title: Electrical properties of epoxy-glue/(Bi12MnO20–BiMn2O5) composite

abstract: Electrical properties of composite consisted of epoxy-glue and powdered bismuth manganite Bi12MnO20–BiMn2O5 ceramics were studied. Samples contained 66 wt% of bismuth manganite. Secondary electron images and backscattered electron images were collected for the epoxy-glue and the epoxy-glue/bismuth manganite samples. Microanalysis shown that majority of bismuth manganite powder was homogenously distributed in epoxy-glue matrix. Lack of changes in crystallographic structure, in 300–450 K range, was deduced from the X-ray diffraction patterns. The electric impedance was measured at radio-frequencies. It was analysed using electric permittivity ɛ*(T, f ) and modulus M″(T, f ) representation. Epoxy-glue/bismuth manganite composite exhibited, at room temperature, conductivity, σ(f ) = 10−5 to 10−7 S m−1, permittivity, ɛ′≈10, losses, tan δ = 0.2–0.8 and dispersion markedly lower than bismuth manganite ceramics. Electric relaxation was induced in epoxy-glue/bismuth manganite composite samples. The Vogel–Fulcher–T...


title: Temporal Attention Model for Neural Machine Translation

abstract: Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address some of these limitations and improve the NMT attention. Specifically, our approach memorizes the alignments temporally (within each sentence) and modulates the attention with the accumulated temporal memory, as the decoder generates the candidate translation. We compare our approach against the baseline NMT model and two other related approaches that address this issue either explicitly or implicitly. Large-scale experiments on two language pairs show that our approach achieves better and robust gains over the baseline and related NMT approaches. Our model further outperforms strong SMT baselines in some settings even without using ensembles.


title: Endogenous and Exogenous Attention Shifts Are Mediated by the Same Large-Scale Neural Network

abstract: Event-related fMRI was used to examine the neural basis of endogenous (top-down) and exogenous (bottom-up) spatial orienting. Shifts of attention were induced by central (endogenous) or peripheral (exogenous) cues. Reaction times on subsequently presented targets showed the expected pattern of facilitation and inhibition in both conditions. No difference in brain activity was observed when the two orienting conditions were contrasted with a liberal threshold, showing that both forms of orienting were mediated by the same neural network. Compared to within-block control trials, both endogenous and exogenous orienting activated a fronto-parietal network consisting of premotor cortex, posterior parietal cortex, medial frontal cortex and right inferior frontal cortex. Within these regions, equally strong activation was observed for both orienting conditions. It is concluded that endogenous and exogenous orienting are mediated by the same large-scale network of frontal and parietal brain areas.


title: The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years

abstract: The Dartmouth College Artificial Intelligence Conference: The Next 50 Years (AI@50) took place July 13-15, 2006. The conference had three objectives: to celebrate the Dartmouth Summer Research Project, which occurred in 1956; to assess how far AI has progressed; and to project where AI is going or should be going. AI@50 was generously funded by the office of the Dean of Faculty and the office of the Provost at Dartmouth College, by DARPA, and by some private donors.


title: A compact LTCC branch-line coupler UsingModified-T equivalent-circuit modelfor transmission line

abstract: In this letter, a compact branch-line coupler is proposed by making good use of the three-dimensional layout capability of the low-temperature co-fired ceramic (LTCC) substrate. This branch-line coupler is accomplished by using lumped-inductors and lumped-capacitors to realize the modified-T equivalent-circuit model for the transmission line so that the circuit size may drastically be reduced. Specifically, a very compact LTCC branch-line coupler with a size of 0.079/spl lambda//spl times/0.0717/spl lambda/ is implemented and carefully examined, where /spl lambda/ is the wavelength of the multilayer structure at the operating frequency f/sub 0/.


title: High-quality passive facial performance capture using anchor frames

abstract: We present a new technique for passive and markerless facial performance capture based on anchor frames. Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.


title: Translation, rotation, and scale-invariant object recognition

abstract: A method for object recognition that is invariant under translation, rotation and scaling is addressed. The first step of the method (pre-processing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor (HHN) algorithm, in which vectors obtained in the pre-processing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper-case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging, since we achieved 98% correct recognition. Tolerance to boundary deformations and random noise was tested. Results for character recognition in "real" images of car plates are presented as well.


title: Big data interoperability challenges for logistics

abstract: Analysis of real time information can be used to leverage logistic value chains and processes. It enables organizations to become more flexible and efficient. With the continued explosion of available data, challenges are aplenty for logistic companies on how to use such data for meaningful service improvements. Capturing, storing and analyzing such data, extracting useful information, are representative of big data challenges in the logistic value chain. In this paper, we elaborate on the uses of big data analytics for logistics and the related interoperability challenges for logistic companies.


title: Intraday Periodicity . . . Financial Markets

abstract: Abstract The pervasive intraday periodicity in the return volatility in foreign exchange and equity markets is shown to have a strong impact on the dynamic properties of high frequency returns. Only by taking account of this strong intraday periodicity is it possible to uncover the complex intraday volatility dynamics that exists both within and across different financial markets. The explicit periodic modeling procedure developed here provides such a framework and thus sets the stage for a formal integration of standard volatility models with market microstructure variables to allow for a more comprehensive empirical investigation of the fundamental determinants behind the volatility clustering phenomenon.


title: Modeling well edge proximity effect on highly-scaled MOSFETs

abstract: Well edge proximity effect caused by ion scattering during implantation in highly-scaled CMOS technology was explored from a process and physics point of view. TCAD simulation was employed to visualize the internal change of the MOSFETs. A new compact model for SPICE was proposed using physics-based understanding and was calibrated with experimental silicon test sets. Circuit simulation using the proposed model was conducted to evaluate the improvement in accuracy


title: 56Gb/s PAM4 and NRZ SerDes transceivers in 40nm CMOS

abstract: This paper presents 56Gb/s PAM4 and NRZ SerDes transceivers (TRXs), designed and fabricated in advance CMOS technology. Incorporating broadband techniques, noise suppression skills, and clock extraction circuits, this work demonstrates feasibility of 56Gb/s SerDes and compares tradeoffs between the two data format.


title: Semantic Video CNNs Through Representation Warping

abstract: In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to augment existing architectures with very little extra computational cost. This module is called NetWarp and we demonstrate its use for a range of network architectures. The main design principle is to use optical flow of adjacent frames for warping internal network representations across time. A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to-end training. Experiments validate that the proposed approach incurs only little extra computational cost, while improving performance, when video streams are available. We achieve new state-of-the-art results on the CamVid and Cityscapes benchmark datasets and show consistent improvements over different baseline networks. Our code and models will be available at this http URL


title: AC Transconductance Dispersion (ACGD): A Method to Profile Oxide Traps in MOSFETs Without Body Contact

abstract: We introduce an ac transconductance dispersion method (ACGD) to profile the oxide traps in an MOSFET without needing a body contact. The method extracts the spatial distribution of oxide traps from the frequency dependence of transconductance, which is attributed to charge trapping as modulated by an ac gate voltage. The results from this method have been verified by the use of the multifrequency charge pumping (MFCP) technique. In fact, this method complements the MFCP technique in terms of the trap depth that each method is capable of probing. We will demonstrate the method with InP passivated InGaAs substrates, along with electrically stressed Si N-MOSFETs.


title: Overview strategy of wind farm in VSC-HVDC power transmission

abstract: The VSC-HVDC system performance has the most competitive and new feasible solution for integration of high wind penetration at offshore sites for efficient power transmission. This paper presents an overview of various control strategy for power converters and the plan of services in VSC-HVDC transmission. For long distance transmission line, reactance limits the transfer capability of high voltage alternating current (HVAC) system. With the advancement of VSC-HVDC technology reactance can be smoothly controlled, hence transfer capability can be enhanced. VSC-HVDC (voltage source converter- high voltage direct current) technology also meets the challenges that are faced by 2-level and multilevel converters. Rapid developments in IGBT open new gate for controlled strategy in converters. Due to this there is a paradigms shift in interfacing renewable energy with electric power system. The real and reactive power can be controlled indepedently by using VSC-HVDC system and black start capability is restored by an AC grid. This paper lays focus on VSC-HVDC transmission technology with integration offshore wind power. It will help operating engineers, designers, and research scholars to focus on this work and decision making. VSC-HVDC transmissions are giving ease of belongingness to energy transition.


title: The Dialog State Tracking Challenge Series: A Review

abstract: In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn. Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress. The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker. This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled.


title: Risk Factors for the Progression of Mild Cognitive Impairment in Different Types of Neurodegenerative Disorders

abstract: Objective. Mild cognitive impairment (MCI) is a transitional state between normal aging and early dementia. It has a heterogeneous etiology and clinical course. This study aimed to examine the factors associated with the progression of MCI in different types of dementia disorders. Method. A retrospective, longitudinal, observational study of outpatients with MCI was conducted at a medical center in northern Taiwan. Patient medical records were reviewed, and risk factors were analyzed by multivariate analysis. Results. Among 279 patients with MCI, 163 (58.4%), 68 (24.4%), and 48 (17.2%) were diagnosed with Alzheimer’s disease, vascular cognitive impairment, and Lewy body diseases, respectively. During the observation period, 37.2% of patients progressed to dementia. Older age and a higher Clinical Dementia Rating Scale-Sum of Boxes were associated with the risk of progression. Hyperlipidemia was associated with a decreased risk. Converters were more likely to receive an antidementia prescription. Conclusion. Our study suggests the importance of comprehensive clinical profiling, risk factor assessment, and detailed drug history evaluations in improving our understanding and management of dementia subtypes.


title: IoT based monitoring of container vehicle for secure and reliable delivery of goods

abstract: Designing a Secured Smart Transportation System for delivering goods to various Fair Price Shops is one of the major goals for all the state governments in the India. In this paper, a hardware model and an application is designed for tracking the moving good carrying vehicle. The data for the proposed application is generated from the different sensors such as Door sensors, Collision Prevention sensor, and GPS/GPRS devices that attached to the hardware model. An alarm is raised to make decision in case of trouble or malfunction. The data generated by these sensors are stored under cloud environment. An alert system is implemented using automatic alarm concept that placed inside goods carrying vehicle. If the current moving path of the vehicle has an obstacle then an alternate path is recommended by the proposed application using intelligent water drops algorithm. From the experiment, it is observed that the proposed hardware model and application meets the objective, such as monitoring the goods carrying container, speedy recovery in case of any incidents happened and secured delivery of ration food items to all the required fair price shops.


title: Solution-aware data flow diagrams for security threat modeling

abstract: Threat modeling refers to a number of systematic approaches for eliciting security and privacy threats. Data Flow Diagrams (DFDs) are the main input for threat modeling techniques such as Microsoft STRIDE or LINDDUN. They represent system-level abstractions that lack any architectural knowledge on existing security solutions. However, this is not how software is built in practice: there are often previously-made security- and privacy-relevant decisions that originate from the technological context or domain, reuse, or external dependencies. Not taking these into account leads to the enumeration of many non-applicable threats during threat modeling. While recording the effect of these decisions on individual elements can provide some relief, the lack of a proper first-class representation causes conflicts when modifying the architecture and inhibits traceability between effect and decision. In this paper, we enrich Data Flow Diagrams with security solution elements, which are taken into account during threat elicitation. Our modeling approach is supported by a proof-of-concept implementation of a threat modeling framework and validated in the context of a STRIDE analysis of an industrial video conferencing solution that is based on WebRTC. The presented DFD enrichments are a key enabler for future efforts towards dynamic and continuous threat modeling.


title: Neuro-guided genetic programming: prioritizing evolutionary search with neural networks

abstract: When search operators in genetic programming (GP) insert new instructions into programs, they usually draw them uniformly from the available instruction set. Prefering some instructions to others would require additional domain knowledge, which is typically unavailable. However, it has been recently demonstrated that the likelihoods of instructions' occurrence in a program can be reasonably well estimated from its input-output behavior using a neural network. We exploit this idea to bias the choice of instructions used by search operators in GP. Given a large sample of programs and their input-output behaviors, a neural network is trained to predict the presence of individual instructions. When applied to a new program synthesis task, the network is first queried on the set of examples that define the task, and the obtained probabilities determine the frequencies of using instructions in initialization and mutation operators. This priming leads to significant improvements of the odds of successful synthesis on a range of benchmarks.


title: BayesGrad: Explaining Predictions of Graph Convolutional Networks

abstract: Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question:"how do we explain the predictions of graph convolutional networks?"A possible approach to answer this question is to visualize evidence substructures responsible for the predictions. For chemical property prediction tasks, the sample size of the training data is often small and/or a label imbalance problem occurs, where a few samples belong to a single class and the majority of samples belong to the other classes. This can lead to uncertainty related to the learned parameters of the machine learning model. To address this uncertainty, we propose BayesGrad, utilizing the Bayesian predictive distribution, to define the importance of each node in an input graph, which is computed efficiently using the dropout technique. We demonstrate that BayesGrad successfully visualizes the substructures responsible for the label prediction in the artificial experiment, even when the sample size is small. Furthermore, we use a real dataset to evaluate the effectiveness of the visualization. The basic idea of BayesGrad is not limited to graph-structured data and can be applied to other data types.


title: Operator Variational Inference

abstract: Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inference uses the Kullback-Leibler divergence to define the optimization. Though this divergence has been widely used, the resultant posterior approximation can suffer from undesirable statistical properties. To address this, we reexamine variational inference from its roots as an optimization problem. We use operators, or functions of functions, to design variational objectives. As one example, we design a variational objective with a Langevin-Stein operator. We develop a black box algorithm, operator variational inference (OPVI), for optimizing any operator objective. Importantly, operators enable us to make explicit the statistical and computational tradeoffs for variational inference. We can characterize different properties of variational objectives, such as objectives that admit data subsampling---allowing inference to scale to massive data---as well as objectives that admit variational programs---a rich class of posterior approximations that does not require a tractable density. We illustrate the benefits of OPVI on a mixture model and a generative model of images.


title: Predicting the Future with Transformational States

abstract: An intelligent observer looks at the world and sees not only what is, but what is moving and what can be moved. In other words, the observer sees how the present state of the world can transform in the future. We propose a model that predicts future images by learning to represent the present state and its transformation given only a sequence of images. To do so, we introduce an architecture with a latent state composed of two components designed to capture (i) the present image state and (ii) the transformation between present and future states, respectively. We couple this latent state with a recurrent neural network (RNN) core that predicts future frames by transforming past states into future states by applying the accumulated state transformation with a learned operator. We describe how this model can be integrated into an encoder-decoder convolutional neural network (CNN) architecture that uses weighted residual connections to integrate representations of the past with representations of the future. Qualitatively, our approach generates image sequences that are stable and capture realistic motion over multiple predicted frames, without requiring adversarial training. Quantitatively, our method achieves prediction results comparable to state-of-the-art results on standard image prediction benchmarks (Moving MNIST, KTH, and UCF101).


Overfitting and undercomputing in machine learning


Document analysis system


title: Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

abstract: Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs) for high-resolution remote sensing image retrieval (HRRSIR). To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.


Concurrent Treaps


title: No incremental clinical information of NT Pro BNP at peak exercise over resting levels in patients with impaired left ventricular function

abstract: Albstract There is an increasing tension to use NT Pro BNP blood levels at peak exercise testing. Their possible superiority over resting levels in congestive heart failure or factors associated with their increase have not been adequately studied. We studied 65 patients, 51 males and 14 females with impaired left ventricular function. Mean left ventricular ejection fraction (LVEF) was 35±9%. Our findings suggest that in patients with heart failure NT Pro BNP plasma levels at peak exercise do not provide incremental clinical information over resting levels. Baseline NT Pro BNP alone can provide sufficient clinical information.


The Rorschach white space response and "oppositional" behavior.


title: Capability Maturity Model and Metrics Framework for Cyber Cloud Security

abstract: Cyber space is affecting all areas of our life. Cloud computing is the cutting-edge technology of this cyber space and has established itself as one of the most important resources sharing technologies for future on-demand services and infrastructures that support Internet of Things (IOTs), big data platforms and software-defined systems/services. More than ever, security is vital for cloud environment. There exist several cloud security models and standards dealing with emerging cloud security threats. However, these models are mostly reactive rather than proactive and they do not provide adequate measures to assess the overall security status of a cloud system. Out of existing models, capability maturity models, which have been used by many organizations, offer a realistic approach to address these problems using management by security domains and security assessment on maturity levels. The aim of the paper is twofold: first, it provides a review of capability maturity models and security metrics; second, it proposes a cloud security capability maturity model (CSCMM) that extends existing cyber security models with a security metric framework.


title: Managing RFID Data: Challenges, Opportunities and Solutions

abstract: The advances of Radio-Frequency Identification (RFID) technology have significantly enhanced the capability of capturing data from pervasive space. It becomes a great challenge in the information era to effectively understand human behavior, mobility and activity through the perceived RFID data. Focusing on RFID data management, this article provides an overview of current challenges, emerging opportunities and recent progresses in RFID. In particular, this article has described and analyzed the research work on three aspects: algorithm, protocol and performance evaluation. We investigate the research progress in RFID with anti-collision algorithms, authentication and privacy protection protocols, localization and activity sensing, as well as performance tuning in realistic settings. We emphasize the basic principles of RFID data management to understand the state-of-the-art and to address directions of future research in RFID.


title: Nonlinear robust observers for ball and beam system: A comparative analysis

abstract: Nonlinear robust observers for the ball and beam system are presented in this paper. Ball and beam control system is a class of nonlinear system which is used to balance a ball on a particular position on the beam. In the designing, Lyapunov theory is used for the stability analysis of the overall system and all the error signals are uniformly bounded. Proposed observers comprise of reduced order and full order robust observers. The presented methodology is easy in implementation. The simulation results demonstrate the important features and satisfactory tracking performance of the proposed observer design approach. Comparison between reduced and full order robust observers is also depicted in this paper.


title: Cancer-associated IDH1 mutations produce 2-hydroxyglutarate

abstract: This corrects the article DOI: 10.1038/nature08617


title: Sequential PAttern mining using a bitmap representation

abstract: We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depth-first search strategy that integrates a depth-first traversal of the search space with effective pruning mechanisms.Our implementation of the search strategy combines a vertical bitmap representation of the database with efficient support counting. A salient feature of our algorithm is that it incrementally outputs new frequent itemsets in an online fashion.In a thorough experimental evaluation of our algorithm on standard benchmark data from the literature, our algorithm outperforms previous work up to an order of magnitude.


Anchors: High-Precision Model-Agnostic Explanations


title: Drivers’ Visual Search Patterns during Overtaking Maneuvers on Freeway

abstract: Drivers gather traffic information primarily by means of their vision. Especially during complicated maneuvers, such as overtaking, they need to perceive a variety of characteristics including the lateral and longitudinal distances with other vehicles, the speed of others vehicles, lane occupancy, and so on, to avoid crashes. The primary object of this study is to examine the appropriate visual search patterns during overtaking maneuvers on freeways. We designed a series of driving simulating experiments in which the type and speed of the leading vehicle were considered as two influential factors. One hundred and forty participants took part in the study. The participants overtook the leading vehicles just like they would usually do so, and their eye movements were collected by use of the Eye Tracker. The results show that participants’ gaze durations and saccade durations followed normal distribution patterns and that saccade angles followed a log-normal distribution pattern. It was observed that the type of leading vehicle significantly impacted the drivers’ gaze duration and gaze frequency. As the speed of a leading vehicle increased, subjects’ saccade durations became longer and saccade angles became larger. In addition, the initial and destination lanes were found to be key areas with the highest visual allocating proportion, accounting for more than 65% of total visual allocation. Subjects tended to more frequently shift their viewpoints between the initial lane and destination lane in order to search for crucial traffic information. However, they seldom directly shifted their viewpoints between the two wing mirrors.


title: A curriculum for teaching computer science through computational textiles

abstract: The field of computational textiles has shown promise as a domain for diversifying computer science culture by drawing a population with broad and non-traditional interests and backgrounds into creating technology. In this paper, we present a curriculum that teaches computer science and computer programming through a series of activities that involve building and programming computational textiles. We also describe two new technological tools, Modkit and the LilyPad ProtoSnap board, that support implementation of the curriculum. In 2011-12, we conducted three workshops to evaluate the impact of our curriculum and tools on students' technological self-efficacy. We conclude that our curriculum both draws a diverse population, and increases students' comfort with, enjoyment of, and interest in working with electronics and programming.


title: Performance Analysis of Mobile Data Offloading in Heterogeneous Networks

abstract: An unprecedented increase in the mobile data traffic volume has been recently reported due to the extensive use of smartphones, tablets, and laptops. This is a major concern for mobile network operators, who are forced to often operate very close to their capacity limits. Recently, different solutions have been proposed to overcome this problem. The deployment of additional infrastructure, the use of more advanced technologies (LTE), or offloading some traffic through Femtocells and WiFi are some of the solutions. Out of these, WiFi presents some key advantages such as its already widespread deployment and low cost. While benefits to operators have already been documented, it is less clear how much and under what conditions the user gains as well. Additionally, the increasingly heterogeneous deployment of cellular networks (partial 4G coverage, small cells, etc.) further complicates the picture regarding both operator- and user-related performance of data offloading. To this end, in this paper we propose a queueing analytic model that can be used to understand the performance improvements achievable by Wi-Fi-based data offloading, as a function of Wi-Fi availability and performance, user mobility and traffic load, and the coverage ratio and respective rates of different cellular technologies available. We validate our theory against simulations for realistic scenarios and parameters, and provide some initial insights as to the offloading gains expected in practice.


title: Correcting A Widespread Error in Unification Algorithms

abstract: The unification of two patterns both containing variables is a ubiquitous operation in logic programming and in many artificial intelligence applications. Thus, many texts present unification algorithms. Unfortunately, at least seven of these presentations are incorrect. The common error occurs when logic variables are represented as binding lists; implementations that destructively update variable cells do not manifest the error. This note gives the examples that uncover the error and presents a correction.


Web-Scale Language-Independent Cataloging of Noisy Product Listings for E-Commerce.


title: Optimal routing control of a construction machine by deep reinforcement learning

abstract: Deep reinforcement learning algorithms are rapidly growing, and expected to be applied to many industrial fields. In this paper, we proposed a method that combines a deep Q-network with batch normalization to generate an optimal route for a grading machine. The goal is to achieve autonomous operation of the grading machine. For the learning platform, a grading simulator was developed to emulate the grading work. The proposed method was evaluated with the grading simulator, and showed better route searching performance results than the conventional method.


title: Mixed Method Research: Instruments, Validity, Reliability and Reporting Findings

abstract: The mixed method approaches have recently risen to prominence. The reason that more researchers are opting for these types of research is that both qualitative and quantitative data are simultaneously collected, analyzed and interpreted. In this article the main research instruments (questionnaire, interview and classroom observation) usually used in the mixed method designs are presented and elaborated on. It is believed that using different types of procedures for collecting data and obtaining that information through different sources (learners, teachers, program staff, etc.) can augment the validity and reliability of the data and their interpretation. Therefore, the various ways of boosting the validity and reliability of the data and instruments are delineated at length. Finally, an outline of reporting the findings in the mixed method approaches is sketched out. It is believed that this article can be useful and beneficial to the researchers in general and postgraduate students in particular who want to start or are involved in the process of conducting research.


title: Understanding the Relationship between Human Behavior and Susceptibility to Cyber Attacks: A Data-Driven Approach

abstract: Despite growing speculation about the role of human behavior in cyber-security of machines, concrete data-driven analysis and evidence have been lacking. Using Symantec’s WINE platform, we conduct a detailed study of 1.6 million machines over an 8-month period in order to learn the relationship between user behavior and cyber attacks against their personal computers. We classify users into 4 categories (gamers, professionals, software developers, and others, plus a fifth category comprising everyone) and identify a total of 7 features that act as proxies for human behavior. For each of the 35 possible combinations (5 categories times 7 features), we studied the relationship between each of these seven features and one dependent variable, namely the number of attempted malware attacks detected by Symantec on the machine. Our results show that there is a strong relationship between several features and the number of attempted malware attacks. Had these hosts not been protected by Symantec’s anti-virus product or a similar product, they would likely have been infected. Surprisingly, our results show that software developers are more at risk of engaging in risky cyber-behavior than other categories.


title: Integrating shape grammars into a generative system for Zhuang ethnic embroidery design exploration

abstract: The integration of shape grammars within a generative system is thought to have good potential to support design applications in the art and cultural domains. Based on a shape decomposing method, two-level generative shape grammars are developed within the application of Zhuang ethnic embroidery design. In our approach, free B-spline curves are used to derive initial design concepts together with a new interpolation algorithm. In order to cope with the potentially huge solution space, a novel aesthetic evaluation model and a design exploration algorithm are developed for designers to find their favorite design solutions based on their subjective assessments. A system is introduced to demonstrate this research with an example of Zhuang ethnic embroidery design.


title: PREDICTING STUDENT PERFORMANCE : AN APPLICATION OF DATA MINING METHODS WITH THE EDUCATIONAL WEB-BASED SYSTEM LON-CAPA

abstract: Newly developed web-based educational technologies offer researchers unique opportunities to study how students learn and what approaches to learning lead to success. Web-based systems routinely collect vast quantities of data on user patterns, and data mining methods can be applied to these databases. This paper presents an approach to classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. We design, implement, and evaluate a series of pattern classifiers and compare their performance on an online course dataset. A combination of multiple classifiers leads to a significant improvement in classification performance. Furthermore, by learning an appropriate weighting of the features used via a genetic algorithm (GA), we further improve prediction accuracy. The GA is demonstrated to successfully improve the accuracy of combined classifier performance, about 10 to 12% when comparing to non-GA classifier. This method may be of considerable usefulness in identifying students at risk early, especially in very large classes, and allow the instructor to provide appropriate advising in a timely manner.


title: Segmentation of the speech stream in a non-human primate: statistical learning in cotton-top tamarins

abstract: Previous work has shown that human adults, children, and infants can rapidly compute sequential statistics from a stream of speech and then use these statistics to determine which syllable sequences form potential words. In the present paper we ask whether this ability reflects a mechanism unique to humans, or might be used by other species as well, to acquire serially organized patterns. In a series of four experimental conditions, we exposed a New World monkey, the cotton-top tamarin (Saguinus oedipus), to the same speech streams used by Saffran, Aslin, and Newport (Science 274 (1996) 1926) with human infants, and then tested their learning using similar methods to those used with infants. Like humans, tamarins showed clear evidence of discriminating between sequences of syllables that differed only in the frequency or probability with which they occurred in the input streams. These results suggest that both humans and non-human primates possess mechanisms capable of computing these particular aspects of serial order. Future work must now show where humans' (adults and infants) and non-human primates' abilities in these tasks diverge.


Hierarchical Convolutional Deep Learning in Computer Vision


title: Flow-sensitive pointer analysis for millions of lines of code

abstract: Many program analyses benefit, both in precision and performance, from precise pointer analysis. An important dimension of pointer analysis precision is flow-sensitivity, which has been shown to be useful for applications such as program verification and static analysis of binary code, among many others. However, flow-sensitive pointer analysis has historically been unable to scale to programs with millions of lines of code. We present a new flow-sensitive pointer analysis algorithm that is an order of magnitude faster than the existing state of the art, enabling for the first time flow-sensitive pointer analysis for programs with millions of lines of code. Our flow-sensitive algorithm is based on a sparse representation of program code created by a staged, flow-insensitive pointer analysis. We explain how this new algorithm is a member of a new family of pointer analysis algorithms that deserves further study.


title: A Deep Embedding Model for Co-occurrence Learning

abstract: Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items. In this paper, we study co-occurrence data using a general energy-based probabilistic model, and we analyze three different categories of energy-based model, namely, the L1, L2 and Lk models, which are able to capture different levels of dependency in the co-occurrence data. We also discuss how several typical existing models are related to these three types of energy models, including the Fully Visible Boltzmann Machine (FVBM) (L2), Matrix Factorization (L2), Log-BiLinear (LBL) models (L2), and the Restricted Boltzmann Machine (RBM) model (Lk). Then, we propose a Deep Embedding Model (DEM) (an Lk model) from the energy model in a principled manner. Furthermore, motivated by the observation that the partition function in the energy model is intractable and the fact that the major objective of modeling the co-occurrence data is to predict using the conditional probability, we apply the maximum pseudo-likelihood method to learn DEM. In consequence, the developed model and its learning method naturally avoid the above difficulties and can be easily used to compute the conditional probability in prediction. Interestingly, our method is equivalent to learning a special structured deep neural network using back-propagation and a special sampling strategy, which makes it scalable on large-scale datasets. Finally, in the experiments, we show that the DEM can achieve comparable or better results than state-of-the-art methods on datasets across several application domains.


title: Legal Advocacy and Education Reform: Litigating School Exclusion

abstract: School exclusion has existed as a dark side of public education since the creation of America's public schools. Several cases in the United States Supreme Court memorably invalidated State and school system efforts to deny equal educational opportunities to marginalized school children and youth. In these cases, the over-riding multiple values of education were poignantly articulated in the majority decisions.School exclusion has stubbornly persisted. It takes many forms. This article surveys the most prominent pathways to school exclusion, highlighting what has been called the "School-To-Prison-Pipeline." Various legal challenges are also evaluated. The pros and cons of litigating school exclusion cases are also assessed.


title: Analyzing the applicability of Internet of Things to the battlefield environment

abstract: As the Internet of Things (IoT) matures in commercial sectors, the promise of diverse new technologies such as data-driven applications, intelligent adaptive systems, and embedded optimized automation will be realized in every environment. An immediate research question is whether contemporary IoT concepts can be applied also to military battlefield environments and can realize benefits similar to those in industry. Military environments, especially those that depend on tactical communications, are much more challenging than commercial environments. Thus it is likely many commercial IoT architectures and technologies may not translate into the military domain and others will require additional research to enable deployment and efficient implementation. This paper investigates these issues and describes potential military operational activities that could benefit from commercial IoT technologies, including logistics, sensing/surveillance, and situation awareness. In addition, the paper lays out a roadmap for future research necessary to leverage IoT and apply it to the tactical battlefield environment.


Lectures On Noncommutative Symmetric Functions


title: A method of requirements change management for global software development

abstract: Context: Requirements change is an inevitable software development activity and can occur due to changes in user requirements, increased understanding of the stakeholders' needs, customer organizational re-structure, and availability of new technologies. Timely management of these changes is vital to successful software development, which can be achieved through a rigorous requirements change management (RCM) process. RCM is not straight forward in collocated software development; and with the presences of geographical, social, cultural and temporal factors in Global Software Development (GSD), it makes RCM even more difficult for GSD. Existing RCM methods do not take into consideration of the GSD issues. Objective: In this paper, we present a method for managing requirements changes for GSD. Method: Our method consists of three stages: (i) an understanding of the changes required between different GSD sites is to be established; (ii) a change analysis is to be performed with respect to the development work, which might be either directly or indirectly affected by the changes; and (iii) a finalization of the changes will be made between GSD sites. Past researchers used student groups in a university environment to play the roles of stakeholders in experiments in GSD studies. We validate our method by applying it to a case study of an online shopping system, where the roles of stakeholders were played by a group of students. Results: The results showed that our method facilitates stakeholders to manage requirements changes for GSD better than the existing methods could. Conclusion: Managing changes in requirements at the right time improves the chance of developing a GSD project successfully by minimizing the risks associated with it.


title: Enhancement of MRI human thigh muscle segmentation by template-based framework

abstract: Image segmentation of anatomic structures is often an essential step in medical image analysis. A variety of segmentation methods have been proposed, but none provides automatic segmentation of the thigh. In magnetic resonance images of the thigh, the segmentation is complicated by factors, such as artifacts (e.g. intensity inhomogeneity and echo) and inconsistency of soft and hard tissue compositions, especially in muscle from older people, where accumulation of intermuscular fat is greater than in young muscles. In this paper, the combination framework that leads to a segmentation enhancement method for region of interest segmentation are demonstrated. Appropriate methods of image pre-processing, thresholding, manual interaction of muscle border, template conversion and deformable contours in combination with image filters are applied. Prior geometrical information in an initial template image is used to automatically derive the muscle outlines by application of snake active contours, in serial images within a single MRI dataset. Our approach has an average segmented output accuracy of 93.34% by Jaccard Similarity Index, and reduced the processing time by 97.73% per image compared to manual segmentation.


title: Dispersal in microbes: fungi in indoor air are dominated by outdoor air and show dispersal limitation at short distances

abstract: The indoor microbiome is a complex system that is thought to depend on dispersal from the outdoor biome and the occupants’ microbiome combined with selective pressures imposed by the occupants’ behaviors and the building itself. We set out to determine the pattern of fungal diversity and composition in indoor air on a local scale and to identify processes behind that pattern. We surveyed airborne fungal assemblages within 1-month time periods at two seasons, with high replication, indoors and outdoors, within and across standardized residences at a university housing facility. Fungal assemblages indoors were diverse and strongly determined by dispersal from outdoors, and no fungal taxa were found as indicators of indoor air. There was a seasonal effect on the fungi found in both indoor and outdoor air, and quantitatively more fungal biomass was detected outdoors than indoors. A strong signal of isolation by distance existed in both outdoor and indoor airborne fungal assemblages, despite the small geographic scale in which this study was undertaken (<500 m). Moreover, room and occupant behavior had no detectable effect on the fungi found in indoor air. These results show that at the local level, outdoor air fungi dominate the patterning of indoor air. More broadly, they provide additional support for the growing evidence that dispersal limitation, even on small geographic scales, is a key process in structuring the often-observed distance–decay biogeographic pattern in microbial communities.


title: Drivers of Finished-Goods Inventory in the U.S. Automobile Industry

abstract: Automobile manufacturers in the U.S. supply chain exhibit significant differences in their days of supply of finished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days of supply than General Motors. This suggests that Toyota's well-documented advantage in manufacturing efficiency, product design, and upstream supply chain management extends to their finished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the effect of several factors on inventory holdings. We find that two factors, the number of dealerships in a manufacturer's distribution network and a manufacturer's production flexibility, explain essentially all of the difference in finished-goods inventory between Toyota and three other manufacturers: Chrysler, Ford, and General Motors.


title: Deep Learning Improves Template Matching by Normalized Cross Correlation

abstract: Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. We improve the robustness of this algorithm by preprocessing images with "siamese" convolutional networks trained to maximize the contrast between NCC values of true and false matches. The improvement is quantified using patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional networks significantly reduce false matches. Furthermore, all false matches can be eliminated by removing a tiny fraction of all matches based on NCC values. The improved accuracy of our method could be essential for connectomics, because emerging petascale datasets may require billions of template matches to assemble 2D images of serial sections into a 3D image stack. Our method is also expected to generalize to many other computer vision applications that use NCC template matching to find image correspondences.


title: Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

abstract: Money laundering has been affecting the global economy for many years. Large sums of money are laundered every year, posing a threat to the global economy and its security. Money laundering encompasses illegal activities that are used to make illegally acquired funds appear legal and legitimate. This paper aims to provide a comprehensive survey of machine learning algorithms and methods applied to detect suspicious transactions. In particular, solutions of anti-money laundering typologies, link analysis, behavioural modelling, risk scoring, anomaly detection, and geographic capability have been identified and analysed. Key steps of data preparation, data transformation, and data analytics techniques have been discussed; existing machine learning algorithms and methods described in the literature have been categorised, summarised, and compared. Finally, what techniques were lacking or under-addressed in the existing research has been elaborated with the purpose of pinpointing future research directions.


title: Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction

abstract: Maintaining accurate world knowledge in a complex and changing environment is a perennial problem for robots and other artificial intelligence systems. Our architecture for addressing this problem, called Horde, consists of a large number of independent reinforcement learning sub-agents, or demons. Each demon is responsible for answering a single predictive or goal-oriented question about the world, thereby contributing in a factored, modular way to the system's overall knowledge. The questions are in the form of a value function, but each demon has its own policy, reward function, termination function, and terminal-reward function unrelated to those of the base problem. Learning proceeds in parallel by all demons simultaneously so as to extract the maximal training information from whatever actions are taken by the system as a whole. Gradient-based temporal-difference learning methods are used to learn efficiently and reliably with function approximation in this off-policy setting. Horde runs in constant time and memory per time step, and is thus suitable for learning online in real-time applications such as robotics. We present results using Horde on a multi-sensored mobile robot to successfully learn goal-oriented behaviors and long-term predictions from off-policy experience. Horde is a significant incremental step towards a real-time architecture for efficient learning of general knowledge from unsupervised sensorimotor interaction.


title: Background subtraction using running Gaussian average and frame difference

abstract: Background Subtraction methods are wildly used to detect moving object from static cameras. It has many applications such as traffic monitoring, human motion capture and recognition, and video surveillance. It is hard to propose a background model which works well under all different situations. Actually, there is no need to propose a pervasive model; it is a good model as long as it works well under a special situation. In this paper, a new method combining Gaussian Average and Frame Difference is proposed. Shadow suppression is not specifically dealt with, because it is considered to be part of the background, and can be subtracted by using an appropriate threshold. At last, a new method is raised to fill small gaps that the detected foreground or the moving objects may contain.


Disparities in child and adolescent mental health and mental health services in the U.S.


title: Fatal Outcomes from Liposuction: Census Survey of Cosmetic Surgeons

abstract: Troubling reports of adverse outcomes after liposuction prompted a census survey of aesthetic plastic surgeons. All 1200 actively practicing North American board-certified ASAPS members were polled by facsimile, then mail, regarding deaths after liposuction. Patient initials together with case summaries precluded data replication yet assured patient anonymity and preserved surgeon privacy. Incomplete returns or ambiguous findings were authenticated, where feasible, by direct follow-up. Total number of lipoplasties performed by plastic surgeons was interpolated from the ASPRS procedure database for the survey time frame of 1994 to mid-1998. Lacking reliable annual case volume estimates, deaths from lipoplasties performed by non-ABPS surgeons were excluded from the actual mortality rate computation but were included in cause-of-death ranking statistics. Responding aesthetic plastic surgeons (917 of 1200) reported 95 uniquely authenticated fatalities in 496,245 lipoplasties. In this census survey, the mortality rate computed to 1 in 5224, or 19.1 per 100,000. A virtually identical 20.3 per 100,000 mortality rate was obtained in a 1997 random survey commissioned by the parent society. Pulmonary thromboembolism remains as the major killer (23.4+/-2.6 percent); lacking consistent medical examiners' toxicology data, the putative role of high-dose lidocaine cardiotoxicity could not be ascertained. Where so stated, many deaths occurred during the first night after discharge home; prudence suggests vigilant observation for residual "hangover" from sedative/anesthetic drugs after lengthy procedures. Taken together, these two independent surveys peg the late 1990s mortality rate from liposuction at about 20 per 100,000, or 1 in every 5000 procedures. Set beside the 16.4 per 100,000 fatality rates of U.S. motor vehicle accidents, liposuction is not an altogether benign procedure. We do not have comparable mortality data for lipoplasties performed by non-ABPS-certified physicians.


title: Online spatio-temporal Gaussian process experts with application to tactile classification

abstract: In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and uncertainty estimates. Experiments on benchmarks demonstrate that our method achieves high accuracies relative to state-of-the-art methods. Second, we contribute an online tactile classifier which uses an array of STORK-GP experts. In contrast to existing work, our classifier is capable of learning new objects as they are presented, improving itself over time. We show that our approach yields results comparable to highly-optimised offline classification methods. Moreover, we conducted experiments with human subjects in a similar online setting with true-label feedback and present the insights gained.


title: Improving Modified Differential Evolution for Fuzzy Clustering

abstract: Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms. In recent times, it has been widely used in different fields of science and engineering. Among recently developed various variants of differential evolution, a modified technique called Modified Differential Evolution based Fuzzy Clustering (MoDEFC-V1), was proposed by the authors of this article to improve the speed and accuracy of convergence of differential evolution with a new mutation operation. However, it has a certain limitation of finding global optimum value while searching in solution space. To overcome the limitation of MoDEFC-V1, in this article, we have proposed two different improved versions of MoDEFC called MoDEFC-V2 and MoDEFC-V3 in order to do the underlying optimization such as clustering of patterns better. The effectiveness of the proposed versions is demonstrated for two synthetic and four real-life datasets. Moreover, the superiority of MoDEFC-V2 and MoDEFC-V3 is shown by comparing with state-of-the-art methods qualitatively and quantitatively. Finally, two sample independent one-tailed t-test is performed in order to judge the superiority of the results produced by the proposed versions.


title: Mobilyzer: An Open Platform for Controllable Mobile Network Measurements

abstract: Mobile Internet availability, performance and reliability have remained stubbornly opaque since the rise of cellular data access. Conducting network measurements can give us insight into user-perceived network conditions, but doing so requires careful consideration of device state and efficient use of scarce resources. Existing approaches address these concerns in ad-hoc ways. In this work we propose Mobilyzer, a platform for conducting mobile network measurement experiments in a principled manner. Our system is designed around three key principles: network measurements from mobile devices require tightly controlled access to the network interface to provide isolation; these measurements can be performed efficiently using a global view of available device resources and experiments; and distributing the platform as a library to existing apps provides the incentives and low barrier to adoption necessary for large-scale deployments. We describe our current design and implementation, and illustrate how it provides measurement isolation for applications, efficiently manages measurement experiments and enables a new class of experiments for the mobile environment.


title: Management of severe acute malnutrition in children

abstract: Severe acute malnutrition (SAM) is defined as a weight-for-height measurement of 70% or less below the median, or three SD or more below the mean National Centre for Health Statistics reference values, the presence of bilateral pitting oedema of nutritional origin, or a mid-upper-arm circumference of less than 110 mm in children age 1-5 years. 13 million children under age 5 years have SAM, and the disorder is associated with 1 million to 2 million preventable child deaths each year. Despite this global importance, child-survival programmes have ignored SAM, and WHO does not recognise the term "acute malnutrition". Inpatient treatment is resource intensive and requires many skilled and motivated staff. Where SAM is common, the number of cases exceeds available inpatient capacity, which limits the effect of treatment; case-fatality rates are 20-30% and coverage is commonly under 10%. Programmes of community-based therapeutic care substantially reduce case-fatality rates and increase coverage rates. These programmes use new, ready-to-use, therapeutic foods and are designed to increase access to services, reduce opportunity costs, encourage early presentation and compliance, and thereby increase coverage and recovery rates. In community-based therapeutic care, all patients with SAM without complications are treated as outpatients. This approach promises to be a successful and cost-effective treatment strategy.


title: Advanced Dicing Technology for Semiconductor Wafer—Stealth Dicing

abstract: "Stealth Dicing (SD) " was developed to solve such inherent problems of dicing process as debris contaminants and unnecessary thermal damage on work wafer. In SD, laser beam power of transmissible wavelength is absorbed only around focal point in the wafer by utilizing temperature dependence of absorption coefficient of the wafer. And these absorbed power forms modified layer in the wafer, which functions as the origin of separation in followed separation process. Since only the limited interior region of a wafer is processed by laser beam irradiation, damages and debris contaminants can be avoided in SD. Besides characteristics of devices will not be affected. Completely dry process of SD is another big advantage over other dicing methods.


title: iCare: Applying IoT Technology for Monitoring Alzheimer's Patients

abstract: Taking care of elderly people is uneasy task, especially when they are diagnosed with Alzheimer's disease. They need a permanent and particular type of care. Therefore, Alzheimer's caregivers have huge burden on them. This project aims to get the benefit of the Internet of Things (IoT) concept along with a mobile application to facilitate Alzheimer's caregiving and avoid burnout. The solution consists of two parts: the first one is that the Alzheimer's patient should have a wearable IoT device which is the Apple Smartwatch. The second part is an iOS application download in the caregiver's iPhone that must be connected to the patient's watch to accessing and storing the patient's heart rate and location data. The application enables the caregivers to track and find their beloved ones, especially if they go out by themselves, also it displays the patients' heart rates to the caregivers when they required. It has another feature which is the reminder list to schedule tasks, such as bathing, exercises, a patient's medication and medical appointments. In addition, the application has a section where the caregivers can get some information about Alzheimer's Disease.


title: Approximate Exploration through State Abstraction

abstract: Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of pseudo-count based exploration bonuses (Bellemare et al., 2016), a practical exploration scheme based on density modelling. As a warm-up, we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and Littman, 2008), when explicitly combined with state aggregation. This allows us to confirm that, as might be expected, approximation allows the agent to trade off between learning speed and quality of the learned policy. Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration. Then, we show that a given density model also defines an implicit abstraction, and find a surprising mismatch between pseudo-counts derived either implicitly or explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.


Multi-Engine Machine Translation with an Open-Source SMT Decoder


title: Current state of the research in agile quality development

abstract: With the changing trends in software development methodologies since 1990's the focus is more on iterative incremental approaches for software development. One of such approach is “Agile Approach” also named as lightweight model for development. Agile is more about communication and collaboration between team members working on software project. For good quality software it's important to select the appropriate model for the development of software. Another factor that is important to be considered while model selection is that in what respect quality gets better if we choose either traditional or agile approach. Quality assurance techniques for traditional heavy weight development rely on inspections, reviews made at end whereas quality assurance techniques for agile light weight development rely on routine activities by teams. This paper aims to review various success factors of agile software development and various challenges faced in terms of assuring quality in agile.


title: Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization

abstract: Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines (SVM) and AdaBoost. In this paper, we use a modified version of LR to approximate the optimization of SVM by a sequence of unconstrained optimization problems. We prove that our approximation will converge to SVM, and propose an iterative algorithm called "MLR-CG" which uses Conjugate Gradient as its inner loop. Multiclass version "MMLR-CG" is also obtained after simple modifications. We compare the MLR-CG with SVMlight over different text categorization collections, and show that our algorithm is much more efficient than SVMlight when the number of training examples is very large. Results of the multiclass version MMLR-CG is also reported.


title: Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

abstract: Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN), Bayesian Classification and Support vector machine (SVM) are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from "DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy" and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.


title: Formal Verification of Neural Network Controlled Autonomous Systems

abstract: In this paper, we consider the problem of formally verifying the safety of an autonomous robot equipped with a Neural Network (NN) controller that processes LiDAR images to produce control actions. Given a workspace that is characterized by a set of polytopic obstacles, our objective is to compute the set of safe initial states such that a robot trajectory starting from these initial states is guaranteed to avoid the obstacles. Our approach is to construct a finite state abstraction of the system and use standard reachability analysis over the finite state abstraction to compute the set of safe initial states. To mathematically model the imaging function, that maps the robot position to the LiDAR image, we introduce the notion of imaging-adapted partitions of the workspace in which the imaging function is guaranteed to be affine. Given this workspace partitioning, a discrete-time linear dynamics of the robot, and a pre-trained NN controller with Rectified Linear Unit (ReLU) non-linearity, we utilize a Satisfiability Modulo Convex (SMC) encoding to enumerate all the possible assignments of different ReLUs. To accelerate this process, we develop a pre-processing algorithm that could rapidly prune the space of feasible ReLU assignments. Finally, we demonstrate the efficiency of the proposed algorithms using numerical simulations with the increasing complexity of the neural network controller.


title: Integration of Electric Vehicles in the Electric Power System

abstract: This paper presents a conceptual framework to successfully integrate electric vehicles into electric power systems. The proposed framework covers two different domains: the grid technical operation and the electricity markets environment. All the players involved in both these processes, as well as their activities, are described in detail. Additionally, several simulations are presented in order to illustrate the potential impacts/benefits arising from the electric vehicles grid integration under the referred framework, comprising steady-state and dynamic behavior analysis.


title: Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle

abstract: Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target's motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.


title: Password Enhancement Based on Semantic Transformation

abstract: With the continuous development of authentication approaches, password-based authentication is still the first choice for various online services today. The security of password-based authentication relies heavily on the strength of the passwords created by users. Password enhancement is a general way to increase the difficulty of cracking a password. An ideal password enhancement strategy should take into account both the usability (mainly the memorability) and the security of passwords. However, it has been found that the higher the password strength, the lower the usability of the password, and vice versa. In order to balance the usability and the security of the password, we propose a password enhancement method based on semantic transformation, which can effectively analyze the semantic structure of a given password. This enhances the password's strength through one or more password semantic transformations to make the password better protected against guessing attacks. Finally, we use publicly available real-world password data sets leaked in previous security incidents to conduct experiments. Our password enhancement strategy significantly reduces the proportion of guesses by a classic password guessing attack, which demonstrates the effectiveness of the method.


title: Association between heart rate variability and fluctuations in resting-state functional connectivity

abstract: Functional connectivity has been observed to fluctuate across the course of a resting state scan, though the origins and functional relevance of this phenomenon remain to be shown. The present study explores the link between endogenous dynamics of functional connectivity and autonomic state in an eyes-closed resting condition. Using a sliding window analysis on resting state fMRI data from 35 young, healthy male subjects, we examined how heart rate variability (HRV) covaries with temporal changes in whole-brain functional connectivity with seed regions previously described to mediate effects of vigilance and arousal (amygdala and dorsal anterior cingulate cortex; dACC). We identified a set of regions, including brainstem, thalamus, putamen, and dorsolateral prefrontal cortex, that became more strongly coupled with the dACC and amygdala seeds during states of elevated HRV. Effects differed between high and low frequency components of HRV, suggesting specific contributions of parasympathetic and sympathetic tone on individual connections. Furthermore, dynamics of functional connectivity could be separated from those primarily related to BOLD signal fluctuations. The present results contribute novel information about the neural basis of transient changes of autonomic nervous system states, and suggest physiological and psychological components of the recently observed non-stationarity in resting state functional connectivity.


title: NUMERICAL ANALYSIS OF A SMALL ULTRA WIDEBAND MICROSTRIP-FED TAP MONOPOLE ANTENNA

abstract: This paper presents a planar microstrip-fed tab monopole antenna for ultra wideband wireless communications applications. The impedance bandwidth of the antenna is improved by adding slit in one side of the monopole,introducing a tapered transition between the monopole and the feed line,and adding two-step staircase notch in the ground plane. Numerical analysis for the antenna dimensional parameters using Ansoft HFSS is performed and presented. The proposed antenna has a small size of 16 × 19 mm,and provides an ultra wide bandwidth from 2.8 to 28 GHz with low VSWR level and good radiation characteristics to satisfy the requirements of the current and future wireless communications systems.


title: A capacitance-to-digital converter for displacement sensing with 17b resolution and 20μs conversion time

abstract: In precision mechatronic systems, such as wafer steppers, the position of critical mechanical components must be dynamically stabilized with sub-nanometer precision. This can be achieved by a servo loop consisting of a displacement sensor and an actuator. Compared to optical interferometers, capacitive displacement sensors offer smaller size and lower cost. However, mechanical tolerances limit their electrode spacing to about 10μm [1], while the targeted resolution is below 100pm rms . This requires a capacitance-to-digital converter (CDC) with more than 17b resolution. Furthermore, its latency must be low enough (20μs) to avoid compromising servo-loop stability. Lastly, it should be stable enough to maintain measurement accuracy during the intervals between system calibrations.


A Resilient, Untethered Soft Robot


title: Lens Antenna for Wide Angle Beam Scanning at 79 GHz for Automotive Short Range Radar Applications

abstract: This communication presents the design and experimental verification of a substrate integrated waveguide fed lens antenna for 79 GHz automotive radar applications. The proposed integrated lens antenna consists of a six layer cylindrical Luneburg lens illuminated by 17 source elements of substrate integrated waveguide fed by a planar log periodic dipole antenna array. The Luneburg lens is developed by using a unique foam material AirexR82 ( $\varepsilon _{r} = 1.12$ ), which is drilled and pressed to achieve the different dielectric constant needed to follow the index law inside the lens. The return loss and the radiation characteristics of the proposed integrated lens antenna are investigated and validated by measurements. A good agreement between the measured and simulated results is observed. The measured results confirm the beam scanning capability of the proposed integrated lens antenna for a wide scan angle of ±85° in azimuth plane having a maximum antenna gain of 15 dBi at 82.5 GHz and gain drop of less than 2.75 dB for the edge feeds. The estimated radiation efficiency of the antenna is found to be 67% at 82.5 GHz.


title: ASSURE: automatic software self-healing using rescue points

abstract: Software failures in server applications are a significant problem for preserving system availability. We present ASSURE, a system that introduces rescue points that recover software from unknown faults while maintaining both system integrity and availability, by mimicking system behavior under known error conditions. Rescue points are locations in existing application code for handling a given set of programmer-anticipated failures, which are automatically repurposed and tested for safely enabling fault recovery from a larger class of (unanticipated) faults. When a fault occurs at an arbitrary location in the program, ASSURE restores execution to an appropriate rescue point and induces the program to recover execution by virtualizing the program's existing error-handling facilities. Rescue points are identified using fuzzing, implemented using a fast coordinated checkpoint-restart mechanism that handles multi-process and multi-threaded applications, and, after testing, are injected into production code using binary patching. We have implemented an ASSURE Linux prototype that operates without application source code and without base operating system kernel changes. Our experimental results on a set of real-world server applications and bugs show that ASSURE enabled recovery for all of the bugs tested with fast recovery times, has modest performance overhead, and provides automatic self-healing orders of magnitude faster than current human-driven patch deployment methods.


title: Improving K-modes Algorithm Considering Frequencies of Attribute Values

abstract: In this paper, we present an experimental study on applying a new dissimilarity measure to the k-modes clustering algorithm to improve its clustering accuracy. The measure is based on the idea that the similarity between a data object and cluster mode, is directly proportional to the sum of relative frequencies of the common values in mode. Experimental results on real life datasets show that, the modified algorithm is superior to the original k-modes algorithm with respect to clustering accuracy.


AN OVERVIEW OF PENETRATION TESTING 1


title: Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots

abstract: State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously updating a common, shared policy. Moreover, decentralized control architectures (e.g., CPGs) can coordinate spatially distributed portions of an articulated robot to achieve system-level objectives. In this work, we investigate the relationship between distributed learning and decentralized control by learning decentralized control policies for the locomotion of articulated robots in challenging environments. To this end, we present an approach that leverages the structure of the asynchronous advantage actor-critic (A3C) algorithm to provide a natural means of learning decentralized control policies on a single articulated robot. Our primary contribution shows individual agents in the A3C algorithm can be defined by independently controlled portions of the robot's body, thus enabling distributed learning on a single robot for efficient hardware implementation. We present results of closed-loop locomotion in unstructured terrains on a snake and a hexapod robot, using decentralized controllers learned offline and online respectively. ::: Preprint of the paper submitted to the IEEE Transactions in Robotics (T-RO) journal in October 2018, and conditionally accepted for publication as a regular paper in January 2019.


title: Practical off-chip meta-data for temporal memory streaming

abstract: Prior research demonstrates that temporal memory streaming and related address-correlating prefetchers improve performance of commercial server workloads though increased memory level parallelism. Unfortunately, these prefetchers require large on-chip meta-data storage, making previously-proposed designs impractical. Hence, to improve practicality, researchers have sought ways to enable timely prefetch while locating meta-data entirely off-chip. Unfortunately, current solutions for off-chip meta-data increase memory traffic by over a factor of three. We observe three requirements to store meta-data off chip: minimal off-chip lookup latency, bandwidth-efficient meta-data updates, and off-chip lookup amortized over many prefetches. In this work, we show: (1) minimal off-chip meta-data lookup latency can be achieved through a hardware-managed main memory hash table, (2) bandwidth-efficient updates can be performed through probabilistic sampling of meta-data updates, and (3) off-chip lookup costs can be amortized by organizing meta-data to allow a single lookup to yield long prefetch sequences. Using these techniques, we develop Sampled Temporal Memory Streaming (STMS), a practical address-correlating prefetcher that keeps predictor meta-data in main memory while achieving 90% of the performance potential of idealized on-chip meta-data storage.


title: An empirical study of PHP feature usage: a static analysis perspective

abstract: PHP is one of the most popular languages for server-side application development. The language is highly dynamic, providing programmers with a large amount of flexibility. However, these dynamic features also have a cost, making it difficult to apply traditional static analysis techniques used in standard code analysis and transformation tools. As part of our work on creating analysis tools for PHP, we have conducted a study over a significant corpus of open-source PHP systems, looking at the sizes of actual PHP programs, which features of PHP are actually used, how often dynamic features appear, and how distributed these features are across the files that make up a PHP website. We have also looked at whether uses of these dynamic features are truly dynamic or are, in some cases, statically understandable, allowing us to identify specific patterns of use which can then be taken into account to build more precise tools. We believe this work will be of interest to creators of analysis tools for PHP, and that the methodology we present can be leveraged for other dynamic languages with similar features.


title: Dynamic analysis and control of lego mindstorms NXT bicycle

abstract: The paper is inspired from a challenging experiment conducted with undergraduate students for bachelor project within Automatic Control and Computer Engineering Faculty, Iasi. A bicycle type system was implemented, analyzed and some control strategies were implemented and tested in order to self-stabilize the system. The bicycle study is enjoyable both to undergraduate students and researchers because it uses a “hi-tech” relevant concrete example. This work encompasses and uses a full range of system-theoretic aspects which covers knowledge from analytical, computational and experimental point of view. The dynamic description of the bicycle is done using a set of dynamic equations that describes the location and orientation of the body, considering four rigid subsystems that form the bicycle. The control issues were conducted in order to follow a desired forward speed using classical PI controller, followed by the LQR approach for steering angle.


title: The KUKA Control Toolbox: motion control of KUKA robot manipulators with MATLAB

abstract: The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the University of Siena, for motion control of KUKA robot manipulators. The toolbox, which is compatible with all 6 DOF small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 40 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display, 3-D animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.


title: Fourier cleaning of fingerprint images

abstract: Usage of finger orient imaging for personal identification is one of the oldest and widely used techniques. One of the important steps in fingerprint matching procedure is to reliably extract the discriminating features called minutiae. However, some of the acquired images are of poor quality. Therefore, one must resort to the pre-processing the images. There are various pre-processing techniques has been identified like the Gabor filtering. However, one limiting factor in these e enhancement techniques is their dependence on the extraction of orientation estimates. These estimates remain no longer reliable under some noisy conditions. In this paper we avoided the explicit estimate of the orientation information and relied on the Fourier sinusoidal pattern. We have used Fourier cleaning in the form of notch filter, highpass filter and binarization along with Gabor filter to enhance the fingerprint images. The conducted experiments do suggest a modest raise of around 20–25% in true minutiae matching rate in these poorly visible fingerprint regions.


title: PCN: Part and Context Information for Pedestrian Detection with CNNs

abstract: Pedestrian detection has achieved great improvements in recent years, while complex occlusion handling is still one of the most important problems. To take advantage of the body parts and context information for pedestrian detection, we propose the part and context network (PCN) in this work. PCN specially utilizes two branches which detect the pedestrians through body parts semantic and context information, respectively. In the Part Branch, the semantic information of body parts can communicate with each other via recurrent neural networks. In the Context Branch, we adopt a local competition mechanism for adaptive context scale selection. By combining the outputs of all branches, we develop a strong complementary pedestrian detector with a lower miss rate and better localization accuracy, especially for occlusion pedestrian. Comprehensive evaluations on two challenging pedestrian detection datasets (i.e. Caltech and INRIA) well demonstrated the effectiveness of the proposed PCN.


Improved single image dehazing using guided filter


title: Consolidated tree classifier learning in a car insurance fraud detection domain with class imbalance

abstract: This paper presents an analysis of the behaviour of Consolidated Trees, CT (classification trees induced from multiple subsamples but without loss of explaining capacity). We analyse how CT trees behave when used to solve a fraud detection problem in a car insurance company. This domain has two important characteristics: the explanation given to the classification made is critical to help investigating the received reports or claims, and besides, this is a typical example of class imbalance problem due to its skewed class distribution. In the results presented in the paper CT and C4.5 trees have been compared, from the accuracy and structural stability (explaining capacity) point of view and, for both algorithms, the best class distribution has been searched.. Due to the different associated costs of different error types (costs of investigating suspicious reports, etc.) a wider analysis of the error has also been done: precision/recall, ROC curve, etc.


title: KPB-SIFT: a compact local feature descriptor

abstract: Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.


title: Multi-view Recurrent Neural Acoustic Word Embeddings

abstract: Recent work has begun exploring neural acoustic word embeddings–fixed dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddings are applicable to speech retrieval and recognition tasks, where reasoning about whole words may make it possible to avoid ambiguous sub-word representations. The main idea is to map acoustic sequences to fixed-dimensional vectors such that examples of the same word are mapped to similar vectors, while different-word examples are mapped to very different vectors. In this work we take a multi-view approach to learning acoustic word embeddings, in which we jointly learn to embed acoustic sequences and their corresponding character sequences. We use deep bidirectional LSTM embedding models and multi-view contrastive losses. We study the effect of different loss variants, including fixed-margin and cost-sensitive losses. Our acoustic word embeddings improve over previous approaches for the task of word discrimination. We also present results on other tasks that are enabled by the multi-view approach, including cross-view word discrimination and word similarity.


title: Förster’s resonance excitation transfer theory: not just a formula

abstract: After 65 years of increasing scrutiny and application, Theodor Forster's treatment of resonance excitation transfer is widely quoted and has acquired the acronym FRET, in which "F" originally and rather curiously stood for "fluorescence." In this brief and mostly qualitative survey, we review some of its history, mention its important limitations, and relate some personal encounters with Forster.


title: Single-phase bidirectional three-level T-type inverter

abstract: This paper proposes a single-phase bidirectional three-level T-type inverter. The proposed inverter has a T-type switching leg and a half-bridge switching leg. The T-type switching leg operates at high switching frequency with sinusoidal pulse width modulation. The half-bridge switching leg operates at the grid frequency according to the voltage polarity of the grid. Due to the three-level switching operation of the proposed inverter, it provides the advantages of low voltage stress, reduced switching loss, and low harmonic components for the single-phase gird-tied applications. The proposed inverter is introduced by describing its circuit configuration, switching technique, operation modes, and control scheme. A 1.0 kW prototype has been designed and experimentally tested to verify the performance of the proposed inverter.


title: Recurrent Convolutional Networks for Pulmonary Nodule Detection in CT Imaging

abstract: Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-automated detection of pulmonary nodules in CT scans. The architecture learns to distinguish nodules and normal structures at the pixel level and generates three-dimensional probability maps highlighting areas that are likely to harbour the objects of interest. Convolutional and recurrent layers are combined to learn expressive image representations exploiting the spatial dependencies across axial slices. We demonstrate that leveraging intra-slice dependencies substantially increases the sensitivity to detect pulmonary nodules without inflating the false positive rate. On the publicly available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet reaches a detection sensitivity of 90.5% with an average of 4.5 false positives per scan. Comparisons with a competing multi-channel convolutional neural network for multi-slice segmentation and other published methodologies using the same dataset provide evidence that ReCTnet offers significant performance gains.


title: Scout: A GPU-Aware System for Interactive Spatio-temporal Data Visualization

abstract: This demo presents Scout; a full-fledged interactive data visualization system with native support for spatio-temporal data. Scout utilizes computing power of GPUs to achieve real-time query performance. The key idea behind Scout is a GPU-aware multi-version spatio-temporal index. The indexing and query processing modules of Scout are designed to complement the GPU hardware characteristics. Front end of Scout provides a user interface to submit queries and view results. Scout supports a variety of spatio-temporal queriesrange, k-NN, and join. We use real data sets to demonstrate scalability and important features of Scout.


title: Restraint Asphyxiation in Excited Delirium

abstract: Eleven cases of sudden death of men restrained in a prone position by police officers are reported. Nine of the men were hogtied, one was tied to a hospital gurney, and one was manually held prone. All subjects were in an excited delirious state when restrained. Three were psychotic, whereas the others were acutely delirious from drugs (six from cocaine, one from methamphetamine, and one from LSD). Two were shocked with stun guns shortly before death. The literature is reviewed and mechanisms of death are discussed. Language: en


Subjective Well-Being, Psychological Well-Being, and Islamic Religiosity