title: Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

abstract: Abstract Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because it requires exploration of the problem’s dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.


title: Wireless power transfer via strongly coupled magnetic reso- nances

abstract: Using self-resonant coils in a strongly coupled regime, we experimentally demonstrated efficient nonradiative power transfer over distances up to 8 times the radius of the coils. We were able to transfer 60 watts with ∼40% efficiency over distances in excess of 2 meters. We present a quantitative model describing the power transfer, which matches the experimental results to within 5%. We discuss the practical applicability of this system and suggest directions for further study.


title: VII. THE HISTORY OF PHYSICAL ACTIVITY AND ACADEMIC PERFORMANCE RESEARCH: INFORMING THE FUTURE

abstract: The study of physical activity, physical fitness, and academic performance research are reviewed from a historical perspective, by providing an overview of existing publications focused on children and adolescents. Using rigorous inclusion criteria, the studies were quantified and qualified using both meta-analytic and descriptive evaluations analyses, first by time-period and then as an overall summary, particularly focusing on secular trends and future directions. This review is timely because the body of literature is growing exponentially, resulting in the emergence of new terminology, methodologies, and identification of mediating and moderating factors. Implications and recommendations for future research are summarized.


A focused backpropagation algorithm for temporal pattern recognition


Neural Utterance Ranking Model for Conversational Dialogue Systems


title: Deep Learning Solutions to Computational Phenotyping in Health Care

abstract: Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent people from applying standard deep learning models directly. In this paper, we discussed three key challenges in this field: how to deal with missing data, how to build scalable models, and how to get interpretations of features and models. We proposed novel and effective deep learning solutions to each of them respectively. All proposed solutions are evaluated on several real-world health care datasets and experimental results demonstrated their superiority over existing baselines.


title: Global Hypothesis Generation for 6D Object Pose Estimation

abstract: This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features, ii) generate a pool of pose-hypotheses, iii) select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-Voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new, efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging Occluded Object Dataset.


title: Operating characteristics of a rank correlation test for publication bias.

abstract: An adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations. The test statistic is a direct statistical analogue of the popular "funnel-graph." The number of component studies in the meta-analysis, the nature of the selection mechanism, the range of variances of the effect size estimates, and the true underlying effect size are all observed to be influential in determining the power of the test. The test is fairly powerful for large meta-analyses with 75 component studies, but has only moderate power for meta-analyses with 25 component studies. However, in many of the configurations in which there is low power, there is also relatively little bias in the summary effect size estimate. Nonetheless, the test must be interpreted with caution in small meta-analyses. In particular, bias cannot be ruled out if the test is not significant. The proposed technique has potential utility as an exploratory tool for meta-analysts, as a formal procedure to complement the funnel-graph.


title: Probably approximately correct learning in stochastic games with temporal logic specifications

abstract: We consider a controller synthesis problem in turn-based stochastic games with both a qualitative linear temporal logic (LTL) constraint and a quantitative discounted-sum objective. For each case in which the LTL specification is realizable and can be equivalently transformed into a deterministic Buchi automaton, we show that there always exists a memoryless almost-sure winning strategy that is "e-optimal with respect to the discounted-sum objective for any arbitrary positive ". Building on the idea of the R-MAX algorithm, we propose a probably approximately correct (PAC) learning algorithm that can learn such a strategy efficiently in an online manner with a-priori unknown reward functions and unknown transition distributions. To the best of our knowledge, this is the first result on PAC learning in stochastic games with independent quantitative and qualitative objectives.


title: ZeroVM: secure distributed processing for big data analytics

abstract: A key challenge for any large-scale computation today, whether in “big data” or in handling large-scale web services, has to do with the management of data. In the big data context, the arbitrary separation of storage and computation increases latency and decreases performance. ZeroVM is a lightweight container-based virtualization platform that provides deterministic process execution and isolation. The philosophy behind ZeroVM is to virtualize applications then move the application to the data. This provides the ability to transform or process data in situ, rather than moving data to where the application is located. With the ability to move and execute application next to data, ZeroVM changes the conventional wisdom on infrastructure centric commuting models and enables even more data centric computing models to be used for Big-Data Analytics. The ZeroVM distributed processing framework proposed in this paper presents new opportunities for processing, storing and using data, particularly in big data analytics.


title: Performance evaluation of electric power steering with IPM motor and drive system

abstract: As the development of microprocessors, power electronic converters and electric motor drives, electric power steering (EPS) system which uses an electric motor came to use a few year ago. Electric power steering systems have many advantages over traditional hydraulic power steering systems in engine efficiency, space efficiency, and environmental compatibility. This paper deals with design and optimization of an interior permanent magnet (IPM) motor for power steering application. Simulated Annealing method is used for optimization. After optimization and finding motor parameters, An IPM motor and drive with mechanical parts of EPS system is simulated and performance evaluation of system is done.


title: Development of Novel Fire Alarm Warning System Using Automated Remote Messaging Method

abstract: This paper discussed on design and development of fire warning system using automated remote messaging method. This device enables to alert the owner whenever fire occur that need rapid attention towards the building. This is maybe due to carelessness of user or gas leakage. Fire warning system is an existing project but it will be enhanced. This project discussed the design and implementation of a fire alarm system using the microcontroller which controlled the entire system. This system comprised of smoke detector that linked to PIC and GSM Modem. When smoke detected, the fire alarm will triggered and send a signal to the PIC. The PIC will process the data and transmit the signal to the GSM modem. The GSM modem will send message to alert the building owner. The owner can make further action by informing the nearest fire department. This module is applied for transferring of GSM SMS message to the owner mobile number. The devices can be the early and fast prevention system for building owner.


title: Language Control and Lexical Competition in Bilinguals: An Event-Related fMRI Study

abstract: Language selection (or control) refers to the cognitive mechanism that controls which language to use at a given moment and context. It allows bilinguals to selectively communicate in one target language while minimizing the interferences from the nontarget language. Previous studies have suggested the participation in language control of different brain areas. However, the question remains whether the selection of one language among others relies on a language-specific neural module or general executive regions that also allow switching between different competing behavioral responses including the switching between various linguistic registers. In this functional magnetic resonance imaging study, we investigated the neural correlates of language selection processes in German-French bilingual subjects during picture naming in different monolingual and bilingual selection contexts. We show that naming in the first language in the bilingual context (compared with monolingual contexts) increased activation in the left caudate and anterior cingulate cortex. Furthermore, the activation of these areas is even more extended when the subjects are using a second weaker language. These findings show that language control processes engaged in contexts during which both languages must remain active recruit the left caudate and the anterior cingulate cortex (ACC) in a manner that can be distinguished from areas engaged in intralanguage task switching.


title: Developing Bug-Free Machine Learning Systems With Formal Mathematics

abstract: Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.


RF-Based Relative Localization for Robot Swarms


title: A Data Mining-Based Solution for Detecting Suspicious Money Laundering Cases in an Investment Bank

abstract: Today, money laundering poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché, of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting money laundering activities. Within the scope of a collaboration project for the purpose of developing a new solution for the anti-money laundering Units in an international investment bank, we proposed a simple and efficient data mining-based solution for anti-money laundering. In this paper, we present this solution developed as a tool and show some preliminary experiment results with real transaction datasets.


title: Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

abstract: Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS’s energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.


title: Dual-regularized one-class collaborative filtering with implicit feedback

abstract: Collaborative filtering plays a central role in many recommender systems. While most of the existing collaborative filtering methods are proposed for the explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). In this article, we propose dual-regularized one-class collaborative filtering models for implicit feedback. In particular, by dividing existing methods into point-wise class and pair-wise class, we first propose a point-wise model by integrating two existing methods and further exploiting the side information from both users and items. Next, we propose to add dual regularization into an existing pair-wise method with a different treatment of the side information. We also propose efficient algorithms to solve the proposed models. Extensive experimental evaluations on three real data sets demonstrate the effectiveness and efficiency of the proposed methods.


title: New Constructions and Proof Methods for Large Universe Attribute-Based Encryption

abstract: We propose two large universe Attribute-Based Encryption constructions. In a large universe ABE construction any string can be used as an attribute and attributes need not be enumerated at system setup. Our first construction establishes a novel large universe Ciphertext-Policy ABE scheme on prime order bilinear groups, while the second achieves a significant efficiency improvement over the large universe Key-Policy ABE systems of Lewko-Waters and Lewko. Both schemes are selectively secure in the standard model under two “q-type” assumptions similar to ones used in prior works. Our work brings back “program and cancel” techniques to this problem. We provide implementations and benchmarks of our constructions in Charm; a programming environment for rapid prototyping of cryptographic primitives.


title: 3D printed FSS arrays for long wavelength applications

abstract: The transmission properties of arrays of triple cross and caltrop FSS elements whose cores have been fabricated using two different three dimensional (3D) printing procedures is assessed. Having four symmetrically located arms, both elements are good candidates for testing the performance of 3D printed frequency selective structures. They allow for placement on different types of lattices including crystal-like configurations. The caltrop, when on a compact triangular lattice, offers good angular stability. The structures in this paper are designed to operate at the long wavelengths employed in wireless communications and are intended to control electromagnetic wave propagation in buildings. The basic performance of the arrays are summarized through simulations and experimental results.


title: A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment

abstract: Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve believable speaker mimicry without introducing processing artifacts; performance assessment of VC, therefore, usually involves both speaker similarity and quality evaluation by a human panel. As a time-consuming, expensive, and non-reproducible process, it hinders rapid prototyping of new VC technology. We address artifact assessment using an alternative, objective approach leveraging from prior work on spoofing countermeasures (CMs) for automatic speaker verification. Therein, CMs are used for rejecting `fake' inputs such as replayed, synthetic or converted speech but their potential for automatic speech artifact assessment remains unknown. This study serves to fill that gap. As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts. Equal error rate (EER) of the CM, a confusability index of VC samples with real human speech, serves as our artifact measure. Two clusters of VCC'18 entries are identified: low-quality ones with detectable artifacts (low EERs), and higher quality ones with less artifacts. None of the VCC'18 systems, however, is perfect: all EERs are < 30 % (the `ideal' value would be 50 %). Our preliminary findings suggest potential of CMs outside of their original application, as a supplemental optimization and benchmarking tool to enhance VC technology.


title: Seismic Response of High Voltage Electrical Transformer-Bushing Systems

abstract: High voltage porcelain bushings mounted on transformers have shown vulnerability during past earthquakes. This is contrary to the generally good performance observed during shake table qualification testing of bushings anchored to a rigid base. The seismic vulnerability of porcelain bushings might be caused by the flexibility of transformer tanks and of bushing attachments. Current seismic qualification guidelines consider an amplification factor of 2.0 between the amplitude of the ground motion and the amplitude of the motion at the base of the bushing. This paper investigates numerically the dynamic response of porcelain bushings mounted on transformer tanks. The results of the numerical study show that large amplification occurs when the fundamental frequency of the porcelain bushing is tuned with the fundamental frequency of the transformer tank.


title: Deep Structure for end-to-end inverse rendering

abstract: Inverse rendering in a 3D format denoted to recovering the 3D properties of a scene given 2D input image(s) and is typically done using 3D Morphable Model (3DMM) based methods from single view images. These models formulate each face as a weighted combination of some basis vectors extracted from the training data. In this paper a deep framework is proposed in which the coefficients and basis vectors are computed by training an autoencoder network and a Convolutional Neural Network (CNN) simultaneously. The idea is to find a common cause which can be mapped to both the 3D structure and corresponding 2D image using deep networks. The empirical results verify the power of deep framework in finding accurate 3D shapes of human faces from their corresponding 2D images on synthetic datasets of human faces.


title: FFT Implementation for Electronic Holograms using Field Programmable Gate Array

abstract: The majority of holograms are made using interference of light and computer-generated holograms. In this work we propose a real time FFT implementation to generate Fourier holograms with a VLSI digital component. Nowadays digital designers are increasingly using field programmable gate arrays (FPGAs), powerful hardware devices combining the main advantages of ASICs and DSPs with the possibility of re-programming, which make them very attractive devices for rapid prototyping. This paper also shows how the Xilinx system generator (XSG) environment can be used to develop hardware-based computer FFT algorithm from a system level approach, which makes it suitable for developing co-design environments


title: JiST/MobNet: combined simulation, emulation, and real-world testbed for ad hoc networks

abstract: Mobile ad hoc networks are a promising technology enabling the spontaneous formation of communication networks without dedicated infrastructure. However, ad hoc networks are not yet ready for large-scale deployment, because several unsolved research challenges persist. Evaluation methods such as analytical modeling, simulation, emulation, and real-world experiments aid in addressing these challenges. Applying a single method alone often leaves doubt as to the accuracy of the obtained results. Thus, there is a strong need for tools that support the task of modeling, evaluation, and analysis. These tools should allow for protocol validation, performance analysis, and proof-of-concept implementation using multiple evaluation methods. In this paper, we present a new approach for conducting simulation, emulation, and real-world experiments in mobile ad hoc networks using a single tool that allows for vertical validation of experiments. We explain the principles behind the architecture of our tool and systematically assess its limitations.


title: Arabic speech recognition using Hidden Markov Model Toolkit(HTK)

abstract: In this paper we discuss the development and implementation of an Arabic automatic speech recognition engine. The engine can recognize both continuous speech and isolated words. The system was developed using the Hidden Markov Model Toolkit. First, an Arabic dictionary was built by composing the words to its phones. Next, Mel Frequency Cepstral Coefficients (MFCC) of the speech samples are derived to extract the speech feature vectors. Then, the training of the engine based on triphones is developed to estimate the parameters for a Hidden Markov Model. To test the engine, the database consisting of speech utterance from thirteen Arabian native speakers is used which is divided into ten speaker-dependent and three speaker-independent samples. The experimental results showed that the overall system performance was 90.62%, 98.01 % and 97.99% for sentence correction, word correction and word accuracy respectively.


title: Modeling Review Spam Using Temporal Patterns and Co-bursting Behaviors

abstract: Online reviews play a crucial role in helping consumers evaluate and compare products and services. However, review hosting sites are often targeted by opinion spamming. In recent years, many such sites have put a great deal of effort in building effective review filtering systems to detect fake reviews and to block malicious accounts. Thus, fraudsters or spammers now turn to compromise, purchase or even raise reputable accounts to write fake reviews. Based on the analysis of a real-life dataset from a review hosting site (this http URL), we discovered that reviewers' posting rates are bimodal and the transitions between different states can be utilized to differentiate spammers from genuine reviewers. Inspired by these findings, we propose a two-mode Labeled Hidden Markov Model to detect spammers. Experimental results show that our model significantly outperforms supervised learning using linguistic and behavioral features in identifying spammers. Furthermore, we found that when a product has a burst of reviews, many spammers are likely to be actively involved in writing reviews to the product as well as to many other products. We then propose a novel co-bursting network for detecting spammer groups. The co-bursting network enables us to produce more accurate spammer groups than the current state-of-the-art reviewer-product (co-reviewing) network.


title: LVRT Capability of DFIG-Based WECS Under Asymmetrical Grid Fault Condition

abstract: In this paper, the low-voltage ride-through (LVRT) capability of the doubly fed induction generator (DFIG)-based wind energy conversion system in the asymmetrical grid fault situation is analyzed, and the control scheme for the system is proposed to follow the requirements defined by the grid codes. As analyzed in the paper, the control efforts of the negative-sequence current are much higher than that of the positive-sequence current for the DFIG. As a result, the control capability of the DFIG restrained by the dc-link voltage will degenerate for the fault type with higher negative-sequence voltage component and 2φ fault turns out to be the most serious scenario for the LVRT problem. When the fault location is close to the grid connection point, the DFIG may be out of control resulting in non-ride-through zones. In the worst circumstance when LVRT can succeed, the maximal positive-sequence reactive current supplied by the DFIG is around 0.4 pu, which coordinates with the present grid code. Increasing the power rating of the rotor-side converter can improve the LVRT capability of the DFIG but induce additional costs. Based on the analysis, an LVRT scheme for the DFIG is also proposed by taking account of the code requirements and the control capability of the converters. As verified by the simulation and experimental results, the scheme can promise the DFIG to supply the defined positive-sequence reactive current to support the power grid and mitigate the oscillations in the generator torque and dc-link voltage, which improves the reliability of the wind farm and the power system.


A Use Case Identification Framework and Use Case Canvas for identifying and exploring relevant Blockchain opportunities


title: Automatic generation of release notes

abstract: This paper introduces ARENA (Automatic RElease Notes generAtor), an approach for the automatic generation of release notes. ARENA extracts changes from the source code, summarizes them, and integrates them with information from versioning systems and issue trackers. It was designed based on the manual analysis of 1,000 existing release notes. To evaluate the quality of the ARENA release notes, we performed three empirical studies involving a total of 53 participants (45 professional developers and 8 students). The results indicate that the ARENA release notes are very good approximations of those produced by developers and often include important information that is missing in the manually produced release notes.


title: A texture-based method for modeling the background and detecting moving objects

abstract: This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model.


title: Transformer inrush transients using Jiles-Atherton model in PSCAD/EMTDC

abstract: To investigate the effect of transformer energisation inrush, accurate modeling of the transformer is vital where a mathematical model must be established for numerical calculations. This paper presents a detailed transformer modelling for the slow transients study. The PSCAD/EMTDC classical transformer combined with the Jiles-Atherton nonlinear hysteresis core model is used for modelling. The developed model enables determination of the B-H curve including consideration of the saturation effects in the magnetic material. For validation, laboratory work has been carried out. A 16kVA, 250V/11kV single phase distribution transformer is used in simulation and experimentation to obtain its magnetising inrush response. Both PSCAD simulation and experimental measurements show good agreement and thus demonstrates the capability of the model to accurately represent the energisation transient. The model shows it can generate the hysteresis loop representative of the magnetic core material. For further validation, MATLAB simulation is also developed for result comparison.


From object-oriented to goal-oriented requirements analysis


title: Plasmodium parasite detection on Red Blood Cell image for the diagnosis of malaria using double thresholding

abstract: Malaria which is caused by Plasmodium parasite is one of the diseases that can cause death in patients. Detection of plasmodium parasites on the Red Blood Cell (RBC) image can help diagnose malaria quickly and accurately, especially in the areas that lacked medical expertise. This research proposes a detection method of plasmodium parasite at RBC using double thresholding for improving accuracy of detection. Our method is able to achieved Predictive Positive Value (PPV) and Sensitivity about 92.85% and 85.52% respectively.


title: Stock return predictability: Is it there?

abstract: We ask whether stock returns in France, Germany, Japan, the UK and the US are predictable by three instruments: the dividend yield, the earnings yield and the short rate. The predictability regression is suggested by a present value model with earnings growth, payout ratios and the short rate as state variables. We use this model imposing a constant risk premium to examine the finite sample evidence on predictability. Not only do we find the short rate to be a relevant state variable theoretically, it is also the only robust short-run predictor of equity returns. The evidence in Lamont (1998) on earnings and dividend yield predictability is not robust to our increased sample period, does not survive finite sample corrections and does not extend to other countries. We find no evidence of long-horizon predictability once we account for finite sample influence. Finally, cross-country predictability appears stronger than predictability using local instruments.


Concepts and Meaning


title: A Massively Parallel Coprocessor for Convolutional Neural Networks

abstract: We present a massively parallel coprocessor for accelerating Convolutional Neural Networks (CNNs), a class of important machine learning algorithms. The coprocessor functional units, consisting of parallel 2D convolution primitives and programmable units performing sub-sampling and non-linear functions specific to CNNs, implement a “meta-operator” to which a CNN may be compiled to. The coprocessor is serviced by distributed off-chip memory banks with large data bandwidth. As a key feature, we use low precision data and further increase the effective memory bandwidth by packing multiple words in every memory operation, and leverage the algorithm’s simple data access patterns to use off-chip memory as a scratchpad for intermediate data, critical for CNNs. A CNN is mapped to the coprocessor hardware primitives with instructions to transfer data between the memory and coprocessor. We have implemented a prototype of the CNN coprocessor on an off-the-shelf PCI FPGA card with a single Xilinx Virtex5 LX330T FPGA and 4 DDR2 memory banks totaling 1GB. The coprocessor prototype can process at the rate of 3.4 billion multiply accumulates per second (GMACs) for CNN forward propagation, a speed that is 31x faster than a software implementation on a 2.2 GHz AMD Opteron processor. For a complete face recognition application with the CNN on the coprocessor and the rest of the image processing tasks on the host, the prototype is 6-10x faster, depending on the host-coprocessor bandwidth.


title: A High-Power Broadband Passive Terahertz Frequency Doubler in CMOS

abstract: To realize a high-efficiency terahertz signal source, a varactor-based frequency-doubler topology is proposed. The structure is based on a compact partially coupled ring that simultaneously achieves isolation, matching, and harmonic filtering for both input and output signals at f0 and 2f0. The optimum varactor pumping/loading conditions for the highest conversion efficiency are also presented analytically along with intuitive circuit representations. Using the proposed circuit, a passive 480-GHz frequency doubler with a measured minimum conversion loss of 14.3 dB and an unsaturated output power of 0.23 mW is reported. Within 20-GHz range, the fluctuation of the measured output power is less than 1.5 dB, and the simulated 3-dB output bandwidth is 70 GHz (14.6%). The doubler is fabricated using 65-nm low-power bulk CMOS technology and consumes near zero dc power.


An evaluation of the design and implementation of an outcomes-based education business studies bridging programme


title: Text extraction from natural scene image: A survey

abstract: With the increasing popularity of portable camera devices and embedded visual processing, text extraction from natural scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text extraction from natural scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images.


A Neuroevolution Approach to Imitating Human-Like Play in Ms. Pac-Man Video Game


A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search.


title: DifNet: Semantic Segmentation by Diffusion Networks

abstract: Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. OurDifNet is trained end-to-end without complex loss functions.


title: A Noisy-Or Discriminative Restricted Boltzmann Machine for Recognizing Handwriting Style Development

abstract: Restricted Boltzmann machines (RBMs) and their variants have attracted a lot of attention recently. They have been applied widely, e.g., In handwriting recognition, document categorization and object recognition. Unfortunately, an RBM requires a large parameter space since it is a fully-connected bipartite graph, especially with high dimensional input spaces. Moreover, it is still unclear how it selects discriminative features for classification problems. This necessitates the selection of effective and discriminative features for recognition. In this paper, we propose a Noisy-Or discriminative restricted Boltzmann machine (Abbr. As NDRBM or Noisy-Or RBM), which combines RBM and Noisy-Or gate function. On the one hand, it can greatly reduce the parameter space. Furthermore, this model extends the RBM into a multiple instance learning scenario-to help select discriminative features or regions. An approximate approach is proposed to make the NDRBM practical. We apply our method on handwriting style development recognition and recognition rates are observed to be better than competitive baselines on two data sets (cursive and handprint data from Grades 2-4).


title: Supervised Adaptive-Transfer PLSA for Cross-Domain Text Classification

abstract: Cross-domain learning is a very promising technique to improve classification in the target (testing) domain whose data distributions are very different from the source (training) domain. Many cross-domain text classification methods are built on topic modeling approaches. However, topic model methods are unsupervised in nature without fully utilizing the label information of the source domain. In addition, almost all cross-domain learning approaches utilize the knowledge of source domain in the later stage of the training process, and this limits the knowledge transfer. In this paper, we propose a model named Supervised Adaptive transfer Probabilistic Latent Semantic Analysis (SAtPLSA) for cross-domain text classification aiming to deal with the above two issues. The proposed model extends the original PLSA to a supervised learning paradigm. By defining the common labeled information from each term across domains, we transfer knowledge in source domain to assist classifying text in target domain. In addition, we adaptively modify the weight value controlling the proportion of the usage of knowledge from source domain in the model learning process. At last, we conducted experiments on nine benchmark datasets in cross domain text classification to compare the performance of our proposed algorithm with two classical supervised learning methods and five state-of-art transfer learning approaches. The experimental results have shown the effectiveness and efficiency of our proposed SAtPLSA algorithm.


title: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

abstract: Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.


title: NLCP: Towards a Compiler for Natural Language

abstract: NLCP - The Natural Language Compiler - is a new programming environment, which features a strong type system and support for a natural language interface. Additionally, it provides an interactive interpreter that can assist in the process of designing an algorithm. This is achieved by allowing the user to embed information about the objectives of algorithms within the type system. We provide an overview of the main design considerations behind NLCP and list several simple examples of algorithms translated from natural language statements to code.


title: Conversion from Paninian Karakas to Universal Dependencies for Hindi Dependency Treebank

abstract: Universal Dependencies (UD) are gaining much attention of late for systematic evaluation of cross-lingual techniques for crosslingual dependency parsing. In this paper we present our work in line with UD. Our contribution to this is manifold. We extend UD to Indian languages through conversion of Pānịnian Dependencies to UD for the Hindi Dependency Treebank (HDTB). We discuss the differences in annotation in both the schemes, present parsing experiments for both the formalisms and empirically evaluate their weaknesses and strengths for Hindi. We produce an automatically converted Hindi Treebank conforming to the international standard UD scheme, making it useful as a resource for multilingual language technology.


Fast and Robust Fixed-Point Algorithms for Independent Component Analysis


title: The Process Matters: Ensuring Data Veracity in Cyber-Physical Systems

abstract: Cyber-physical systems are characterized by an IT infrastructure controlling effects in the physical world. Attacks are intentional actions trying to cause undesired physical effects. When process data originating in the physical world is manipulated before being handed to the IT infrastructure, the data security property called "veracity" or trustworthiness will be violated. There is no canonical IT security solution guaranteeing that the inputs from a sensor faithfully represent reality. However, the laws of physics may help the defender to detect impossible or implausible sensor readings. This paper proposes a process-aware approach to detect when a sensor signal is being maliciously manipulated. We present a set of lightweight real-time algorithms for spoofing sensor signals directly at the microcontroller of the field device. The detection of spoofed measurements takes the form of plausibility and consistency checks with the help of the correlation entropy in a cluster of related sensors. We use the Tennessee Eastman challenge process to demonstrate the performance of our approach and to highlight aspects relevant to the detection effectiveness.


title: Informing the Design of Proxemic Interactions

abstract: Proxemic interactions can help address six key challenges of ubicomp interaction design and how devices can sense or capture proxemic information via five dimensions-distance, orientation, movement, identity, and location.


title: Securing Encryption Algorithms against DPA at the Logic Level: Next Generation Smart Card Technology

abstract: This paper describes a design method to secure encryption algorithms against Differential Power Analysis at the logic level. The method employs logic gates with a power consumption, which is independent of the data signals, and therefore the technique removes the foundation for DPA. In a design ex- periment, a fundamental component of the DES algorithm has been imple- mented. Detailed transistor level simulations show a perfect security whenever the layout parasitics are not taken into account.


title: Complex-order dynamics in hexapod locomotion

abstract: This paper studies the dynamics of foot-ground interaction in hexapod locomotion systems. For that objective the robot motion is characterized in terms of several locomotion variables and the ground is modelled through a non-linear spring-dashpot system, with parameters based on the studies of soil mechanics. Moreover, it is adopted an algorithm with foot-force feedback to control the robot locomotion. A set of model-based experiments reveals the influence of the locomotion velocity on the foot-ground transfer function, which presents complex-order dynamics.


title: Gaussian Lower Bound for the Information Bottleneck Limit

abstract: The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It generalizes the notion of minimal sufficient statistics from classical parametric statistics to a broader information-theoretic sense. The IB curve defines the optimal trade-off between representation complexity and its predictive power. Specifically, it is achieved by minimizing the level of mutual information (MI) between the representation and the original variables, subject to a minimal level of MI between the representation and the target. This problem is shown to be in general NP hard. One important exception is the multivariate Gaussian case, for which the Gaussian IB (GIB) is known to obtain an analytical closed form solution, similar to Canonical Correlation Analysis (CCA). In this work we introduce a Gaussian lower bound to the IB curve; we find an embedding of the data which maximizes its "Gaussian part", on which we apply the GIB. This embedding provides an efficient (and practical) representation of any arbitrary data-set (in the IB sense), which in addition holds the favorable properties of a Gaussian distribution. Importantly, we show that the optimal Gaussian embedding is bounded from above by non-linear CCA. This allows a fundamental limit for our ability to Gaussianize arbitrary data-sets and solve complex problems by linear methods.


title: The AD8: A brief informant interview to detect dementia

abstract: Background:Brief measures that accurately discriminate normal cognitive aging from very mild dementia are lacking. Cognitive tests often are insensitive to very mild dementia. Informant-based measures may be more sensitive in detecting early dementia. Objective:To identify informant-reported clinical variables that differentiate cognitively normal individuals from those with very mild dementia. Methods:A 55-item battery of informant queries regarding an individual's cognitive status was derived from a semistructured interview and a consensus panel of dementia experts. The battery was evaluated with informants for 189 consecutive participants of a longitudinal study of memory and aging and compared with an independently obtained Clinical Dementia Rating (CDR) score for the participant. Multiple regression and receiver operator characteristic curves assessed subsets of the items to discriminate between CDR 0 (no dementia) and CDR 0.5 (very mild dementia). Results:The final version (AD8) querying memory, orientation, judgment, and function was administered to an additional sample of 112 CDR 0 and 68 CDR 0.5 participants. Using a cut-off of two items endorsed, the area under the curve was 0.834, suggesting good to excellent discrimination, sensitivity was 74%, and specificity was 86% (prevalence of 0.38 for very mild dementia). Inclusion of 56 additional individuals with mild to severe dementia (increasing dementia prevalence to 0.53) increased sensitivity to 85%. Conclusions:The AD8 is a brief, sensitive measure that reliably differentiates between nondemented and demented individuals. Use of the AD8 in conjunction with a brief assessment of the participant could improve diagnostic accuracy in general practice.


title: Integrated path planning and dynamic steering control for autonomous vehicles

abstract: A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals. Simulation results are presented to demonstrate the control scheme.


title: A graph-based friend recommendation system using Genetic Algorithm

abstract: A social network is composed by communities of individuals or organizations that are connected by a common interest. Online social networking sites like Twitter, Facebook and Orkut are among the most visited sites in the Internet. Presently, there is a great interest in trying to understand the complexities of this type of network from both theoretical and applied point of view. The understanding of these social network graphs is important to improve the current social network systems, and also to develop new applications. Here, we propose a friend recommendation system for social network based on the topology of the network graphs. The topology of network that connects a user to his friends is examined and a local social network called Oro-Aro is used in the experiments. We developed an algorithm that analyses the sub-graph composed by a user and all the others connected people separately by three degree of separation. However, only users separated by two degree of separation are candidates to be suggested as a friend. The algorithm uses the patterns defined by their connections to find those users who have similar behavior as the root user. The recommendation mechanism was developed based on the characterization and analyses of the network formed by the user's friends and friends-of-friends (FOF).


title: Detecting depression and mental illness on social media: an integrative review

abstract: Although rates of diagnosing mental illness have improved over the past few decades, many cases remain undetected. Symptoms associated with mental illness are observable on Twitter, Facebook, and web forums, and automated methods are increasingly able to detect depression and other mental illnesses. In this paper, recent studies that aimed to predict mental illness using social media are reviewed. Mentally ill users have been identified using screening surveys, their public sharing of a diagnosis on Twitter, or by their membership in an online forum, and they were distinguishable from control users by patterns in their language and online activity. Automated detection methods may help to identify depressed or otherwise at-risk individuals through the large-scale passive monitoring of social media, and in the future may complement existing screening procedures.


title: Malware Propagations in Wireless Ad Hoc Networks

abstract: Accurate malware propagation modeling in wireless ad hoc networks (WANETs) represents a fundamental and open research issue which shows distinguished challenges due to complicated access competition, severe channel interference, and dynamic connectivity. As an effort towards the issue, in this paper, we investigate the malware propagation under two spread schemes including Unicast and Broadcast, in Spread Mode and Communication Mode, respectively. We highlight our contributions in three-fold in the light of previous literature works. First, a bound of malware infection rate for each scheme is provided by applying the wireless network capacity theories. Second, the impact of mobility on malware propagations has been studied. Third, discussion of the relationship between different schemes and practical applications is provided. Numerical simulations and detailed performance analysis show that the Broadcast Scheme with Spread Mode is most dangerous in the sense of malware propagation speed in WANETs, and mobility will greatly increase the risk further. The results achieved in this paper not only provide insights on the malware propagation characteristics in WANETs, but also serve as fundamental guidelines on designing defense schemes.


title: Switching converters with wide DC conversion range

abstract: Compared to basic converter topologies (buck, boost, buck-boost, Cuk, etc.), pulse-width modulation (PWM) converters with quadratic DC conversion ratios, M(D)=D/sup 2/, M(D)=D/sup 2//(1-D) or M(D)=D/sup 2//(1-D)/sup 2/, offer a significantly wider conversion range. For a given minimum ON-time and, consequently, for a given minimum duty ratio D/sub min/, D/sup 2/ in the numerator of M(D) yields a much lower limit on the minimum attainable conversion ratio. By applying a systematic synthesis procedure, six novel single-transistor converter configurations with quadratic DC conversion ratios are found. The simpler, single-transistor realization is the most important advantage over the straightforward cascade of two basic converters. As far as conversion efficiency is concerned, it is clear that a single-stage converter is usually a better choice than a two-stage converter. The quadratic converters proposed are intended for applications where conventional single-stage converters are inadequate-for high-frequency applications where the specified range of input voltages and the specified range of output voltages call for an extremely large range of conversion ratios. >


title: Zero is not enough: On the lower limit of agent intelligence for continuous double auction markets

abstract: agent continuous double auction, trade Gode and Sunder's (1993) results from using "zero-intelligence" (ZI) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous double-auction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of ZI traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, 'zero-intelligence-plus' (ZIP) traders are introduced: like ZI traders, these simple agents make stochastic bids. Unlike ZI traders, they employ an elementary form of machine learning. Groups of ZIP traders interacting in experimental markets similar to those used by Smith (1962) and Gode and Sunder (1993) are demonstrated, and we show that the performance of ZIP traders is significantly closer to the human data than is the performance of Gode and Sunder's ZI traders. Because ZI traders are too simple, we offer ZIP traders as a slight upward revision of the intelligence required to achieve equilibrium in continuous double auction markets.


Information modeling and relational databases (2. ed.)


title: Measuring Target Range and Velocity

abstract: Increasing demand for industrial radars has spurred research aimed toward designing better transceiver (TRx) integrated circuits (ICs) combined with advanced packaging technologies [1]-[6], innovative antenna concepts, very precise wirebonding schemes [5]-[8], and high-performance baseband processing systems [9]. With a well-optimized package of such products, the resulting application fields can vary from avionics and robotics to automotive, monitoring, level-tracking, and vital-sign-detection systems, among many others. All these applications will require that the final product satisfy certain metrics such as range, resolution, accuracy, power consumption, and (as always) production cost, which is ultimately the most crucial criterion.


title: Modeling the customer in electronic commerce

abstract: This paper reviews interface design of web pages for e-commerce. Different tasks in e-commerce are contrasted. A systems model is used to illustrate the information flow between three subsystems in e-commerce: store environment, customer, and web technology. A customer makes several decisions: to enter the store, to navigate, to purchase, to pay, and to keep the merchandize. This artificial environment must be designed so that it can support customer decision-making. To retain customers it must be pleasing and fun, and create a task with natural flow. Customers have different needs, competence and motivation, which affect decision-making. It may therefore be important to customize the design of the e-store environment. Future ergonomics research will have to investigate perceptual aspects, such as presentation of merchandize, and cognitive issues, such as product search and navigation, as well as decision making while considering various economic parameters. Five theories on e-commerce research are presented.


Hyper-Realistic Characters and the Existence of the Uncanny Valley in Animation Films


title: Risk Prediction Models for Hospital Readmission

abstract: Context Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison. Objective To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. Data Sources and Study Selection The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in both derivation and validation cohorts. Data Extraction Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Data Synthesis Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health. Conclusions Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.


title: Facial performance sensing head-mounted display

abstract: There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs). The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques. To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time. Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions. These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement. To map the input signals to a 3D face model, we perform a single-instance offline training session for each person. For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use. The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds.


title: The Road to Immersive Communication

abstract: Communication has seen enormous advances over the past 100 years including radio, television, mobile phones, video conferencing, and Internet-based voice and video calling. Still, remote communication remains less natural and more fatiguing than face-to-face. The vision of immersive communication is to enable natural experiences and interactions with remote people and environments in ways that suspend disbelief in being there. This paper briefly describes the current state-of-the-art of immersive communication, provides a vision of the future and the associated benefits, and considers the technical challenges in achieving that vision. The attributes of immersive communication are described, together with the frontiers of video and audio for achieving them. We emphasize that the success of these systems must be judged by their impact on the people who use them. Recent high-quality video conferencing systems are beginning to deliver a natural experience-when all participants are in custom-designed studios. Ongoing research aims to extend the experience to a broader range of environments. Augmented reality has the potential to make remote communication even better than being physically present. Future natural and effective immersive experiences will be created by drawing upon intertwined research areas including multimedia signal processing, computer vision, graphics, networking, sensors, displays and sound reproduction systems, haptics, and perceptual modeling and psychophysics.


title: Secular trends in BMI and the prevalence of obesity in young Polish males from 1965 to 2010.

abstract: BACKGROUND ::: Obesity is a serious public health problem, the prevalence of which is increasing dramatically all over the world. The aim of this study was to examine trends in body mass index (BMI) and the proportion of overweight and obese individuals among 19-year-old Polish males reporting for mandatory military fitness exams from 1965 to 2010. ::: ::: ::: METHODS ::: Height, weight and BMI [weight (kg)/height (m(2))] in five 10% nationwide random samples of 19-year-old conscripts examined in 1965, 1986, 1995, 2001 and 2010 were analysed. ::: ::: ::: RESULTS ::: From 1965 to 2010, mean BMI in 19-year-old Polish males increased from 21.7 to 22.9. The rate of change was not uniform, with a rapid increase in mean BMI from 1995 to 2010. Beginning in 1965, the proportion of men with a BMI over 25 has been steadily increasing from one decade to the next, and was four times higher in 2010 than it was in 1965. The rate of increase per decade was twice as high from 2001 to 2010 than it was from 1995 to 2001. In 2010, only 70.8% of young men were of ideal weight. ::: ::: ::: CONCLUSION ::: Increase in obesity can be attributed to the social and economic changes brought about by the transformation of the country from a communist to a free-market economy in 1989. The challenges of the obesity epidemic for public health services and its impact on morbidity and life expectancy are also discussed.


title: Metafraud: a meta-learning framework for detecting financial fraud

abstract: Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. ::: ::: In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.


title: Dual Based DSP Bidding Strategy and its Application

abstract: In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determination strategy which maximizes revenue or performance under budget and ROI(Return On Investment) constraints in P4P(Pay For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1) formalizing the DSP problem as a constrained optimization problem, 2) proposing the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with general solution, 3) and demonstrating the DSP problem is a special case of the augmented MMKP and deriving specialized strategy. Our strategy is verified through simulation and outperforms state-of-the-art strategies in real application. To the best of our knowledge, our solution is the first dual based DSP bidding framework that is derived from strict second price auction assumption and generally applicable to the multiple ads scenario with various objectives and constraints.


title: Deep Neural Network for Learning to Rank Query-Text Pairs

abstract: This paper considers the problem of document ranking in information retrieval systems by Learning to Rank. We propose ConvRankNet combining a Siamese Convolutional Neural Network encoder and the RankNet ranking model which could be trained in an end-to-end fashion. We prove a general result justifying the linear test-time complexity of pairwise Learning to Rank approach. Experiments on the OHSUMED dataset show that ConvRankNet outperforms systematically existing feature-based models.


title: A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

abstract: We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset. We revisit and discuss diverse popular methods in NL2SQL literature, take a full advantage of BERT {Devlin et al., 2018) through an effective table contextualization method, and coherently combine them, outperforming the previous state of the art by 8.2% and 2.5% in logical form and execution accuracy, respectively. We particularly note that BERT with a seq2seq decoder leads to a poor performance in the task, indicating the importance of a careful design when using such large pretrained models. We also provide a comprehensive analysis on the dataset and our model, which can be helpful for designing future NL2SQL datsets and models. We especially show that our model's performance is near the upper bound in WikiSQL, where we observe that a large portion of the evaluation errors are due to wrong annotations, and our model is already exceeding human performance by 1.3% in execution accuracy.


title: Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization

abstract: Accurately determining the position and orientation from which an image was taken, i.e., computing the camera pose, is a fundamental step in many Computer Vision applications. The pose can be recovered from 2D-3D matches between 2D image positions and points in a 3D model of the scene. Recent advances in Structure-from-Motion allow us to reconstruct large scenes and thus create the need for image-based localization methods that efficiently handle large-scale 3D models while still being effective, i.e., while localizing as many images as possible. This paper presents an approach for large scale image-based localization that is both efficient and effective. At the core of our approach is a novel prioritized matching step that enables us to first consider features more likely to yield 2D-to-3D matches and to terminate the correspondence search as soon as enough matches have been found. Matches initially lost due to quantization are efficiently recovered by integrating 3D-to-2D search. We show how visibility information from the reconstruction process can be used to improve the efficiency of our approach. We evaluate the performance of our method through extensive experiments and demonstrate that it offers the best combination of efficiency and effectiveness among current state-of-the-art approaches for localization.


title: DOA estimation of coherent waves for 77GHZ automotive radar with three receiving antennas

abstract: This paper proposes a novel scheme suitable for mm-wave automotive radars to estimate the direction-of-arrivals (DOAs) of coherent waves with only three receiving antennas. Reflected waves completely correlated from two targets can be resolved with calibrated 3-channel array antenna by using only one snapshot data. The resolution capability can be further improved with the use of multiple snapshot data. A 77GHz prototype radar mounted on a vehicle successfully measured the directions of two stationary targets in real time computation.


title: DeViSE: A Deep Visual-Semantic Embedding Model

abstract: Modern visual recognition systems are often limited in their ability to scale to large numbers of object categories. This limitation is in part due to the increasing difficulty of acquiring sufficient training data in the form of labeled images as the number of object categories grows. One remedy is to leverage data from other sources - such as text data - both to train visual models and to constrain their predictions. In this paper we present a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text. We demonstrate that this model matches state-of-the-art performance on the 1000-class ImageNet object recognition challenge while making more semantically reasonable errors, and also show that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training. Semantic knowledge improves such zero-shot predictions achieving hit rates of up to 18% across thousands of novel labels never seen by the visual model.


On-line new event detection and tracking


title: The Princeton Web Transparency and Accountability Project

abstract: When you browse the web, hidden “third parties” collect a large amount of data about your behavior. This data feeds algorithms to target ads to you, tailor your news recommendations, and sometimes vary prices of online products. The network of trackers comprises hundreds of entities, but consumers have little awareness of its pervasiveness and sophistication. This chapter discusses the findings and experiences of the Princeton Web Transparency Project (https://webtap.princeton.edu/), which continually monitors the web to uncover what user data companies collect, how they collect it, and what they do with it. We do this via a largely automated monthly “census” of the top 1 million websites, in effect “tracking the trackers”. Our tools and findings have proven useful to regulators and investigatory journalists, and have led to greater public awareness, the cessation of some privacy-infringing practices, and the creation of new consumer privacy tools. But the work raises many new questions. For example, should we hold websites accountable for the privacy breaches caused by third parties? The chapter concludes with a discussion of such tricky issues and makes recommendations for public policy and regulation of privacy.


title: Contextual Action Recognition with R*CNN

abstract: There are multiple cues in an image which reveal what action a person is performing. For example, a jogger has a pose that is characteristic for jogging, but the scene (e.g. road, trail) and the presence of other joggers can be an additional source of information. In this work, we exploit the simple observation that actions are accompanied by contextual cues to build a strong action recognition system. We adapt RCNN to use more than one region for classification while still maintaining the ability to localize the action. We call our system R*CNN. The action-specific models and the feature maps are trained jointly, allowing for action specific representations to emerge. R*CNN achieves 90.2% mean AP on the PASAL VOC Action dataset, outperforming all other approaches in the field by a significant margin. Last, we show that R*CNN is not limited to action recognition. In particular, R*CNN can also be used to tackle fine-grained tasks such as attribute classification. We validate this claim by reporting state-of-the-art performance on the Berkeley Attributes of People dataset.


title: Six views of embodied cognition

abstract: The emerging viewpoint of embodied cognition holds that cognitive processes are deeply rooted in the body’s interactions with the world. This position actually houses a number of distinct claims, some of which are more controversial than others. This paper distinguishes and evaluates the following six claims: (1) cognition is situated; (2) cognition is time-pressured; (3) we off-load cognitive work onto the environment; (4) the environment is part of the cognitive system; (5) cognition is for action; (6) offline cognition is body based. Of these, the first three and the fifth appear to be at least partially true, and their usefulness is best evaluated in terms of the range of their applicability. The fourth claim, I argue, is deeply problematic. The sixth claim has received the least attention in the literature on embodied cognition, but it may in fact be the best documented and most powerful of the six claims.


title: Design and Verification of Model-based Nonlinear Controller for Fluidic Soft Actuators

abstract: Soft actuators are of great interests in recent years due to inherent compliance and adaptability. However, most of current studies focus on the physical actuation phenomenon and their manufaction, few results can be found on the modeling and closed-loop control due to their complicated elastic deformation. This work studies physical modeling, parameter identification, and nonlinear robust backstepping control for fluidic soft bending actuators governed by high-speed on-off solenoid valves. A second-order transfer function is used to describe the dynamic behavior from driving pneumatic pressure to soft actuator bending angle, and the model parameters are identified by with different operating frequencies. The nonlinear dynamical model of pneumatic valves is also built and identified by using least square method. Based on the identified high-order model, a robust backstepping control algorithm is designed for soft actuator to deal with nonlinearities and parameter uncertainties. Experimental results show that the closed-loop stability is guaranteed and the good tracking performance is achieved by the proposed method.


title: COP: to compress and protect main memory

abstract: Protecting main memories from soft errors typically requires special dual-inline memory modules (DIMMs) which incorporate at least one extra chip per rank to store error-correcting codes (ECC). This increases the cost of the DIMM as well as its power consumption. To avoid these costs, some proposals have suggested protecting non-ECC DIMMs by allocating a portion of memory space to store ECC metadata. However, such proposals can significantly shrink the available memory space while degrading performance due to extra memory accesses. In this work, we propose a technique called COP which uses block-level compression to make room for ECC check bits in DRAM. Because a compressed block with check bits is the same size as an uncompressed block, no extra memory accesses are required and the memory space is not reduced. Unlike other approaches that require explicit compression-tracking metadata, COP employs a novel mechanism that relies on ECC to detect compressed data. Our results show that COP can reduce the DRAM soft error rate by 93% with no storage overhead and negligible impact on performance. We also propose a technique using COP to protect both compressible and incompressible data with minimal storage and performance overheads.


title: Global path planning method of mobile robot in uncertain environment

abstract: Path planning is one of the key technologies in the robot research. The aim of it is to find the shortest safe path in the objective environments. Firstly, the robot is transformed into particle by expanding obstacles method; the obstacle is transformed into particle by multi-round enveloping method. Secondly, we make the Voronoi graph of the particles of obstacle and find the skeleton topology about the feasible path. Following, a new arithmetic named heuristic bidirectional ant colony algorithm is proposed by joining the merit of ant colony algorithm, Dijkstra algorithm and heuristic algorithm, with which we can find the shortest path of the skeleton topology. After transforming the path planning into n-dimensions quadrate feasible region by coordinate transformation and solving it with particle swarm optimization, the optimization of the path planning is acquired.


title: Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets

abstract: Modern machine learning (ML) models are being used heavily in business domains to build effective decision support systems. As a primary requirement, supervised ML models need large labeled datasets. However, obtaining a high volume of labeled training data is both expensive and time-consuming. Researchers have proposed several labeling approaches to avoid manual labeling efforts. Active learning (AL) and Data Programming (DP) are two state-of-the-art techniques used to label datasets. Nevertheless, both approaches have their strengths and weaknesses. For example, AL is computationally expensive to apply on large industrial datasets; and labels generated by DP are often inaccurate and difficult to interpret. To address these challenges, in this paper, we propose a novel hybrid method that integrates the scalability of DP with the user engagement and accuracy of AL. The proposed approach aims at optimizing the labeling process by applying DP to generate initial noisy training data and then use AL to query the user to label only those points that maximize the accuracy of the final labels with a minimum annotation cost. To evaluate the proposed approach, we have used five open source datasets and a real-world business dataset of 1.5 million records. We use traditional active learning and data programming techniques as baselines to compare the performance and annotation cost of our proposed approach. The results show that the proposed method can achieve higher labeling accuracy than data programming. It also can minimize the labeling cost in real-world business scenarios, while delivering a comparable level of performance (accuracy) with active learning.


title: Breaking out of the box of recommendations: from items to packages

abstract: Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from. Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.


title: The drawing assistant: automated drawing guidance and feedback from photographs

abstract: We present an interactive drawing tool that provides automated guidance over model photographs to help people practice traditional drawing-by-observation techniques. The drawing literature describes a number of techniques to %support this task and help people gain consciousness of the shapes in a scene and their relationships. We compile these techniques and derive a set of construction lines that we automatically extract from a model photograph. We then display these lines over the model to guide its manual reproduction by the user on the drawing canvas. Finally, we use shape-matching to register the user's sketch with the model guides. We use this registration to provide corrective feedback to the user. Our user studies show that automatically extracted construction lines can help users draw more accurately. Furthermore, users report that guidance and corrective feedback help them better understand how to draw.


title: A practical theory of language-integrated query

abstract: Language-integrated query is receiving renewed attention, in part because of its support through Microsoft's LINQ framework. We present a practical theory of language-integrated query based on quotation and normalisation of quoted terms. Our technique supports join queries, abstraction over values and predicates, composition of queries, dynamic generation of queries, and queries with nested intermediate data. Higher-order features prove useful even for constructing first-order queries. We prove a theorem characterising when a host query is guaranteed to generate a single SQL query. We present experimental results confirming our technique works, even in situations where Microsoft's LINQ framework either fails to produce an SQL query or, in one case, produces an avalanche of SQL queries.


title: Automatic Generation of Pencil-Sketch Like Drawings from Personal Photos

abstract: In this paper, we present an algorithm for automatically generating pencil-sketch like drawings from personal photos. On top of the core step of gradient computation, some proper transformations are introduced to achieve the best visual effects. It is found that the popular Sobel operator and Laplacian operator are both not desirable for this task, and thus we propose a computationally simple algorithm for gradient estimation. Experimental results show that the proposed method can generate visually appealing pencil-sketch like images from personal photos


title: Comparison of Dynamic Characteristics Between Virtual Synchronous Generator and Droop Control in Inverter-Based Distributed Generators

abstract: In recent researches on inverter-based distributed generators, disadvantages of traditional grid-connected current control, such as no grid-forming ability and lack of inertia, have been pointed out. As a result, novel control methods like droop control and virtual synchronous generator (VSG) have been proposed. In both methods, droop characteristics are used to control active and reactive power, and the only difference between them is that VSG has virtual inertia with the emulation of swing equation, whereas droop control has no inertia. In this paper, dynamic characteristics of both control methods are studied, in both stand-alone mode and synchronous-generator-connected mode, to understand the differences caused by swing equation. Small-signal models are built to compare transient responses of frequency during a small loading transition, and state-space models are built to analyze oscillation of output active power. Effects of delays in both controls are also studied, and an inertial droop control method is proposed based on the comparison. The results are verified by simulations and experiments. It is suggested that VSG control and proposed inertial droop control inherits the advantages of droop control, and in addition, provides inertia support for the system.


Examining Barriers to Maternal Health Care in Kenya Using the Three-Delay Framework


title: Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO with Lens Antenna Array

abstract: Beamspace channel estimation is essential for wideband millimeter-wave (mmWave) MIMO with lens antenna array to achieve substantial increase in data rates with considerably reduced number of radio-frequency chains. However, most of existing beamspace channel estimation schemes are designed for narrowband mmWave systems, while rather scarce wideband schemes ideally assume beamspace channel enjoys the common support in frequency-domain. In this paper, inspired by the classical successive interference cancellation for multi-user detection, we propose an efficient successive support detection (SSD) based scheme without the assumption of common support. Specifically, we first demonstrate that each path component of the wideband beamspace channel exhibits a unique frequency-varying sparse structure. Based on this, we then successively estimate all sparse path components. For each path component, its supports at different frequencies are jointly estimated to improve the estimation accuracy, and then its influence is removed to estimate the remained path components. Once all path components have been estimated, the wideband beamspace channel can be recovered at a low complexity. Simulation results verify that the proposed SSD based beamspace channel estimation scheme achieves higher accuracy than existing wideband schemes.


title: The Effect of Syntactic Representation on Semantic Role Labeling

abstract: Almost all automatic semantic role labeling (SRL) systems rely on a preliminary parsing step that derives a syntactic structure from the sentence being analyzed. This makes the choice of syntactic representation an essential design decision. In this paper, we study the influence of syntactic representation on the performance of SRL systems. Specifically, we compare constituent-based and dependency-based representations for SRL of English in the FrameNet paradigm. ::: ::: Contrary to previous claims, our results demonstrate that the systems based on dependencies perform roughly as well as those based on constituents: For the argument classification task, dependency-based systems perform slightly higher on average, while the opposite holds for the argument identification task. This is remarkable because dependency parsers are still in their infancy while constituent parsing is more mature. Furthermore, the results show that dependency-based semantic role classifiers rely less on lexicalized features, which makes them more robust to domain changes and makes them learn more efficiently with respect to the amount of training data.


title: An Explication of Social Norms

abstract: This article identifies four factors for consideration in norms-based research to enhance the predictive ability of theoretical models. First, it makes the distinction between perceived and collective norms and between descriptive and injunctive norms. Second, the article addresses the role of important moderators in the relationship between descriptive norms and behaviors, including outcome expectations, group identity, and ego involvement. Third, it discusses the role of both interpersonal and mass communication in normative influences. Lastly, it outlines behavioral attributes that determine susceptibility to normative influences, including behavioral ambiguity and the public or private nature of the behavior. The study of norms is of particular importance to communication scholarship because, by definition, norms are social phenomena, and they are propagated among group members through communication (Kincaid, 2004). Communication plays a part not only in formulating perceptions about norms (as when people use the preponderance of a behavior depicted in the media to form their perceptions about the prevalence of the behavior), but also in acting as a conduit of influence (when people base their decisions to act in a situation on the support for their actions that is communicated to them). This article is based on the premise that, given the centrality of communicative processes in propagating information about norms, its inclusion would enhance the explanatory power of theories of normative influences. The purpose of this article is to explore the intersection between social norms and communication by specifying the meaning of norms, delineating the moderators in the relationship between norms and behavior, and highlighting some of the attributes of behaviors that determine their susceptibility to normative influences.


title: The Benefits of Early Book Sharing (BEBS) for child cognitive and socio-emotional development in South Africa: study protocol for a randomised controlled trial

abstract: Background ::: Children in low and middle-income countries (LMICs) are at risk for problems in their cognitive, social and behavioural development. Factors such as a lack of cognitive stimulation, harsh parenting practices, and severe and persistent aggression in early childhood are central to the genesis of these problems. Interventions that target the intersection between early childhood development, parenting, and early violence prevention are required in order to meaningfully address these problems.


title: VARIANT STRUCTURE IN METAL-ORGANIC-CHEMICAL-VAPOR-DEPOSITION-DERIVED SNO2 THIN FILMS ON SAPPHIRE (0001)

abstract: Tin oxide (SnO{sub 2}) thin films were deposited on sapphire (0001) substrate by metal-organic chemical vapor deposition (MOCVD) at temperatures of 600 and 700 {degree}C. The microstructure of the deposited films was characterized by x-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM). At the growth conditions studied, films were single-phase rutile and epitaxial, but showed variant structures. Three distinct in-plane epitaxial relationships were observed between the films and the substrate. A crystallographic model is proposed to explain the film morphology. This model can successfully predict the ratio of the width to the length of an averaged grain size based upon the lattice mismatch of the film-substrate interface.


Particle-Imaging Techniques for Experimental Fluid Mechanics


Subcharacter Information in Japanese Embeddings: When Is It Worth It?


title: Design of transition from coaxial line to ridge gap waveguide

abstract: This paper presents the design of a simple transition from ridge gap waveguide to coaxial line transition. First, a straight ridge gap waveguide with single transition on end is simulated. Then a ridge gap waveguide with two 90° bends is simulated with two transitions on each end. Available commercial FDTD simulation tool was used to design the transitions. A return loss of −10 dB was achieved over the frequency band of 12– 15.75 GHz.


title: Application of LSTM Neural Networks in Language Modelling

abstract: Artificial neural networks have become state-of-the-art in the task of language modelling on a small corpora. While feed-forward networks are able to take into account only a fixed context length to predict the next word, recurrent neural networks (RNN) can take advantage of all previous words. Due the difficulties in training of RNN, the way could be in using Long Short Term Memory (LSTM) neural network architecture.


title: Air Pollution, Health, and Socio-economic Status: The Effect of Outdoor Air Quality on Childhood Asthma

abstract: Abstract This paper estimates the effect of air pollution on child hospitalizations for asthma using naturally occurring seasonal variations in pollution within zip codes. Of the pollutants considered, carbon monoxide (CO) has a significant effect on asthma for children ages 1–18: if 1998 pollution levels were at their 1992 levels, there would be a 5–14% increase in asthma admissions. Also, households respond to information about pollution with avoidance behavior, suggesting it is important to account for these endogenous responses when measuring the effect of pollution on health. Finally, the effect of pollution is greater for children of lower socio-economic status (SES), indicating that pollution is one potential mechanism by which SES affects health.


title: Data Loss Prevention Based on Data-Driven Usage Control

abstract: Inadvertent data disclosure by insiders is considered as one of the biggest threats for corporate information security. Data loss prevention systems typically try to cope with this problem by monitoring access to confidential data and preventing their leakage or improper handling. Current solutions in this area, however, often provide limited means to enforce more complex security policies that for instance specify temporal or cardinal constraints on the execution of events. This paper presents UC4Win, a data loss prevention solution for Microsoft Windows operating systems that is based on the concept of data-driven usage control to allow such a fine-grained policy-based protection. UC4Win is capable of detecting and controlling data-loss related events at the level of individual function calls. This is done with function call interposition techniques to intercept application calls to the Windows API in combination with methods to track the flows of confidential data through the system.


title: Development of Triboelectrostatic Separation Technique for Material Separation of ABS and PS Mixed Plastic Waste

abstract: Due to the environmental problem caused by plastics largely used in various fields, the importance of recycling is being emphasized. A research on material separation of ABS and PS mixed plastic waste, using a triboelectrostatic separator, was carried out for recovery the ABS. As a results of research on charging characteristic for choosing charging material, it was confirmed that ABS was optimum charging material for a tribo-charger in the material separation of ABS and PS. In the material separation using ABS charger, ABS grade of 99.5% and recovery of 92.5% were achieved at 20 kV, splitter position +2 cm from the center and 30% relative humidity. Therefore, material separation technique for recycling ABS and PS mixed plastic waste was established.


title: Repurchase intention in B2C e-commerce - A relationship quality perspective

abstract: Information systems professionals must pay attention to online customer retention. Drawing on the relationship marketing literature, we formulated and tested a model to explain B2C user repurchase intention from the perspective of relationship quality. The model was empirically tested through a survey conducted in Northern Ireland. Results showed that online relationship quality and perceived website usability positively impacted customer repurchase intention. Moreover, online relationship quality was positively influenced by perceived vendor expertise in order fulfillment, perceived vendor reputation, and perceived website usability, whereas distrust in vendor behavior negatively influenced online relationship quality. Implications of these findings are discussed.