30^{\circ}$ and $+30^{\circ}$ from the broadside. Increasing the number of parasitic elements generally will only provide a mere 3 $^{\circ}$ –5 $^{\circ}$ improvement in terms of the beam-scanning range. In this work, the scanning range is improved by approximately 57% via a novel approach combining the additional parasitic elements and ground plane reduction techniques. Numerical and experimental results have validated that the array is capable of producing five different beam patterns directed towards $-50^{\circ}$ , $-30^{\circ}$ , $0^{\circ}$ , $+30^{\circ}$ , and $+50^{\circ}$ steerable at the $xz$ -plane using an optimized switching method and careful switch location selection on the parasitic elements.


Question Condensing Networks for Answer Selection in Community Question Answering


title: TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

abstract: Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF, random mutations of inputs to a neural network are guided by a coverage metric toward the goal of satisfying user-specified constraints. We describe how fast approximate nearest neighbor algorithms can provide this coverage metric. We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models. Finally, we release an open source library called TensorFuzz that implements the described techniques.


title: Combining static analysis with probabilistic models to enable market-scale Android inter-component analysis

abstract: Static analysis has been successfully used in many areas, from verifying mission-critical software to malware detection. Unfortunately, static analysis often produces false positives, which require significant manual effort to resolve. In this paper, we show how to overlay a probabilistic model, trained using domain knowledge, on top of static analysis results, in order to triage static analysis results. We apply this idea to analyzing mobile applications. Android application components can communicate with each other, both within single applications and between different applications. Unfortunately, techniques to statically infer Inter-Component Communication (ICC) yield many potential inter-component and inter-application links, most of which are false positives. At large scales, scrutinizing all potential links is simply not feasible. We therefore overlay a probabilistic model of ICC on top of static analysis results. Since computing the inter-component links is a prerequisite to inter-component analysis, we introduce a formalism for inferring ICC links based on set constraints. We design an efficient algorithm for performing link resolution. We compute all potential links in a corpus of 11,267 applications in 30 minutes and triage them using our probabilistic approach. We find that over 95.1% of all 636 million potential links are associated with probability values below 0.01 and are thus likely unfeasible links. Thus, it is possible to consider only a small subset of all links without significant loss of information. This work is the first significant step in making static inter-application analysis more tractable, even at large scales.


title: Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling

abstract: In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, hrLDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of hrLDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from hrLDA are very competitive with the ontologies created by domain experts.


title: Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit

abstract: Online banking (Internet banking) has emerged as one of the most profitable e-commerce applications over the last decade. Although several prior research projects have focused on the factors that impact on the adoption of information technology or Internet, there is limited empirical work which simultaneously captures the success factors (positive factors) and resistance factors (negative factors) that help customers to adopt online banking. This paper explores and integrates the various advantages of online banking to form a positive factor named perceived benefit. In addition, drawing from perceived risk theory, five specific risk facets - financial, security/privacy, performance, social and time risk - are synthesized with perceived benefit as well as integrated with the technology acceptance model (TAM) and theory of planned behavior (TPB) model to propose a theoretical model to explain customers' intention to use online banking. The results indicated that the intention to use online banking is adversely affected mainly by the security/privacy risk, as well as financial risk and is positively affected mainly by perceived benefit, attitude and perceived usefulness. The implications of integrating perceived benefit and perceived risk into the proposed online banking adoption model are discussed.