title: Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

abstract: The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.


title: Selective review of offline change point detection methods

abstract: This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.


title: Sensor-Less Five-Level Packed U-Cell (PUC5) Inverter Operating in Stand-Alone and Grid-Connected Modes

abstract: In this paper, a new mode of operation has been introduced for packed U-cell (PUC) inverter. A sensor-less voltage control based on redundant switching states is designed for the five-level packed U-cell (PUC5) inverter, which is integrated into switching process. The sensor-less voltage control is in charge of fixing the dc capacitor voltage at half of the dc source value results in generating symmetric five-level voltage waveform at the output with low harmonic distortion. The sensor-less voltage regulator reduces the complexity of the control system, which makes the proposed converter appealing for industrial applications. An external current controller has been applied for grid-connected application of the introduced sensor-less PUC5 to inject active and reactive power from inverter to the grid with arbitrary power factor, while the PUC auxiliary dc bus is regulated only by sensor-less controller combined with new switching pattern. Experimental results obtained in stand-alone and grid-connected operating modes of proposed PUC5 inverter prove the fast response and good dynamic performance of the designed sensor-less voltage control in balancing the dc capacitor voltage at desired level.


title: The status of HTS ship propulsion motor developments

abstract: The development of ship propulsion synchronous motors with high temperature superconductor (HTS) field windings for Naval electric ship applications has progressed to the point where a full scale motor is now under construction. A 5 MW, 230-rpm prototype ship propulsion motor was built and tested by the Center for Advanced Power Systems (CAPS) on behalf of U.S. Office of Naval Research (ONR). It met or exceeded all its design goals. Currently, a 36.5 MW, 120-rpm ship propulsion motor is being built for delivery to ONR at the end of 2006. This paper presents test results of the 5 MW motor and the status of the 36.5 MW motor.


title: Cleaning up after a face tracker: False positive removal

abstract: Automatic person identification in TV series has gained popularity over the years. While most of the works rely on using face-based recognition, errors during tracking such as false positive face tracks are typically ignored. We propose a variety of methods to remove false positive face tracks and categorize the methods into confidence- and context-based. We evaluate our methods on a large TV series data set and show that up to 75% of the false positive face tracks are removed at the cost of 3.6% true positive tracks. We further show that the proposed method is general and applicable to other detectors or trackers.


title: Object detection using a max-margin Hough transform

abstract: We present a discriminative Hough transform based object detector where each local part casts a weighted vote for the possible locations of the object center. We show that the weights can be learned in a max-margin framework which directly optimizes the classification performance. The discriminative training takes into account both the codebook appearance and the spatial distribution of its position with respect to the object center to derive its importance. On various datasets we show that the discriminative training improves the Hough detector. Combined with a verification step using a SVM based classifier, our approach achieves a detection rate of 91.9% at 0.3 false positives per image on the ETHZ shape dataset, a significant improvement over the state of the art, while running the verification step on at least an order of magnitude fewer windows than in a sliding window approach.


title: Early forest fire detection by vision-enabled wireless sensor networks

abstract: Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland–urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 × 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate.


title: Mindfulness-Action Based Cognitive Behavioral Therapy for concurrent Binge Eating Disorder and Substance Use Disorders.

abstract: Individuals with Binge Eating Disorder (BED) often evidence comorbid Substance Use Disorders (SUD), resulting in poor outcome. This study is the first to examine treatment outcome for this concurrent disordered population. In this pilot study, 38 individuals diagnosed with BED and SUD participated in a 16-week group Mindfulness-Action Based Cognitive Behavioral Therapy (MACBT). Participants significantly improved on measures of objective binge eating episodes; disordered eating attitudes; alcohol and drug addiction severity; and depression. Taken together, MACBT appears to hold promise in treating individuals with co-existing BED-SUD.


title: Impedance measurement for balanced UHF RFID tag antennas

abstract: Dipole antennas in UHF RFID tags are driven by differential signals; it is difficult to measure the antenna input impedance accurately using single-ended probes. Furthermore, the testing fixture used in the antenna measurement process also induces additional parasitic effects; accurate measurement cannot be achieved without taking the fixture impact into consideration. In this paper, we propose to characterize the dipole antenna's input impedance using mixed mode S-parameters, the testing fixture impact can also be quantified with port extension and de-embedding techniques. Experiments demonstrate that the proposed measurement method achieves very consistent results over UHF frequency band with very high accuracy.


title: Extended Naive Bayes classifier for mixed data

abstract: Naive Bayes induction algorithm is very popular in classification field. Traditional method for dealing with numeric data is to discrete numeric attributes data into symbols. The difference of distinct discredited criteria has significant effect on performance. Moreover, several researches had recently employed the normal distribution to handle numeric data, but using only one value to estimate the population easily leads to the incorrect estimation. Therefore, the research for classification of mixed data using Naive Bayes classifiers is not very successful. In this paper, we propose a classification method, Extended Naive Bayes (ENB), which is capable for handling mixed data. The experimental results have demonstrated the efficiency of our algorithm in comparison with other classification algorithms ex. CART, DT and MLP's.


title: Factors Influencing Software Development Productivity - State of the Art and Industrial Experiences

abstract: Abstract Managing software development productivity is a key issue in software organizations. Business demands for shorter time‐to‐market while maintaining high product quality force software organizations to look for new strategies to increase development productivity. Traditional, simple delivery rates employed to control hardware production processes have turned out not to work when simply transferred to the software domain. The productivity of software production processes may vary across development contexts dependent on numerous influencing factors. Effective productivity management requires considering these factors. Yet, there are thousands of possible factors and considering all of them would make no sense from the economical point of view. Therefore, productivity modeling should focus on a limited number of factors with the most significant impact on productivity. In this chapter, we present a comprehensive overview of productivity factors recently considered by software practitioners. The study results are based on the review of 126 publications as well as international experiences of the Fraunhofer Institute, including the most recent 13 industrial projects, four workshops, and eight surveys on software productivity. The aggregated results show that the productivity of software development processes still depends significantly on the capabilities of developers as well as on the tools and methods they use.


title: Distributed Wireless Power Transfer with Energy Feedback

abstract: Energy beamforming (EB) is a key technique for achieving efficient radio-frequency transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they can be constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of EB waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain practically, especially in a distributed system with ETs at separate locations. In this paper, we study practical and efficient channel training methods to achieve optimal EB in a distributed WET system. We propose two protocols with and without centralized coordination, respectively, where distributed ETs either sequentially or in parallel adapt their transmit phases based on a low-complexity energy feedback from the ER. The energy feedback only depends on the received power level at the ER, where each feedback indicates one particular transmit phase that results in the maximum harvested power over a set of previously used phases. Simulation results show that the two proposed training protocols converge very fast in practical WET systems even with a large number of distributed ETs, while the protocol with sequential ET phase adaptation is also analytically shown to converge to the optimal EB design with perfect CSI by increasing the training time. Numerical results are also provided to evaluate the performance of the proposed distributed EB and training designs as compared to other benchmark schemes.


title: Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

abstract: A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12×10 pixel and basically satisfy the practical requirements of recognition. The further comparison of oth...


title: Markov models for offline handwriting recognition: a survey

abstract: Since their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic offline handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. It is therefore the goal of this survey to provide a comprehensive overview of the application of Markov models in the research field of offline handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, we will introduce the typical architecture of a Markov-model-based offline handwriting recognition system and make the reader familiar with the essential theoretical concepts behind Markovian models. Then, we will give a thorough review of the solutions proposed in the literature for the open problems how to apply Markov-model-based approaches to automatic offline handwriting recognition.


title: COMPUTER GRAPHICS forum Low-cost Subpixel Rendering for Diverse Displays

abstract: Subpixel rendering increases the apparent display resolution by taking into account the subpixel structure of a given display. In essence, each subpixel is addressed individually, allowing the underlying signal to be sampled more densely. Unfortunately, naive subpixel sampling introduces colour aliasing, as each subpixel only displays a specific colour usually R, G and B subpixels are used. As previous work has shown, chromatic aliasing can be reduced significantly by taking the sensitivity of the human visual system into account. In this work, we find optimal filters for subpixel rendering for a diverse set of 1D and 2D subpixel layout patterns. We demonstrate that these optimal filters can be approximated well with analytical functions. We incorporate our filters into GPU-based multi-sample anti-aliasing to yield subpixel rendering at a very low cost 1-2 ms filtering time at HD resolution. We also show that texture filtering can be adapted to perform efficient subpixel rendering. Finally, we analyse the findings of a user study we performed, which underpins the increased visual fidelity that can be achieved for diverse display layouts, by using our optimal filters.


title: OmniPage vs. Sakhr: paired model evaluation of two Arabic OCR products

abstract: Characterizing the performance of Optical Character Recognition (OCR) systems is crucial for monitoring technical progress, predicting OCR performance, providing scientific explanations for the system behavior and identifying open problems. While research has been done in the past to compare performances of two or more OCR systems, all assume that the accuracies achieved on individual documents in a dataset are independent when, in fact, they are not. In this paper we show that accuracies reported on any dataset are correlated and invoke the appropriate statistical technique--the paired model--to compare the accuracies of two recognition systems. Theoretically we show that this method provides tighter confidence intervals than methods used in OCR and computer vision literature. We also proposed a new visualization method, which we call the accuracy scatter plot, for providing a visual summary of performance results. This method summarizes the accuracy comparisons on the entire corpus while simultaneously allowing the researcher to visually compare the performances on individual document images. Finally, we report on the accuracy and speed performances as a function of scanning resolution. Contrary to what one might expect, the performance of one of the systems degrades when the image resolution is increased beyond 300 dpi. Furthermore, the average time taken to OCR a document image, after increasing almost linearly as a function of resolution, suddenly becomes a constant beyond 400 dpi. This behavior is most likely because the OCR algorithm samples the images at resolutions 400 dpi and higher to a standard resolution. The two products that we compare are the Arabic OmniPage 2.0 and the Automatic Page Reader 3.01 from Sakhr. The SAIC Arabic dataset was used for the evaluations. The statistical and visualization methods presented in this article are very general and can be used for comparing accuracies of any two recognition systems, not just OCR systems.


title: Recurrent Convolutional Neural Networks for Scene Labeling

abstract: The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accuracy, it is essential for a model to capture long range (pixel) label dependencies in images. In a feed-forward architecture, this can be achieved simply by considering a sufficiently large input context patch, around each pixel to be labeled. We propose an approach that consists of a recurrent convolutional neural network which allows us to consider a large input context while limiting the capacity of the model. Contrary to most standard approaches, our method does not rely on any segmentation technique nor any task-specific features. The system is trained in an end-to-end manner over raw pixels, and models complex spatial dependencies with low inference cost. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. Our approach yields state-of-the-art performance on both the Stanford Background Dataset and the SIFT Flow Dataset, while remaining very fast at test time.


title: Appropriating and assessing heuristics for mobile computing

abstract: Mobile computing presents formidable challenges not only to the design of applications but also to each and every phase of the systems lifecycle. In particular, the HCI community is still struggling with the challenges that mobile computing poses to evaluation. Expert-based evaluation techniques are well known and they do enable a relatively quick and easy evaluation. Heuristic evaluation, in particular, has been widely applied and investigated, most likely due to its efficiency in detecting most of usability flaws at front of a rather limited investment of time and human resources in the evaluation. However, the capacity of expert-based techniques to capture contextual factors in mobile computing is a major concern. In this paper, we report an effort for realizing usability heuristics appropriate for mobile computing. The effort intends to capture contextual requirements while still drawing from the inexpensive and flexible nature of heuristic-based techniques. This work has been carried out in the context of a research project task geared toward developing a heuristic-based evaluation methodology for mobile computing. This paper describes the methodology that we adopted toward realizing mobile heuristics. It also reports a study that we carried out in order to assess the relevance of the realized mobile heuristics by comparing their performance with that of the standard/traditional usability heuristics. The study yielded positive results in terms of the number of usability flaws identified and the severity ranking assigned.


Computational Intelligence Models for Insurance Fraud Detection : A Review of a Decade of Research


title: Cutting the Cord: Static and Dynamic Inductive Wireless Charging of Electric Vehicles

abstract: In this article, we have reviewed the state of the art of IPT systems and have explored the suitability of the technology to wirelessly charge battery powered vehicles. the review shows that the IPT technology has merits for stationary charging (when the vehicle is parked), opportunity charging (when the vehicle is stopped for a short period of time, for example, at a bus stop), and dynamic charging (when the vehicle is moving along a dedicated lane equipped with an IPT system). Dynamic wireless charging holds promise to partially or completely eliminate the overnight charging through a compact network of dynamic chargers installed on the roads that would keep the vehicle batteries charged at all times, consequently reducing the range anxiety and increasing the reliability of EVs. Dynamic charging can help lower the price of EVs by reducing the size of the battery pack. Indeed, if the recharging energy is readily available, the batteries do not have to support the whole driving range but only supply power when the IPT system is not available. Depending on the power capability, the use of dynamic charging may increase driving range and reduce the size of the battery pack.


title: Intrusion detection using clustering of network traffic flows

abstract: This paper investigates the continued need for intrusion detection systems (IDS) in computer networks. It explores some of the ways that data mining techniques can be used to improve IDS, and looks at how others have implemented those techniques. It then highlights a method for developing an intrusion detection model using DBSCAN clustering and presents the results of the clustering algorithm as applied to a real-world data set. Finally, the paper concludes that clustering as an intrusion detection technique produces accurate results, but that special considerations must be made both with regard to outliers and the type of traffic flowing across the network.


title: Data Mining: Concepts, Models and Techniques

abstract: Introduction to Data Mining.- The "data-mine".- Exploratory data analysis.- Classification and decision trees.- Data mining techniques and models.- Classification performance evaluation.


title: Exploring the longitudinal relationships between the use of grammar in text messaging and performance on grammatical tasks

abstract: Research has demonstrated that use of texting slang (textisms) when text messaging does not appear to impact negatively on children’s literacy outcomes and may even benefit children’s spelling attainment. However, less attention has been paid to the impact of text messaging on the development of children’s and young people’s understanding of grammar. This study therefore examined the interrelationships between children’s and young adults’ tendency to make grammatical violations when texting and their performance on formal assessments of spoken and written grammatical understanding, orthographic processing and spelling ability over the course of 1 year. Zero-order correlations showed patterns consistent with previous research on textism use and spelling, and there was no evidence of any negative associations between the development of the children’s performance on the grammar tasks and their use of grammatical violations when texting. Adults’ tendency to use ungrammatical word forms (‘does you’) was positively related to performance on the test of written grammar. Grammatical violations were found to be positively associated with growth in spelling for secondary school children. However, not all forms of violation were observed to be consistently used in samples of text messages taken 12 months apart or were characteristic of typical text messages. The need to differentiate between genuine errors and deliberate violation of rules is discussed, as are the educational implications of these findings.


title: Learning Semantic Correspondences in Technical Documentation

abstract: We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates. Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn. Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model. We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.


title: An Event-driven Multi-threading Real-time Operating System Dedicated to Wireless Sensor Networks

abstract: At present, the OSs (Operating system) employed for WSN (wireless sensor networks) are either satisfied with only one or two application classes or unsuitable for strict-constrained resources. In view of a variety of WSN applications, there is a need of developing a self-adaptable and self-configurable embedded real-time operating system (RTOS). This paper presents a resource-aware and low-power RTOS termed LIMOS. This kernel adopts a component-based three-level system architecture: action (system operation), thread (component) and event (container). In accordance, a predictable and deterministic two-level scheduling mechanism is proposed: 'non pre-emption priority based' high level scheduling for events and 'preemptive priority-based' low level scheduling for threads. Employing the concepts of LINDA language, LIMOS provides a simplified tuple space and a light IN/OUT system primitive-pair to achieve system communication and synchronization. LIMOS is capable of self-adapting to run on two operation modes: event-driven and multi-threading, with respect to the application diversity. The performance evaluation and comparison shows LIMOS has tiny resource consumption and is fit for the real-time applications. Currently LIMOS has been ported on several hardware platforms for different WSN applications.


title: Mortality After Atypical Femoral Fractures: A Cohort Study.

abstract: Although osteoporotic fracture rates can be reduced by bisphosphonates, prolonged therapy is associated with higher risk of atypical femoral fractures. Ordinary fragility fractures are linked to high mortality rates. We aimed to determine whether atypical femoral fractures also confer excess mortality. Radiographs were reviewed for all patients aged ≥55 years who had experienced a subtrochanteric or femoral shaft fracture in Sweden in 2008 to 2010. The fractures were classified as either atypical or ordinary. Data on medication use, coexisting conditions, and date of death were obtained from national registers. We estimated multivariable-adjusted relative risks of death after atypical femoral fractures compared with ordinary subtrochanteric or femoral shaft fractures and calculated age- and sex-standardized mortality ratios (SMRs) for atypical and ordinary fractures compared with the population average. During a mean of 4 years of follow-up, 39 of 172 (23%) patients with an atypical fracture had died compared with 588 of 952 (62%) with an ordinary fracture, corresponding to a relative risk of 0.51 (95% confidence interval [CI] 0.38-0.68). The lower risk was evident in both users and nonusers of bisphosphonates. No patient with atypical fracture died in the first year after fracture. Individuals with an ordinary fracture had a higher mortality risk than the general population (SMR = 1.82; 95% CI 1.69-1.99), but no excess risk was found in patients with atypical fracture (SMR = 0.92; 95% CI 0.65-1.26). We conclude that in contrast to ordinary subtrochanteric and femoral shaft fractures, atypical femoral fractures are not associated with excess mortality.


title: The analysis of a semi-active suspension system

abstract: Automotive industry is growing rapidly by the involving of multi-fields not only mechanical engineering but also electrical and electronic engineering, material and more. As a key system in vehicles, suspension system and its control has been researched in a long time. A well-controlled suspension system will provide high vehicle handling, good driving pleasure, high comfort for passengers, and good isolation from road noise and vibration. In order to enhance the comfort and handling of light-weight transportation vehicles, the semi-active suspension systems is a considerable issues and had been proposed in a number of papers. The semi-active suspension system requires less space than traditional suspension systems. Therefore, it is suitable for small compact car body known as light vehicles. Moreover, the system complexity and maintenance caused by difficulty of assembly will be reduced This paper focuses on the analysis and control of a semi-active suspension system for light vehicles. Models of a quarter-car suspension with air spring and magneto rheological damper were built. Because components in the system involve nonlinear dynamic characteristics, a self-tuning Fuzzy logic controller was designed to the semi-active suspension system. The simulation results showed that the designed suspension system and its controller had good performance for vibration suppression. The work in this paper was divided into three parts: (1) modeling the semi-active suspension system including air spring and magneto-rheological fluid damper (MRF damper); (2) controller design of a self-tuning Fuzzy logic controller; (3) simulation and data analysis.


title: High-efficiency, high step-up DC-DC converters

abstract: Many applications call for high step-up DC-DC converters that do not require isolation. Some DC-DC converters can provide high step-up voltage gain, but with the penalty of either an extreme duty ratio or a large amount of circulating energy. DC-DC converters with coupled inductors can provide high voltage gain, but their efficiency is degraded by the losses associated with leakage inductors. Converters with active clamps recycle the leakage energy at the price of increasing topology complexity. A family of high-efficiency, high step-up DC-DC converters with simple topologies is proposed in this paper. The proposed converters, which use diodes and coupled windings instead of active switches to realize functions similar to those of active clamps, perform better than their active-clamp counterparts. High efficiency is achieved because the leakage energy is recycled and the output rectifier reverse-recovery problem is alleviated.


title: Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis

abstract: In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural networks to obtain a more diverse tweet representation. The network is trained on top of GloVe word embeddings pre-trained on the Common Crawl dataset. Both neural networks are used to obtain a fixed length representation of variable sized tweets, and the concatenation of these vectors is supplied to a fully connected softmax layer with dropout regularization. The system is evaluated on benchmark datasets from the Sentiment Analysis in Twitter task of the SemEval 2016 challenge where our model achieves best and second highest results on the 2-point and 5-point quantification subtasks respectively. Despite not relying on any hand-crafted features, our system manages the second highest average rank on the considered subtasks.


title: A cloud visitation platform to facilitate cloud federation and fog computing

abstract: Evolving from hybrid clouds to true cloud federations and, ultimately, fog computing will require that cloud platforms allow for--and embrace--local hardware awareness.


title: Variational Adaptive-Newton Method for Explorative Learning

abstract: We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations that are similar to continuous optimization methods. Our theoretical contribution reveals that VAN is a second-order method that unifies existing methods in distinct fields of continuous optimization, variational inference, and evolution strategies. Our experimental results show that VAN performs well on a wide-variety of learning tasks. This work presents a general-purpose explorative-learning method that has the potential to improve learning in areas such as active learning and reinforcement learning.


title: Dynamic path planning based on improved boundary value problem for unmanned aerial vehicle

abstract: When unmanned aerial vehicles (UAVs) track a moving target in complex environment, most path planning methods are difficult to combine real-time calculation and optimality. A real-time path planning method based on boundary value problem (BVP) is studied. First, a grid model of terrain is established based on UAV constraints. Then the structure of receding horizon planning ensures the real-time tracking to moving target, and the path is determined based on BVP in each horizon window. The sub-goal of horizon window is designed by line-of-sight, and the method of updating potential field dynamically and the calculation of flight direction are proposed. By comparison with other methods, the simulation results show that the method can ensure the path smooth and feasible. It can track the moving target in real-time and have some optimality, which is suitable for the mission of UAV to track moving target in complex environment.


title: Fully Homomorphic Encryption without Squashing Using Depth-3 Arithmetic Circuits

abstract: All previously known fully homomorphic encryption (FHE) schemes use Gentry's blueprint:* SWHE: Construct a somewhat homomorphic encryption (SWHE) scheme -- roughly, an encryption scheme that can homomorphically evaluate polynomials up to some degree.* Squash: ``Squash" the decryption function of the SWHE scheme, so that the scheme can evaluate functions twice as complex (in terms of polynomial degree) than its own decryption function. Do this by adding a ``hint " to the SHWE public key -- namely, a large set of vectors that has a secret sparse subset that sums to the original secret key.* Bootstrap: Given a SWHE scheme that can evaluate functions twice as complex as its decryption function, apply Gentry's transformation to get a ``leveled" FHE scheme. To get ``pure" (non-leveled) FHE, one assumes circular security. Here, we describe a new blueprint for FHE. We show how to eliminate the squashing step, and thereby eliminate the need to assume that the sparse subset sum problem (SSSP) is hard, as all previous leveled FHE schemes have done. Using our new blueprint, we obtain the following results:* A ``simple" leveled FHE scheme where we replace SSSP with Decision Diffie-Hellman!* The first leveled FHE scheme based entirely on worst-case hardness}. Specifically, we give a leveled FHE scheme with security based on the shortest independent vector problem over ideal lattices (ideal-SIVP).* Some efficiency improvements for FHE.} While the new blueprint does not yet improve computational efficiency, it reduces cipher text length. As in the previous blueprint, we obtain pure FHE by assuming circular security. Our main technique is to express the decryption function of SWHE schemes as a depth-3 ($\sum \prod \sum$) arithmetic circuit. When we evaluate this decryption function homomorphically, we temporarily switch to a multiplicatively homomorphic encryption (MHE) scheme, such as Elgamal, to handle the $\prod$ part, after which we translate the result from the MHE scheme back to the SWHE scheme by evaluating the MHE scheme's decryption function within the SWHE scheme. The SWHE scheme only needs to be able to evaluate the MHE scheme's decryption function (plus minor operations), and does not need to have the self-referential property of being able to evaluate its {\em own} decryption function, a property that necessitated squashing in the original blueprint.


title: Design and motion control of a cable-driven dexterous robotic arm

abstract: Robots with hyper degrees of freedom motion are highly flexible, hence they are the ideal candidates to work in highly-confined spaces. These dexterous robotic arms are classified into two categories, i.e., discrete and continuum. As the weight of the discrete robots is a major concern, this paper discusses about the possibility of reducing their weight by replacing the heavy actuators with light cables. The proposed cable-driven robot consists of a series of identical parallel-structured modules connected to one another via universal joints. Experimental results showed that the cables have demonstrated a good step response with no steady-state errors and a fast settling time of 0.042 seconds. By using the kinematics formulations analysed in this paper, position control has been successfully implemented on a two-module cable-driven prototype. This allows the robot to be controlled in a co-ordinated manner while maintaining a smooth trajectory.


title: Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection

abstract: In this paper, we propose a novel text-based traffic sign detection framework with two deep learning components. More precisely, we apply a fully convolutional network to segment candidate traffic sign areas providing candidate regions of interest (RoI), followed by a fast neural network to detect texts on the extracted RoI. The proposed method makes full use of the characteristics of traffic signs to improve the efficiency and accuracy of text detection. On one hand, the proposed two-stage detection method reduces the search area of text detection and removes texts outside traffic signs. On the other hand, it solves the problem of multi-scales for the text detection part to a large extent. Extensive experimental results show that the proposed method achieves the state-of-the-art results on the publicly available traffic sign data set: Traffic Guide Panel data set. In addition, we collect a data set of text-based traffic signs including Chinese and English traffic signs. Our method also performs well on this data set, which demonstrates that the proposed method is general in detecting traffic signs of different languages.


title: Reinforcement Learning on a Futures Market Simulator

abstract: In recent years, it becomes vigorous to forecast a market by using machine learning methods. Since they assume that each trader's individual decisions do not affect market prices at all, most existing works use a past market data set. Meanwhile there is an attempt to analyze economic phenomena by constructing a virtual market simulator, where human and artificial traders really make trades. Since prices in the market are determined by every trader's decisions, it is more realistic and the assumption cannot be applied any more. In this work, we design and evaluate several reinforcement learners on a futures market simulator U-Mart (Unreal Market as an Artificial Research Testbed). After that, we compare our learner to the previous champions of U-Mart competitions.


title: eGovernment service quality assessed through the public value lens

abstract: This paper assesses the role of e-government service quality in the creation of public value from the citizen perspective. By assessing the added value of e-government services through a public value lens we aim to explore more deeply how e-government service quality impacts on public value creation. We propose a conceptual framework based on the theoretical perspectives of public value and e-service quality to support the examination of e-government service quality from the citizens' viewpoint. An exploration of the literature on public value, e-service quality, and e-government indicates that the creation of public value is highly dependent on the level of quality of a service delivered by a public organization. The framework draws together the elements of public value as determined by Moore [1] and Kelly [2], and quality dimensions from the updated IS success model by DeLone and McLean [3].


title: A novel approach to classify and detect bean diseases based on image processing

abstract: The early detection of plant diseases is one of the main reasons that can reduce the world crop losses. It requires a tremendous amount of effort, money, and time. Therefore, the algorithms used in image processing make it possible to detect plant leaf diseases automatically. This paper focuses on detecting two types of bean leaf diseases including bacterial brown spot and powdery mildew. The detecting process involves acquisition, preprocessing, segmentation, feature extraction, and classification. The training and testing images are taken from a public database. The developed methodology can successfully detect the two types of plant leaf diseases with an accuracy of 100%.


title: Closed structure soft robotic gripper

abstract: This work describes a new kind of soft gripper. Inspiration on nature is usual in Soft Robotics device creation. In this case, the inspiration came from a sea lamprey in order to create a closed structure soft robotic gripper. Usually the grippers have one or more soft actuators that act like fingers. Since it is difficult to know whether the fingers are really grasping an object, it is being proposed a closed structure to deal with this problem. While the proposed gripper involves the object as whole, a homogeneous force is applied on it. In addition to the details of the closed structure actuator manufacture description, two procedure tests are presented: an analysis of load versus pressure function and of the force versus pressure characteristics. At the end, some new research topics to the proposed soft gripper are discussed.


title: Deep reinforcement learning policy in Hex game system

abstract: Hex game is a zero-sum chess game. It has a large solution space when using 11×11 size of chess board. In recent years, deep reinforcement learning-based Go game systems, i.e. AlphaGo and AlphaGo Zero, have gotten huge achievement. In this paper, we design the self-learning method and system structure of Hex game, design policy network and value network referred to residual network, and use asynchronous advantage actor-critic algorithm to train policy network and value network. The comparison of deep reinforcement learning-based policy network and fixed strategy proves better effect of self-learning.


title: The causes and molecular consequences of polyploidy in flowering plants

abstract: Polyploidy is an important force shaping plant genomes. All flowering plants are descendants of an ancestral polyploid species, and up to 70% of extant vascular plant species are believed to be recent polyploids. Over the past century, a significant body of knowledge has accumulated regarding the prevalence and ecology of polyploid plants. In this review, we summarize our current understanding of the causes and molecular consequences of polyploidization in angiosperms. We also provide a discussion on the relationships between polyploidy and adaptation and suggest areas where further research may provide a better understanding of polyploidy.


title: A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

abstract: In Information-Centric Internet of Things (ICIoT), Internet of Things (IoT) data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However, in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-Driven secure data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPD-ICIoT, newly introduced attribute manifest is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an automatic attribute self-update mechanism is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CP-ABE scheme.


title: Affective video content representation and modeling

abstract: This paper looks into a new direction in video content analysis - the representation and modeling of affective video content . The affective content of a given video clip can be defined as the intensity and type of feeling or emotion (both are referred to as affect) that are expected to arise in the user while watching that clip. The availability of methodologies for automatically extracting this type of video content will extend the current scope of possibilities for video indexing and retrieval. For instance, we will be able to search for the funniest or the most thrilling parts of a movie, or the most exciting events of a sport program. Furthermore, as the user may want to select a movie not only based on its genre, cast, director and story content, but also on its prevailing mood, the affective content analysis is also likely to contribute to enhancing the quality of personalizing the video delivery to the user. We propose in this paper a computational framework for affective video content representation and modeling. This framework is based on the dimensional approach to affect that is known from the field of psychophysiology. According to this approach, the affective video content can be represented as a set of points in the two-dimensional (2-D) emotion space that is characterized by the dimensions of arousal (intensity of affect) and valence (type of affect). We map the affective video content onto the 2-D emotion space by using the models that link the arousal and valence dimensions to low-level features extracted from video data. This results in the arousal and valence time curves that, either considered separately or combined into the so-called affect curve, are introduced as reliable representations of expected transitions from one feeling to another along a video, as perceived by a viewer.


title: Evaluation of the inhibitory effect of dimethyl dicarbonate (DMDC) against wine microorganisms

abstract: Abstract Several microbial species associated with wine were challenged against increasing concentrations of dimethyl dicarbonate (DMDC). The concentration inducing complete cell death upon addition to red wine was regarded as the minimum inhibitory concentration (MIC). In dry red wines with 12% (v/v) ethanol and pH 3.50, the inactivation depended on the initial cell concentration. For an initial inoculum of 500 CFU/ml, the MIC of the yeasts species Schizosaccharomyces pombe, Dekkera bruxellensis , Saccharomyces cerevisiae and Pichia guilliermondii was 100 mg/l. The most sensitive strains belong to Zygosaccharomyces bailii , Zygoascus hellenicus and Lachancea thermotolerans , with MIC of 25 mg/l DMDC. For inoculation rates of about 10 6 CFU/ml, the maximum dose of DMDC legally authorised (200 mg/l) was not effective against the most resistant species. The addition of 100 mg/l potassium metabisulphite (PMB), equivalent to 1 mg/l molecular sulphur dioxide, increased the inactivation effect of 100 mg/l DMDC over initial yeast populations of 10 6 CFU/ml but did not fully kill S. pombe and S. cerevisiae . Lactic acid and acetic acid bacteria were not killed by the addition of 300 mg/l of DMDC. Trials performed in wines before bottling showed that in most samples indigenous bacterial populations were not affected by 200 mg/l DMDC. Therefore, under winery practice, DMDC at the maximum dose legally permitted may be regarded as an efficient preservative to control low contamination rates of yeasts but ineffective against lactic acid and acetic acid bacteria.


title: S3Email: A method for securing emails from service providers

abstract: We often send our confidential information such as passport, credit card, social security numbers over email without concern about the security of email services. Existing network security mechanisms provide adequate security from external malicious adversaries and eavesdroppers, but they don't guarantee that the email service providers (ESPs) wouldn't or can't access our email data themselves, which in some cases could be highly confidential. One of the ways to protect email data from ESPs is to use Pretty Good Privacy (PGP) that has many limitations including key storage problem and dependability on third party services, making it cumbersome to use in practice. In this paper, we present S3Email method that provides email security against ESPs. The proposed method uses a cryptographic secret sharing technique in a novel way and encrypts the email metadata, body and attachments before the email is sent. In the proposed solution, the email sender and receiver must have at least two email accounts on the existing ESPs, which is not unusual today. Experiments and analysis show that the S3Email method provides information theoretic security with minimal computational overhead.


title: Survey propagation: an algorithm for satisfiability

abstract: We study the satisfiability of randomly generated formulas formed by $M$ clauses of exactly $K$ literals over $N$ Boolean variables. For a given value of $N$ the problem is known to be most difficult with $\alpha=M/N$ close to the experimental threshold $\alpha_c$ separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when $\alpha$ is close to (but smaller than) $\alpha_c$. We introduce a new type of message passing algorithm which allows to find efficiently a satisfiable assignment of the variables in the difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: it passes messages that are surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic.


title: Echocardiographic diagnosis of a cardiac fibrosarcoma in the right atrium of a sheep.

abstract: This is a case report of a six-year-old female White Alpine sheep with a cardiac fibrosarcoma in the right atrium. Clinically, the sheep had right-sided cardiac insufficiency with tachycardia, engorgement of the jugular veins, brisket edema, and ascites. Chronic congestion of the liver resulted in increased hepatic enzyme activity. Based on clinical findings, a tentative diagnosis of endocarditis or pericarditis was made. Radiography of the thorax revealed hydrothorax. An echogenic mass was observed in the right atrium via echocardiography; it was interpreted as a tumor or thrombus. Ultrasonography of the abdomen revealed severe ascites and chronic congestion of the liver attributable to right-sided cardiac insufficiency. The clinical and sonographic findings were verified at post mortem. The mass in the right atrium was a pedunculated fibrosarcoma.


title: Nonparametric kernel-based sequential investment strategies

abstract: The purpose of this paper is to introduce sequential investment strategies that guarantee an optimal rate of growth of the capital, under minimal assumptions on the behavior of the market. The new strategies are analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth matches the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the performance of the proposed investment strategies measured on past nyse and currency exchange data is solid, and sometimes even spectacular.


title: SCEPter: Semantic Complex Event Processing over End-to-End Data Flows

abstract: Emerging Complex Event Processing (CEP) applications in cyber physical systems like Smart Power Grids present novel challenges for end-to-end analysis over events, flowing from heterogeneous information sources to persistent knowledge repositories. CEP for these applications must support two distinctive features – easy specification patterns over diverse information streams, and integrated pattern detection over realtime and historical events. Existing work on CEP has been limited to relational query patterns, and engines that match events arriving after the query has been registered. We propose SCEPter, a semantic complex event processing framework which uniformly processes queries over continuous and archived events. SCEPteris built around an existing CEP engine with innovative support for semantic event pattern specification and allows their seamless detection over past, present and future events. Specifically, we describe a unified semantic query model that can operate over data flowing through event streams to event repositories. Compile-time and runtime semantic patterns are distinguished and addressed separately for efficiency. Query rewriting is examined and analyzed in the context of temporal boundaries that exist between event streams and their repository to avoid duplicate or missing results. The design and prototype implementation of SCEPterare analyzed using latency and throughput metrics for scenarios from the Smart Grid domain.


title: Subspace video stabilization based on matrix transformation and Bezier curve

abstract: Video stabilization improves video quality by removing undesirable jitter to receive stable and comfortable video sequences. This paper proposes a new approach for subspace video stabilization. To make feature trajectories factorization more accurate, we segment feature trajectories into fragments to construct local trajectory matrices, then obtain smooth trajectories based on subspace constraint, matrix transformation and Bezier curve. Finally, according to the original feature points and the final corresponding stable feature points, we use mesh warp to receive high-quality and plausible videos. Experiments show that our method can generate comparable results with regard to some other state-of-the-art video stabilization methods, furthermore in some scenes our results are better than theirs.


title: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

abstract: Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called Co-teaching for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.


title: The Analysis and Optimization of KNN Algorithm Space-Time Efficiency for Chinese Text Categorization

abstract: The performance of any algorithm for text classification are reflected in the of reliability classification results and classification algorithm is high efficient. We analyze the space-time efficiency of different stages based on the traditional KNN algorithm process for Chinese text classification and ensure the reliability of classification. And we optimize efficiency of the algorithm and the feasibility in the practical application from these aspects including feature extraction, feature weighting, similarity computing etc.


title: Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference

abstract: We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward inference. IDP adds monotonically non-increasing coefficients, referred to as a "profile", to the channels during training. The profile orders the contribution of each channel in non-increasing order. At inference time, the number of channels used can be dynamically adjusted to trade off accuracy for lowered power consumption and reduced latency by selecting only a beginning subset of channels. This approach allows for a single network to dynamically scale over a computation range, as opposed to training and deploying multiple networks to support different levels of computation scaling. Additionally, we extend the notion to multiple profiles, each optimized for some specific range of computation scaling. We present experiments on the computation and accuracy trade-offs of IDP for popular image classification models and datasets. We demonstrate that, for MNIST and CIFAR-10, IDP reduces computation significantly, e.g., by 75%, without significantly compromising accuracy. We argue that IDP provides a convenient and effective means for devices to lower computation costs dynamically to reflect the current computation budget of the system. For example, VGG-16 with 50% IDP (using only the first 50% of channels) achieves 70% in accuracy on the CIFAR-10 dataset compared to the standard network which achieves only 35% accuracy when using the reduced channel set.


title: Integration of Standardized and Non-Standardized Product Data.

abstract: Smart products adapt to environments, process contexts, users and other products. Standardized product data such as BMECat, eCl@ss, and EPC global formats are designed to support the exploitation of product data and therefore contribute to more efficient supply chains. Non-standardized product data mainly target soft benefits which targets the exploration of product data [Su04]. We present an extended product data model (SPDO) that integrates standardized and non-standardized product data. The SPDO model consists of five product information types that receive product data and, if available, associated explicit semantics. Explicit semantics are used for advanced services, such as compatibility testing or product bundling. Exemplified by the GS1 approach, we present how standardized product data is retrieved via RFID-based Electronic Product Codes (EPC) and stored in SPDO instantiations.


title: Multibeam antennas for very high throughput satellites in Europe: Technologies and trends

abstract: This paper proposes an overview of recent European developments related to broadband satellite applications. Ka-band multi-beam antennas for user links and Q/V-band antennas for feeder links are crucial components to cope with stringent VHTS mission requirements. A brief presentation of technical trends since early 2000 is exposed with a specific focus on multi-beam antenna technology. Also, the main needs for upcoming VHTS systems and the key developments needed to increase significantly the overall system capacity of current satellites while maintaining total system cost competitive are presented.


title: A hybrid symbolic execution assisted fuzzing method

abstract: We present a new automated method for efficient detection of security vulnerabilities in binary programs. This method starts with a bounded symbolic execution of the target program so as to explore as many paths as possible. Constraints of the explored paths are collected and solved for inputs. The inputs will then be fed to the following interleaved coverage-based fuzzing and concolic execution. As the paths explored by the bounded symbolic execution may cover some unique paths that can be rarely reached by random testing featured fuzzing and locality featured concolic execution, the efficiency and effectiveness of the overall exploration can be greatly enhanced. In particular, the bounded symbolic execution can effectively prevent the fuzzing guided exploration from converging to the less interesting but easy-to-fuzz branches.


title: Developing an intelligent cloud for higher education

abstract: With rapid development in the IT world, technologies are becoming more dynamic and advanced. Today, technologies are changing with customer requirements. In the IT world, research is carried out to make technology better to meet the requirements that change with time. With the advancement in the IT world, online services have proliferated. Now a days, cloud computing is the hottest buzzword in the IT world. Cloud computing is not limited to the E-Governance and business worlds, but is also making a great impact in the education world. With growing demand for education, technologies and research, all universities and education institutions have their eyes on cloud computing. The main pillars of educational institutions are students, faculties, administrations and libraries. Faculty and students do research and need quality data while students of a particular field need a subject-oriented knowledge. Manually getting these kinds of data is time consuming as students depend on literature, books, different kind of software and hardware. With cloud computing in higher education, cost-effective measures can be taken to minimize the dependency on books, hardware and software. In this paper, we discuss how Artifical Intelligence based cloud computing in higher education will improve quality and ease the process of getting e-resources (software/hardware platform, storage etc.). This study will help in understanding effective cost-cutting measures. We also discuss how cloud computing in the library and administration will brighten the education prospects.


title: 12.1 A 7nm 256Mb SRAM in high-k metal-gate FinFET technology with write-assist circuitry for low-VMIN applications

abstract: The growing demand for battery powered mobile devices is a major driver for reducing power and continued area scaling in SOC chips. Continued scaling of the transistor and metal interconnection geometry is accompanied by increasing random Vt variation and increased wire routing resistance and capacitance variation in advanced technologies. Such variation degrades SRAM performance and its minimum operating voltage, which then seriously impact the battery life of mobile devices. FinFET technology provides a superior short-channel effect and less random dopant fluctuation. However, the quantized channel width and length force constrains on transistor sizing of high density SRAM bitcells. Figure 12.1.1(a) shows the layout of a high density 6T SRAM bit cell with a 0.027µm2 area in a leading edge 7nm FinFET technology. In order to achieve minimum area, all transistors (PU, PG, PD) in this bitcell have to be sized as single fin. Figure 12.1.1(b) shows a contention between the pull-up (PU) and the pass-gate (PG) transistors during a write operation. A stronger PU transistor results in better read stability, but the write margin is significantly degraded and results in elevation of minimum operation voltage for write operation. The negative bit-line (NBL) technique was proposed to improve write VMIN in previous work [1-6]. In addition to transistor scaling, the geometric scaling of metal and via routing increases the back-end wire RC load, which also significantly degrades SRAM operation speed. In this work, we use a flying BL (FBL) and double WL (DWL) design to mitigate the RC wire load impact in order to improve SRAM array access performance.


title: Extracting normative relationships from business contracts

abstract: The normative concepts offer a principled basis for engineering flexible multiagent systems for business and other cross-organizational settings. However, producing suitable specifications is nontrivial: the difficulty is an obstacle to the adoption of multiagent systems in industry. This paper considers normative relationships of six main types, namely, commitments (both practical and dialectical), authorizations, powers, prohibitions, and sanctions. It applies natural language processing and machine learning to extract these relationships from business contracts, establishing that they are realistic and their encoding can assist modelers, thereby lowering a barrier to adoption. A ten-fold cross-validation over more than 800 sentences randomly drawn from a corpus of real-life contracts (and manually labeled) yields promising results for the viability of this approach.


title: Consumer socialization of children: A retrospective look at twenty-five years of research

abstract: Twenty-five years of consumer socialization research have yielded an impressive set of findings. The purpose of our article is to review these findings and assess what we know about children's development as consumers. Our focus is on the developmental sequence characterizing the growth of consumer knowledge, skills, and values as children mature throughout childhood and adolescence. In doing so, we present a conceptual framework for understanding consumer socialization as a series of stages, with transitions between stages occurring as children grow older and mature in cognitive and social terms. We then review empirical findings illustrating these stages, including children's knowledge of products, brands, advertising, shopping, pricing, decision-making strategies, parental influence strategies, and consumption motives and values. Based on the evidence reviewed, implications are drawn for future theoretical and empirical development in the field of consumer socialization. Copyright 1999 by the University of Chicago.


title: Real-time fire and flame detection in video

abstract: The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.


title: Comprehending reality - practical barriers to industrial adoption of software maintenance automation

abstract: Recent years have seen many significant advances in program comprehension and software maintenance automation technology. In spite of the enormous potential savings in software maintenance costs, for the most part adoption of these ideas in industry remains at the experimental prototype stage. In this paper the author explores some of the practical reasons for industrial resistance to adoption of software maintenance automation. Based on the experience of six years of software maintenance automation services to the financial industry involving more than 4.5 Gloc of code at Legasys Corporation, the author discusses some of the social, technical and business realities that lie at the root of this resistance, outlines various Legasys attempts overcome these barriers, and suggests some approaches to software maintenance automation that may lead to higher levels of industrial acceptance in the future.


title: Dynamic effects of mismatched time constants in DC-DC converters with inductor DCR current sensing

abstract: This paper analyzes the effects of time constant mismatch on the dynamic behavior of current-controlled DC-DC converters that employ the lossless DCR inductor current sensing technique. Modeling of such mismatch is often oversimplified in the literature and treated as a simple modification of the small-signal modulator gain. The analysis proposed in this paper, on the contrary, demonstrates that time constant mismatch also affects the current loop gain in the low frequency region. Furthermore, the analysis indicates that the mismatch can even be intentionally exploited so as to achieve specific advantages in current-controlled DC-DC converters. Modified small-signal models for both peak and average current-mode control techniques are proposed and discussed, and a two-phase synchronous Buck converter for battery charger applications is considered to experimentally validate the theoretical analysis.


title: Multiphase System for Metal Disc Induction Heating: Modeling and RMS Current Control

abstract: This paper presents a multiphase induction system modeling for a metal disc heating and further industrial applications such as hot strip mill. An original architecture, with three concentric inductors supplied by three resonant current inverters, leads to a reduced element system, without any coupling transformers, phase loop, mobile screens, or mobile magnetic cores as it could be found in classical solutions. A simulation model is built, based on simplified equivalent models of electric and thermal phenomena. It takes into account the data extracted from Flux2D finite-element software, concerning the energy transfer between the inductor currents and the piece to be heated. It is implemented in a versatile software PSIM, initially dedicated to power electronics. An optimization procedure calculates the optimal supply currents in the inverters in order to obtain a desired power density profile in the work piece. This paper deals with the simulated and experimental results which are compared in open loop and closed loop. This paper ends with a current control method which sets rms inductor currents in continuous and digital conditions.


title: Developing Web-based assessment strategies for facilitating junior high school students to perform self-regulated learning in an e-Learning environment

abstract: This research refers to the self-regulated learning strategies proposed by Pintrich (1999) in developing a multiple-choice Web-based assessment system, the Peer-Driven Assessment Module of the Web-based Assessment and Test Analysis system (PDA-WATA). The major purpose of PDA-WATA is to facilitate learner use of self-regulatory learning behaviors to perform self-regulated learning and in turn improve e-Learning effectiveness. PDA-WATA includes five main strategies: 'Adding Answer Notes,' 'Stating Confidence,' 'Reading Peer Answer Notes,' 'Recommending Peer Answer Notes' and 'Querying Peers' Recommendation on Personal Answer Notes'. Using these strategies, examinees are allowed to add answer notes to explain why they chose a certain option as the correct answer and state their confidence in their own answer and answer notes, for peers' reference. In addition to reading peer answer notes, examinees can also recommend peer answer notes as valuable references. The recommendation information can also be queried by all examinees. Quasi-experimental design was adopted to understand the effectiveness of PDA-WATA in facilitating learner use of self-regulatory learning behaviors to perform self-regulated learning and in improving learner e-Learning effectiveness. Participants were 123 seventh-grade junior high school students from four classes. These four classes were randomly divided into the PDA-WATA group (n=63) and the N-WBT group (n=60). Before e-Learning instruction, all students took the pre-test of the Learning Process Inventory (LPI), used to understand how often learners use self-regulatory learning behaviors in the learning process, and the pre-test of the summative assessment. After a two-week e-Learning instruction, the students all took the post-test of the LPI and the summative assessment. Results indicate that students in the PDA-WATA group appear to be more willing to take the Web-based formative assessment than students in the N-WBT group. In addition, PDA-WATA appears to be significantly more effective than N-WBT in facilitating learner use of self-regulatory learning behaviors to perform self-regulated learning and in improving their e-Learning effectiveness. Moreover, this research also finds that in the PDA-WATA group, there is no significant difference between the learning effectiveness of students with a low level of self-regulated learning and students with a high level of self-regulated learning, but similar result cannot be found in the N-WBT group.


title: Interconnected dynamic systems: An overview on distributed control

abstract: Control problems such as multirobot control, distributed intelligence, swarm intelligence, distributed decision, distributed cognition, congestion control in networks, collective motion in biology, oscillator synchronization in physics, parallelization in optimization theory, distributed estimation, cooperative estimation, equilibria in economics, social interaction modeling, and game theory may be analyzed under the theory of interconnected dynamic systems. Those topics have several overlapping research communities; for that reason they are characterized by different definitions and a variety of approaches ranging from rigorous mathematical analysis to trial-and-error experimental study or emulation by observation of natural phenomena. The areas involved concern robotics, dynamic systems, computer science, signal theory, biology, economics, and mathematics. A shared taxonomy is missing; for example, dynamic systems can be identified in robots, agents, nodes, processors, and entities. An ensemble is called a group, network, platoon, swarm, team, and cluster, and the algorithms are defined as controllers, protocols, and dynamics. In the following, the term agent is used to denote the single dynamic system and network or collective the ensemble.


title: High performance GaN single-chip frontend for compact X-band AESA systems

abstract: A next generation of AESA antennas will be challenged with the need for lower size, weight, power and cost (SWAP-C). This leads to enhanced demands especially with regard to the integration density of the RF-part inside a T/R module. The semiconductor material GaN has proven its capacity for high power amplifiers, robust receive components as well as switch components for separation of transmit and receive mode. This paper will describe the design and measurement results of a GaN-based single-chip T/R module frontend (HPA, LNA and SPDT) using UMS GH25 technology and covering the frequency range from 8 GHz to 12 GHz. Key performance parameters of the frontend are 13 W minimum transmit (TX) output power over the whole frequency range with peak power up to 17 W. The frontend in receive (RX) mode has a noise figure below 3.2 dB over the whole frequency range, and can survive more than 5 W input power. The large signal insertion loss of the used SPDT is below 0.9 dB at 43 dBm input power level.


An Empirical Taxonomy of Crowdfunding Intermediaries Completed Research Paper


title: Using CommonKADS Method to Build Prototype System in Medical Insurance Fraud Detection

abstract: At present, the false claim cases related to insurance fraud emerge endlessly. In respect of frequent fraud cases of social insurance undertaken by government organizations, the inspection procedure usually relies on experts’ experience for verification and experienced personnel in charge of checking. However, due to heavy work load insufficient manpower and lack of experience, the ratio of miscarriages of justice is very high, which leads to improper settlement of claims and the waste of social resources. In this paper, we used rule technology to improve the above inefficiency. We employ a knowledge engineering methodology to analyze problems and construct knowledge modesl, including the domain schema and rules. We implement the knowledge model along with the existing database applications. The benefits generated by the research are: (1) establishing a knowledge system with expertise reasoning to solve the review problems of massive cases, (2) significantly reducing the large labor cost and consumed time of the existing reviewing system, and (3) improving the application level of traditional database in the expert reasoning system.


title: Mobile Banking Information Security and Protection Methods

abstract: With the help of modern information communication technology, mobile banking as a new type of financial services carrier can provide efficient and effective financial services for clients. Compare with Internet banking, mobile banking is more secure and user friendly. The implementation of wireless communication technologies may result in more complicated information security problems. Based on the principles of information security, this paper presented issues of information security of mobile banking and discussed the security protection measures such as: encryption technology, identity authentication, digital signature, WPKI technology.


title: Robust visual odometry to irregular illumination changes with RGB-D camera

abstract: Sensitivity to illumination conditions poses a challenge when utilizing visual odometry (VO) in various applications. To make VO robust with respect to illumination conditions, they need to be considered explicitly. In this paper, we propose a direct visual odometry method which can handle illumination changes by considering an affine illumination model to compensate abrupt, local light variations during direct motion estimation process. The core of our proposed method is to estimate the relative camera pose and the parameters of the illumination changes by minimizing the sum of squared photometric error with efficient second-order minimization. We evaluate the performance of the proposed algorithm on synthetic and real RGB-D datasets with ground-truth. Our result implies that the proposed method successfully estimates 6-DoF pose under significant illumination changes whereas existing direct visual odometry methods either fail or lose accuracy.


title: HypoEnergy. Hybrid supercapacitor-battery power-supply optimization for Energy efficiency

abstract: This paper presents HypoEnergy, a framework for extending the hybrid battery-supercapacitor power supply lifetime. HypoEnergy combines high energy density and reliable workload supportability of an electrochemical battery with high power density and high number of recharge cycles of supercapacitors. The lifetime optimizations consider nonlinear battery characteristics and supercapacitors' charging overhead. HypoEnergy-KI studies the hybrid supply lifetime optimization for a preemptively known workload and for one ideal superca-pacitor. We show a mapping of HypoEnergy-KI to the multiple-choice knapsack problem and use dynamic programming to address the problem. HypoEnergy-KN considers the optimization for the known workload but in the case of having a nonideal supercapacitor bank that leaks energy. Evaluations on iPhone load measurements demonstrate the efficiency and applicability of the HypoEnergy framework in extending the system's lifetime.


title: How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking

abstract: This paper proposes an approach to applying standardized entity data to improve job search quality and to make search results more personalized. Specifically, we explore three types of entity-aware features and incorporate them into the job search ranking function. The first is query-job matching features which extract and standardize entities mentioned in queries and documents, then semantically match them based on these entities. The second type, searcher-job expertise homophily, aims to capture the fact that job searchers tend to be interested in the jobs requiring similar expertise as theirs. To measure the similarity, we use standardized skills in job descriptions and searchers' profiles as well as skills that we infer searchers might have but not explicitly list in their profiles. Third, we propose a concept of entity-faceted historical click-through-rates (CTRs) to capture job document quality. Faceting jobs by their standardized companies, titles, locations, etc., and computing historical CTRs at the facet level instead of individual job level alleviate sparseness issue in historical action data. This is particularly important in job search where job lifetime is typically short. Both offline and online experiments confirm the effectiveness of the features. In offline experiment, using the entity-aware features gives improvements of +20%, +12.1% and +8.3% on Precision@1, MRR and NDCG@25, respectively. Online A/B test shows that a new model with these features is +11.3% and +5.3% better than the baseline in terms of click-through-rate and apply rate.


title: A study of hippocampal shape anomaly in schizophrenia and in families multiply affected by schizophrenia or bipolar disorder

abstract: Hippocampal shape anomaly (HSA), characterised by a rounded hippocampus, has been documented in congenital malformations and epileptic patients. Subtle structural hippocampal abnormalities have been demonstrated in patients with schizophrenia. We tested the hypothesis that HSA is more frequent in schizophrenia, particularly in patients from families multiply affected by schizophrenia, and that HSA is transmitted within these families. We also aimed to define the anatomical features of the hippocampus and other cerebral structures in the HSA spectrum and to determine the prevalence of HSA in a control group. We reviewed the magnetic resonance imaging of a large number of subjects with schizophrenia and bipolar disorder, many of who came from multiply affected families, relatives of the affected probands, and controls. Quantitative measures of hippocampal shape and position and other qualitative anatomical measures were performed (including depth of dominant sulcus cortical cap, angle of dominant sulcus and hippocampal fissure, bulk of collateral white matter, prominence of temporal horn lateral recess and blurring of internal hippocampal architecture) on subjects with HSA. A spectrum of mild, moderate and severe HSA was defined. The prevalence of HSA was, 7.8% for the controls (n=218), 9.3% for all schizophrenic subjects (n=151) and 12.3% for familial schizophrenic subjects (n=57). There was a greater prevalence of moderate or severe forms of HSA in familial schizophrenics than controls. However, there was no increase in the prevalence of HSA in the unaffected first-degree relatives of schizophrenic patients or in patients with familial bipolar disorder. HSA was rarely transmitted in families. HSA was frequently associated with a deep, vertical collateral/occipito-temporal sulcus and a steep hippocampal fissure. Our data raise the possibility that HSA is linked to disturbances of certain neurodevelopmental genes associated with schizophrenia. However, the lack of any increase in prevalence in the unaffected relatives of patients and the lack of clustering within individual pedigrees argues against this developmental anomaly being commonly associated with genetic predisposition to the illness.


title: Aiding Power System Support by Means of Voltage Control With Intelligent Distribution Substation

abstract: AC power systems have to continuously balance generation and consumption in order to maintain a stable system frequency. The mismatches between the electricity generated and consumed have to be compensated by means of ancillary services, usually provided by conventional power plants. However, the recent developments in power distribution and automation will allow some of those services to be provided by distribution networks. Therefore, in this paper voltage control, at distribution substations, is used in connection with demand response (DR) for aiding system support in the Netherlands. The available capacity, which could be accessible by applying active voltage control, is evaluated and quantified as a function of supplied load over time. The DR capacity is estimated and the implications on power system imbalances are demonstrated in a case study for the Netherlands. In addition, the impact on the network losses is addressed and evaluated.


Universal grapheme-based speech synthesis.


title: Bag of Attributes for Video Event Retrieval

abstract: In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can be understood as a high-level codebook. This model is used to map low-level frame vectors into high-level vectors (e.g., classifier probability scores). Then, we apply pooling operations on the frame vectors to create the final bag of attributes for the video. In the BoA representation, each dimension corresponds to one category (or attribute) of the semantic space. Other interesting properties are: compactness, flexibility regarding the classifier, and ability to encode multiple semantic concepts in a single video representation. Our experiments considered the semantic space created by a deep convolutional neural network (OverFeat) pre-trained on 1000 object categories of ImageNet. OverFeat was then used to classify each video frame and max pooling combined the frame vectors in the BoA representation for the video. Results using BoA outperformed the baselines with statistical significance in the task of video event retrieval using the EVVE dataset.


title: Exploring the effects of source credibility on information adoption on YouTube

abstract: This research-in-progress paper explores the effects of information source credibility (brands versus vloggers), information type (how-to tutorial versus product demonstration), and viewer characteristics on perceptions of information quality, information usefulness, information satisfaction, and information adoption in the context of YouTube videos regarding a technology product, the Apple Watch. The primary goal of this study is to understand how users process information provided through YouTube videos by brands and vloggers, as well as extend existing models of information adoption that solely focus on information and source characteristics without considering characteristics of the user or viewer. Envisioned future steps in this project are discussed as well as implications for research and practice. Data collection will be completed prior to the HCII conference, where results will be presented.


title: Seven-Level PWM Inverter Employing Series-Connected Capacitors Paralleled to a Single DC Voltage Source

abstract: This paper presents an effective circuit configuration of a multilevel inverter that can increase the number of output voltage levels with a reduced number of circuit components. The proposed seven-level pulsewidth-modulation inverter consists of a single dc voltage source with a series of capacitors, diodes, active switches for synthesizing output voltage levels, and an H-bridge cell. After theoretical analysis, we carry out computer-aided simulations and experiments to verify the validity of the proposed approach. Here, we also introduce a modified switching strategy to solve the capacitor voltage unbalancing that occurs in series-connected capacitors.


title: Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video

abstract: We present a generic and real-time time-varying point cloud codec for 3D immersive video. This codec is suitable for mixed reality applications in which 3D point clouds are acquired at a fast rate. In this codec, intra frames are coded progressively in an octree subdivision. To further exploit inter-frame dependencies, we present an inter-prediction algorithm that partitions the octree voxel space in $N \times N \times N$ macroblocks ( $N=8,16,32$ ). The algorithm codes points in these blocks in the predictive frame as a rigid transform applied to the points in the intra-coded frame. The rigid transform is computed using the iterative closest point algorithm and compactly represented in a quaternion quantization scheme. To encode the color attributes, we defined a mapping of color per vertex attributes in the traversed octree to an image grid and use legacy image coding method based on JPEG. As a result, a generic compression framework suitable for real-time 3D tele-immersion is developed. This framework has been optimized to run in real time on commodity hardware for both the encoder and decoder. Objective evaluation shows that a higher rate-distortion performance is achieved compared with available point cloud codecs. A subjective study in a state-of-the-art mixed reality system shows that introduced prediction distortions are negligible compared with the original reconstructed point clouds. In addition, it shows the benefit of reconstructed point cloud video as a representation in the 3D virtual world. The codec is available as open source for integration in immersive and augmented communication applications and serves as a base reference software platform in JTC1/SC29/WG11 (MPEG) for the further development of standardized point-cloud compression solutions.


title: The Bluetooth radio system

abstract: A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.


title: Model Driven Game Development: 2D Platform Game Prototyping.

abstract: A class-D amplifier circuit ( 30; 30 ') providing improved open-loop error for base-band frequencies, such as the audio band, is disclosed. The amplifier circuit ( 30; 30 ') includes a comparator ( 35 ) for generating a pulse-width-modulated output signal that is applied to an output power stage ( 37 ). An LC filter ( 32 ) is at the output of the power stage ( 37 ). The amplifier circuit ( 30; 30 ') includes a loop filter having multiple feedback loop paths, with at least one feedback loop path coupled to the output of the power stage ( 37 ), and optionally, at least one feedback loop path coupled to the output of the LC filter ( 32 ). The transfer function (Hmae(s)) of the loop filter has a real part that has a much steeper slope (on the order of 80 dB/decade) at frequencies above the pulse-width-modulation switching frequency than the slope of its magnitude characteristic at frequencies below this switching frequency.


Principles of Object Perception


title: Ontology based semantic-predictive model for reconfigurable automation systems

abstract: Due to increasing product variety and complexity, capability to support reconfiguration is a key competitiveness indicator for current automation system within large enterprises. Reconfigurable manufacturing systems could efficiently reuse existing knowledge in order to decrease the required skills and design time to launch new products. However, most of the software tools developed to support design of reconfigurable manufacturing system lack integration of product, process and resource knowledge, and the design data is not transferred from domain-specific engineering tools to a collaborative and intelligent platform to capture and reuse design knowledge. The focus of this research study is to enable integrated automation systems design to support a knowledge reuse approach to predict process and resource changes when product requirements change. The proposed methodology is based on a robust semantic-predictive model supported by ontology representations and predictive algorithms for the integration of Product, Process, Resource and Requirement (PPRR) data, so that future automation system changes can be identified at early design stages.


title: The Influence of Partisan Motivated Reasoning on Public Opinion

abstract: Political parties play a vital role in democracies by linking citizens to their representatives. Nonetheless, a longstanding concern is that partisan identification slants decision-making. Citizens may support (oppose) policies that they would otherwise oppose (support) in the absence of an endorsement from a political party—this is due in large part to what is called partisan motivated reasoning where individuals interpret information through the lens of their party commitment. We explore partisan motivated reasoning in a survey experiment focusing on support for an energy law. We identify two politically relevant factors that condition partisan motivated reasoning: (1) an explicit inducement to form an “accurate” opinion, and (2) cross-partisan, but not consensus, bipartisan support for the law. We further provide evidence of how partisan motivated reasoning works psychologically and affects opinion strength. We conclude by discussing the implications of our results for understanding opinion formation and the overall quality of citizens’ opinions.