title: CNN- and LSTM-based Claim Classification in Online User Comments

abstract: When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes claims according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.


title: Bayesian super-resolved surface reconstruction from images

abstract: Bayesian inference has been used successfully for many problems where the aim is to infer the parameters of a model of interest. In this paper we formulate the three dimensional reconstruction problem as the problem of inferring the parameters of a surface model from image data, and show how Bayesian methods can be used to estimate the parameters of this model given the image data. Thus we recover the three dimensional description of the scene. This approach also gives great flexibility. We can specify the geometrical properties of the model to suit our purpose, and can also use different models for how the surface reflects the light incident upon it. In common with other Bayesian inference problems, the estimation methodology requires that we can simulate the data that would have been recorded for any values of the model parameters. In this application this means that if we have image data we must be able to render the surface model. However it also means that we can infer the parameters of a model whose resolution can be chosen irrespective of the resolution of the images, and may be super-resolved. We present results of the inference of surface models from simulated aerial photographs for the case of super-resolution, where many surface elements project into a single pixel in the low-resolution images.


title: Impact of authentic leadership on performance: Role of followers' positive psychological capital and relational processes

abstract: Summary Authentic leadership has received considerable attention and research support over the past decade. Now the time has come to refine and better understand how it impacts performance. This study investigates the moderating role followers’ positive psychological capital (PsyCap) and the mediating role that leader– member exchange (LMX) may play in influencing the relationship between authentic leadership and followers’ performance. Specifically, we tested this mediated moderation model with matched data from 794 followers and their immediate leaders. We found that authentic leadership is positively related to LMX and consequently followers’ performance, and to a larger degree, among followers who have low rather than high levels of PsyCap. Our discussion highlights the benefits of understanding the roles of relational processes and followers’ positive psychological resources involved in the effectiveness of authentic leadership and how they can be practically implemented. Copyright © 2012 John Wiley & Sons, Ltd.


title: Sonographic Evaluation of a Tibial Periosteal Ganglion With an Intraosseous Component

abstract: Aperiosteal ganglion cyst is a rare condition that can easily be misdiagnosed as other types of surface bony lesions because of cortical erosion and suggestive periosteal reactions on plain radiography. We encountered a periosteal ganglion located over the anterolateral aspect of the metaphysis of the distal tibia, which was connected to an intraosseous ganglion cyst through a cortical defect "on the anterolateral surface of the distal end. To our knowledge, such a case has not been reported previously. We present the sonographic features and the magnetic resonance (MR) imaging and computed tomographic (CT) findings of this case. Overall, sonography can be used to characterize the location and cystic nature of a periosteal ganglion.


title: Studies on the synthesis and crystal structure of a magnesium complex with pyruvic acid isonicotinoyl hydrazone

abstract: Abstract A magnesium complex with pyruvic acid isonicotinoyl hydrazone (H2L), having the general formula [MgL(H2O)31 · H2O, has been synthesized and its crystal structure determined by X-ray diffraction analyses. The structural parameters and typical IR spectra bands for H2L complexes with Mg and other metals are discussed.


title: Behavioral Diversity Generation in Autonomous Exploration through Reuse of Past Experience

abstract: The production of behavioral diversity---producing a diversity of effects---is an essential strategy for robots exploring the world when facing situations where interaction possibilities are unknown or non-obvious. It allows to discover new aspects of the environment that cannot be inferred or deduced from available knowledge. However, creating behavioral diversity in situations where it is the most crucial---new and unknown ones---is far from trivial. In particular in large and redundant sensorimotor spaces, only small areas are interesting to explore for any practical purpose. When the environment does not provide clues or gradient toward those areas, trying to discover those areas relies on chance. To address this problem, we introduce a method to create behavioral diversity in a new sensorimotor task by re-enacting actions that allowed to produce behavioral diversity in a previous task, along with a measure that quantifies this diversity. We show that our method can learn how to interact with an object by reusing experience from another, that it adapts to instances of morphological changes and of dissimilarity between tasks, and how scaffolding behaviors can emerge by simply switching the attention of the robot to different parts of the environment. Finally, we show that the method can robustly use simulated experiences and crude cognitive models to generate behavioral diversity in real robots.


0414-001 2 APK File Dynamic Analysis Emulator Android OS Dalvik VM Analysis Report Static Analysis Post Processing Network Protocols Code Coverage


title: SC-FDMA & OFDMA in LTE physical layer

abstract: This paper provides the introduction of LTE and the key components of its physical layer. These descriptions are simplified version of the detailed descriptions provided by 3gpp. In LTE, the OFDMA is used as downlink and SCFDMA as uplink modulation schemes; with OFDM as the basic building block. OFDMA is used for achieving high spectral efficiency in communication system; whereas, SC-FDMA for uplink multiple access scheme in LTE system. This paper evaluates the performance of SC-FDMA and OFDMA of LTE physical layer by considering different modulation schemes (BPSK, QPSK, 16QAM and 64QAM) on the basis of PAPR, BER and error probability. From the simulated results, it is observed that for a particular value of SNR, the BER increases for high order modulation (16- QAM and 64-QAM) in both OFDMA and SC-FDMA. However, the lower order modulation schemes (BPSK and QPSK) experience less BER at receiver; thus lower order modulations improve the system performance in terms of BER and SNR. In terms of bandwidth efficiency, the higher order modulation accommodates more data within a given bandwidth and is more bandwidth efficient as compared to lower order modulation. Thus, there exists a tradeoff between BER and bandwidth efficiency among these modulation schemes used in LTE. It is also observed that the error probability increases as order of modulation scheme increases. Therefore, the selection of modulation schemes in adaptive modulation is quite crucial based on these results.


title: Analysis and Evaluation of Current Graph-Based Text Mining Researches

abstract: Traditionally, since text mining has been assumed unformatted text (document), it is necessary to represent text as a simplified model. One of most commonly used model is the vector space model, in which text is represented as a bag of words. Recently, much research tried to apply a graph-based text mod- el for representing semantic relationships between words. In this paper, we sur- vey research trends of graph-based text representation models for text mining. We summarized each model, its features and forecasted further researches.


title: A Tensor-Based Algorithm for High-Order Graph Matching

abstract: This paper addresses the problem of establishing correspondences between two sets of visual features using higher order constraints instead of the unary or pairwise ones used in classical methods. Concretely, the corresponding hypergraph matching problem is formulated as the maximization of a multilinear objective function over all permutations of the features. This function is defined by a tensor representing the affinity between feature tuples. It is maximized using a generalization of spectral techniques where a relaxed problem is first solved by a multidimensional power method and the solution is then projected onto the closest assignment matrix. The proposed approach has been implemented, and it is compared to state-of-the-art algorithms on both synthetic and real data.


title: Abstraction in Artificial Intelligence and Complex Systems

abstract: Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRAmodel, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRAmodel, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.


title: Collaborative Sparse Coding for Multiview Action Recognition

abstract: Multiview action recognition has received increasing attention over the past decade. Various approaches have been proposed to extract view-invariant features; among them, self-similarity matrices (SSMs) have shown outstanding performance. However, SSMs become sensitive when there's a very large view change. To make SSMs more robust to viewpoint changes, the authors propose a collaborative sparse coding framework. They integrate the classifier training process and sparse coding process into a unified collaborative filtering framework; this lets more discriminative sparse video representations and classifiers be learned by optimizing the dictionary and classifier jointly. Experimental results demonstrate the effectiveness of the framework.


title: Holistic measurement of well-being: psychometric properties of the physical, mental and social well-being scale (PMSW-21) for adults.

abstract: Background. A holistic approach to health requires the development of tools that would allow to measure the inner world of individuals within its physical, mental and social dimensions. Objectives. To create the Physical, Mental and Social Well-being scale (PMSW-21) that allows a holistic representation of various dimensions of well-being in such a way as they are perceived by the individuals and how affected their health. Material and methods. The study was conducted on the sample of 406 inhabitants of Warsaw involving in the Social Participation in Health Reform project. The PMSW-21 scale included: headache, tiredness, abdominal pain, palpitation, joint pain, backache, sleep disturbance (physical domain), anxiety, guiltiness, helplessness, hopelessness, sadness, self-dissatisfaction, hostility (mental domain), security, communicability, protection, loneliness, rejection, sociability and appreciation (social domain). The five criterial variables of health and seven of life experiences were adopted to assess the discriminative power of the PMSW-21 scale. Results. The total well-being scale as well as its physical, mental and social domains showed high reliability (Cronbach a 0.81, 0.77, 0.90, 0.72, respectively). The analysis confirmed the construct validity. All the items stronger correlated with their own domain than with the others (ranges for physical: 0.41 – 0.55, mental: 0.49 – 0.80 and social: 0.31 – 0.50). The total scale demonstrate high sensitivity; it significantly differentiated almost all criterial variables. Physical domain showed high sensitivity for health as well as for negative life events variables, while the mental and social domains were more sensitive for life events. Conclusions. The analysis confirmed the usefulness of PMSW-21 scale for measure the holistic well-being. The reliability of the total scale and its domains, construct validity and sensitivity for health and life determinants were at acceptable level.


title: A Review on Massive E-Learning (MOOC) Design, Delivery and Assessment

abstract: MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.


Face Synthesis from Facial Identity Features.


title: Image-based logical document structure recognition

abstract: The paper presents a complete solution for recognition of textual and graphic structures in various types of documents acquired from the Internet. In the proposed approach, the document structure recognition problem is divided into sub-problems. The first one is localizing logical structure elements within the document. The second one is recognizing segmented logical structure elements. The input to the method is an image of document page, the output is the XML file containing all graphic and textual elements included in the document, preserving the reading order of document blocks. This file contains information about the identity and position of all logical elements in the document image. The paper describes all details of the proposed method and shows the results of the experiments validating its effectiveness. The results of the proposed method for paragraph structure recognition are comparable to the referenced methods which offer segmentation only.


title: How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

abstract: In the last few years, we have seen the rise of deep learning applications in a broad range of chemistry research problems. Recently, we reported on the development of Chemception, a deep convolutional neural network (CNN) architecture for general-purpose small molecule property prediction. In this work, we investigate the effects of systematically removing and adding basic chemical information to the image channels of the 2D images used to train Chemception. By augmenting images with only 3 additional basic chemical information, we demonstrate that Chemception now outperforms contemporary deep learning models trained on more sophisticated chemical representations (molecular fingerprints) for the prediction of toxicity, activity, and solvation free energy, as well as physics-based free energy simulation methods. Thus, our work demonstrates that a firm grasp of first-principles chemical knowledge is not a pre-requisite for deep learning models to accurately predict chemical properties. Lastly, by altering the chemical information content in the images, and examining the resulting performance of Chemception, we also identify two different learning patterns in predicting toxicity/activity as compared to solvation free energy, and these patterns suggest that Chemception is learning about its tasks in the manner that is consistent with established knowledge.


title: Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms

abstract: Brain Computer interfaces (BCI) has immense potentials to improve human lifestyle including that of the disabled. BCI has possible applications in the next generation human-computer, human-robot and prosthetic/assistive devices for rehabilitation. The dataset used for this study has been obtained from the BCI competition-II 2003 databank provided by the University of Technology, Graz. After pre-processing of the signals from their electrodes (C3 & C4), the wavelet coefficients, Power Spectral Density of the alpha and the central beta band and the average power of the respective bands have been employed as features for classification. This paper presents a comparative study of different classification methods including linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN) algorithm, linear support vector machine (SVM), radial basis function (RBF) SVM and naive Bayesian classifiers algorithms in differentiating the raw EEG data obtained, into their associative left/right hand movements. Performance of left/right hand classification is studied using both original features and reduced features. The feature reduction here has been performed using Principal component Analysis (PCA). It is as observed that RBF kernelised SVM classifier indicates the highest performance accuracy of 82.14% with both original and reduced feature set. However, experimental results further envisage that all the other classification techniques provide better classification accuracy for reduced data set in comparison to the original data. It is also noted that the KNN classifier improves the classification accuracy by 5% when reduced features are used instead of the original.


title: From regular expressions to deterministic automata

abstract: The main theorem allows an elegant algorithm to be refined into an efficient one. The elegant algorithm for constructing a finite automaton from a regular expression is based on ‘derivatives of’ regular expressions; the efficient algorithm is based on ‘marking of’ regular expressions. ::: ::: Derivatives of regular expressions correspond to state transitions in finite automata. When a finite automaton makes a transition under input symbol a, a leading a is stripped from the remaining input. Correspondingly, if the input string is generated by a regular expression E, then the derivative of E by a generates the remaining input after a leading a is stripped. Brzozowski (1964) used derivatives to construct finite automata; the state for expression E has a transition under a to the state for the derivative of E by a. This approach extends to regular expressions with new operators, including intersection and complement; however, explicit computation of derivatives can be expensive. ::: ::: Marking of regular expressions yields an expression with distinct input symbols. Following McNaughton and Yamada (1960), we attach subscripts to each input symbol in an expression; (ab + b)∗ba becomes (a1b2+b3)∗b4a5. Conceptually, the efficient algorithm constructs an automaton for the marked expression. The marks on the transitions are then erased, resulting in a nondeterministic automaton for the original unmarked expression. This approach works for the usual operations of union, concatenation, and iteration; however, intersection and complement cannot be handled because marking and unmarking do not preserve the languages generated by regular expressions with these operators.


title: A Cascaded Multilevel Inverter Based on Switched-Capacitor for High-Frequency AC Power Distribution System

abstract: The increase of transmission frequency reveals more merits than low- or medium-frequency distribution among different kinds of power applications. High-frequency inverter serves as source side in high-frequency ac (HFAC) power distribution system (PDS). However, it is complicated to obtain a high-frequency inverter with both simple circuit topology and straightforward modulation strategy. A novel switched-capacitor-based cascaded multilevel inverter is proposed in this paper, which is constructed by a switched-capacitor frontend and H-Bridge backend. Through the conversion of series and parallel connections, the switched-capacitor frontend increases the number of voltage levels. The output harmonics and the component counter can be significantly reduced by the increasing number of voltage levels. A symmetrical triangular waveform modulation is proposed with a simple analog implementation and low modulation frequency comparing with traditional multicarrier modulation. The circuit topology, symmetrical modulation, operation cycles, Fourier analysis, parameter determination, and topology enhancement are examined. An experimental prototype with a rated output frequency of 25 kHz is implemented to compare with simulation results. The experimental results agreed very well with the simulation that confirms the feasibility of proposed multilevel inverter.


title: Automated reprojection-based pixel shader optimization

abstract: We present a framework and supporting algorithms to automate the use of temporal data reprojection as a general tool for optimizing procedural shaders. Although the general strategy of caching and reusing expensive intermediate shading calculations across consecutive frames has previously been shown to provide an effective trade-off between speed and accuracy, the critical choices of what to reuse and at what rate to refresh cached entries have been left to a designer. The fact that these decisions require a deep understanding of a procedure's semantic structure makes it challenging to select optimal candidates among possibly hundreds of alternatives. Our automated approach relies on parametric models of the way possible caching decisions affect the shader's performance and visual fidelity. These models are trained using a sample rendering session and drive an interactive profiler in which the user can explore the error/performance trade-offs associated with incorporating temporal reprojection. We evaluate the proposed models and selection algorithm with a prototype system used to optimize several complex shaders and compare our approach to current alternatives.


title: Social Skills Interventions for Children with Asperger’s Syndrome or High-Functioning Autism: A Review and Recommendations

abstract: This paper reviews the literature examining social skills training (SST) programs for youth with AS/HFA, with an emphasis on critically evaluating efficacy and highlighting areas of future research. The review highlights the disparity between SST programs described in the extant literature, including lack of a universal definition of social skills, various levels of intensity and duration of treatment, divergent theoretical backgrounds, and variety in services provided in clinic or classroom settings. Overall, it is clear that, despite their widespread clinical use, empirical support for SST programs for children with AS/HFA is minimal at this time. Based on this critical review, a “roadmap” for future research, consistent with recommendations put forth by a leading group of autism researchers, is presented.


title: Synaesthesia : The prevalence of atypical cross-modal experiences

abstract: Sensory and cognitive mechanisms allow stimuli to be perceived with properties relating to sight, sound, touch, etc, and ensure, for example, that visual properties are perceived as visual experiences, rather than sounds, tastes, smells, etc. Theories of normal development can be informed by cases where this modularity breaks down, in a condition known as synaesthesia. Conventional wisdom has held that this occurs extremely rarely (0.05% of births) and affects women more than men. Here we present the first test of synaesthesia prevalence with sampling that does not rely on self-referral, and which uses objective tests to establish genuineness. We show that (a) the prevalence of synaesthesia is 88 times higher than previously assumed, (b) the most common variant is coloured days, (c) the most studied variant (grapheme-colour synaesthesia)-previously believed most common-is prevalent at 1%, and (d) there is no strong asymmetry in the distribution of synaesthesia across the sexes. Hence, we suggest that female biases reported earlier likely arose from (or were exaggerated by) sex differences in self-disclosure.


title: Using 360 Degree Feedback Instruments up, down and around the world: Implications for global implementation and use of Multi‐Rater Feedback

abstract: This report highlights some of the challenges faced when using multi-rater feedback around the world. It is based on the observations of coaches who have worked with a variety of managers from different cultures using a 360 degree feedback instrument — the PROFILOR®. Reference is made to some of the research on cultural differences in order to clarify, or otherwise, some of the issues raised.


title: Content-Aware Bit Shuffling for Maximizing PCM Endurance

abstract: Recently, phase change memory (PCM) has been emerging as a strong replacement for DRAM owing to its many advantages such as nonvolatility, high capacity, low leakage power, and so on. However, PCM is still restricted for use as main memory because of its limited write endurance. There have been many methods introduced to resolve the problem by either reducing or spreading out bit flips. Although many previous studies have significantly contributed to reducing bit flips, they still have the drawback that lower bits are flipped more often than higher bits because the lower bits frequently change their bit values. Also, interblock wear-leveling schemes are commonly employed for spreading out bit flips by shifting input data, but they increase the number of bit flips per write. In this article, we propose a noble content-aware bit shuffling (CABS) technique that minimizes bit flips and evenly distributes them to maximize the lifetime of PCM at the bit level. We also introduce two additional optimizations, namely, addition of an inversion bit and use of an XOR key, to further reduce bit flips. Moreover, CABS is capable of recovering from stuck-at faults by restricting the change in values of stuck-at cells. Experimental results showed that CABS outperformed the existing state-of-the-art methods in the aspect of PCM lifetime extension with minimal overhead. CABS achieved up to 48.5% enhanced lifetime compared to the data comparison write (DCW) method only with a few metadata bits. Moreover, CABS obtained approximately 9.7% of improved write throughput than DCW because it significantly reduced bit flips and evenly distributed them. Also, CABS reduced about 5.4% of write dynamic energy compared to DCW. Finally, we have also confirmed that CABS is fully applicable to BCH codes as it was able to reduce the maximum number of bit flips in metadata cells by 32.1%.


title: K-Means for Parallel Architectures Using All-Prefix-Sum Sorting and Updating Steps

abstract: We present an implementation of parallel K-means clustering, called Kps-means, that achieves high performance with near-full occupancy compute kernels without imposing limits on the number of dimensions and data points permitted as input, thus combining flexibility with high degrees of parallelism and efficiency. As a key element to performance improvement, we introduce parallel sorting as data preprocessing and updating steps. Our final implementation for Nvidia GPUs achieves speedups of up to 200-fold over CPU reference code and of up to three orders of magnitude when compared with popular numerical software packages.


title: A Pilot Computer Science and Programming Course for Primary School Students

abstract: Computer Science and programming are being introduced to school curricula in many western countries in an effort to equip students with Computational Thinking skills. However, as these subjects are still relatively new to pre-tertiary education there is much investigation to be done into how best to present these topics and how to prepare teachers. In this study we focus on the presentation of topics relating to computing, Computational Thinking, and Computer Science for primary schools. We analyse English-language curricula that have been published, specifically the English, Australian and CSTA curricula for primary schools. From this we establish the main topics covered, and how they are positioned to be suitable for students from the first year of school to approximately their eighth year. We then report on a pilot study of a curriculum based on Computational Thinking; long term the study will encompass a range of topics and year levels, but the pilot focused on topics suitable for 11 to 12 year old students. Here we detail the design of this part of the curriculum, the manner of its delivery, and the experiences and observations of the generalist teacher who taught the course. Through assessment data, student responses to an attitude survey, and class observations we have evaluated the pilot curriculum. The findings of this study are being used to inform the design of a planned larger scale study.


title: Resnet in Resnet: Generalizing Residual Architectures

abstract: Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.


title: Orthogonal AMP

abstract: Approximate message passing (AMP) is a low-cost iterative signal recovery algorithm for linear system models. When the system transform matrix has independent identically distributed (IID) Gaussian entries, the performance of AMP can be asymptotically characterized by a simple scalar recursion called state evolution (SE). However, SE may become unreliable for other matrix ensembles, especially for ill-conditioned ones. This imposes limits on the applications of AMP. In this paper, we propose an orthogonal AMP (OAMP) algorithm based on de-correlated linear estimation (LE) and divergence-free non-linear estimation (NLE). The Onsager term in standard AMP vanishes as a result of the divergence-free constraint on NLE. We develop an SE procedure for OAMP and show numerically that the SE for OAMP is accurate for general unitarily-invariant matrices, including IID Gaussian matrices and partial orthogonal matrices. We further derive optimized options for OAMP and show that the corresponding SE fixed point coincides with the optimal performance obtained via the replica method. Our numerical results demonstrate that OAMP can be advantageous over AMP, especially for ill-conditioned matrices


title: Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes

abstract: Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural networks. On the other hand, layer normalization normalizes the activations across all activities within a layer. This was shown to work well in the recurrent setting. In this paper we propose a unified view of normalization techniques, as forms of divisive normalization, which includes layer and batch normalization as special cases. Our second contribution is the finding that a small modification to these normalization schemes, in conjunction with a sparse regularizer on the activations, leads to significant benefits over standard normalization techniques. We demonstrate the effectiveness of our unified divisive normalization framework in the context of convolutional neural nets and recurrent neural networks, showing improvements over baselines in image classification, language modeling as well as super-resolution.


title: Bonus or not? learn to reward in crowdsourcing

abstract: Recent work has shown that the quality of work produced in a crowdsourcing working session can be influenced by the presence of performance-contingent financial incentives, such as bonuses for exceptional performance, in the session. We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session. Specifically, we propose and train an input-output hidden Markov model to learn the impact of bonuses on work quality and then use this model to dynamically decide whether to offer a bonus on each task in a working session to maximize a requester's utility. Experiments on Amazon Mechanical Turk show that our approach leads to higher utility for the requester than fixed and random bonus schemes do. Simulations on synthesized data sets further demonstrate the robustness of our approach against different worker population and worker behavior in improving requester utility.


title: All Your DNS Records Point to Us: Understanding the Security Threats of Dangling DNS Records

abstract: In a dangling DNS record (Dare), the resources pointed to by the DNS record are invalid, but the record itself has not yet been purged from DNS. In this paper, we shed light on a largely overlooked threat in DNS posed by dangling DNS records. Our work reveals that Dare can be easily manipulated by adversaries for domain hijacking. In particular, we identify three attack vectors that an adversary can harness to exploit Dares. In a large-scale measurement study, we uncover 467 exploitable Dares in 277 Alexa top 10,000 domains and 52 edu zones, showing that Dare is a real, prevalent threat. By exploiting these Dares, an adversary can take full control of the (sub)domains and can even have them signed with a Certificate Authority (CA). It is evident that the underlying cause of exploitable Dares is the lack of authenticity checking for the resources to which that DNS record points. We then propose three defense mechanisms to effectively mitigate Dares with little human effort.


title: Systematic review of interventions addressing social isolation and depression in aged care clients

abstract: OBJECTIVE ::: A systematic review was undertaken of studies reporting interventions for reducing social isolation and depression in older people receiving aged care services (community or residential). ::: ::: ::: METHODS ::: Gray literature and relevant electronic databases were systematically searched for studies published in English between January 2009 and December 2013. Two reviewers independently screened studies for selection using predetermined inclusion and exclusion criteria and independently completed methodological quality review at study level. Studies of poor methodological quality were excluded. Data were extracted at study level by one reviewer and independently checked by a second reviewer, using a standardized form. The results across studies were qualitatively synthesized with outcomes described and summarized at last follow-up. ::: ::: ::: RESULTS ::: Although the original objective was to review rural studies, no intervention studies based in rural areas met criteria for inclusion in the review, and only urban studies could be reviewed. Of 403 articles, six articles representing five studies with moderate-to-low risk of bias were included for review. All study participants were older adults ranging in age from 77 to 86 years. All studies had small sample sizes, ranging from 26 to 113 participants. Three of the five included intervention studies successfully reduced social isolation; one also successfully reduced depression. ::: ::: ::: CONCLUSIONS ::: Only one intervention, group-based reminiscence therapy, was reported as successful in reducing both social isolation and depression in older people within an urban aged care setting. More research is needed to explore transferability of interventions across different aged care settings and into rural areas.


title: A New Approach for Idiom Identification Using Meanings and the Web

abstract: There is a great deal of knowledge available on the Web, which represents a great opportunity for automatic, intelligent text processing and understanding, but the major problems are finding the legitimate sources of information and the fact that search engines provide page statistics not occurrences. This paper presents a new, domain independent, general-purpose idiom identification approach. Our approach combines the knowledge of the Web with the knowledge extracted from dictionaries. This method can overcome the limitations of current techniques that rely on linguistic knowledge or statistics. It can recognize idioms even when the complete sentence is not present, and without the need for domain knowledge. It is currently designed to work with text in English but can be extended to other languages.


title: Dielectric Image Line-Based Leaky-Wave Antenna for Wide Range of Beam Scanning Through Broadside

abstract: In this communication, a dielectric image line (DIL)-based leaky-wave antenna is proposed with planar feeding. The dominant mode of DIL is perturbed by making holes periodically in the DIL and fast-wave space harmonics are generated. The proposed antenna is designed for Ku-band (12–18 GHz) and its impedance bandwidth in the fast-wave region is 36% (11.8–17 GHz). The scan range of the proposed antenna in the working band is 90° (−65° to 25°) and the gain is varying from 10 to 16 dBi in the fast-wave region. The proposed antenna can be directly scaled to other frequency bands by taking into account the corresponding dispersion characteristic in respective frequency bands. This scaling property is demonstrated in $V$ -band. A prototype is fabricated and measured in Ku-band, which fairly agrees with simulated results.


title: Image Captioning with Deep Bidirectional LSTMs

abstract: This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are proposed to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale and vertical mirror are proposed to prevent overfitting in training deep models. We visualize the evolution of bidirectional LSTM internal states over time and qualitatively analyze how our models "translate" image to sentence. Our proposed models are evaluated on caption generation and image-sentence retrieval tasks with three benchmark datasets: Flickr8K, Flickr30K and MSCOCO datasets. We demonstrate that bidirectional LSTM models achieve highly competitive performance to the state-of-the-art results on caption generation even without integrating additional mechanism (e.g. object detection, attention model etc.) and significantly outperform recent methods on retrieval task


title: A Syntactic Approach To Discourse Semantics

abstract: A correct structural analysis of a discourse is a prerequisite for understanding it. This paper sketches the outline of a discourse grammar which acknowledges several different levels of structure. This grammar, the "Dynamic Discourse Model", uses an Augmented Transition Network parsing mechanism to build a representation of the semantics of a discourse in a stepwise fashion, from left to right, on the basis of the semantic representations of the individual clauses which constitute the discourse. The intermediate states of the parser model the intermediate states of the social situation which generates the discourse.The paper attempts to demonstrate that a discourse may indeed be viewed as constructed by means of sequencing and recursive nesting of discourse constituents. It gives rather detailed examples of discourse structures at various levels, and shows how these structures are described in the framework proposed here.


The Impact of Digital Technology on Learning : A Summary for the Education Endowment Foundation


title: The neurobiology of antisocial personality disorder: The quest for rehabilitation and treatment

abstract: Psychopathy is perhaps one of the most misused terms in the American public, which is in no small part due to our obsession with those who have no conscience, and our boldness to try and profile others with this disorder. Here, I present how psychopathy is seen today, before discussing the classification of psychopathy. I also explore the neurological differences in the brains of those with psychopathy, before finally taking a look at genetic risk factors. I conclude by raising some questions about potential treatment.


title: Cerebrospinal Fluid and Interstitial Fluid Motion via the Glymphatic Pathway Modelled by Optimal Mass Transport

abstract: It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the ‘glymphatic pathway’ plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs1,2. It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through peri-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach3 to model the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.


Mapping Crime: Understanding Hot Spots


title: Neural-network-based signature recognition for harmonic source identification

abstract: This paper proposes a neural-network (NN)-based approach to nonintrusive harmonic source identification. In this approach, NNs are trained to extract important features from the input current waveform to uniquely identify various types of devices using their distinct harmonic "signatures". Such automated, noninvasive device identification will be critical in future power-quality monitoring and enhancement systems. Several NN-based classification models including multilayer perceptron (MLP), radial basis function (RBF) network, and support vector machines (SVM) with linear, polynomial, and RBF kernels were developed for signature extraction and device identification. These models were trained and tested using spike train data gathered from the Fourier analysis of the input current waveform in the presence of multiple devices. The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed that MLPs and SVM were both able to determine the presence of devices based on their harmonic signatures with high accuracy. MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.


title: PID control of a nonlinear half-car active suspension system via force feedback

abstract: This paper presents the design of a two-loop, force/suspension travel PID control system, for a four degree-of-freedom (DOF), nonlinear, half-car active vehicle suspension system (AVSS). The two-loop system consists of an inner PID hydraulic actuator force control loop and an outer PID suspension travel control loop. Performance of the PID based AVSS is compared to a passive, nonlinear, half-car suspension system with the same model parameters. The simulation results showed the superior performance of the AVSS in the presence of the deterministic road disturbance.


title: Enabling Agile Testing through Continuous Integration

abstract: A Continuous Integration system is often considered one of the key elements involved in supporting an agile software development and testing environment. As a traditional software tester transitioning to an agile development environment it became clear to me that I would need to put this essential infrastructure in place and promote improved development practices in order to make the transition to agile testing possible. This experience report discusses a continuous integration implementation I led last year. The initial motivations for implementing continuous integration are discussed and a pre and post-assessment using Martin Fowler's" Practices of Continuous Integration" is provided along with the technical specifics of the implementation. The report concludes with a retrospective of my experiences implementing and promoting continuous integration within the context of agile testing.


title: Personal Recommendation using Weighted Bipartite Graph Projection

abstract: This work is a study of personal recommendation algorithm employing the projection of weighted bipartite consumer-product network. The weight of the edges is directly the rate that a customer giving on a product. Following a network based resource allocation process we get similarities between every pair of consumers, which is then used to produce prediction and recommendation. We show this is also a two step random walk process in the bipartite. Since the weighted graph is more informative, we would expect higher predict accuracy.


title: On the analysis of the Zeus botnet crimeware toolkit

abstract: In this paper, we present our reverse engineering results for the Zeus crimeware toolkit which is one of the recent and powerful crimeware tools that emerged in the Internet underground community to control botnets. Zeus has reportedly infected over 3.6 million computers in the United States. Our analysis aims at uncovering the various obfuscation levels and shedding the light on the resulting code. Accordingly, we explain the bot building and installation/infection processes. In addition, we detail a method to extract the encryption key from the malware binary and use that to decrypt the network communications and the botnet configuration information. The reverse engineering insights, together with network traffic analysis, allow for a better understanding of the technologies and behaviors of such modern HTTP botnet crimeware toolkits and opens an opportunity to inject falsified information into the botnet communications which can be used to defame this crimeware toolkit.


title: Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images

abstract: This paper investigates different batch-mode active-learning (AL) techniques for the classification of remote sensing (RS) images with support vector machines. This is done by generalizing to multiclass problem techniques defined for binary classifiers. The investigated techniques exploit different query functions, which are based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is associated to the confidence of the supervised algorithm in correctly classifying the considered sample, while the diversity criterion aims at selecting a set of unlabeled samples that are as more diverse (distant one another) as possible, thus reducing the redundancy among the selected samples. The combination of the two criteria results in the selection of the potentially most informative set of samples at each iteration of the AL process. Moreover, we propose a novel query function that is based on a kernel-clustering technique for assessing the diversity of samples and a new strategy for selecting the most informative representative sample from each cluster. The investigated and proposed techniques are theoretically and experimentally compared with state-of-the-art methods adopted for RS applications. This is accomplished by considering very high resolution multispectral and hyperspectral images. By this comparison, we observed that the proposed method resulted in better accuracy with respect to other investigated and state-of-the art methods on both the considered data sets. Furthermore, we derived some guidelines on the design of AL systems for the classification of different types of RS images.


title: Mindfulness-based stress reduction and health benefits: a meta-analysis

abstract: Broadly, the present invention relates to a magnetically active organometallic Schiff base polymer having a molecular weight of between about 4,000 and 50,000 daltons. In an additional aspect, the present invention relates to a process for the preparation of magnetically active organometallic polymer having a molecular weight of between about 4,000 and 50,000 daltons, wherein the polymer comprises a multiplicity of Schiff base tridentate chelating groups having the capacity to combine with the iron (II) or iron (III) ion of an organic salt, which process comprises: (a) combining at least one organic diamine including hydrazine with at least one organic diketone or dialdehyde in an anhydrous dipolar aprotic solvent for a time sufficient to produce the polymer at a temperature of between about 50 DEG and 200 DEG C.; (b) optionally recovering the polymer produced in step (a); (c) combining the polymer of step (b) with a solution of an iron ion for a time sufficient to produce the organometallic polymer; (d) recovering the organometallic polymer of step (c); (e) heating the organic polymer up to about 200 DEG C. for a time sufficient to observe at least a five-times increase in magnetic susceptibility which is retained when the organometallic polymer is cooled to ambient temperature or below; and (f) recovering the magnetically enhanced organo-metallic polymer of step (e). These polymers are useful as optical magnetic information storage devices.


title: Machine Learning Paradigms for Next-Generation Wireless Networks

abstract: Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.


title: Passivity-Based Stability Assessment of Grid-Connected VSCs—An Overview

abstract: The interconnection stability of a grid-connected voltage-source converter (VSC) can be assessed by the passivity properties of the VSC input admittance. If critical grid resonances fall within regions where the input admittance acts passively, i.e., has nonnegative real part, then their destabilization is generally prevented. This paper presents an overview of passivity-based stability assessment, including techniques for space-vector modeling of VSCs whereby expressions for the input admittance can be derived. Design recommendations for minimizing the negative-real-part region are given as well.


title: A Generalized Computational Method to Determine Stability of a Multi-inverter Microgrid

abstract: Microgrid-containing parallel-connected inverters, where each inverter is controlled by decentralized active power/voltage frequency and reactive power/voltage magnitude droop control laws results in flexible and expandable systems. These systems have been known to have stability problems for large values of active power/voltage frequency droop control gain. However, so far the stability analysis of multi-inverter systems has always been performed in a computationally intensive manner by considering the entire microgrid. In a practical microgrid, where the number of inverters may be large or the capacity of the units may differ, it becomes essential to develop a method by which stability can be examined without much computational burden. The system of differential algebraic equations has been simplified using justifiable assumptions to result in a final expression that allows the stability of the microgrid to be examined separately with respect to the droop control laws of each inverter transformed into an equivalent network. Moreover, the procedure allows taking into consideration the R/X ratio of the interconnecting cables. Analysis of final expressions validate the stability results reported in literature. Experimental results on hardware show the stable operation of the microgrid.


title: Design of 4×4 microstrip Quasi-Yagi beam-steering antenna array operation at 3.5GHz for future 5G vehicle applications

abstract: In this paper, a novel design of 4×4 microstrip Quasi-Yagi beam-steering antenna array operation at 3.5GHz for future 5G vehicle applications is proposed. This array consists of sixteen element antennas with dimension of 374×374×1.15mm3, which exhibits good bandwidth (impedance bandwidth of single antenna element about 440MHz for S 11 less than −10dB at the center frequency of 3.5GHz) and high gain (for single antenna about 7dBi and for antenna array about 5.8∶8.76dBi). The beam-steering characteristics in the operation band can nearly achieve omni-directional radiation.


title: Autonomous Robot Skill Acquisition

abstract: Among the most impressive of aspects of human intelligence is skill acquisition—the ability to identify important behavioral components, retain them as skills, refine them through practice, and apply them in new task contexts. Skill acquisition underlies both our ability to choose to spend time and effort to specialize at particular tasks, and our ability to collect and exploit previous experience to become able to solve harder and harder problems over time with less and less cognitive effort. ::: Hierarchical reinforcement learning provides a theoretical basis for skill acquisition, including principled methods for learning new skills and deploying them during problem solving. However, existing work focuses largely on small, discrete problems. This dissertation addresses the question of how we scale such methods up to high-dimensional, continuous domains, in order to design robots that are able to acquire skills autonomously. This presents three major challenges; we introduce novel methods addressing each of these challenges. ::: First, how does an agent operating in a continuous environment discover skills? Although the literature contains several methods for skill discovery in discrete environments, it offers none for the general continuous case. We introduce skill chaining, a general skill discovery method for continuous domains. Skill chaining incrementally builds a skill tree that allows an agent to reach a solution state from any of its start states by executing a sequence (or chain) of acquired skills. We empirically demonstrate that skill chaining can improve performance over monolithic policy learning in the Pinball domain, a challenging dynamic and continuous reinforcement learning problem. ::: Second, how do we scale up to high-dimensional state spaces? While learning in relatively small domains is generally feasible, it becomes exponentially harder as the number of state variables grows. We introduce abstraction selection, an efficient algorithm for selecting skill-specific, compact representations from a library of available representations when creating a new skill. Abstraction selection can be combined with skill chaining to solve hard tasks by breaking them up into chains of skills, each defined using an appropriate abstraction. We show that abstraction selection selects an appropriate representation for a new skill using very little sample data, and that this leads to significant performance improvements in the Continuous Playroom, a relatively high-dimensional reinforcement learning problem. ::: Finally, how do we obtain good initial policies? The amount of experience required to learn a reasonable policy from scratch in most interesting domains is unrealistic for robots operating in the real world. We introduce CST, an algorithm for rapidly constructing skill trees (with appropriate abstractions) from sample trajectories obtained via human demonstration, a feedback controller, or a planner. We use CST to construct skill trees from human demonstration in the Pinball domain, and to extract a sequence of low-dimensional skills from demonstration trajectories on a mobile robot. The resulting skills can be reliably reproduced using a small number of example trajectories. ::: Finally, these techniques are applied to build a mobile robot control system for the uBot-5, resulting in a mobile robot that is able to acquire skills autonomously. We demonstrate that this system is able to use skills acquired in one problem to more quickly solve a new problem.


Nonparametric Tests for the Interaction in Two-way Factorial Designs Using R


title: A Query Substitution-Search Result Refinement Approach for Long Query Web Searches

abstract: Long queries are widely used in current Web applications, such as literature searches, news searches, etc. However, since long queries are frequently expressed as natural language texts but not keywords, the current keywords-based search engines, like GOOGLE, perform worse with long queries than with short ones. This paper proposes a query substitution and search result refinement approach for long query Web searches. First, we retrieved several short queries related to a long query from the users’ query history. Then, we constructed the short query clusters and selected the most representative queries to substitute the original long query. However, since searching relevant short queries may ignore contexts and terms in the original long query and thus obtain diverse results and neighboring information, we compared the contexts from search results with the contexts from original long query and filtered non-relevant results. The experiments show that our approach achieves high precision for long query Web searches.


title: Automatic stage scoring of single-channel sleep EEG based on multiscale permutation entropy

abstract: Multiscale entropy is a recently developed method to estimate complexity associated with the long-range temporal correlation of a time series. Since sleep EEG patterns also change regularly from light to deep sleep states, we firstly applied multiscale permutation entropy (MPE) to analysis sleep EEG to investigate the relations between changes of sleep stages and the MPE values. It was observed that correlation coefficient between the averaged MPE values of sleep EEG and the manual scoring of sleep stages can reach over 0.7. Then a MPE-based sleep scoring method for single channel EEG was developed. After training based on the data from 10 subjects, the overall sensitivity of the proposed automatic sleep scoring method combining MPE, autoregressive models, and linear discriminant analysis can reach 89.1% evaluated by the data of the other 10 subjects. Due to high accuracy and requiring only single-channel EEG, the proposed method has good applicability for sleep monitoring and home cares.


title: A real-time multi-class multi-object tracker using YOLOv2

abstract: Multi-class multi-object tracking is an important problem for real-world applications like surveillance system, gesture recognition, and robot vision system. However, building a multi-class multi-object tracker that works in real-time is difficult due to low processing speed for detection, classification, and data association tasks. By using fast and reliable deep learning based algorithm YOLOv2 together with fast detection to tracker algorithm, we build a real-time multi-class multi-object tracking system with competitive accuracy.


A fistful of Astragalus : phenotypic and genotypic basis of the most taxon rich species in the North American flora


title: Evaluation of the Importance of Centrifugation as a Component of Zinc Sulfate Fecal Flotation Examinations

abstract: Fifty canine fecal samples were evaluated by five flotation procedures to compare the sensitivity of the zinc sulfate (ZnSO4) centrifugation flotation test with ZnSO4 flotation tests using benchtop incubation during the flotation period. One or more parasite species were detected in 40 samples. Results showed that centrifugation with ZnSO4 solution was significantly more likely to detect a positive sample than benchtop procedures. The difference in procedures was due primarily to increased detection of Trichuris eggs and Giardia cysts by centrifugal flotation. No significant difference was seen in the ability of benchtop procedures to detect positive samples when tests sat either for 5 or 10 minutes before examination.


title: Practicing Differential Privacy in Health Care: A Review

abstract: Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It has also been proposed as an appropriate model for protecting health data. In this paper we review the current literature on differential privacy and highlight important general limitations to the model and the proposed mechanisms. We then examine some practical challenges to the application of differential privacy to health data. The most severe limitation is the theoretical nature of the privacy parameter epsilon. It has implications on our ability to quantify the level of anonymization that would be guaranteed to patients, as well as assessing responsibilities when a privacy breach occurs. The review concludes by identifying the areas that researchers and practitioners need to address to increase the adoption of differential privacy for health data.


title: Feature based terrain generation using diffusion equation

abstract: This paper presents a diffusion method for generating terrains from a set of parameterized curves that characterize the landform features such as ridge lines, riverbeds or cliffs. Our approach provides the user with an intuitive vector-based feature-oriented control over the terrain. Different types of constraints (such as elevation, slope angle and roughness) can be attached to the curves so as to define the shape of the terrain. The terrain is generated from the curve representation by using an efficient multigrid diffusion algorithm. The algorithm can be efficiently implemented on the GPU, which allows the user to interactively create a vast variety of landscapes.


title: A new paradigm for intelligent tutoring systems: example-tracing tutors

abstract: Key success criteria for an ITS authoring tool are that (1) the tool supports the creation of effective tutoring systems, (2) the tool can be used to build tutors across a wide range of application domains, (3) authoring with the tool is cost-effective, (4) the tool supports easy deployment and delivery of tutors in a variety of technical contexts, (5) tutors created with the tool are maintainable, and (6) if tutors are used in a research context, the tool must support research-related functionality. The Cognitive Tutor Authoring Tools (CTAT) address all of these requirements to a substantial degree, fully meeting most of them. CTAT supports the creation of both Cognitive Tutors (Koedinger & Corbett, 2006) and a newer type of tutors called example-tracing tutors. This paper focuses on the latter. Example-tracing tutors evaluate student behavior by flexibly comparing it against examples of correct and incorrect problem-solving behaviors. Example-tracing tutors are capable of sophisticated tutoring behaviors: they provide step-by-step guidance on complex problems while recognizing multiple student strategies and maintaining multiple interpretations of student behavior. On that basis, they should be deemed intelligent tutoring systems. Example-tracing tutors can be built without programming, through drag-and-drop techniques and programming by demonstration. Exampletracing tutors have been built and used in real educational settings for a wide range of application areas. Development time estimates from a large number of projects that have used CTAT suggest that CTAT improves the cost-effectiveness of ITS development by a factor of 4-8, compared to “historical” estimates of tutor development time. Although there is a lot of variability in these kinds of estimates, they nonetheless support our hope that lowering the skill requirements for tutor creation is a key step toward widespread use of ITS technology. The main contributions of the work are the example-tracing tutor technology and tools for building these types of tutors without programming.


title: The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: evidence for a breakdown in self-monitoring

abstract: Background. To test the hypothesis that certain psychotic symptomatology is due to a defect in selfmonitoring, we investigated the ability of groups of psychiatric patients to dierentiate perceptually between self-produced and externally produced tactile stimuli. Methods. Responses to tactile stimulation were assessed in three groups of subjects: schizophrenic patients; patients with bipolar aective disorder or depression; and normal control subjects. Within the psychiatric groups subjects were divided on the basis of the presence or absence of auditory hallucinations and}or passivity experiences. The subjects were asked to rate the perception of a tactile sensation on the palm of their left hand. The tactile stimulation was either self-produced by movement of the subject’s right hand or externally produced by the experimenter. Results. Normal control subjects and those psychiatric patients with neither auditory hallucinations nor passivity phenomena experienced self-produced stimuli as less intense, tickly and pleasant than identical, externally produced tactile stimuli. In contrast, psychiatric patients with these symptoms did not show a decrease in their perceptual ratings for tactile stimuli produced by themselves as compared with those produced by the experimenter. This failure to show a dierence in perception between self-produced and externally produced stimuli appears to relate to the presence of auditory hallucinations and}or passivity experiences rather than to the diagnosis of schizophrenia. Conclusions. We propose that auditory hallucinations and passivity experiences are associated with an abnormality in the self-monitoring mechanism that normally allows us to distinguish selfproduced from externally produced sensations.


title: A study of secure DBaaS with encrypted data transactions

abstract: The emergence of cloud computing allowed different IT services to be outsourced to cloud service providers (CSP). This includes the management and storage of user's structured data called Database as a Service (DBaaS). However, DBaaS requires users to trust the CSP to protect their data, which is inherent in all cloud-based services. Enterprises and Small-to-Medium Businesses (SMB) see this as a roadblock in adopting cloud services (and DBaaS) because they do not have full control of the security and privacy of the sensitive data they are storing on the cloud. One of the solutions is for the data owners to store their sensitive data in the cloud's storage services in encrypted form. However, to take full advantage of DBaaS, there should be a solution to manage the structured data while it is encrypted. Upcoming technologies like Secure Multi-Party Computing (MPC) and Fully Homomorphic Encryption (FHE) are recent advances in security that allow computation on encrypted data. FHE is considered as the holy grail of cryptography and the original blue print's processing performance is in the order of 1014 times longer than without encryption. Our work gives an insight on how far the state-of-the-art is into realizing it into a practical and viable solution for cloud computing data services. We achieved this by comparing two types of encrypted database management system (DBMS). We performed well-known complex database queries and measured the performance results of the two DBMS. We used an FHE-encrypted relational DBMS (RDBMS) and for specific query sets it takes only a few milliseconds, and the highest is in the order of 104 times longer than encrypted object-oriented DBMS (OODBMS). Aside from focusing on performance of the two databases, we also evaluated the network resource usage, standards availability, and application integration.


title: Analysis of Dynamic Task Allocation in Multi-Robot Systems

abstract: Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of tasks, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with results of embodied simulations.


title: Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network

abstract: The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture the emotional content for various styles of speaking, and while on the other, machine learning algorithms need to be insensitive to outliers while being able to model the context. Whereas the latter has been tackled by the use of Long Short-Term Memory (LSTM) networks, the former is still under very active investigations, even though more than a decade of research has provided a large set of acoustic descriptors. In this paper, we propose a solution to the problem of ‘context-aware’ emotional relevant feature extraction, by combining Convolutional Neural Networks (CNNs) with LSTM networks, in order to automatically learn the best representation of the speech signal directly from the raw time representation. In this novel work on the so-called end-to-end speech emotion recognition, we show that the use of the proposed topology significantly outperforms the traditional approaches based on signal processing techniques for the prediction of spontaneous and natural emotions on the RECOLA database.


title: Unsupervised Alignment of Embeddings with Wasserstein Procrustes

abstract: We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recently shown that it is possible to infer a bilingual lexicon, without supervised data, by aligning word embeddings trained on monolingual data. These recent advances are based on adversarial training to learn the mapping between the two embeddings. In this paper, we propose to use an alternative formulation, based on the joint estimation of an orthogonal matrix and a permutation matrix. While this problem is not convex, we propose to initialize our optimization algorithm by using a convex relaxation, traditionally considered for the graph isomorphism problem. We propose a stochastic algorithm to minimize our cost function on large scale problems. Finally, we evaluate our method on the problem of unsupervised word translation, by aligning word embeddings trained on monolingual data. On this task, our method obtains state of the art results, while requiring less computational resources than competing approaches.


title: Investigation of Self-Heating Effect on Ballistic Transport Characterization for Si FinFETs Featuring Ultrafast Pulsed IV Technique

abstract: In this paper, we investigate the carrier transport characteristics of the ultrascaled Si FinFETs. Traditional dc characterization technique is compared with the ultrafast pulsed IV method. Due to the severe self-heating effect introduced in the characterization process, the ballistic transport parameters extracted using dc method would show essential discrepancies from those in the real high-speed IC circuits. Therefore, an ultrafast pulsed IV measurement technique is proposed for accurate ballistic transport characterization. Furthermore, since the series resistance ( ${R}_{{\textbf {SD}}}$ ) of the Si FinFETs is temperature-dependent, a modified backscattering model is adopted to extract the ballistic transport parameters without the influence of the temperature-variant ${R}_{{\textbf {SD}}}$ . A ballisticity scaling model was established to predict the scalability of the ballistic transport parameters. It is found that very high ballisticity could be achieved for FinFETs with sub-10-nm technology nodes.


Motion Planni 35 . Motion Planning and Obstacle Avoidance


title: A Low Phase Noise 100MHz Silicon BAW Reference Oscillator

abstract: The paper presents a temperature compensated 100MHz reference oscillator based on a capacitive silicon Bulk Acoustic Wave (BAW) resonator interfaced with a CMOS amplifier. The resonator is optimized for high quality factor (92000) and low impedance. The CMOS IC comprises of a trans-impedance amplifier to sustain oscillations and an oven control mechanism for temperature control. A phase noise floor of ?136dBc/Hz was measured for the oscillator and the temperature drift of frequency was measured to be 56ppm over 100°C.


title: Multi-Modal Audio, Video and Physiological Sensor Learning for Continuous Emotion Prediction

abstract: The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. The Audio Video Emotion Challenge (AVEC) 2016 provides a well-defined framework for developing and rigorously evaluating innovative approaches for estimating the arousal and valence states of emotion as a function of time. It presents the opportunity for investigating multimodal solutions that include audio, video, and physiological sensor signals. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities. It includes a number of technical contributions, including the development of novel high- and low-level features for modeling emotion in the audio, video, and physiological channels. Low-level features include modeling arousal in audio with minimal prosodic-based descriptors. High-level features are derived from supervised and unsupervised machine learning approaches based on sparse coding and deep learning. Finally, a state space estimation approach is applied for score fusion that demonstrates the importance of exploiting the time-series nature of the arousal and valence states. The resulting system outperforms the baseline systems [10] on the test evaluation set with an achieved Concordant Correlation Coefficient (CCC) for arousal of 0.770 vs 0.702 (baseline) and for valence of 0.687 vs 0.638. Future work will focus on exploiting the time-varying nature of individual channels in the multi-modal framework.


title: LMS Adaptive Filters for Noise Cancellation: A Review

abstract: This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.


title: Neural Sign Language Translation

abstract: Sign Language Recognition (SLR) has been an active research field for the last two decades. However, most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language. In contrast, we introduce the Sign Language Translation (SLT) problem. Here, the objective is to generate spoken language translations from sign language videos, taking into account the different word orders and grammar. We formalize SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge). This allows us to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language. To evaluate the performance of Neural SLT, we collected the first publicly available Continuous SLT dataset, RWTH-PHOENIX-Weather 2014T1. It provides spoken language translations and gloss level annotations for German Sign Language videos of weather broadcasts. Our dataset contains over .95M frames with >67K signs from a sign vocabulary of >1K and >99K words from a German vocabulary of >2.8K. We report quantitative and qualitative results for various SLT setups to underpin future research in this newly established field. The upper bound for translation performance is calculated at 19.26 BLEU-4, while our end-to-end frame-level and gloss-level tokenization networks were able to achieve 9.58 and 18.13 respectively.


title: The Conditional Analogy GAN: Swapping Fashion Articles on People Images

abstract: We present a novel method to solve image analogy problems : it allows to learn the relation between paired images present in training data, and then generalize and generate images that correspond to the relation, but were never seen in the training set. Therefore, we call the method Conditional Analogy Generative Adversarial Network (CAGAN), as it is based on adversarial training and employs deep convolutional neural networks. An especially interesting application of that technique is automatic swapping of clothing on fashion model photos. Our work has the following contributions. First, the definition of the end-to-end trainable CAGAN architecture, which implicitly learns segmentation masks without expensive supervised labeling data. Second, experimental results show plausible segmentation masks and often convincing swapped images, given the target article. Finally, we discuss the next steps for that technique: neural network architecture improvements and more advanced applications.


Techno-economic analysis of PV / H 2 systems


title: Parallelization and characterization of pattern matching using GPUs

abstract: Pattern matching is a highly computationally intensive operation used in a plethora of applications. Unfortunately, due to the ever increasing storage capacity and link speeds, the amount of data that needs to be matched against a given set of patterns is growing rapidly. In this paper, we explore how the highly parallel computational capabilities of commodity graphics processing units (GPUs) can be exploited for high-speed pattern matching. We present the design, implementation, and evaluation of a pattern matching library running on the GPU, which can be used transparently by a wide range of applications to increase their overall performance. The library supports both string searching and regular expression matching on the NVIDIA CUDA architecture. We have also explored the performance impact of different types of memory hierarchies, and present solutions to alleviate memory congestion problems. The results of our performance evaluation using off-the-self graphics processors demonstrate that GPU-based pattern matching can reach tens of gigabits per second on different workloads.


title: Developing Adaptive Business Intelligence Systems for Agile Supply Chain Analytics

abstract: Agile supply chain analytics is about ability to adapt business intelligence (BI) systems to meet the changing needs of the supply network and its environment. Agile BI incorporates both technical and process-oriented approach to development, management, and delivery of BI solutions. To achieve agile BI, supply chains must provide and establish the necessary infrastructure, architecture, tools and processes in a way that enables them to deliver BI systems capable of quickly and effectively adapting to the rapidly changing business needs and environment factors. However, most supply chains still lack methods, processes and tools to successfully design and implement these systems. The paper first provides an analysis of current situation and a concise background research, followed by the introduction of the integrated and adaptive supply chain business intelligence model. This model enables design of pervasive analytical systems for collaborative decision-making, monitoring and management of the supply network. The main components of the model are described, as well as the architecture and features of the specialized supply chain intelligence web portal which demonstrates usefulness and applicability of the proposed model. This system allows creation of a new breed of flexible and agile BI systems which ultimately results in more efficient, responsive and adaptive supply chains.


Achieving high and consistent rendering performance of Java AWT/Swing on multiple platforms


title: Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

abstract: This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN) and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6% and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3% and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8%, 9.5% and 2.5% over ANN, BNN and DBN classifiers respectively.


title: Ontology Evolution and Challenges

abstract: Information semantics and semantic interoperability among applications, systems, and services are mostly based on ontology. Its increase usage in Information Systems and Knowledge Sharing Systems raises the importance of ontology maintenance. Ontology change management incorporates areas like ontology engineering, evolution versioning, merging, integration, and maintenance. Changes are made to the body of knowledge as experts develop a better understanding of the domain. As a result, the body of knowledge evolves from one state to another. Preserving consistency, while accommodating new changes, is a crucial task that needs special attention. This paper aims at providing a comprehensive review on key approaches followed in the field of ontology evolution. The analysis reveals that different individual components have been developed but a complete integrated system for automated ontology evolution is not available yet. This paper introduces some unfolded challenges in the field of ontology evolution, which must be tackled to complete the process automatically. Moreover, the new changes could affect the dependent data, applications, systems, and services. Therefore, this paper also discusses in detail why special attention must be paid to minimize the after effects of ontology evolution and proposes some possible solutions to achieve this goal.


title: A hierarchical aspect-sentiment model for online reviews

abstract: To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.


title: Learning to Classify Documents According to Formal and Informal Style

abstract: This paper discusses an important issue in computational linguistics: classifying texts as formal or informal style. Our work describes a genre-independent methodology for building classifiers for formal and informal texts. We used machine learning techniques to do the automatic classification, and performed the classification experiments at both the document level and the sentence level. First, we studied the main characteristics of each style, in order to train a system that can distinguish between them. We then built two datasets: the first dataset represents general-domain documents of formal and informal style, and the second represents medical texts. We tested on the second dataset at the document level, to determine if our model is sufficiently general, and that it works on any type of text. The datasets are built by collecting documents for both styles from different sources. After collecting the data, we extracted features from each text. The features that we designed represent the main characteristics of both styles. Finally, we tested several classification algorithms, namely Decision Trees, Naive Bayes, and Support Vector Machines, in order to choose the classifier that generates the best classification results.


title: Understanding Visualization: A Formal Approach using Category Theory and Semiotics

abstract: This paper combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: Relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This paper generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not.


title: Ubik: efficient cache sharing with strict qos for latency-critical workloads

abstract: Chip-multiprocessors (CMPs) must often execute workload mixes with different performance requirements. On one hand, user-facing, latency-critical applications (e.g., web search) need low tail (i.e., worst-case) latencies, often in the millisecond range, and have inherently low utilization. On the other hand, compute-intensive batch applications (e.g., MapReduce) only need high long-term average performance. In current CMPs, latency-critical and batch applications cannot run concurrently due to interference on shared resources. Unfortunately, prior work on quality of service (QoS) in CMPs has focused on guaranteeing average performance, not tail latency. In this work, we analyze several latency-critical workloads, and show that guaranteeing average performance is insufficient to maintain low tail latency, because microarchitectural resources with state, such as caches or cores, exert inertia on instantaneous workload performance. Last-level caches impart the highest inertia, as workloads take tens of milliseconds to warm them up. When left unmanaged, or when managed with conventional QoS frameworks, shared last-level caches degrade tail latency significantly. Instead, we propose Ubik, a dynamic partitioning technique that predicts and exploits the transient behavior of latency-critical workloads to maintain their tail latency while maximizing the cache space available to batch applications. Using extensive simulations, we show that, while conventional QoS frameworks degrade tail latency by up to 2.3x, Ubik simultaneously maintains the tail latency of latency-critical workloads and significantly improves the performance of batch applications.


title: Evaluating Battery Models in Wireless Sensor Networks

abstract: Recent measurements highlight the importance of battery-aware evaluation of energy efficiency in wireless sensor networks. However, existing battery models been not investigated in the context of the low duty cycle, short duration loads that are typical of sensor networks. We evaluate three battery models with regard to their applicability in the WSN context. Our evaluation focuses on how the models reflect two key battery discharge behaviors, the rate capacity effect and charge recovery. We find that the models handle the former better than the latter and are more sensitive to a load’s peak current than to its timing.


title: The impact of the global economic downturn on communities and poverty in the UK

abstract: How people on low incomes experienced the recent global economic downturn. This report examines what being part of the global economy means for people living in poverty, focusing on experiences of the 20092010 recession. It identifies areas where welfare reform is necessary to protect poorer people from a more volatile world economy. The report: examines the effects of the recession on work and local economies in deprived areas in rural Northern Ireland, East Sussex and Lancashire; identifies the impact of high food and fuel prices on everyday life and well-being; discusses forms of state and non-state support that helped people on low incomes during the recession; and looks at how global social and economic connections shaped people's experiences of, and responses to, the recession.


title: Including surge arresters in the lightning performance analysis of 132kV transmission line

abstract: Line arresters are considered as an effective way to improve the lightning performance of transmission lines, especially in parts of line that suffer from high soil resistivity and lightning ground flash density. This paper presents results of the application of line surge arresters on the 132KV double circuit transmission line in EMTP-RV and all the practical scenarios for installation of surge arresters. The study has shown that a significant level of improvement can be reached by installing arresters at all or only some of the line phases. It can increase the strength of the line to withstand lightning currents up to −292kA. the probability of having this lightning current, is practically zero.


title: Multimedia Big Data: Content Analysis and Retrieval

abstract: This chapter surveys recent developments in the area of multimedia big data, the biggest big data. One core problem is how to best process this multimedia big data in an efficient and scalable way. We outline examples of the use of the MapReduce framework, including Hadoop, which has become the most common approach to a truly scalable and efficient framework for common multimedia processing tasks, e.g., content analysis and retrieval. We also examine recent developments on deep learning which has produced promising results in large-scale multimedia processing and retrieval. Overall the focus has been on empirical studies rather than the theoretical so as to highlight the most practically successful recent developments and highlight the associated caveats or lessons learned.


title: Design of plant disease detection system: A transfer learning approach work in progress

abstract: The use of ICT has become essential to help farmers collect important and updated information and knowledge which are valuable resources that farming depends on. The study embarked investigates the problem sources of unavailability or lack of timely, relevant and accurate farming information and knowledge for small-scale farmers. The main target is to deal with plant diseases and how to manage them by carefully diagnosing the plants leaves. This work proposes to use image analysis and convolutional neural networks and the ever increasing capability of machine learning such as supervised learning to offer a mobile solution. A Design Science Research Methodology was followed in shaping skeleton of the proposed prototype. The developed prototype will be subjected to three usability measures to test if indeed it is timely, relevant and accurate as the farmers need it to be.