title: Crossing the Disciplinary Boundary: Teaching ESL Composition in the English Department.

abstract: Although the number of English as a Second Language (ESL) students in United States colleges and universities has increased over the past decades, there has been no corresponding .increase in the level of attention given to the special needs of these students in composition classrooms. ESL students are likely to be found in writing classrooms at any level, but ESL-trained instructors may or may not be trained in the teaching of ESL writing. Existing definitions of the field discourage teachers of composition from teaching or learning about ESL students. An alternative symbiotic model, however, situates second language writing studies in various instructional contexts, unlike the division of labor model which sees ESL writing as the responsibility of ESL specialists and not of composition professionals. The intersection model (which sees second language writing situated at the intersection of second language studies and composition studies) places second language writing as a subfield of second language studies. The symbiotic model sees the constituting disciplines as interdependent; second language writing provides more than a site of interaction between the two disciplines--it connects theory and research on second language writing with praxes that are situated in both second language and composition classrooms. This model can facilitate the development of research methodologies that are appropriate for the unique problems of composing in a second language. (Contains 4 figures and a table of models of second language writing.) (NKA) *******************************************************************1, Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Disciplinary Boundary 1 Crossing the Disciplinary Boundary: Teaching ESL Composition in English Department vim) Conference on College Composition and Communication, Milwaukee, WI. 29 March 1996. c) by PAUL KEI MATSUDA Purdue University Although the number of English as a Second Language (ESL) students in US colleges and universities has increased over the past few decades, there has not been a corresponding increase in the level of attention given to the special needs of these students in composition classrooms. For example, the distribution of ESL-trained teachers in college-level writing programs has been limited to some of the special ESL courses, while ESL students are likely to be found in writing classrooms at any level (see fig. 1). This tendency may be related to the way the field of second language writing has been positioned in relation to its neighboring disciplines. In this paper, I problematize existing definitions of the fieldthe division of labor model and the intersection modelthat discourage teachers of composition from teaching or learning about ESL students. I will then propose an alternative modelthe symbiotic modelthat situates second language writing studies in various instructional contexts. Fig. 1. Distribution of ESL-Trained Teachers Upper Division Writing Courses Technical Writing Business Writing Advanced Composition Composition II r Composition I ,/ % Basic Writing 4..: BEST COPY AVAILABLE 2 Figure 1 is a generalized representation of composition programs in four-year colleges and universities. Shaded areas represent the distribution of ESL-trained instructors, who may or may not be trained in the teaching of ESL writing (Roy). Writing centers and ESL programs outside of English department are not included in this diagram. Not all institutions have the same structure, and many do not have any ESL components. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement ED yCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Disciplinary Boundary 2 Division of Labor Model Teachers and administrators of composition tend to think that ESL writing is the responsibility of ESL specialists and not of composition professionals (Roy). This "division of labor" model (see fig. 2) views second language writing as part of second language studies. This Fig. 2. Division of Labor Model Second Language Studies Composition Studies Second Langauge First Language model does not acknowledge some composition specialists' effort to help ESL writers, nor does it encourage interdisciplinary cooperation in addressing the needs of ESL writers (Santos,


title: Rain Streak Removal Using Layer Priors

abstract: This paper addresses the problem of rain streak removal from a single image. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision algorithms. Rain streak removal can be formulated as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either dictionary learning methods or impose a low rank structure on the appearance of the rain streaks. While these methods can improve the overall visibility, they tend to leave too many rain streaks in the background image or over-smooth the background image. In this paper, we propose an effective method that uses simple patch-based priors for both the background and rain layers. These priors are based on Gaussian mixture models and can accommodate multiple orientations and scales of the rain streaks. This simple approach removes rain streaks better than the existing methods qualitatively and quantitatively. We overview our method and demonstrate its effectiveness over prior work on a number of examples.


title: Quantitative Analysis of Graph Algorithms: Models and Optimization Methods

abstract: With the prevalence of graph data in real-world applications and their ever-increasing size, many graph computing systems have been developed in recent years to scale the processing and analyzing of massive graphs. However, few study focuses on modeling the performance of graph algorithms and systems to identify the bottleneck of existing machines under the workload of large-scale graphs. In this paper, we propose an analytical performance model considering computation capacity, communication bandwidth and communication latency to quantify the performance of graph algorithms. Based on this model, we find the majority of graph processing runtime is spent on random access of neighbouring vertices, causing a high cache missrate, which makes the performance bounded by communication latency. We further present performance optimization methods from perspectives of both hardware and software to alleviate the bottlenecks. Experiment results from both synthesis graphs andreal graphs show that these optimization methods can produce up to 4X performance improvement in a variety of graph algorithms.


title: Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service

abstract: We designed and deployed automatic alt-text (AAT), a system that applies computer vision technology to identify faces, objects, and themes from photos to generate photo alt-text for screen reader users on Facebook. We designed our system through iterations of prototyping and in-lab user studies. Our lab test participants had a positive reaction to our system and an enhanced experience with Facebook photos. We also evaluated our system through a two-week field study as part of the Facebook iOS app for 9K VoiceOver users. We randomly assigned them into control and test groups and collected two weeks of activity data and their survey feedback. The test group reported that photos on Facebook were easier to interpret and more engaging, and found Facebook more useful in general. Our system demonstrates that artificial intelligence can be used to enhance the experience for visually impaired users on social networking sites (SNSs), while also revealing the challenges with designing automated assistive technology in a SNS context.


title: Components of bottom-up gaze allocation in natural images

abstract: Recent research [Parkhurst, D., Law, K., & Niebur, E., 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Research 42 (1) (2002) 107–123] showed that a model of bottom-up visual attention can account in part for the spatial locations fixated by humans while free-viewing complex natural and artificial scenes. That study used a definition of salience based on local detectors with coarse global surround inhibition. Here, we use a similar framework to investigate the roles of several types of non-linear interactions known to exist in visual cortex, and of eccentricity-dependent processing. For each of these, we added a component to the salience model, including richer interactions among orientation-tuned units, both at spatial short range (for clutter reduction) and long range (for contour facilitation), and a detailed model of eccentricity-dependent changes in visual processing. Subjects free-viewed naturalistic and artificial images while their eye movements were recorded, and the resulting fixation locations were compared with the models’ predicted salience maps. We found that the proposed interactions indeed play a significant role in the spatiotemporal deployment of attention in natural scenes; about half of the observed inter-subject variance can be explained by these different models. This suggests that attentional guidance does not depend solely on local visual features, but must also include the effects of interactions among features. As models of these interactions become more accurate in predicting behaviorally-relevant salient locations, they become useful to a range of applications in computer vision and human-machine interface design.


title: The promoter of a gene encoding an isoflavone reductase-like protein in coffee (Coffea arabica) drives a stress-responsive expression in leaves

abstract: A cDNA clone (designated CaIRL) encoding an isoflavone reductase-like protein from coffee (Coffea arabica) was retrieved during a search for genes showing organ/tissue-specific expression among the expressed sequence tags (EST) of the Brazilian coffee EST database. The CaIRL cDNA contains a single open reading frame of 946 nucleotides (nt) encoding 314 amino acids (predicted molecular weight of 34 kDa). Several features identified the predicted CaIRL protein as a new member of the PIP family of NADPH-dependent reductases. Expression studies demonstrated that CaIRL is expressed exclusively in coffee leaves and its transcript level is markedly increased in response to fungal infection and mechanical injury. Analysis of transgenic tobacco plants harboring a CaIRL 5'-flanking region (862 nt) fused to uidA reporter gene (GUS) confirmed the responsiveness of the putative promoter to abiotic stress in wounded leaves. In turn, a 5' deletion to -404 completely abolished promoter activation by abiotic stimulus in transgenic plants. The lack of GUS expression in non-wounded leaf tissues in transgenic tobacco was in contrast to the basal level of CaIRL expression observed in non-stressed healthy coffee leaves.


title: Social Area Analysis , Data Mining , and GIS

abstract: There is a long cartographic tradition of describing cities through a focus on the characteristics of their residents. A review of the history of this type of urban social analysis highlights some persistent challenges. In this paper existing geodemographic approaches are extended through coupling the Kohonen Self-Organizing Map algorithm (SOM), a data-mining technique, with geographic information systems (GIS). This approach allows the construction of linked maps of social (attribute) and geographic space. This novel type of geodemographic classification allows ad hoc hierarchical groupings and exploration of the relationship between social similarity and geographic proximity. It allows one to filter complex demographic datasets and is capable of highlighting general social patterns while retaining the fundamental social fingerprints of a city. A dataset describing 79 attributes of the 2217 census tracts in New York City is analyzed to illustrate the technique. Pairs of social and geographic maps are formally compared using simple pattern metrics. Our analysis of New York City calls into question some assumptions about the functional form of spatial relationships that underlie many modeling and statistical techniques.


title: LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning

abstract: A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (“insight jumps”) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming.


title: ELS: a word-level method for entity-level sentiment analysis

abstract: We introduce ELS, a new method for entity-level sentiment classification using sequence modeling by Conditional Random Fields (CRF). The CRF is trained to identify the sentiment of each word in a document, which is then used to determine the sentiment for the entity, based on where it appears in the text. Due to its sequential nature, the CRF classifier performs better than the common bag-of-words approaches, especially when we target the local sentiment in small parts of a larger document. Identifying the sentiment about a specific entity, mentioned in a blog post or a larger product review, is a special case of such local sentiment classification. Furthermore, the proposed approach performs well even in short pieces of text, where bag-of-words approaches usually fail, due to the sparseness of the resulting feature vector. We have implemented and tested the proposed method on a publicly available benchmark corpus of short product reviews in English. The results that we present in this paper improve significantly upon published results on the same data, thus confirming our intuition about the approach.


title: Controversies in ocular trauma classification and management: review

abstract: Ocular trauma is a topic of unresolved controversies and there are continuous controversial and debatable management strategies for open-globe injuries (OGIs). International classification of ocular trauma proposed almost 15 years ago needs to be reviewed and to be more robust in predicting the outcome in the setting of OGIs. Anterior segment trauma involves controversies related to patching for corneal abrasion, corneal laceration repair, and medical management of hyphema. Timing of cataract surgery and intraocular lens implantation in the setting of trauma is still debated worldwide. There are unresolved issues regarding the management of OGIs involving the posterior segment. Timing of vitrectomy has been and will continue to be debated by proponents of early versus delayed intervention. The use of prophylactic cryotherapy and scleral buckle is still practiced differently throughout the world. The role of intravitreal antibiotics in posterior segment trauma in the absence of infection is still debated. Similarly, the use of vitrectomy versus vitreous tap in the setting of traumatic endophthalmitis is not fully resolved. In optic neuropathy, the role of intravenous methylprednisolone versus conservative management is always debated and still there are no evidence-based guidelines about the beneficial role of pulse steroid therapy. The role of optic canal decompression in the setting of acute traumatic optic neuropathy is also not conclusive. Orbital and adnexal trauma has been shown to adversely affect the outcome of OGI patients but both lids and orbital injury are not taken as preoperative variables in international ocular trauma classification. The timing of intervention in blow-out fracture is still debated. The pediatric age group, owing to the high risk of amblyopia and intraocular inflammation as well as strong vitreoretinal adhesions, has to be managed by different principles. Although the risk of sympathetic ophthalmia is very rare, it is always one of the key debated issues while managing traumatized eyes with no light perception vision. Prospective, controlled clinical studies are not possible in the OGI setting and this article reviews pertinent data regarding these management issues and controversies, and provides recommendations for treatment based on the available published data and the authors' personal experience.


title: Low latency book handling in FPGA for high frequency trading

abstract: Recent growth in algorithmic trading has caused a demand for lowering the latency of systems for electronic trading. FPGA cards are widely used to reduce latency and accelerate market data processing. To create a low latency trading system, it is crucial to effectively build a representation of the market state (book) in hardware. Thus, we have designed a new hardware architecture, which updates the book with the best bid/offer prices based on the incoming messages from the exchange. For each message a corresponding financial instrument needs to be looked up and its record needs to be updated. Proposed architecture is utilizing cuckoo hashing for the book handling, which enables low latency symbol lookup and high memory utilization. In this paper we discuss a trade-off between lookup latency and memory utilization. With average latency of 253 ns the proposed architecture is able to handle 119 275 instruments while using only 144 Mbit QDR SRAM.


oro.open.ac.uk Semantic Sentiment Analysis of Twitter


title: MIMO FMCW Reader Concept for Locating Backscatter Transponders

abstract: This paper presents a multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) reader concept for locating backscatter radio-frequency identification (RFID) transponders. The proposed MIMO concept enables transmitter sided digital beamforming after a measurement has been executed. An algorithm is presented to decompose the MIMO response to match a digital beamforming input matrix. A 2.4-GHz experimental reconfigurable localization system based on FMCW technology was set up to demonstrate the concept. Multichannels are implemented to allow the possibility of testing the MIMO concept. A frequency-division multiplexing was implemented into an MIMO FMCW RFID reader and successful tested. Moreover, the system is able to locate multiple transponders simultaneously. Measurements confirm the functionality of the concept. The implementation of a digital beamforming algorithm shows the possibility of transmitting and receiving sided spatial filtering. Angle and distance precision were determined in an antenna chamber as well as in a harsh multipath environment. To help readers to compare the system with others, the mean absolute error of the position shows the system accuracy in a laboratory environment. Finally, an error analysis of the decomposition algorithm verified its robustness.


title: A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles

abstract: Kappa-error diagrams are used to gain insights about why an ensemble method is better than another on a given data set. A point on the diagram corresponds to a pair of classifiers. The x-axis is the pairwise diversity (kappa), and the y-axis is the averaged individual error. In this study, kappa is calculated from the 2 × 2 correct/wrong contingency matrix. We derive a lower bound on kappa which determines the feasible part of the kappa-error diagram. Simulations and experiments with real data show that there is unoccupied feasible space on the diagram corresponding to (hypothetical) better ensembles, and that individual accuracy is the leading factor in improving the ensemble accuracy.


title: The Reactor: A Sample-Efficient Actor-Critic Architecture

abstract: In this work we present a new reinforcement learning agent, called Reactor (for Retrace-actor), based on an off-policy multi-step return actor-critic architecture. The agent uses a deep recurrent neural network for function approximation. The network outputs a target policy {\pi} (the actor), an action-value Q-function (the critic) evaluating the current policy {\pi}, and an estimated behavioral policy {\hat \mu} which we use for off-policy correction. The agent maintains a memory buffer filled with past experiences. The critic is trained by the multi-step off-policy Retrace algorithm and the actor is trained by a novel {\beta}-leave-one-out policy gradient estimate (which uses both the off-policy corrected return and the estimated Q-function). The Reactor is sample-efficient thanks to the use of memory replay, and numerical efficient since it uses multi-step returns. Also both acting and learning can be parallelized. We evaluated our algorithm on 57 Atari 2600 games and demonstrate that it achieves state-of-the-art performance.


title: Semi-supervised distance metric learning for collaborative image retrieval and clustering

abstract: Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as “Laplacian Regularized Metric Learning” (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.


Millennials ’ Awareness and Approach to Social 2 Responsibility and Investment – Case of Czech 3 Republic 4


title: Allele-specific FKBP5 DNA demethylation mediates gene–childhood trauma interactions

abstract: Although the fact that genetic predisposition and environmental exposures interact to shape development and function of the human brain and, ultimately, the risk of psychiatric disorders has drawn wide interest, the corresponding molecular mechanisms have not yet been elucidated. We found that a functional polymorphism altering chromatin interaction between the transcription start site and long-range enhancers in the FK506 binding protein 5 (FKBP5) gene, an important regulator of the stress hormone system, increased the risk of developing stress-related psychiatric disorders in adulthood by allele-specific, childhood trauma-dependent DNA demethylation in functional glucocorticoid response elements of FKBP5. This demethylation was linked to increased stress-dependent gene transcription followed by a long-term dysregulation of the stress hormone system and a global effect on the function of immune cells and brain areas associated with stress regulation. This identification of molecular mechanisms of genotype-directed long-term environmental reactivity will be useful for designing more effective treatment strategies for stress-related disorders.


title: A Review on Distribution System State Estimation

abstract: Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed.


title: Slant Plane CSAR Processing Using Householder Transform

abstract: Fourier analysis-based focusing of synthetic aperture radar (SAR) data collected during circular flight path is a recent advancement in SAR signal processing. This paper uses the Householder transform to obtain a ground plane circular SAR (CSAR) signal phase history from the slant plane CSAR phase history by inverting the linear shift-varying system model, thereby circumventing the need for explicitly computing a pseudo-inverse. The Householder transform has recently been shown to have improved error bounds and stability as an underdetermined and ill-conditioned system solver, and the Householder transform is computationally efficient.


title: DevOpSlang – Bridging the Gap between Development and Operations

abstract: DevOps is an emerging paradigm to eliminate the split and barrier between developers and operations personnel that traditionally exists in many enterprises today. The main promise of DevOps is to enable continuous delivery of software in order to enable fast and frequent releases. This enables quick responses to changing requirements of customers and thus may be a critical competitive advantage. In this work we propose a language called DevOpSlang in conjunction with a methodology to implement DevOps as an efficient means for collaboration and automation purposes. Efficient collaboration and automation are the key enablers to implement continuous delivery and thus to react to changing customer requirements quickly.


title: The Xbox recommender system

abstract: A recent addition to Microsoft's Xbox Live Marketplace is a recommender system which allows users to explore both movies and games in a personalized context. The system largely relies on implicit feedback, and runs on a large scale, serving tens of millions of daily users. We describe the system design, and review the core recommendation algorithm.


title: A Maximum Power Point Tracking System With Parallel Connection for PV Stand-Alone Applications

abstract: This paper presents the analysis, design, and implementation of a parallel connected maximum power point tracking (MPPT) system for stand-alone photovoltaic power generation. The parallel connection of the MPPT system reduces the negative influence of power converter losses in the overall efficiency because only a part of the generated power is processed by the MPPT system. Furthermore, all control algorithms used in the classical series-connected MPPT can be applied to the parallel system. A simple bidirectional dc-dc power converter is proposed for the MPPT implementation and presents the functions of battery charger and step-up converter. The operation characteristics of the proposed circuit are analyzed with the implementation of a prototype in a practical application.


title: Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

abstract: This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding at the transmitter and equalization at the receiver) for multicarrier multiple-input multiple-output (MIMO) channels under a variety of design criteria. Instead of considering each design criterion in a separate way, we generalize the existing results by developing a unified framework based on considering two families of objective functions that embrace most reasonable criteria to design a communication system: Schur-concave and Schur-convex functions. Once the optimal structure of the transmit-receive processing is known, the design problem simplifies and can be formulated within the powerful framework of convex optimization theory, in which a great number of interesting design criteria can be easily accommodated and efficiently solved, even though closed-form expressions may not exist. From this perspective, we analyze a variety of design criteria, and in particular, we derive optimal beamvectors in the sense of having minimum average bit error rate (BER). Additional constraints on the peak-to-average ratio (PAR) or on the signal dynamic range are easily included in the design. We propose two multilevel water-filling practical solutions that perform very close to the optimal in terms of average BER with a low implementation complexity. If cooperation among the processing operating at different carriers is allowed, the performance improves significantly. Interestingly, with carrier cooperation, it turns out that the exact optimal solution in terms of average BER can be obtained in closed form.


title: Factors of s-commerce influencing trust and purchase intention

abstract: This paper aims to study factors influencing trust and purchase intention on the Internet, develop model of trust and purchase intention by empirical data, and analyze path of antecedents influencing purchase intention. This study identifies the factors proposed in the existing social commerce constructs: recommendation and referrals, rating and reviews, forums and communities, together with three additional important factors in online environments to get comprehensive results comprising security concerns, communication, and word of mouth. It has been found that the four investigated factors: recommendation and referrals, rating and reviews, communication, and WOM, have positive effects on both trust and purchase intention. The rest of the investigated factors: forums and communities, and security concerns have positive effects specific to trust. The results also indicate that the more consumers trust an s-commerce, the more likely they are to exhibit purchase intention.


Sparse connection and pruning in large dynamic artificial neural networks


title: Realtime facial animation with on-the-fly correctives

abstract: We introduce a real-time and calibration-free facial performance capture framework based on a sensor with video and depth input. In this framework, we develop an adaptive PCA model using shape correctives that adjust on-the-fly to the actor's expressions through incremental PCA-based learning. Since the fitting of the adaptive model progressively improves during the performance, we do not require an extra capture or training session to build this model. As a result, the system is highly deployable and easy to use: it can faithfully track any individual, starting from just a single face scan of the subject in a neutral pose. Like many real-time methods, we use a linear subspace to cope with incomplete input data and fast motion. To boost the training of our tracking model with reliable samples, we use a well-trained 2D facial feature tracker on the input video and an efficient mesh deformation algorithm to snap the result of the previous step to high frequency details in visible depth map regions. We show that the combination of dense depth maps and texture features around eyes and lips is essential in capturing natural dialogues and nuanced actor-specific emotions. We demonstrate that using an adaptive PCA model not only improves the fitting accuracy for tracking but also increases the expressiveness of the retargeted character.


Nonnegative matrix approximation: algorithms and applications


Data Augmentation for Morphological Reinflection


title: Dual-Band Circularly-Polarized Unidirectional Patch Antenna for RFID Reader Applications

abstract: A dual-band 0.92/2.45 GHz circularly-polarized (CP) unidirectional antenna using the wideband dual-feed network, two orthogonally positioned asymmetric H-shape slots, and two stacked concentric annular-ring patches is proposed for RF identification (RFID) applications. The measurement result shows that the antenna achieves the impedance bandwidths of 15.4% and 41.9%, the 3-dB axial-ratio (AR) bandwidths of 4.3% and 21.5%, and peak gains of 7.2 dBic and 8.2 dBic at 0.92 and 2.45 GHz bands, respectively. Moreover, the antenna provides stable symmetrical radiation patterns and wide-angle 3-dB AR beamwidths in both lower and higher bands for unidirectional wide-coverage RFID reader applications. Above all, the dual-band CP unidirectional patch antenna presented is beneficial to dual-band RFID system on configuration, implementation, as well as cost reduction.


title: What’s wrong with cross-cultural comparisons of subjective Likert scales? The reference-group problem

abstract: Social comparison theory maintains that people think about themselves compared with similar others. Those in one culture, then, compare themselves with different others and standards than do those in another culture, thus potentially confounding cross-cultural comparisons. A pilot study and Study 1 demonstrated the problematic nature of this reference-group effect: Whereas cultural experts agreed that East Asians are more collectivistic than North Americans, cross-cultural comparisons of trait and attitude measures failed to reveal such a pattern. Study 2 found that manipulating reference groups enhanced the expected cultural differences, and Study 3 revealed that people from different cultural backgrounds within the same country exhibited larger differences than did people from different countries. Crosscultural comparisons using subjective Likert scales are compromised because of different reference groups. Possible solutions are discussed.


title: Learning Less-Overlapping Representations

abstract: In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regulariza- tion approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regu- larized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.


title: THE NEUROIMAGING OF LOVE AND DESIRE: REVIEW AND FUTURE DIRECTIONS

abstract: We review recent neuroimaging research on the experiences of romantic love and sexual desire, focusing specifically on the question of links and distinctions between the brain regions involved in these experiences. We conclude that although love and desire are associated with distinct patterns of brain activation, certain regions (such as the caudate, putamen, insula, and anterior cingulate cortex) have shown activation during both experiences, raising the possibility that certain types of love and desire may be relatively distinct from one another (on an experiential and neural level) whereas others are more interconnected. We outline several promising directions for future research on this possibility, for example testing for differences between the neurobiological bases of different types of sexual desires (i.e., those directed toward strangers versus romantic partners; those which are more “responsive” versus automatic, and those which are more or less dependent on an emotional context). We also discuss future research directions related to the study of female sexual desire and orientation.


title: CoTO: A novel approach for fuzzy aggregation of semantic similarity measures

abstract: Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.


title: Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays.

abstract: A tetrazolium salt has been used to develop a quantitative colorimetric assay for mammalian cell survival and proliferation. The assay detects living, but not dead cells and the signal generated is dependent on the degree of activation of the cells. This method can therefore be used to measure cytotoxicity, proliferation or activation. The results can be read on a multiwell scanning spectrophotometer (ELISA reader) and show a high degree of precision. No washing steps are used in the assay. The main advantages of the colorimetric assay are its rapidity and precision, and the lack of any radioisotope. We have used the assay to measure proliferative lymphokines, mitogen stimulations and complement-mediated lysis.


title: Terrestrial radar and laser scanning for deformation monitoring: first steps towards assisted radar scanning

abstract: Abstract. Deformation analysis based on multi-temporal Terrestrial Laser Scanning (TLS) surveys has been applied for many years in commercial and academic problem domains. Downsides of this technique are the sequential data acquisition as well as its limited accuracy compared to terrestrial InSAR or InRAR based technologies that are currently gaining attention of the geodetic community. A drawback of these instruments is its relative operation method while beneficially measurements within the entire field of view can be carried out simultaneously at high frequencies. At first comparative studies between TLS and InRAR scanning are presented which have been carried out at a quarry. Over the course of roughly two hours six epochs have been measured where geometric changes of different degrees have been purposely made. In order to ensure comparability concerning the outcome, both instrument coordinate systems have been transformed into a common coordinate system by applying corner cube reflectors. At last an assisted ground based RADAR approach is presented where advantages of both applied techniques are incorporated.


title: Grey level co-occurrence matrix and its application to seismic data

abstract: Christoph Georg Eichkitz, John Davies, Johannes Amtmann, Marcellus Gregor Schreilechner and Paul de Groot demonstrate how grey level co-occurrence matrix can be adapted to work on 3D imaging of seismic data. ::: ::: Texture analysis is the extraction of textural features from images (Tuceryan and Jain, 1998). The meaning of texture varies, depending on the area of science in which it is used. In general, texture refers to the physical character of an object or the appearance of an image. In image analysis, texture is defined as a function of the spatial variation in intensities of pixels (Tuceryan and Jain, 1998). Seismic texture refers to the magnitude and variability of neighbouring amplitudes at sample locations and is physically related to the distribution of scattering objects (geological texture) within a small volume at the corresponding subsurface location (Gao, 2008). Four principal methods have been developed for the analysis of seismic texture (Figure 1). These are texture classification, segmentation, synthesis, and shape.


title: The Potential of Using Quantum Theory to Build Models of Cognition

abstract: Quantum cognition research applies abstract, mathematical principles of quantum theory to inquiries in cognitive science. It differs fundamentally from alternative speculations about quantum brain processes. This topic presents new developments within this research program. In the


title: Separate Neural Systems Value Immediate and Delayed Monetary Rewards

abstract: When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.


title: An IEC 61850-90-5 gateway for IEEE C37.118.2 synchrophasor data transfer

abstract: This work describes the implementation and validation of a library named Khorjin to receive and parse synchrophasor data from a PMU/PDC based on IEEE C37.118.2 protocol, map it to the IEC 61850 data model and transmit the synchrophasor data through either Routed-Sampled Value or Routed-GOOSE services defined in the IEC 61850-90-5 protocol. In addition, the library is capable of acting as the receiver and parser of IEC 61850-90-5 messages, extracting raw synchrophasor data and feeding to subscriber applications. The functionality of the Khorjin library is validated in a Real-Time Hardware-in-the-Loop (HIL) simulation environment to asses its conformance to the functional requirements of the IEC 61850-90-5 standard.


title: Crowdsourcing, collaboration and creativity

abstract: While many organizations turn to human computation labor markets for jobs with black-or-white solutions, there is vast potential in asking these workers for original thought and innovation.


title: Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously

abstract: We propose a novel framework for simultaneously generating and manipulating the face images with desired attributes. While the state-of-the-art attribute editing technique has achieved the impressive performance for creating realistic attribute effects, they only address the image editing problem, using the input image as the condition of model. Recently, several studies attempt to tackle both novel face generation and attribute editing problem using a single solution. However, their image quality is still unsatisfactory. Our goal is to develop a single unified model that can simultaneously create and edit high quality face images with desired attributes. A key idea of our work is that we decompose the image into the latent and attribute vector in low dimensional representation, and then utilize the GAN framework for mapping the low dimensional representation to the image. In this way, we can address both the generation and editing problem by learning the generator. For qualitative and quantitative evaluations, the proposed algorithm outperforms recent algorithms addressing the same problem. Also, we show that our model can achieve the competitive performance with the state-of-the-art attribute editing technique in terms of attribute editing quality.


title: Comparison of Different Control Theories on a Two Wheeled Self Balancing Robot

abstract: . This paper is aimed to discuss and compare three of the most famous Control Theories on a Two wheeled Self Balancing Robot Simulation using Robot Operating System (ROS) and Gazebo. Two Wheeled Self Balancing Robots are one of the most fascinating applications of Inverted Pendulum System. In this paper, PID, LQR and Fuzzy logic controllers are discussed . Also,0 the modeling and algorithms of the robot simulation is discussed. The primary objectives of this paper is to discuss about the building of a robot model in ROS and Gazebo , experimenting different control theories on them, documenting the whole process with the analysis of the robot and comparison of different control theories on the system.


title: Detection of microaneurysms using multi-scale correlation coefficients

abstract: This paper presents a new approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR)-a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since microaneurysms are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on multi-scale correlation filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, microaneurysm candidate detection (coarse level) and true microaneurysm classification (fine level). The approach was evaluated based on two public datasets-ROC (retinopathy on-line challenge, http://roc.healthcare.uiowa.edu) and DIARETDB1 (standard diabetic retinopathy database, http://www.it.lut.fi/project/imageret/diaretdb1). We conclude our method to be effective and efficient.


title: Exploration in model-based reinforcement learning by empirically estimating learning progress

abstract: Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error. For example, PAC-MDP approaches such as R-MAX base their model certainty on the amount of collected data, while Bayesian approaches assume a prior over the transition dynamics. We propose extensions to such approaches which drive exploration solely based on empirical estimates of the learner's accuracy and learning progress. We provide a "sanity check" theoretical analysis, discussing the behavior of our extensions in the standard stationary finite state-action case. We then provide experimental studies demonstrating the robustness of these exploration measures in cases of non-stationary environments or where original approaches are misled by wrong domain assumptions.


title: Modeling Cyber-Insurance: Towards a Unifying Framework

abstract: We propose a comprehensive formal framework to classify all market models of cyber-insurance we are aware of. The framework features a common terminology and deals with the specific properties of cyber-risk in a unified way: interdependent security, correlated risk, and information asymmetries. A survey of existing models, tabulated according to our framework, reveals a discrepancy between informal arguments in favor of cyber-insurance as a tool to align incentives for better network security, and analytical results questioning the viability of a market for cyber-insurance. Using our framework, we show which parameters should be considered and endogenized in future models to close this gap.


title: Optimal counterforensics for histogram-based forensics

abstract: There has been a recent interest in counterforensics as an adversarial approach to forensic detectors. Most of the existing counterforensics strategies, although successful, are based on heuristic criteria, and their optimality is not proven. In this paper the optimal modification strategy of a content in order to fool a histogram-based forensics detector is derived. The proposed attack relies on the assumption of a convex cost function; special attention is paid to the Euclidean norm, obtaining the optimal attack in the MSE sense. In order to prove the usefulness of the proposed strategy, we employ it to successfully attack a well-known algorithm for detecting double JPEG compression.


title: The Costly Pursuit of Self-Esteem

abstract: Researchers have recently questioned the benefits associated with having high self-esteem. The authors propose that the importance of self-esteem lies more in how people strive for it rather than whether it is high or low. They argue that in domains in which their self-worth is invested, people adopt the goal to validate their abilities and qualities, and hence their self-worth. When people have self-validation goals, they react to threats in these domains in ways that undermine learning; relatedness; autonomy and self-regulation; and over time, mental and physical health. The short-term emotional benefits of pursuing self-esteem are often outweighed by long-term costs. Previous research on self-esteem is reinterpreted in terms of self-esteem striving. Cultural roots of the pursuit of self-esteem are considered. Finally, the alternatives to pursuing self-esteem, and ways of avoiding its costs, are discussed.


title: Pose-Guided Photorealistic Face Rotation

abstract: Face rotation provides an effective and cheap way for data augmentation and representation learning of face recognition. It is a challenging generative learning problem due to the large pose discrepancy between two face images. This work focuses on flexible face rotation of arbitrary head poses, including extreme profile views. We propose a novel Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN) to generate both neutral and profile head pose face images. The head pose information is encoded by facial landmark heatmaps. It not only forms a mask image to guide the generator in learning process but also provides a flexible controllable condition during inference. A couple-agent discriminator is introduced to reinforce on the realism of synthetic arbitrary view faces. Besides the generator and conditional adversarial loss, CAPG-GAN further employs identity preserving loss and total variation regularization to preserve identity information and refine local textures respectively. Quantitative and qualitative experimental results on the Multi-PIE and LFW databases consistently show the superiority of our face rotation method over the state-of-the-art.


title: A High-Speed LED Driver That Sweeps Out the Remaining Carriers for Visible Light Communications

abstract: The modulation speed of light-emitting diodes (LEDs) must be increased to improve the speed, increase the bandwidth, and miniaturize the hardware of visible light communication (VLC) systems. The LED modulation speed is limited by the remaining carriers that remain in the depletion capacitance. In this paper, we evaluate the increase in optical transmission rate for an LED driver for the first time by sweeping out the remaining carriers in a GaN-based LED for VLC system. The driver is fabricated using discrete GaAs FETs and passive elements on a board. An optical transmission experiment is performed. The experimental results demonstrate that the driver increases the maximum error-free bit rate for a NRZ PRBS signal from 69 to 95 Mb/s (38% increase). However, the additional current path for carrier sweep-out increases the power dissipation of the driver. To reduce the power dissipation, we employ a CMOS inverter, in which a PMOS FET sweeps out the carriers. The driver is fabricated using a 0.18-μm CMOS IC process and then implemented in a package. The experimental results demonstrate that this design increased the maximum error-free bit rate from 27.5 to 51.8 Mb/s (88% increase). The maximum bit rate for the packaged driver is 20% higher than that of the driver measured on the wafer.


title: D.: A Survey of Semantics-Based Approaches for Context Reasoning

abstract: A key issue in the study of Ambient Intelligence is reasoning about context. The aim of context reasoning is to deduce new knowledge, based on the available context data. The endmost goal is to make the ambient services more ”intelligent”; closer to the specific needs of their users. The main challenges of this effort derive from the imperfect context information, and the dynamic and heterogeneous nature of the ambient environments. In this paper, we focus on semantics-based approaches for reasoning about context. We describe how each approach addresses the requirements of ambient environments, identify their limitations, and propose possible future research directions.


title: Asymptotically tight bounds for composing ORAM with PIR

abstract: Oblivious RAM (ORAM) is a cryptographic primitive that allows a trusted client to outsource storage to an untrusted server while hiding the client’s memory access patterns to the server. The last three decades of research on ORAMs have reduced the bandwidth blowup of ORAM schemes from \(O(\sqrt{N})\) to O(1). However, all schemes that achieve a bandwidth blowup smaller than \(O(\log N)\) use expensive computations such as homomorphic encryptions. In this paper, we achieve a sub-logarithmic bandwidth blowup of \(O(\log _{d} N)\) (where d is a free parameter) without using expensive computation. We do so by using a d-ary tree and a two server private information retrieval (PIR) protocol based on inexpensive XOR operations at the servers. We also show a \(\varOmega (\log _{cD} N)\) lower bound on bandwidth blowup in the modified model involving PIR operations. Here, c is the number of blocks stored by the client and D is the number blocks on which PIR operations are performed. Our construction matches this lower bound implying that the lower bound is tight for certain parameter ranges. Finally, we show that C-ORAM (CCS 15) and CHf-ORAM violate the lower bound. Combined with concrete attacks on C-ORAM/CHf-ORAM, we claim that there exist security flaws in these constructions.


title: Productivity growth in Korea: efficiency improvement or technical progress?

abstract: This paper shows that the productivity gains in Korean manufacturing are mostly from efficiency improvement rather than from technical progress. These findings are contrary to those of previous sectoral studies of Korean and Taiwanese manufacturing, but are consistent with those of cross-country studies. Regression results show that both domestic and foreign R&D played an important role in increasing efficiency and technical progress in Korean manufacturing. However, domestic R&D has more effect on technical progress, while foreign R&D has played a relatively stronger role in efficiency improvement.


title: A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis

abstract: We measure the knowledge flows between countries by analysing publication and citation data, arguing that not all citations are equally important. Therefore, in contrast to existing techniques that utilize absolute citation counts to quantify knowledge flows between different entities, our model employs a citation context analysis technique, using a machine-learning approach to distinguish between important and non-important citations. We use 14 novel features (including context-based, cue words-based and text-based) to train a Support Vector Machine (SVM) and Random Forest classifier on an annotated dataset of 20,527 publications downloaded from the Association for Computational Linguistics anthology (http://allenai.org/data.html). Our machine-learning models outperform existing state-of-the-art citation context approaches, with the SVM model reaching up to 61% and the Random Forest model up to a very encouraging 90% Precision–Recall Area Under the Curve, with 10-fold cross-validation. Finally, we present a case study to explain our deployed method for datasets of PLoS ONE full-text publications in the field of Computer and Information Sciences. Our results show that a significant volume of knowledge flows from the United States, based on important citations, are consumed by the international scientific community. Of the total knowledge flow from China, we find a relatively smaller proportion (only 4.11%) falling into the category of knowledge flow based on important citations, while The Netherlands and Germany show the highest proportions of knowledge flows based on important citations, at 9.06 and 7.35% respectively. Among the institutions, interestingly, the findings show that at the University of Malaya more than 10% of the knowledge produced falls into the category of important. We believe that such analyses are helpful to understand the dynamics of the relevant knowledge flows across nations and institutions.


title: The microbiome: stress, health and disease

abstract: Bacterial colonisation of the gut plays a major role in postnatal development and maturation of key systems that have the capacity to influence central nervous system (CNS) programming and signaling, including the immune and endocrine systems. Individually, these systems have been implicated in the neuropathology of many CNS disorders and collectively they form an important bidirectional pathway of communication between the microbiota and the brain in health and disease. Regulation of the microbiome–brain–gut axis is essential for maintaining homeostasis, including that of the CNS. Moreover, there is now expanding evidence for the view that commensal organisms within the gut play a role in early programming and later responsivity of the stress system. Research has focused on how the microbiota communicates with the CNS and thereby influences brain function. The routes of this communication are not fully elucidated but include neural, humoral, immune and metabolic pathways. This view is underpinned by studies in germ-free animals and in animals exposed to pathogenic bacterial infections, probiotic agents or antibiotics which indicate a role for the gut microbiota in the regulation of mood, cognition, pain and obesity. Thus, the concept of a microbiome–brain–gut axis is emerging which suggests that modulation of the gut microflora may be a tractable strategy for developing novel therapeutics for complex stress-related CNS disorders where there is a huge unmet medical need.


title: Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum

abstract: Clinical manifestations in diseases affecting the dopamine system include deficits in emotional, cognitive, and motor function. Although the parallel organization of specific corticostriatal pathways is well documented, mechanisms by which dopamine might integrate information across different cortical/basal ganglia circuits are less well understood. We analyzed a collection of retrograde and anterograde tracing studies to understand how the striatonigrostriatal (SNS) subcircuit directs information flow between ventromedial (limbic), central (associative), and dorsolateral (motor) striatal regions. When viewed as a whole, the ventromedial striatum projects to a wide range of the dopamine cells and receives a relatively small dopamine input. In contrast, the dorsolateral striatum (DLS) receives input from a broad expanse of dopamine cells and has a confined input to the substantia nigra (SN). The central striatum (CS) receives input from and projects to a relatively wide range of the SN. The SNS projection from each striatal region contains three substantia nigra components: a dorsal group of nigrostriatal projecting cells, a central region containing both nigrostriatal projecting cells and its reciprocal striatonigral terminal fields, and a ventral region that receives a specific striatonigral projection but does not contain its reciprocal nigrostriatal projection. Examination of results from multiple tracing experiments simultaneously demonstrates an interface between different striatal regions via the midbrain dopamine cells that forms an ascending spiral between regions. The shell influences the core, the core influences the central striatum, and the central striatum influences the dorsolateral striatum. This anatomical arrangement creates a hierarchy of information flow and provides an anatomical basis for the limbic/cognitive/motor interface via the ventral midbrain.


title: Managing Knowledge in Organizations: An Integrative Framework and Review of Emerging Themes

abstract: In this concluding article to the Management Science special issue on "Managing Knowledge in Organizations: Creating, Retaining, and Transferring Knowledge," we provide an integrative framework for organizing the literature on knowledge management. The framework has two dimensions. The knowledge management outcomes of knowledge creation, retention, and transfer are represented along one dimension. Properties of the context within which knowledge management occurs are represented on the other dimension. These properties, which affect knowledge management outcomes, can be organized according to whether they are properties of a unit (e.g., individual, group, organization) involved in knowledge management, properties of relationships between units or properties of the knowledge itself. The framework is used to identify where research findings about knowledge management converge and where gaps in our understanding exist. The article discusses mechanisms of knowledge management and how those mechanisms affect a unit's ability to create, retain and transfer knowledge. Emerging themes in the literature on knowledge management are identified. Directions for future research are suggested.


title: Multivariate monitoring for the industrialisation of a continuous wet granulation tableting process

abstract: The pharmaceutical industry is undergoing a significant change in product development and manufacturing strategies with the progressive shift from batch to continuous processes. These typically feature vast volumes of data generated by the numerous sensors connected to several unit operations running over the period of several hours or even days and that demand the application of increasingly efficient tools for process understanding, monitoring and control. This paper describes the use of multivariate statistical process modeling by means of chemometric methods to monitor the continuous wet granulation tableting process for a drug product currently under development. Models are tailored to the different units that make up the continuous tableting line, from material feeding and granulation up to tablet compression, where the solutions devised reflect the different dynamics of each unit and are used as maintenance and intervention tools to optimise manufacturing and associated operations retrospectively as well as in real-time, as part of the product industrialisation programme.


title: Real-time one-pass decoding with recurrent neural network language model for speech recognition

abstract: This paper proposes an efficient one-pass decoding method for realtime speech recognition employing a recurrent neural network language model (RNNLM). An RNNLM is an effective language model that yields a large gain in recognition accuracy when it is combined with a standard n -gram model. However, since every word probability distribution based on an RNNLM is dependent on the entire history from the beginning of the speech, the search space in Viterbi decoding grows exponentially with the length of the recognition hypothesesand makescomputation prohibitivelyexpensive. Therefore, an RNNLM is usually used by N -best rescoring or by approximating it to a back-off n -gram model. In this paper, we present another approach that enables one-pass Viterbi decoding with an RNNLM without approximation, where the RNNLM is represented as a prefix tree of possible word sequences, and only the part needed for decoding is generated on-the-fly and used to rescore each hypothesis using an on-the-fly composition technique we previously proposed. Experimental results on the MIT lecture transcription task show that our proposed method enables one-pass decoding with small overhead for the RNNLM and achieves a slightly higher accuracy than 1000-best rescoring. Furthermore, it reduces the latency from the end of each utterance in two-pass decoding by a factor of 10.


title: Systematic Survey of Big Data and Data Mining in Internet of Things

abstract: Abstract In recent years, the Internet of Things (IoT) has emerged as a new opportunity. Thus, all devices such as smartphones, transportation facilities, public services, and home appliances are used as data creator devices. All the electronic devices around us help our daily life. Devices such as wrist watches, emergency alarms, and garage doors and home appliances such as refrigerators, microwaves, air conditioning, and water heaters are connected to an IoT network and controlled remotely. Methods such as big data and data mining can be used to improve the efficiency of IoT and storage challenges of a large data volume and the transmission, analysis, and processing of the data volume on the IoT. The aim of this study is to investigate the research done on IoT using big data as well as data mining methods to identify subjects that must be emphasized more in current and future research paths. This article tries to achieve the goal by following the conference and journal articles published on IoT-big data and also IoT-data mining areas between 2010 and August 2017. In order to examine these articles, the combination of Systematic Mapping and literature review was used to create an intended review article. In this research, 44 articles were studied. These articles are divided into three categories: Architecture & Platform, framework, and application. In this research, a summary of the methods used in the area of IoT-big data and IoT-data mining is presented in three categories to provide a starting point for researchers in the future.


title: Metric Factorization: Recommendation beyond Matrix Factorization

abstract: In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations. Nevertheless, the dot product adopted in matrix factorization based recommender models does not satisfy the inequality property, which may limit their expressiveness and lead to sub-optimal solutions. To overcome this problem, we propose a novel recommender technique dubbed as {\em Metric Factorization}. We assume that users and items can be placed in a low dimensional space and their explicit closeness can be measured using Euclidean distance which satisfies the inequality property. To demonstrate its effectiveness, we further designed two variants of metric factorization with one for rating estimation and the other for personalized item ranking. Extensive experiments on a number of real-world datasets show that our approach outperforms existing state-of-the-art by a large margin on both rating prediction and item ranking tasks.


title: Face Synthesis for Eyeglass-Robust Face Recognition

abstract: In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.


title: A Distributed Avionics Communication Network

abstract: Today, the avionics platforms are developedaccording to the Integrated Modular Avionics (IMA) concept, allowing one processing module to host oneor several applications in order to reduce Space, Weight, Power (SWaP) and costs. According to thisevolution, network architectures were developed inwhich modules are interconnected and communicatethrough a deterministic network which shouldsupport critical inter system communications and alsoa part of intra system communications. The favored answer fully compliant with IMAPlatform properties for the Avionic Data Networkarchitecture is a centralized communication systemusing several Avionic Switches as centralequipment's compliant to the standard ARINC 664Part7, which defines a deterministic switchedcommunication network at 100Mbps per link usingan Ethernet frame structure. However, a centralized communication systemfor some aircrafts or helicopters requiring a "small"Avionics Data Network is a major overhead for theavionics suite in term of size, weight and globallycost like to the fact that to fulfill the constraints ofavailability and segregation inside thecommunication system at least a number of 2 or 4Switches shall be used. This negative impact isstressed by the necessity to upload the configurationtables for each location and to monitor A664 Part7Switch as avionics equipment. A promising approach that allows optimizingSWaP (less Size, Weight and Power) than the currentmain A664 Avionic Data Network is the evolutionfrom the centralized communication system to adistributed communication system without anyadditional dedicated communication equipment. Nevertheless, to fulfill issues like dataflowlatency mastering, segregation, network availabilityin case of subscriber loss, configuration should beaddressed while keeping the ARINC 664 propertiesas dataflow partitioning, monitoring and frame structure.


title: Technology as an operant resource in service (eco)systems

abstract: In this paper, we explore the role and scope of technology in value co-creation, service innovation and service systems--value co-creation configurations of people technology and value propositions (Maglio and Spohrer in J Acad Mark Sci 36:18---20, 2008). We draw on a structurational model of technology (Orlikowsky in Organ Sci 3(3):398---427, 1992) to provide a framework for considering the role of technology in service systems and how it influences and is influenced by human actions (i.e., practices) and institutions. We broaden the scope of technology in this model, beyond a material artifact, or outcome of human actions, by applying an S-D logic, service ecosystems (Vargo and Lusch in J Market 68(1):1---17, 2004, Ind Mark Manag 40(2):181---187, 2011a) approach, which focuses on the processes by which value is co-created and new ways of creating value (i.e., innovation) emerge. In this view, technology can be conceptualized as an operant resource--one that is capable of acting on other resources to create value--and, thus, becomes a critical resource for value co-creation, service innovation and systems (re)formation. We argue that the consideration of technology as an operant resource in service (eco)systems provides a more encompassing view for systematically studying the way in which technologies are integrated as resources, value is collaboratively created, and service is innovated.


title: CloudSVM : Training an SVM Classifier in Cloud Computing Systems

abstract: In conventional distributed machine learning methods, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM converges to the optimal classifier function. Single computer is incapable to train SVM algorithm with large scale data sets. The results of this study are important for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing environment using SVM will converge to a global optimal classifier in finite iteration size.


title: Personalized machine learning for robot perception of affect and engagement in autism therapy

abstract: Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the childrens affective states and engagement during robot-assisted autism therapy. The key to our approach is a novel shift from the traditional ML paradigm - instead of using 'one-size-fits-all' ML models, our personalized ML framework is optimized for each child by leveraging relevant contextual information (demographics and behavioral assessment scores) and individual characteristics of each child. We designed and evaluated this framework using a dataset of multi-modal audio, video and autonomic physiology data of 35 children with autism (age 3-13) and from 2 cultures (Asia and Europe), participating in a 25-minute child-robot interaction (~500k datapoints). Our experiments confirm the feasibility of the robot perception of affect and engagement, showing clear improvements due to the model personalization. The proposed approach has potential to improve existing therapies for autism by offering more efficient monitoring and summarization of the therapy progress.


title: Design of a Low-Cost Air Levitation System for Teaching Control Engineering

abstract: Air levitation is the process by which an object is lifted without mechanical support in a stable position, by providing an upward force that counteracts the gravitational force exerted on the object. This work presents a low-cost lab implementation of an air levitation system, based on open solutions. The rapid dynamics makes it especially suitable for a control remote lab. Due to the system’s nature, the design can be optimized and, with some precision trade-off, kept affordable both in cost and construction effort. It was designed to be easily adopted to be used as both a remote lab and as a hands-on lab.


title: Motion control for robotic arm with rotational counterweights

abstract: This paper proposes a new control method for the robotic arm with rotational counterweights. This robotic arm is actuated by gravitational and inertia torque of the counterweights. To date, the control law which achieves the angle control of the robot has been proposed. However, in the research, only 1 DOF robot is discussed. Moreover, the stability analysis of the dynamics hasn't been shown. The new control method proposed in this paper achieves the angle control of 2 DOFs, and we can prove the dynamic stability. In this paper, we show the convergence of the dynamics considering the method. Also we conduct numerical simulations for position control of 2 DOFs robotic arm. Verifying the angles of the robotic arm converge to its desired angles, the utility of the method is demonstrated.


Excuse me, do you have a moment to talk about version control?


title: Enterprise Competitive Analysis and Consumer Sentiments on Social Media

abstract: The utilization of Social media tools in business enterprises has tremendously increased with an increased number of users and a corresponding upsurge in time spent online. Online social media services such as Facebook and Twitter are used by companies to introduce new products and services, provide various supports and interact with customers on daily basis. This regular interaction of businesses and consumers results in huge amount of customer-generated content which is becoming a source of insight in analysing the often erratic consumer behaviour. For companies to harness the business potential of social media to increase competitive advantage, sentiments behind textual data of both their customers and that of their competitors must be keenly monitored and analysed. This paper demonstrates how companies especially those in the Telecommunication industry can seize the opportunity presented by social media to mine textual data to gain advantage over competitors by cumulatively understanding consumer opinions, frustrations and satisfaction. Using Facebook and Twitter sites of the top three telecommunication companies in Ghana: MTN, Vodafone and Tigo the paper reveals insights from unstructured texts of customers of these three companies. The results show (1) the exponential growth of social media users in Ghana (2) impact and numbers behind active social media participation in the telecommunication industry (3) the power of social media opinion mining for competitive analysis (4) how business value could be extracted from the huge unstructured textual data available on social media and (5) the company that is more responsive to customer concerns.


title: Time-Domain Analysis of Intermodulation Distortion of Closed-Loop Class-D Amplifiers

abstract: This paper presents a time-domain analysis of the intermodulation distortion (IMD) of a closed-loop Class-D amplifier (amp) with either first- or second-order loop filter. The derived expression for the IMD indicates that there exist significant third-order intermodulation products (3rd-IMPs) within the output spectrum, which may lead to even greater distortion than the intrinsic harmonic components. In addition, the output expressions are compact, precise, and suitable for hand calculation so that the parametric relationships between the IMD and the magnitude and frequency of the input signals, as well as the effect of the loop filter design are straightforwardly investigated. In order to accurately represent the IMD performance of class-D amp, a modified testing setup is introduced to account for the dominantly large 3rd-IMPs when the International Telecommunication Union Radiocommunication Sector (ITU-R) standard is applied.


title: Reference-Dependent Job Search: Evidence from Hungary

abstract: We propose a model of job search with reference-dependent preferences, where the reference point is given by recent income. Newly unemployed individuals search hard given that they are at a loss, but over time they get used to lower income, and thus reduce their search effort. In anticipation of a benefit cut their search effort rises again, then declines once they get used to the lower benefit level. The model fits the typical pattern of the exit from unemployment, even with no unobserved heterogeneity. The model also makes distinguishing predictions regarding the response to benefit changes, which we evaluate using a unique reform. In 2005, Hungary switched from a single-step UI system to a two-step system, with unchanged overall generosity. The system generated increased hazard rates in anticipation of, and especially following, benefit cuts in ways the standard model has a hard time explaining. We estimate a model with optimal consumption and endogenous search effort, as well as unobserved heterogeneity. The reference-dependent model fits the hazard rates substantially better than most versions of the standard model. We estimate a slow-adjusting reference point and substantial impatience, likely reflecting present-bias. Habit formation and a variety of alternative models do not match the fit of the reference-dependent model. We discuss one model which also fits well, but is at odds with calibrated values and other evidence.


title: Tactile Display Device Using Distributed Lateral Skin Stretch

abstract: In the past, tactile displays were of one of two kinds: they were either shape displays, or relied on distributed vibrotactile stimulation. A tactile display device is described in this paper which is distinguished by the fact that it relies exclusively on lateral skin stretch stimulation. It is constructed from an array of 64 closely packed piezoelectric actuators connected to a membrane. The deformations of this membrane cause an array of 112 skin contactors to create programmable lateral stress fields in the skin of the finger pad. Some preliminary observations are reported with respect to the sensations that this kind of display can produce. INTRODUCTION Tactile displays are devices used to provide subjects with the sensation of touching objects directly with the skin. Previously reported tactile displays portray distributed tactile stimulation as a one of two possibilities [1]. One class of displays, termed “shape displays”, typically consists of devices having a dense array of skin contactors which can move orthogonally to the surface of the skin in an attempt to display the shape of objects via its spatially sampled approximation. There exist numerous examples of such displays, for recent designs see [2; 3; 4; 5]. In the interest of brevity, the distinction between “pressure displays” and shape displays is not made here. However, an important distinction with regard to the focus of this paper must be made between displays intended to cause no slip between the contactors and the skin and those intended for the opposite case.1 Displays which are intended to be used without slip can be mounted on a carrier device [6; 2]. 1Braille displays can be found in this later category. Another class of displays takes advantage of vibrotactile stimulation. With this technique, an array of tactilly active sites stimulates the skin using an array of contactors vibrating at a fixed frequency. This frequency is selected to maximize the loudness of the sensation (200–300 Hz). Tactile images are associated, not to the quasi-static depth of indentation, but the amplitude of the vibration [7].2 Figure 1. Typical Tactile Display. Shape displays control the rising movement of the contactors (resp. the force applied to). In a vibrotactile display, the contactors oscillate at a fixed frequency. Devices intended to be used as general purpose tactile displays cause stimulation by independently and simultaneously activated skin contactors according to patterns that depend both on space and on time. Such patterns may be thought of as “tactile images”, but because of the rapid adaptation of the skin mechanoreceptors, the images should more accurately be described as “tactile movies”. It is also accepted that the separation between these contactors needs to be of the order of one millimeter so that the resulting percept fuse into one single continuous image. In addition, when contactors apply vibratory signals to the skin at a frequency, which may range from a few Hertz to a few kiloHertz, a perception is derived which may be described 2The Optacon device is a well known example [8]. Proceedings of the Haptic Interfaces for Virtual Environment and Teleoperator Systems Symposium, ASME International Mechanical Engineering Congress & Exposition 2000, Orlando, Florida, USA . pp. 1309-1314


title: Quantization-based Markov feature extraction method for image splicing detection

abstract: In this paper, we propose an efficient Markov feature extraction method for image splicing detection using discrete cosine transform coefficient quantization. The quantization operation reduces the information loss caused by the coefficient thresholding used to restrict the number of Markov features. The splicing detection performance is improved because the quantization method enlarges the discrimination of the probability distributions between the authentic and the spliced images. In this paper, we present two Markov feature selection algorithms. After quantization operation, we choose the sum of three directional Markov transition probability values at the corresponding position in the probability matrix as a first feature vector. For the second feature vector, the maximum value among the three directional difference values of the three color channels is used. A fixed number of features, regardless of the color channels and test datasets, are used in the proposed algorithm. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. The average detection accuracy is over than 97% on three well-known splicing detection image datasets without the use of additional feature reduction algorithms. Furthermore, we achieve reasonable forgery detection performance for more modern and realistic dataset.


title: BigBand : GHz-Wide Sensing and Decoding on Commodity Radios

abstract: The goal of this paper is to make sensing and decoding GHz of spectrum simple, cheap, and low power. Our thesis is simple: if we can build a technology that captures GHz of spectrum using commodity Wi-Fi radios, it will have the right cost and power budget to enable a variety of new applications such as GHzwide dynamic access and concurrent decoding of diverse technologies. This vision will change today’s situation where only expensive power-hungry spectrum analyzers can capture GHz-wide spectrum. Towards this goal, the paper harnesses the sparse Fourier transform to compute the frequency representation of a sparse signal without sampling it at full bandwidth. The paper makes the following contributions. First, it presents BigBand, a receiver that can sense and decode a sparse spectrum wider than its own digital bandwidth. Second, it builds a prototype of its design using 3 USRPs that each samples the spectrum at 50 MHz, producing a device that captures 0.9 GHz — i.e., 6× larger bandwidth than the three USRPs combined. Finally, it extends its algorithm to enable spectrum sensing in scenarios where the spectrum is not sparse.


title: Intrinsic Motivation and Engagement as “Active Ingredients” in Garden-Based Education: Examining Models and Measures Derived From Self-Determination Theory

abstract: Building on self-determination theory, this study presents a model of intrinsic motivation and engagement as “active ingredients” in garden-based education. The model was used to create reliable and valid measures of key constructs, and to guide the empirical exploration of motivational processes in garden-based learning. Teacher- and student-reports of garden engagement, administered to 310 middle school students, demonstrated multidimensional structures, good measurement properties, convergent validity, and the expected correlations with self-perceptions in the garden, garden learning, achievement, and engagement in science and school. Exploratory path analyses, calculated using multiple regression, provided initial support for the self-determination model of motivation: students’ perceived autonomy, competence, and intrinsic motivation uniquely predicted their engagement in the garden, which in turn, predicted learning in the gardens and achievement in school.


title: Image enhancement in simple fingerprint minutiae extraction algorithm using crossing number on valley structure

abstract: Most of the existing fingerprint extraction techniques currently available are based on ridge structure. The ridge usually has thicker structure than the valley, so that more processing time is needed to extract the ridge than extracting the valley. Taking the advantage of the thin structure of the valley, we proposed an algorithm that reduces the time needed for minutiae extraction. The algorithm was developed in Matlab environment using fingerprint images from FVC2004. In order to show the performance of the algorithm, numerical results are presented.


title: Design and implementation of a tdd-based 128-antenna massive MIMO prototype system

abstract: Owing to the dramatic mobile IP growth, the emerging Internet of Things, and cloud-based applications, wireless networking is witnessing a paradigm shift. By fully exploiting spatial degrees of freedom, massive multiple-input-multiple-output (MIMO) systems promise significant gains in data rates and link reliability. Although the research community has recognized the theoretical benefits of these systems, building the hardware of such complex systems is a challenge in practice. This paper presents a time division duplex (TDD)-based 128-antenna massive MIMO prototype system from theory to reality. First, an analytical signal model is provided to facilitate the setup of a feasible massive MIMO prototype system. Second, a link-level simulation consistent with practical TDDbased massive MIMO systems is conducted to guide and validate the massive MIMO system design. We design and implement the TDDbased 128-antenna massive MIMO prototype system with the guidelines obtained from the link-level simulation. Uplink real-time video transmission and downlink data transmission under the configuration of multiple single-antenna users are achieved. Comparisons with state-of-the-art prototypes demonstrate the advantages of the proposed system in terms of antenna number, bandwidth, latency, and throughput. The proposed system is also equipped with scalability, which makes the system applicable to a wide range of massive scenarios.


title: PixelNet: Towards a General Pixel-level Architecture

abstract: We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that (1) stratified sampling allows us to add diversity during batch updates and (2) sampled multi-scale features allow us to explore more nonlinear predictors (multiple fully-connected layers followed by ReLU) that improve overall accuracy. Finally, our objective is to show how a architecture can get performance better than (or comparable to) the architectures designed for a particular task. Interestingly, our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context, surface normal estimation on NYUDv2 dataset, and edge detection on BSDS without contextual post-processing.


title: Trust-Based Access Control for Secure Cloud Computing

abstract: Multi-tenancy, elasticity and dynamicity pose several novel challenges for access control in a cloud environment. Accessing subjects may dynamically change, resources requiring protection may be created or modified, and subject access requirements to resources may change during the course of the application execution. Users may need to acquire different permissions from different administrative domains based on the services in cloud computing environment. Traditional identity-based access control models such as attribute-based access control (ABAC), role-based access control (RBAC), discretionary access control (DAC), or mandatory access control (MAC) cannot be applied directly in clouds. In this chapter, we explore challenges of cloud access control, identify desirable properties of access control models, and introduce the novel graph-theoretic semantics of access control model. We specify how authorization occurs in the proposed model, and present how to incorporate features such as separation of duty (SoD).


title: Supply chain product visibility: Methods, systems and impacts

abstract: Supply chain product visibility may be defined to mean the capacity of the supply chain to have a view of a product's lifecycle, from its conception, manufacturing, distribution, delivery to the end customer, customer's experience of the product, and the product's end-of-life activities and processes. This implies developing and keeping a record of the product's materials and components, its physical state throughout the supply chain, the product's forward movement to the user-customer, customer's experience of the product, and the reverse logistics and reuse or termination of the product. The aim of visibility is to foster planning, control and agility of operations associated with the product and to improve customer experience of the product. ''Tracking'' is the term often used to describe the determination of the identity and state of a product in the forward direction (from manufacturing to the end user), while ''tracing'' is used to infer the product's path and history from downstream to upstream of the supply chain. In recent times there has been an upsurge of academic and commercial interest in product visibility. This interest has translated into numerous architectures, technologies and software for product visibility, both at the atomic (item) and composite (or aggregate) levels. Based on an extensive content analysis of academic and trade literature, including websites and documents of vendors and users of the technologies, this paper captures, analyses, compares and contrasts the design choices, essence, results and current and potential future impacts of some of the recent developments. The study also used survey by questionnaire across industries to assess user requirements of tracking and tracing systems and structures. The paper also charts future research directions for end-to-end visibility of product classes and their instances in supply chains.


title: A Dynamical View of High School Attendance: An Assessment of Short-term and Long-term Dependencies in Five Urban Schools.

abstract: While school attendance is a critical mediator to academic achievement, its time dependent characteristics are rarely investigated. To remedy situation, this paper reports on the analysis of daily attendance rates in five urban high schools over a seven-year period. Traditional time series analyses were conducted to estimate short-range and cyclical dependencies in the data. An Autoregressive Fractional Integrated Moving Average (ARFIMA) approach was used to address long-range correlational patterns, and detect signs of self-organized criticality. The analysis reveals a strong cyclical pattern (weekly) in all five schools, and evidence for self-organized criticality in one of the five. These findings illustrate the insufficiency of traditional statistical summary measures to characterize the distribution of daily attendance, and they suggest that daily attendance is not necessarily the stable and predictable feature of school effectiveness it is conventionally assumed to be. While educational practitioners can probably attest to the many of the irregularities in attendance patterns as well as some of their sources, a systematic description of these temporal aspects needs to be included in our assessment of daily attendance behavior to inform policy decisions, if only to better align formal research in this area with existing local knowledge about those patterns.


title: Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network

abstract: We introduce a new top-down pipeline for scene text detection. We propose a novel Cascaded Convolutional Text Network (CCTN) that joints two customized convolutional networks for coarse-to-fine text localization. The CCTN fast detects text regions roughly from a low-resolution image, and then accurately localizes text lines from each enlarged region. We cast previous character based detection into direct text region estimation, avoiding multiple bottom- up post-processing steps. It exhibits surprising robustness and discriminative power by considering whole text region as detection object which provides strong semantic information. We customize convolutional network by develop- ing rectangle convolutions and multiple in-network fusions. This enables it to handle multi-shape and multi-scale text efficiently. Furthermore, the CCTN is computationally efficient by sharing convolutional computations, and high-level property allows it to be invariant to various languages and multiple orientations. It achieves 0.84 and 0.86 F-measures on the ICDAR 2011 and ICDAR 2013, delivering substantial improvements over state-of-the-art results [23, 1].


title: Neural Named Entity Recognition Using a Self-Attention Mechanism

abstract: We propose a novel supervised approach for text tagging and multi-label text classification based on a multi-head encoder-decoder neural network architecture. Our method predicts which subset of possible tags best matches an input text. It efficiently spends computational resources, exploiting dependencies between tags by encoding an input text into a compact representation which is then passed to multiple decoder classifier heads. We test our architecture on a Twitter hashtag prediction task, comparing it to a baseline model with multiple feedforward networks and a baseline model with multiple recurrent neural networks with GRU cells. We show that our approach achieves a significantly better performance than baselines with an equivalent number of parameters.