title: Comparison of classifier fusion methods for classification in pattern recognition tasks

abstract: This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.


title: A Survey on Sharing Economy and Its Effect on Human Behavior Changes

abstract: Advances in computer science prompt the creation of new economic models. One of them in urban life is Sharing Economy. This new economic model receives high attentions and has been applied to a wide range of fields in recent years. We argue that sharing economy, and its applications, may be taken as a way for researchers to understand human behavior, and thus this paper targets a growing issue on the relationship between sharing economy and its impacts on human behavior. It especially looks at the change of behaviors while the sharing economy comes into our daily lives. The Airbnb, one of the most significant examples of sharing economy, is taken as a starting point of this survey, and results are revealed that young generation tends to accept this new economic experience as well as willing to be involved, not only as traveler but host, in this new phenomenon.


title: Street drug markets beyond favelas in Belo Horizonte, Brazil

abstract: This study examines whether social disorganization mechanisms that explain clusters of street drug markets in socially disorganized neighborhoods in developed countries can also help explain geographical patterns of drug dealing across neighborhoods in Belo Horizonte, Brazil. Data for this study includes drug arrests from 2007 to 2011 and socio demographic data from the 2010 Census. To examine the influence of exploratory variables on drug market locations, the Negative Binominal regression model was used at two levels of analysis—the Belo Horizonte city center and other neighborhoods including favelas. The findings show that a high hot spot of street drug markets located in the city center is positively associated with housing quality as well as negatively associated with residential tenure. Low hot spots were found in remaining neighborhoods, including impoverished areas of favelas and are related to key social disorganization indicators such as socio-economic status, age at risk, and residential tenure. This study has important implications for crime prevention policies and provides the basis for further comparative research on street drug markets across many different countries.


title: The tangible user interface and its evolution

abstract: Users sculpt and manipulate digital information throughsuch tangible media as clay, sand, and building models, coupled with underlying computation for design and analysis.


title: Visualizing Deep Convolutional Neural Networks Using Natural Pre-images

abstract: Image representations, from SIFT and bag of visual words to convolutional neural networks (CNNs) are a crucial component of almost all computer vision systems. However, our understanding of them remains limited. In this paper we study several landmark representations, both shallow and deep, by a number of complementary visualization techniques. These visualizations are based on the concept of "natural pre-image", namely a natural-looking image whose representation has some notable property. We study in particular three such visualizations: inversion, in which the aim is to reconstruct an image from its representation, activation maximization, in which we search for patterns that maximally stimulate a representation component, and caricaturization, in which the visual patterns that a representation detects in an image are exaggerated. We pose these as a regularized energy-minimization framework and demonstrate its generality and effectiveness. In particular, we show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.


title: A Pragmatic Guide to Geoparsing Evaluation

abstract: Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems. Evaluation is further made inconsistent, even unrepresentative of real world usage by the lack of distinction between the different types of toponyms, which necessitates new guidelines, a consolidation of metrics and a detailed toponym taxonomy with implications for Named Entity Recognition (NER) and beyond. To address these deficiencies, our manuscript introduces a new framework in three parts. (Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms. (Part 2) Metrics: discussed and reviewed for a rigorous evaluation including recommendations for NER/Geoparsing practitioners. (Part 3) Evaluation data: shared via a new dataset called GeoWebNews to provide test/train examples and enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping and evaluating machine learning NLP models.


title: Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance

abstract: This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical features: large-scale, hierarchical tag system, severe annotation incompleteness and data imbalance. Considering these characteristics, an amount of strategies are employed, including SNIPER, soft sampling, class-aware sampling (CAS), hierarchical non-maximum suppression (HNMS) and so on. In virtue of these effective strategies, and further using the powerful SENet154 armed with feature pyramid module and deformable ROIalign as the backbone, our best single model could achieve a mAP of 56.9%. After a further ensemble with 9 models, the final mAP is boosted to 62.2% in the public leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked the 3rd place, slightly inferior to the 1st place by only 0.04 point).


title: Double-sided cooling design for novel planar module

abstract: A novel packaging structure for medium power modules featuring power semiconductor switches sandwiched between two symmetric substrates that fulfill electrical conduction and insulation functions is presented. Large bonding areas between dies and substrates allow this packaging technology to offer significant improvements in electrical, thermal performance. Double-sided cooling system was dedicatedly analyzed and designed for different applications.


title: Measuring Political Polarization: Twitter shows the two sides of Venezuela

abstract: We say that a population is perfectly polarized when divided in two groups of the same size and opposite opinions. In this paper, we propose a methodology to study and measure the emergence of polarization from social interactions. We begin by proposing a model to estimate opinions in which a minority of influential individuals propagate their opinions through a social network. The result of the model is an opinion probability density function. Next, we propose an index to quantify the extent to which the resulting distribution is polarized. Finally, we apply the proposed methodology to a Twitter conversation about the late Venezuelan president, Hugo Chavez, finding a good agreement between our results and offline data. Hence, we show that our methodology can detect different degrees of polarization, depending on the structure of the network.


title: Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification

abstract: With the considerable growth of user-generated content, online reviews are becoming extremely valuable sources for mining customers' opinions on products and services. However, most of the traditional opinion mining methods are coarse-grained and cannot understand natural languages. Thus, aspect-based opinion mining and summarization are of great interest in academic and industrial research. In this paper, we study an approach to extract product and service aspect words, as well as sentiment words, automatically from reviews. An unsupervised dependency analysis-based approach is presented to extract Appraisal Expression Patterns (AEPs) from reviews, which represent the manner in which people express opinions regarding products or services and can be regarded as a condensed representation of the syntactic relationship between aspect and sentiment words. AEPs are high-level, domain-independent types of information, and have excellent domain adaptability. An AEP-based Latent Dirichlet Allocation (AEP-LDA) model is also proposed. This is a sentence-level, probabilistic generative model which assumes that all words in a sentence are drawn from one topic – a generally true assumption, based on our observation. The model also assumes that every review corpus is composed of several mutually corresponding aspect and sentiment topics, as well as a background word topic. The AEP information is incorporated into the AEP-LDA model for mining aspect and sentiment words simultaneously. The experimental results on reviews of restaurants, hotels, MP3 players, and cameras show that the AEP-LDA model outperforms other approaches in identifying aspect and sentiment words.


title: Open-Ended Video Games: A Model for Developing Learning for the Interactive Age

abstract: With Grand Theft Education: Literacy in the Age of Video Games gracing the cover of Harper’s September 2006 magazine, video games and education, once the quirky interest of a few rogue educational technologists and literacy scholars, reached broader public awareness. The idea of combining video games and education is not new; twenty years ago, Ronald Reagan praised video games for their potential to train “a new generation of warriors.” Meanwhile, Surgeon General C. Everett Koop declared video games among the top health risks facing Americans.1 Video games, like any emerging medium, are disruptive, challenging existing social practices, while capturing our dreams and triggering our fears. Today’s gaming technologies, which allow for unprecedented player exploration and expression, suggest new models of what educational gaming can be.2 As educational games leave the realm of abstraction and become a reality, the field needs to move beyond rhetoric and toward grounded examples not just of good educational games, but effective game-based learning environments that leverage the critical aspects of the medium as they apply to the needs of a twenty-first-century educational system. We need rigorous research into what players do with games (particularly those that don’t claim explicit status as educational), and a better understanding of the thinking that is involved in playing them.3 We need precise language for what we mean by “video games,” and better understandings of how specific design features and patterns operate,4 and compelling evidence of game-based learning environments. In short, the study of games and learning is ready to come of age. Researchers have convinced the academy that games are worthy of study, and that games hold potential for learning. The task now is to provide effective models of how they operate.5 This chapter offers a theoretical model for video game-based learning environments as designed experiences. To be more specific, it suggests that we can take one particular type of video game—open-ended simulation, or “sandbox” games—and use its capacity to recruit diverse interests, creative problem solving, and productive acts (e.g., creating art work, game


title: IMAD: in-execution malware analysis and detection

abstract: The sophistication of computer malware is becoming a serious threat to the information technology infrastructure, which is the backbone of modern e-commerce systems. We, therefore, advocate the need for developing sophisticated, efficient, and accurate malware classification techniques that can detect a malware on the first day of its launch -- commonly known as "zero-day malware detection". To this end, we present a new technique, IMAD, that can not only identify zero-day malware without any apriori knowledge but can also detect a malicious process while it is executing (in-execution detection). The capability of in-execution malware detection empowers an operating system to immediately kill it before it can cause any significant damage. IMAD is a realtime, dynamic, efficient, in-execution zero-day malware detection scheme, which analyzes the system call sequence of a process to classify it as malicious or benign. We use Genetic Algorithm to optimize system parameters of our scheme. The evolutionary algorithm is evaluated on real world synthetic data extracted from a Linux system. The results of our experiments show that IMAD achieves more than 90% accuracy in classifying in-execution processes as benign or malicious. Moreover, our scheme can classify approximately 50% of malicious processes within first 20% of their system calls.


Splines: A Perfect Fit for Signal/Image Processing


title: Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus

abstract: This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.


title: Deep Q-Networks for Accelerating the Training of Deep Neural Networks

abstract: In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN. The state features of the agent are learned from the weight statistics of the optimizee during training. The reward function of this agent is designed to learn policies that minimize the optimizee's training time given a certain performance goal. The actions of the agent correspond to changing the learning rate for the optimizee during training. As far as we know, this is the first attempt to use deep RL to learn how to optimize a large-sized DNN. We perform extensive experiments on a standard benchmark dataset and demonstrate the effectiveness of the policies learned by our approach.


title: A critical evaluation of the emotional intelligence construct

abstract: Abstract This study critically evaluated the Emotional Intelligence (EI) construct (the ability to perceive, understand, and manage emotions), as measured by the Multi-factor Emotional Intelligence Scale (MEIS in press). We administered the MEIS to Australian undergraduates along with a battery of IQ, personality, and other theoretically relevant criterion measures, including life satisfaction and relationship quality. We also induced moods in the students and examined whether people high in EI were better than others at managing their moods and preventing their moods from biasing their social judgments. Analyses revealed that EI was not related to IQ but was related, as expected, to specific personality measures (e.g., empathy) and to other criterion measures (e.g., life satisfaction) even after controlling for IQ and personality traits. EI was also related to people’s ability to manage their moods, but not to their ability to prevent moods from biasing their judgments. IQ was surprisingly related to both these mood processes. The results suggest that the EI construct is distinctive and useful, but that traditional IQ may also be important in understanding emotional processes.


title: Generalized neural-network representation of high-dimensional potential-energy surfaces.

abstract: The accurate description of chemical processes often requires the use of computationally demanding methods like density-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.


title: Online app review analysis for identifying emerging issues

abstract: Detecting emerging issues (e.g., new bugs) timely and precisely is crucial for developers to update their apps. App reviews provide an opportunity to proactively collect user complaints and promptly improve apps' user experience, in terms of bug fixing and feature refinement. However, the tremendous quantities of reviews and noise words (e.g., misspelled words) increase the difficulties in accurately identifying newly-appearing app issues. In this paper, we propose a novel and automated framework IDEA, which aims to IDentify Emerging App issues effectively based on online review analysis. We evaluate IDEA on six popular apps from Google Play and Apple's App Store, employing the official app changelogs as our ground truth. Experiment results demonstrate the effectiveness of IDEA in identifying emerging app issues. Feedback from engineers and product managers shows that 88.9% of them think that the identified issues can facilitate app development in practice. Moreover, we have successfully applied IDEA to several products of Tencent, which serve hundreds of millions of users.


title: BAD: Blockchain Anomaly Detection

abstract: Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks targeting blockchain-based systems. In this paper we present BAD: the first Blockchain Anomaly Detection solution. BAD leverages blockchain meta-data, named forks, in order to collect potentially malicious activities in the network/system. BAD enjoys the following features: (i) it is distributed (thus avoiding any central point of failure), (ii) it is tamper-proof (making not possible for a malicious software to remove or to alter its own traces), (iii) it is trusted (any behavioral data is collected and verified by the majority of the network) and (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is validated via both experimental results and theoretical complexity analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.


title: Modeling of Soft Fiber-Reinforced Bending Actuators

abstract: Soft fluidic actuators consisting of elastomeric matrices with embedded flexible materials are of particular interest to the robotics community because they are affordable and can be easily customized to a given application. However, the significant potential of such actuators is currently limited as their design has typically been based on intuition. In this paper, the principle of operation of these actuators is comprehensively analyzed and described through experimentally validated quasi-static analytical and finite-element method models for bending in free space and force generation when in contact with an object. This study provides a set of systematic design rules to help the robotics community create soft actuators by understanding how these vary their outputs as a function of input pressure for a number of geometrical parameters. Additionally, the proposed analytical model is implemented in a controller demonstrating its ability to convert pressure information to bending angle in real time. Such an understanding of soft multimaterial actuators will allow future design concepts to be rapidly iterated and their performance predicted, thus enabling new and innovative applications that produce more complex motions to be explored.


title: Comparative effects of test‐enhanced learning and self‐explanation on long‐term retention

abstract: Context ::: ::: Educators often encourage students to engage in active learning by generating explanations for the material being learned, a method called self-explanation. Studies have also demonstrated that repeated testing improves retention. However, no studies have directly compared the two learning methods. ::: ::: ::: ::: Methods ::: ::: Forty-seven Year 1 medical students completed the study. All students participated in a teaching session that covered four clinical topics and was followed by four weekly learning sessions. In the learning sessions, students were randomised to perform one of four learning activities for each topic: testing with self-generated explanations (TE); testing without explanations (T); studying a review sheet with self-generated explanations (SE), and studying a review sheet without explanations (S). Students repeated the same activity for each topic in all four sessions. Six months later, they took a free-recall clinical application test on all four topics. ::: ::: ::: ::: Results ::: ::: Repeated testing led to better long-term retention and application than repeatedly studying the material (p SE = 29% [p = 0.001, d = 0.70]; T = 36% > SE = 29% [p = 0.02, d = 0.48]). Studying with self-explanation led to better retention and application than studying without self-explanation (SE = 29% > S = 20%; p = 0.001, d = 0.68). Our analyses showed significant interaction by topic (p = 0.001, η2 = 0.06), indicating some variation in the effectiveness of the interventions among topics. ::: ::: ::: ::: Conclusions ::: ::: Testing and generating self-explanations are both learning activities that can be used to produce superior long-term retention and application of knowledge, but testing is generally more effective than self-explanation alone.


title: Alternative life-history styles of cartilaginous fishes in time and space

abstract: Cartilaginous fishes, the sharks, rays and chimaeras (class Chondrichthyes), are a very old and successful group of jawed fishes that currently contains between 900 and 1100 known living species. Chondrichthyians show a high morphological diversity during most of their evolutionary career from the Paleozoic to the present day. They are relatively large predators which have remained a major, competitive element of marine ecosystems despite the varied rivalry of numerous other marine vertebrate groups over at least 400 million years. Although restricted in their ecological roles by morphology, reproduction and other factors, the living cartilaginous fishes are highly diverse and show numerous alternative life-history styles which are multiple answers to exploiting available niches permitted by chondrichthyian limitations. Chondrichthyians living and fossil can be divided into at least eighteen ecomorphotypes, of which the littoral ecomorphotype is perhaps the most primitive and can serve as an evolutionary origin for numerous specialist ecomorphotypes with benthic, high-speed, superpredatory, deep-slope and oceanic components. Reproductive modes in cartilaginous fishes are of six types, ranging from primitive extended oviparity through retained oviparity and yolk-sac viviparity (previously ovoviviparity) to three derived forms of viviparity. Reproductive modes are not strongly correlated with ecomorphotypes and with the phylogeny of living elasmobranchs. The success and importance of cartilaginous fishes is largely underrated by marine biologists and by the public, and requires new and ‘heretical’ emphasis to overcome the present inadequacies of chondrichthyian research and the problems of overexploitation that cartilaginous fishes face.


title: Brain activities associated with gaming urge of online gaming addiction.

abstract: The aim of this study was to identify the neural substrates of online gaming addiction through evaluation of the brain areas associated with the cue-induced gaming urge. Ten participants with online gaming addiction and 10 control subjects without online gaming addiction were tested. They were presented with gaming pictures and the paired mosaic pictures while undergoing functional magnetic resonance imaging (fMRI) scanning. The contrast in blood-oxygen-level dependent (BOLD) signals when viewing gaming pictures and when viewing mosaic pictures was calculated with the SPM2 software to evaluate the brain activations. Right orbitofrontal cortex, right nucleus accumbens, bilateral anterior cingulate and medial frontal cortex, right dorsolateral prefrontal cortex, and right caudate nucleus were activated in the addicted group in contrast to the control group. The activation of the region-of-interest (ROI) defined by the above brain areas was positively correlated with self-reported gaming urge and recalling of gaming experience provoked by the WOW pictures. The results demonstrate that the neural substrate of cue-induced gaming urge/craving in online gaming addiction is similar to that of the cue-induced craving in substance dependence. The above-mentioned brain regions have been reported to contribute to the craving in substance dependence, and here we show that the same areas were involved in online gaming urge/craving. Thus, the results suggest that the gaming urge/craving in online gaming addiction and craving in substance dependence might share the same neurobiological mechanism.


title: Modeling Label Ambiguity for Neural List-Wise Learning to Rank

abstract: List-wise learning to rank methods are considered to be the state-of-the-art. One of the major problems with these methods is that the ambiguous nature of relevance labels in learning to rank data is ignored. Ambiguity of relevance labels refers to the phenomenon that multiple documents may be assigned the same relevance label for a given query, so that no preference order should be learned for those documents. In this paper we propose a novel sampling technique for computing a list-wise loss that can take into account this ambiguity. We show the effectiveness of the proposed method by training a 3-layer deep neural network. We compare our new loss function to two strong baselines: ListNet and ListMLE. We show that our method generalizes better and significantly outperforms other methods on the validation and test sets.


title: From turtles to Tangible Programming Bricks: explorations in physical language design

abstract: This article provides a historical overview of educational computing research at MIT from the mid-1960s to the present day, focusing on physical interfaces. It discusses some of the results of this research: electronic toys that help children develop advanced modes of thinking through free-form play. In this historical context, the article then describes and discusses the author's own research into tangible programming, culminating in the development of the Tangible Programming Bricks system--a platform for creating microworlds for children to explore computation and scientific thinking.


title: Anti-social media

abstract: To inform the discussion over free speech and hate speech, this study examines the way racial, religious and ethnic slurs are employed on Twitter. Executive summary: How to define the limits of free speech is a central debate in most modern democracies. This is particularly difficult in relation to hateful, abusive and racist speech. The pattern of hate speech is complex. But there is increasing focus on the volume and nature of hateful or racist speech taking place online; and new modes of communication mean it is easier than ever to find and capture this type of language. How and whether to respond to certain types of language use without curbing freedom of expression in this online space is a significant question for policy makers, civil society groups, law enforcement agencies and others. This short study aims to inform these difficult decisions by examining specifically the way racial and ethnic slurs (henceforth, ‘slurs’) are used on the popular microblogging site, Twitter. Slurs relate specifically to a set of words, terms, or nicknames which are used to refer to groups in a society in a derogatory, pejorative or insulting manner. Slurs can be used in a hateful way, but that is not always the case. Therefore, this research is not about hate speech per se, but about epistemology and linguistics: word use and meaning. In this study, we aim to answer two following questions: (a) In what ways are slurs being used on Twitter, and in what volume? (b) What is the potential for automated machine learning techniques to accurately identify and classify slurs?


title: Comparing Kalman Filters and Observers for Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks

abstract: Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced and implemented. Various Kalman filters and the observer are then tested on the 16-machine, 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for Kalman filter routines and observers.


title: A Study of Turbine Engine PID Control Method Based on Closed-Loop Gain Shaping Algorithm

abstract: This paper aims at the application of closed-loop gain shaping algorithm in turbine engine PID control. It introduces calculation examples, showing how to build a PID controller through parameters with physical significances; it also establishes mathematic models of turbine engine and fuel supply system, in order to test the performances of the PID controller, based on the comparison between PID controllers of series type and closed-loop gain shaping. Simultaneously, simulations are conducted using Matlab. The Simulations indicate no overshoot, proper control time as well as a narrow range of amplitude of oscillation. Therefore, the study succeeds in proving that the closed-loop gain shaping PID controller has excellent performance and good robustness, which is particularly useful for turbine engine control system.


title: Multi-label Classification using Logistic Regression Models for NTCIR-7 Patent Mining Task

abstract: We design a multi-label classification system based on a machine learning approach for the NTCIR-7 Patent Mining Task. In our system, we employ a logistic regression model for each International Patent Classification (IPC) code that determines the IPC code assignment of research papers. The logistic regressionmodels are trainedby usingpatentdocuments providedby task organizers. To mitigate the overfitting of the logistic regression models to the patent documents, we design the feature vectors of the patent documents with feature weighting and component selection methods utilizing a research paper set. Using a test collection for the Japanese subtask of the NTCIR7 Patent Mining Task, we confirmed the effectiveness of our multi-label classification system.


title: Cognitive analysis of a case of pure dysgraphia

abstract: Abstract An Italian patient with a pure dysgraphia who incorrectly spelled words and nonwords is described. The spelling errors made by the patient were not affected by lexical factors (e.g., frequency, form class) and were qualitatively the same for words and nonwords. The pattern of writing performance is discussed in relation to current models of writing and, specifically, in relation to the role of the Output Grapheme Buffer and Phoneme-Grapheme Conversion in writing.


title: Edge Detection of CT scan Spine disc image using Canny Edge Detection Algorithm based on Magnitude and Edge Length

abstract: In this paper, a Spinal unit otherwise known as vertebral column consists of 24 separate bony vertebrae together with 5 fused vertebrae, it is the unique interaction between the solid and fluid components that provide the disc the strength and flexibility required to bear loading of the lumbar spine. In this work Magnitude and Edge Length based Canny Edge Detection Algorithm has been proposed to pre processing of boundary detection of the CT scan Spine disc image. To find the correct boundary in noisy image of spine disc is still a difficult one. The proposed Canny Edge Detection algorithm has been used to detect the boundaries of spine disc image from the noisy image. The performance of proposed technique has been verified and validated with the standard medical values. The results show that the proposed technique performs well and produced very near to the optimal solution. This method is robust for all kinds of noisy images.


title: Validation of the Computer Literacy Scale CLS

abstract: Successful use of ICT requires domain knowledge and interaction knowledge. It shapes and is shaped by the use of ICT and is less common among older adults. This paper focus on the validation of the computer literacy scale CLS introduced by [14]. The CLS is an objective knowledge test of ICT-related symbols and terms commonly used in the graphical user interface of interactive computer technology. It has been designed specifically for older adults with little computer knowledge and is based on the idea that knowing common symbols and terms is as necessary for using computers, as it is for reading and writing letters and books. In this paper the Computer literacy scale is described and compared with related measures for example computer expertise CE, Computer Proficiency CPQ and computer anxiety CATS. In addition criterion validity is described with predictions of successful ICT use exemplified with 1 the use of different data entry methods and 2 the use of different ticket vending machine TVM designs.


title: A Generative Model for category text generation

abstract: The neural network model has been the fulcrum of the so-called AI revolution. Although very powerful for pattern-recognition tasks, however, the model has two main drawbacks: it tends to overfit when the training dataset is small, and it is unable to accurately capture category information when the class number is large. In this paper, we combine reinforcement learning, generative adversarial networks, and recurrent neural networks to build a new model, termed category sentence generative adversarial network (CS-GAN). Not only the proposed model is able to generate category sentences that enlarge the original dataset, but also it helps improve its generalization capability during supervised training. We evaluate the performance of CS-GAN for the task of sentiment analysis. Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information.


title: PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping

abstract: Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 $\pi$ ) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.


title: Neu-Laxova syndrome: A terrible phenotypic appearance caused by an undefined genetic alteration

abstract: Neu-Laxova syndrome (NLS) is a rare, uniformly lethal congenital disease characterized by abnormalities of placentation, marked intrauterine growth retardation (IUGR), limb contractures, edema, ichthyosis, central nervous system (CNS) developmental defects and unique facial appearance. The degrees and presence of these findings are variable in reported cases. Therefore the differential diagnosis includes very different disorders.Data from the previously described cases in the literature suggest that this disorder is inherited in an autosomal recessive manner and represents heterogeneous genetic alterations. We herein present a stillborn female at 31 weeks of gestation to consanguineous parents who had one previous offspring affected with non-immune hydrops fetalis. She had typical facial features including severe proptosis with ectropion, micrognathia, flattened nose, gaping mouth and malformed ears. In addition limb deformities and skin lacerations resulting from severe skin restriction were determined. Absence of CNS malformations was the most different feature of reported case. In this report, we aimed to call attention that 6.25% of all reported NLS cases are Turkish and to date most reported cases were from the countries that have high rates of consanguineous marriage such as Turkey. We emphasized that consanguinity could increase the risk of inheriting of NSL for both the partners to share the


title: Applicability of methanol as new marker for paper degradation in power transformers

abstract: This study was designed to examine methanol as a new aging marker of insulating paper. An analytical GC-MS method coupled to headspace injection was developed, followed by a validation. To further validate the possible applicability of methanol for different transformer conditions some laboratory studies were performed such as a stability and an aging test. Oil samples of power transformers of several Belgian nuclear and coal-fired power plants have been analyzed since mid 2009 and are followed-up periodically to correlate the results with the analysis of the furanic compounds which have been used since mid 1980s as oil soluble degradation products of the insulating winding paper. It seemed that while no 2FAL was present some considerable amount of MeOH was detected which revealed some paper degradation. A second real case study was started to further correlate MeOH and 2FAL results. From these laboratory and real case studies it seemed that MeOH is a very promising marker for paper degradation in power transformers.


title: The intrinsic origin of hysteresis in MoS2 field effect transistors

abstract: We investigate the hysteresis and gate voltage stress effect in MoS2 field effect transistors (FETs). We observe that both the suspended and the SiO2-supported FETs have large hysteresis in their transfer curves under vacuum which cannot be attributed to the traps at the interface between the MoS2 and the SiO2 or in the SiO2 substrate or the gas adsorption/desorption effect. Our findings indicate that the hysteresis we observe comes from the MoS2 itself, revealing an intrinsic origin of the hysteresis besides some extrinsic factors. The fact that the FETs based on thinner MoS2 have larger hysteresis than that with thicker MoS2 suggests that the surface of MoS2 plays a key role in the hysteresis. The gate voltage sweep range, sweep direction, sweep time and loading history all affect the hysteresis observed in the transfer curves.


title: Modulating the Gut Micro-Environment in the Treatment of Intestinal Parasites

abstract: The interactions of micro-organisms cohabitating with Homo sapiens spans millennia, with microbial communities living in a symbiotic relationship with the host. Interacting to regulate and maintain physiological functions and immunological tolerance, the microbial community is able to exert an influence on host health. An example of micro-organisms contributing to an intestinal disease state is exhibited by a biodiverse range of protozoan and bacterial species that damage the intestinal epithelia and are therefore implicated in the symptoms of diarrhea. As a contentious exemplar, Blastocystis hominis is a ubiquitous enteric protist that can adversely affect the intestines. The symptoms experienced are a consequence of the responses of the innate immune system triggered by the disruption of the intestinal barrier. The infiltration of the intestinal epithelial barrier involves a host of immune receptors, including toll like receptors and IgM/IgG/IgA antibodies as well as CD8+ T cells, macrophages, and neutrophils. Whilst the mechanisms of interactions between the intestinal microbiome and protozoan parasites remain incompletely understood, it is acknowledged that the intestinal microbiota is a key factor in the pathophysiology of parasitic infections. Modulating the intestinal environment through the administration of probiotics has been postulated as a possible therapeutic agent to control the proliferation of intestinal microbes through their capacity to induce competition for occupation of a common biotype. The ultimate goal of this mechanism is to prevent infections of the like of giardiasis and eliminate its symptoms. The differing types of probiotics (i.e., bacteria and yeast) modulate immunity by stimulating the host immune system. Early animal studies support the potential benefits of probiotic administration to prevent intestinal infections, with human clinical studies showing probiotics can reduce the number of parasites and the severity of symptoms. The early clinical indications endorse probiotics as adjuncts in the pharmaceutical treatment of protozoan infections. Currently, the bar is set low for the conduct of well-designed clinical studies that will translate the use of probiotics to ameliorate protozoan infections, therefore the requisite is for further clinical research.


title: Space as an Invention of Active Agents

abstract: The question of the nature of space around us has occupied thinkers since the dawn of humanity, with scientists and philosophers today implicitly assuming that space is something that exists objectively. Here we show that this does not have to be the case: the notion of space could emerge when biological organisms seek an economic representation of their sensorimotor flow. The emergence of spatial notions does not necessitate the existence of real physical space, but only requires the presence of sensorimotor invariants called 'compensable' sensory changes. We show mathematically and then in simulations that naive agents making no assumptions about the existence of space are able to learn these invariants and to build the abstract notion that physicists call rigid displacement, independent of what is being displaced. Rigid displacements may underly perception of space as an unchanging medium within which objects are described by their relative positions. Our findings suggest that the question of the nature of space, currently exclusive to philosophy and physics, should also be addressed from the standpoint of neuroscience and artificial intelligence.


title: SYNTHETIC APERTURE RADAR IMAGE FORMATON PROCESS: APPLICATION TO A REGION OF NORTH ALGERIA

abstract: Synthetic aperture radar imaging provides high resolution images of large areas. The intensities of pixels in a SAR image are based on the spatial orientation, roughness, and dielectric constant of the surface imaged. SAR is an active sensor, transmitting its own energy, and then measuring the return scattered by the earth’s surface back to the satellite’s antenna. SAR processing is the transformation of raw SAR signal data into a spatial image. In this communication, we present a contribution concerning implementation of a SAR image formation with the IDL language. This process is performed in a frequency domain correlation of the received signal including range compression, range migration, and azimuth compression. The output is the final step in which the data is transposed back into its original format and written to an output file. The data used are raw SAR image of scene of north Algeria of size 5616*28000 pixels of the 16 of February 1996. Evaluation of the method studied here is made on a Region of Reghaia lake ( near of Algiers city) of 2048*5616 pixels.


Social identity and its discontents


title: A radio frequency identification and sensor-based system for the transportation of food

abstract: The damage caused by the degeneration of the quality of perishable products usually results in a great loss to transportation enterprises. To improve the delivery system for perishable products, a real-time monitoring and online decision support system with Radio frequency identification (RFID), a sensor network and a decision rule base has been developed in this study. First of all, the value degeneration process is described, using several mathematical models for handing different ways of perishing. Based on the mathematical models, and data from RFID and the sensor network, the quality of the goods can be predicted by the forecast module. When something abnormal occurs, the warning function will send an alarm signal to the users, then, the rule-based decision module will provide the user with suggestions as how to cope with the abnormality. The results from the simulation have shown that the monitoring and decision support system is an efficient tool for reducing the transportation losses of perishable products for the enterprises in cold chain.


title: De-identification for privacy protection in multimedia content

abstract: Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents. Privacy protection in multimedia.Taxonomy of the personal identifiers in multimedia contents.De-identification of non-biometrical identifiers.De-identification of physiological, behavioural and soft biometric identifiers.


title: Effective Pattern Discovery for Text Mining

abstract: Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.


title: Yield prediction in apple orchards based on image processing

abstract: It has been suggested that apple ( Malus * domestica Borkh) flowering distribution maps can be used for site-specific management decisions. The objectives of this study were (i) to study the flower density variability in an apple orchard using image analysis and (ii) to model the correlation between flower density as determined from image analysis and fruit yield. The research was carried out in a commercial apple orchard in Central Greece. In April 2007, when the trees were at full bloom, photos of the trees were taken following a systematic uniform random sampling procedure. In September 2007, yield mapping was carried out measuring yield per ten trees and recording the position of the centre of the ten trees. Using this data (the measured yield of the trees and the pictures samples, representing the flower distribution), an image processing-based algorithm was developed that predicts tree yield by analyzing the picture of the tree at full bloom. For the evaluation of the algorithm, a case study scenario is presented where the error of the predicted yield was set at 18%. These results indicated that potential yield could be predicted early in the season from flowering distribution maps and could be used for orchard management during the growing season.


title: Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

abstract: Over the last 5 years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the Labeled Faces in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the LFW and Youtube Face datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos. Some open issues regarding DCNNs for face verification problems are then discussed.


title: A Systematic Review of Cloud Modeling Languages

abstract: Modern cloud computing environments support a relatively high degree of automation in service provisioning, which allows cloud service customers (CSCs) to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by cloud computing environments and support different application scenarios, such as migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate in the research community on what a CML is, and what aspects of a cloud application and its target cloud computing environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grain level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support CSCs in selecting the CML that fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda.


title: An approach to discriminate GNSS spoofing from multipath fading

abstract: GNSS signals are vulnerable to various types of interference including jamming and spoofing attacks. Spoofing signals are designed to deceive target GNSS receivers without being detected by conventional receiver quality monitoring metrics. This paper focuses on detecting an overlapped spoofing attack where the correlation peaks of the authentic and spoofing signals interact during the attack. Several spoofing detection and signal quality monitoring (SQM) metrics are introduced. This paper proposes a spoofing detection architecture utilizing combination of different metrics to detect spoofing signals and distinguish them from multipath signals. Experimental results show that the pre-despreading spoofing detection metrics such as variance analysis are not sensitive to multipath propagation and can be used in conjunction with post-despreading methods to correctly detect spoofing signals. Several test scenarios based on different spoofing and multipath cases are performed to demonstrate the effectiveness of the proposed architecture to correctly detect spoofing attack ands distinguish them from multipath.


title: Knowledge management metrics via a balanced scorecard methodology

abstract: IT professionals are finding that more of their IT investments are being measured against a knowledge management (KM) metric. Those who want to deploy foundation technologies such as groupware, CRM or decision support tools, but fail to justify them on the basis of their contribution to KM, may find it difficult to get funding unless they can frame them within the KM context. Determining KM's pervasiveness and impact is analogous to measuring the contribution of marketing, employee development, or any other management or organizational competency. The paper addresses the problem of developing measurement models for KM metrics and discusses what current KM metrics are in use, and examines their sustainability and soundness in assessing knowledge utilization and retention of generating revenue. The paper then discusses the use of a Balanced Scorecard approach to determine a business-oriented relationship between strategic KM usage and IT strategy and implementation.


title: Actual Cases and Analysis of IT Convergence for Green IT

abstract: Eco-friendly or green is a key word which we use in our life. There are examples such as eco-friendly laundry detergent and agricultural products or green car and home, Due to the indiscriminate development, it was caused severe environmental pollution. Green IT is a necessary technology to solve the energy and environmental problems and to increase the productivity of corporations. Therefore, we studied contents related with green IT. The history for green IT has changed from 'Green of IT' to 'IT for Green'. Studies for 'IT for Green' progress extensively on today. In this paper, we described a transition progress and characteristics of green IT. Lastly, we analyzed smart work in the examples of the top.


CARMA : A Deep Reinforcement Learning Approach to Autonomous Driving


title: GraB: Visual Saliency via Novel Graph Model and Background Priors

abstract: We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors. The input image is represented as an undirected graph with superpixels as nodes. Feature vectors are extracted from each node to cover regional color, contrast and texture information. A novel graph model is proposed to effectively capture local and global saliency cues. To obtain more accurate saliency estimations, we optimize the saliency map by using a robust background measure. Comprehensive evaluations on benchmark datasets indicate that our algorithm universally surpasses state-of-the-art unsupervised solutions and performs favorably against supervised approaches.


title: Image classification using the web graph

abstract: Image classification is a well-studied and hard problem in computer vision. We extend a proven solution for classifying web spam to handle images. We exploit the link structure of the web graph: a web page related to a given category is normally linked to other pages describing related objects. Our approach combines information from the webgraph structure with semi-supervised learning from all the unlabeled images to create a superior image-classification model for multimedia data. We show that fusing image, text and web-graph features gives a 12% improvement (in the area under the ROC curve) over content features alone in an adult image-classification experiment.


title: Effects of crustacean cardioactive peptide on the hearts of two Orthopteran insects, and the demonstration of a Frank-Starling-like effect.

abstract: Like vertebrate cardiovascular systems, the dorsal vessel of the Orthopteran insects Baculum extradentatum and Locusta migratoria is under myogenic as well as neural control, through the action of neurotransmitters, neuromodulators and neurohormones. It was previously shown that the excitatory neuropeptide, crustacean cardioactive peptide (CCAP), triggers an increase in heart rate in B. extradentatum, and CCAP-like immunoreactivity is present in the innervation to the heart in many insects. In the present study, CCAP resulted in a dose-dependent increase in heart rate and hemolymph flow velocity, or cardiac output, in B. extradentatum. In contrast, CCAP led to a significant increase in stroke volume and cardiac output in L. migratoria without modifying heart rate or aortic contraction frequency. Hemolymph flow through the excurrent ostia of L. migratoria, small openings or valves on the posterior aorta and anterior heart, was inhibited with increasing concentrations of CCAP, with complete inhibition seen at 10(-7) M CCAP. In the locust, CCAP increases the volume of hemolymph in the dorsal vessel by the synchronous closing of the excurrent ostia, resulting in more forceful heart contractions and increased stroke volume and cardiac output, without modifying heart rate through a physiological mechanism analogous to the Frank-Starling mechanism in vertebrates. Therefore, crustacean cardioactive peptide alters the contractile properties of cardiac tissue in both B. extradentatum and L. migratoria, allowing for an increase in blood flow and circulation.


title: A Planar Integrated Folded Reflectarray Antenna With Circular Polarization

abstract: This communication presents the complete design of a circularly polarized (CP) folded reflectarray (FRA) antenna with an integrated planar structure for the first time in the open literature. To achieve circularly polarized, a printed meander-line polarizer is designed and integrated with the linearly polarized (LP) FRA. To achieve a low-profile planar structure, an integrated $2 \times 2$ planar array is designed as the feed source instead of a horn. Thus, the whole antenna, including the feed source, LPFRA, and meander-line polarizer, can be fully integrated and fabricated using low-cost printed circuit board technology. To validate the concept, a right-handed CPFRA operating in C-band is designed, fabricated, and measured. The broadside axial ratio (AR) of the proposed CPFRA is lower than 1 dB over a bandwidth from 5.22 to 5.46 GHz. In addition, the maximum gain of 22.8 dBic is obtained at 5.38 GHz with the antenna efficiency of 27%. The antenna is promising for applications in satellite communication due to advantages of low profile, easy fabrication, low cost, and high gain.


title: A survey of table recognition Models , observations , transformations , and inferences

abstract: Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.


title: Towards an Analysis of Security Issues, Challenges, and Open Problems in the Internet of Things

abstract: The Internet of Things (IoT) devices have become popular in diverse domains such as e-Health, e-Home, e-Commerce, and e-Trafficking, etc. With increased deployment of IoT devices in the real world, they can be, and in some cases, already are subject to malicious attacks to compromise the security and privacy of the IoT devices. While a number of researchers have explored such security challenges and open problems in IoT, there is an unfortunate lack of a systematic study of the security challenges in the IoT landscape. In this paper, we aim at bridging this gap by conducting a thorough analysis of IoT security challenges and problems. We present a detailed analysis of IoT attack surfaces, threat models, security issues, requirements, forensics, and challenges. We also provide a set of open problems in IoT security and privacy to guide the attention of researchers into solving the most critical problems.


title: Leveraging String Kernels for Malware Detection

abstract: Signature-based malware detection will always be a step behind as novel malware cannot be detected. On the other hand, machine learning-based methods are capable of detecting novel malware but classification is frequently done in an offline or batched manner and is often associated with time overheads that make it impractical. We propose an approach that bridges this gap. This approach makes use of a support vector machine (SVM) to classify system call traces. In contrast to other methods that use system call traces for malware detection, our approach makes use of a string kernel to make better use of the sequential information inherent in a system call trace. By classifying system call traces in small sections and keeping a moving average over the probability estimates produced by the SVM, our approach is capable of detecting malicious behavior online and achieves great accuracy.


title: The Crossfire Attack

abstract: We present the Crossfire attack -- a powerful attack that degrades and often cuts off network connections to a variety of selected server targets (e.g., servers of an enterprise, a city, a state, or a small country) by flooding only a few network links. In Crossfire, a small set of bots directs low intensity flows to a large number of publicly accessible servers. The concentration of these flows on the small set of carefully chosen links floods these links and effectively disconnects selected target servers from the Internet. The sources of the Crossfire attack are undetectable by any targeted servers, since they no longer receive any messages, and by network routers, since they receive only low-intensity, individual flows that are indistinguishable from legitimate flows. The attack persistence can be extended virtually indefinitely by changing the set of bots, publicly accessible servers, and target links while maintaining the same disconnection targets. We demonstrate the attack feasibility using Internet experiments, show its effects on a variety of chosen targets (e.g., servers of universities, US states, East and West Coasts of the US), and explore several countermeasures.


title: Uncertainty in Self-Adaptive Software Systems

abstract: The ever-growing complexity of software systems coupled with their stringent availability requirements are challenging the manual management of software after its deployment. This has motivated the development of self-adaptive software systems. Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior at runtime. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation. This has been one of the key inhibitors to widespread adoption of self-adaption techniques in risk-averse real-world applications. Uncertainty in this setting is a vaguely understood term. In this paper, we characterize the sources of uncertainty in self-adaptive software system, and demonstrate its impact on the system’s ability to satisfy its objectives. We then provide an alternative notion of optimality that explicitly incorporates the uncertainty underlying the knowledge (models) used for decision making. We discuss the state-of-the-art for dealing with uncertainty in this setting, and conclude with a set of challenges, which provide a road map for future research.


title: Proactive Drone-Cell Deployment: Overload Relief for a Cellular Network Under Flash Crowd Traffic

abstract: This paper is concerned with providing radio access network (RAN) elements (supply) for flash crowd traffic demands. The concept of multi-tier cells [heterogeneous networks (HetNets)] has been introduced in 5G network proposals to alleviate the erratic supply–demand mismatch. However, since the locations of the RAN elements are determined mainly based on the long-term traffic behavior in 5G networks, even the HetNet architecture will have difficulty in coping up with the cell overload induced by flash crowd traffic. In this paper, we propose a proactive drone-cell deployment framework to alleviate overload conditions caused by flash crowd traffic in 5G networks. First, a hybrid distribution and three kinds of flash crowd traffic are developed in this framework. Second, we propose a prediction scheme and an operation control scheme to solve the deployment problem of drone cells according to the information collected from the sensor network. Third, the software-defined networking technology is employed to seamlessly integrate and disintegrate drone cells by reconfiguring the network. Our experimental results have shown that the proposed framework can effectively address the overload caused by flash crowd traffic.


title: Online Anomaly Detection in Crowd Scenes via Structure Analysis

abstract: Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural context descriptor (SCD) for describing the crowd individual, which originally introduces the potential energy function of particle’s interforce in solid-state physics to intuitively conduct vision contextual cueing. For computing the crowd SCD variation effectively, we then design a robust multi-object tracker to associate the targets in different frames, which employs the incremental analytical ability of the 3-D discrete cosine transform (DCT). By online spatial-temporal analyzing the SCD variation of the crowd, the abnormality is finally localized. Our contribution mainly lies on three aspects: 1) the new exploration of abnormal detection from structure modeling where the motion difference between individuals is computed by a novel selective histogram of optical flow that makes the proposed method can deal with more kinds of anomalies; 2) the SCD description that can effectively represent the relationship among the individuals; and 3) the 3-D DCT multi-object tracker that can robustly associate the limited number of (instead of all) targets which makes the tracking analysis in high density crowd situation feasible. Experimental results on several publicly available crowd video datasets verify the effectiveness of the proposed method.


title: 3D G-CNNs for Pulmonary Nodule Detection

abstract: Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. In this paper we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions (G-Convs) instead of the more conventional translational convolutions. These 3D G-CNNs were applied to the problem of false positive reduction for pulmonary nodule detection, and proved to be substantially more effective in terms of performance, sensitivity to malignant nodules, and speed of convergence compared to a strong and comparable baseline architecture with regular convolutions, data augmentation and a similar number of parameters. For every dataset size tested, the G-CNN achieved a FROC score close to the CNN trained on ten times more data.


A Modular Hybrid Linear Stepper Motor


title: Asynchronous Distributed ADMM for Consensus Optimization

abstract: Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method of multipliers (ADMM) algorithm. However, this suffers from the straggler problem as its updates have to be synchronized. In this paper, we propose an asynchronous ADMM algorithm by using two conditions to control the asynchrony: partial barrier and bounded delay. The proposed algorithm has a simple structure and good convergence guarantees (its convergence rate can be reduced to that of its synchronous counterpart). Experiments on different distributed ADMM applications show that asynchrony reduces the time on network waiting, and achieves faster convergence than its synchronous counterpart in terms of the wall clock time.


title: Broadband Half Mode Substrate Integrated Waveguide Cruciform Coupler

abstract: In this paper, a cruciform coupler with half mode substrate integrated waveguide structure is presented. The prototype coupler consists of four half mode SIW structures crossing each other in right angle and two metallic posts are inserted in free section of junction in order to reach the coupling properties. Compact size and broadband operation are the features of this coupler. The size of coupler is reduced about 24% compared to previous HMSIW cruciform coupler and arround 40% smaller than the SIW cruciform coupler. a fractional bandwidth of about 35 percent is obtained. The coupler has been designed and simulated with HFSS13 and CST microwave studio for full wave simulation. Here, the simulation results have been compared with experimental results.


title: Design of Flexible Plant Layouts

abstract: In this paper, we present an approach for the design of plant layouts in stochastic environments. We consider systems where the product mix and product demand are subject to variability and where duplicates of the same department type may exist in the same facility. In contrast to a job shop layout, we allow these duplicates to be placed in non-adjacent locations on the plant floor and for flow allocation between pairs of individual departments to be made as a function of the layout and the product demand realization. We present a scenario-based procedure that iteratively solves for layout and flow allocation. We show that having duplicates of the same departments, which can be strategically located in different areas of the plant floor, can significantly reduce material handling cost while effectively hedging against fluctuations in flow patterns and volumes. We show that the effect of duplication is of the diminishing kind, with most of the cost reduction occurring with relatively few duplicates. We also show that the quality of the obtained layouts can be quite insensitive to inaccuracies in estimating demand scenario probabilities.


title: Investigation of various factors affecting encapsulation on the In-Cap automatic capsule-filling machine

abstract: The purpose of this study was to determine the factors that influence fill weight and weight variability of capsules produced on the In-Cap and to assess any differences in terms of capsule defects between gelatin and HPMC (Quali-V) shells. The In-Cap is an automatic tamping type capsule-filling machine and the low output of approximately 3000 capsules/hour makes it ideal for early formulation development and phase I/IIa clinical supplies manufacture. Four commonly used excipients (Avicel PH101, Avicel PH302, A-Tab, and Prosolv HD90) and a poorly flowing drug blend were encapsulated at various pin settings and powder bed heights. The average fill weight and coefficient of weight variation were determined. The percentage of defective capsules formed during encapsulation was calculated. Results of the study showed that pin setting was critical for controlling the fill weight and the weight variation. The order of pin setting with pin 1 (closer to the powder chute) set to a relatively higher position and pin 4 (before ejection) set to a lower position was found to give higher fill weights with relatively lower weight variability. The powder bed height influenced the fill weight for poorly flowing powders. The capsule machine speed did not appear to significantly influence the fill weight. The fill weight and weight variation were found to depend on the flow property of the material. A large percentage of defective capsules was obtained using HPMC shell size #00. Some of the commonly observed defects included split caps and improperly closed filled capsules. In general, appropriate selection of pin settings and bed height can reduce the weight variability seen, especially with poorly flowing high-dose formulations.


title: Patent retrieval: a literature review

abstract: With the ever increasing number of filed patent applications every year, the need for effective and efficient systems for managing such tremendous amounts of data becomes inevitably important. Patent retrieval (PR) is considered the pillar of almost all patent analysis tasks. PR is a subfield of information retrieval (IR) which is concerned with developing techniques and methods that effectively and efficiently retrieve relevant patent documents in response to a given search request. In this paper, we present a comprehensive review on PR methods and approaches. It is clear that recent successes and maturity in IR applications such as Web search cannot be transferred directly to PR without deliberate domain adaptation and customization. Furthermore, state-of-the-art performance in automatic PR is still around average in terms of recall. These observations motivate the need for interactive search tools which provide cognitive assistance to patent professionals with minimal effort. These tools must also be developed in hand with patent professionals considering their practices and expectations. We additionally touch on related tasks to PR such as patent valuation, litigation, licensing, and highlight potential opportunities and open directions for computational scientists in these domains.


title: Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy

abstract: Morally judicious behavior forms the fabric of human sociality. Here, we sought to investigate neural activity associated with different facets of moral thought. Previous research suggests that the cognitive and emotional sources of moral decisions might be closely related to theory of mind, an abstract-cognitive skill, and empathy, a rapid-emotional skill. That is, moral decisions are thought to crucially refer to other persons’ representation of intentions and behavioral outcomes as well as (vicariously experienced) emotional states. We thus hypothesized that moral decisions might be implemented in brain areas engaged in ‘theory of mind’ and empathy. This assumption was tested by conducting a large-scale activation likelihood estimation (ALE) meta-analysis of neuroimaging studies, which assessed 2,607 peak coordinates from 247 experiments in 1,790 participants. The brain areas that were consistently involved in moral decisions showed more convergence with the ALE analysis targeting theory of mind versus empathy. More specifically, the neurotopographical overlap between morality and empathy disfavors a role of affective sharing during moral decisions. Ultimately, our results provide evidence that the neural network underlying moral decisions is probably domain-global and might be dissociable into cognitive and affective sub-systems.


title: On the Role of the Shrinkage Parameter in Local Linear Smoothing

abstract: It has been shown that local linear smoothing possesses a variety of very attractive properties, not least being its mean square performance. However, such results typically refer only to asymptotic mean squared error, meaning the mean squared error of the asymptotic distribution, and in fact, the actual mean squared error is often infinite. See Seifert and Gasser (1996). This difficulty may be overcome by shrinking the local linear estimator towards another estimator with bounded mean square. However, that approach requires information about the size of the shrinkage parameter. From at least a theoretical viewpoint, very little is known about the effects of shrinkage. In particular, it is not clear how small the shrinkage parameter may be chosen without affecting first-order properties, or whether infinitely supported kernels such as the Gaussian require shrinkage in order to achieve first-order optimal performance. In the present paper we provide concise and definitive answers to such questions, in the context of general ridged and shrunken local linear estimators. We produce necessary and sufficient conditions on the size of the shrinkage parameter that ensure the traditional mean squared error formula. We show that a wide variety of infinitely-supported kernels, with tails even lighter than those of the Gaussian kernel, do not require any shrinkage at all in order to achieve traditional first-order optimal mean square performance.


title: Oscillator design and computer simulation

abstract: This second edition of the number one guide to oscillator design presents a comprehensive, unified approach to oscillator design that can be used with a wide range of active devices and resonator types. Resonator types covered include: L-C, crystal, SAW, dielectric resonator, coaxial line, stripline and microstrip. This text covers modern CAD synthesis and analysis techniques and is valuable to experienced engineers as well as to those new to oscillator design. The books topics include: Analysis fundamentals, oscillator fundamentals, limiting and starting, biasing, noise, computer simulation and examples and case studies.


title: Semi-Supervised Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are labeled, the performance of clustering or classification is improved if the information on class labels is incorporated into NMF. To this end, we present semi-supervised NMF (SSNMF), where we jointly incorporate the data matrix and the (partial) class label matrix into NMF. We develop multiplicative updates for SSNMF to minimize a sum of weighted residuals, each of which involves the nonnegative 2-factor decomposition of the data matrix or the label matrix, sharing a common factor matrix. Experiments on document datasets and EEG datasets in BCI competition confirm that our method improves clustering as well as classification performance, compared to the standard NMF, stressing that semi-supervised NMF yields semi-supervised feature extraction.


title: Multi-view learning with supervision for transformed bottleneck features

abstract: Previous work has shown that acoustic features can be improved by unsupervised learning of transformations based on canonical correlation analysis (CCA) using articulatory measurements that are available at training time. In this paper, we investigate whether this second view (articulatory data) still helps even when labels are also available at training time. We begin with strong baseline bottleneck features, which can be learned when the training set is phonetically labeled. We then compare several options for learning transformations of the bottleneck features in the presence of both articulatory measurements and phonetic labels for the training data. The methods compared include combinations of LDA and CCA, as well as a three-view extension of CCA that simultaneously uses the labels and articulatory measurements as additional views. Phonetic recognition experiments on data from the University of Wisconsin X-ray microbeam database show that the learned features improve performance over using either just the labels or just the articulatory measurements for learning acoustic transformations.


title: The use of pit and fissure sealants.

abstract: This paper reviews key issues of sealant use and methodology and poses recommendations to inform the discussion toward a consensus statement by participants. A comprehensive review of sealant literature, including policy recommendations from previous conferences that reviewed best practices for sealant use, was completed. Building on the review paper by Simonsen and on previous policy statements by dental and public health groups, this paper discusses key questions about sealant use in light of contemporary caries data and cost-benefit analyses. In addition, newest material advancements are reviewed to establish the next step in sealant improvement for young patients.


title: A Survey on Methods and Systems for Graph Compression

abstract: We present an informal survey (meant to accompany another paper) on graph compression methods. We focus on lossless methods, briefly list available pproaches, and compare them where possible or give some indicators on their compression ratios. We also mention some relevant results from the field of lossy compression and algorithms specialized for the use on large graphs. --- Note: The comparison is by no means complete. This document is a first draft and will be updated and extended.


title: Texture overlay onto deformable surface for virtual clothing

abstract: In this article, we describe a method for overlaying arbitrary texture image onto surface of T-shirt worn by a user. In this method, the texture image is previously divided into a number of patches. On the T-shirt, markers are printed at the positions corresponding to the vertices of the patches. The markers on the surface of the T-shirt are tracked in the motion image taken by a camera. The texture image is warped according to the tracked positions of the markers, which is overlaid onto the captured image. This article presents experimental results with the pilot system of virtual clothing implemented based on the proposed method.


title: Aesthetic Electronics: Designing, Sketching, and Fabricating Circuits through Digital Exploration

abstract: As interactive electronics become increasingly intimate and personal, the design of circuitry is correspondingly developing a more playful and creative aesthetic. Circuit sketching and design is a multidimensional activity which combines the arts, crafts, and engineering broadening participation of electronic creation to include makers of diverse backgrounds. In order to support this design ecology, we present Ellustrate, a digital design tool that enables the functional and aesthetic design of electronic circuits with multiple conductive and dielectric materials. Ellustrate guides users through the fabrication and debugging process, easing the task of practical circuit creation while supporting designers' aesthetic decisions throughout the circuit authoring workflow. In a formal user study, we demonstrate how Ellustrate enables a new electronic design conversation that combines electronics, materials, and visual aesthetic concerns.


title: High breakdown Voltage undoped AlGaN-GaN power HEMT on sapphire substrate and its demonstration for DC-DC converter application

abstract: Undoped AlGaN-GaN power high electron mobility transistors (HEMTs) on sapphire substrate with 470-V breakdown voltage were fabricated and demonstrated as a main switching device for a high-voltage dc-dc converter. The fabricated power HEMT realized a high breakdown voltage with a field plate structure and a low on-state resistance of 3.9 m/spl Omega//spl middot/cm/sup 2/, which is 10 /spl times/ lower than that of conventional Si MOSFETs. The dc-dc converter operation of a down chopper circuit was demonstrated using the fabricated device at the input voltage of 300 V. These results show the promising possibilities of the AlGaN-GaN power HEMTs on sapphire substrate for future switching power devices.


title: A New Clustering Method Based On Type-2 Fuzzy Similarity and Inclusion Measures

abstract: Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable. Similarity measures and Yang and Shih’s algorithm are combined as a clustering method for type-2 fuzzy data. Clustering results demonstrate that the proposed similarity measures are better than the existing measures.


title: Brain-computer interface: Next generation thought controlled distributed video game development platform

abstract: In this paper we present a system that uses the human ability to control a video game on a mobile device using electroencephalographic (EEG) Mu rhythms. The signals were obtained using a specially designed electrode cap and equipment, and sent through a Bluetooth connection to a PC that processes it in real time. The signal was then mapped onto two control signals and sent through wireless connection to a mobile gaming device BreakOut. We have also investigated the human's ability to play the video game by manipulating neuronal motor cortex activity in the presence of a visual feedback environment. The participants played the video game by using their thoughts only with up to 80% accuracy over controlling the target.


title: The Relationship between Game Genres, Learning Techniques and Learning Styles in Educational Computer Games

abstract: Educational computer game has many similar characteristics like any other genres of games. However its particular aspect is designed to teach, and in which main objective involves in learning a topic. To develop an effective educational computer game, different game genres, learning activities and techniques, and learning styles are important issues for consideration. This paper presents an analysis by comparing and establishing relationships between the game genres and learning techniques based on the types of learning and potential game style of Prensky [1] and learning styles based on the study of Chong et al. [2].


title: A New Pose Estimation Algorithm Using a Perspective-Ray-Based Scaled Orthographic Projection with Iteration

abstract: Pose estimation aims at measuring the position and orientation of a calibrated camera using known image features. The pinhole model is the dominant camera model in this field. However, the imaging precision of this model is not accurate enough for an advanced pose estimation algorithm. In this paper, a new camera model, called incident ray tracking model, is introduced. More importantly, an advanced pose estimation algorithm based on the perspective ray in the new camera model, is proposed. The perspective ray, determined by two positioning points, is an abstract mathematical equivalent of the incident ray. In the proposed pose estimation algorithm, called perspective-ray-based scaled orthographic projection with iteration (PRSOI), an approximate ray-based projection is calculated by a linear system and refined by iteration. Experiments on the PRSOI have been conducted, and the results demonstrate that it is of high accuracy in the six degrees of freedom (DOF) motion. And it outperforms three other state-of-the-art algorithms in terms of accuracy during the contrast experiment.


title: Detecting Faces Using Region-based Fully Convolutional Networks

abstract: Face detection has achieved great success using the region-based methods. In this report, we propose a region-based face detector applying deep networks in a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully Convolutional Networks (R-FCN), our face detector is more accurate and computational efficient compared with the previous R-CNN based face detectors. In our approach, we adopt the fully convolutional Residual Network (ResNet) as the backbone network. Particularly, We exploit several new techniques including position-sensitive average pooling, multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy. Over two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, Face R-FCN achieves superior performance over state-of-the-arts.


title: Tellurium: A Python Based Modeling and Reproducibility Platform for Systems Biology

abstract: In this article, we present Tellurium, a powerful Python-based integrated environment designed for model building, analysis, simulation and reproducibility in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source integrated development environment (IDE) composed of multiple libraries, plugins, and specialized modules and methods. Tellurium ensures exchangeability and reproducibility of computational models by supporting SBML (Systems Biology Markup Language), SED-ML (Simulation Experiment Description Markup Language), the COMBINE archive, and SBOL (Synthetic Biology Open Language). Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution independent of other local Python installations on the target machine. The main interface is based on the Spyder IDE which has a highly accessible user interface akin to MATLAB (https://www.mathworks.com/). Tellurium uses libRoadRunner as the default SBML simulation engine due to its superior performance, scalability and ease of integration. libRoadRunner supports deterministic simulations, stochastic simulations and steady state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from visualization tools to complex algorithms for bifurcation analysis and multi-dimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and simulation.


title: A comparison of machine learning techniques for handwritten |Xam word recognition

abstract: The Bleek and Lloyd collection contains 19th century handwritten notebooks that document the language and culture of the |Xam-speaking people who lived in Southern Africa. Access to this rich data could be enhanced by transcriptions of the text; however, the complex diacritics used in the notebooks complicate the process of transcription. Machine learning techniques could be used to perform this transcription, but it is not known which techniques would produce the best results. This paper thus reports on a comparison of 3 popular techniques applied to this problem: artificial neural networks (ANN); hidden Markov models (HMM); and support vector machines (SVM). It was found that an SVM-based classifier using histograms of oriented gradients as features resulted in the best word recognition accuracy of 58.4%. Furthermore, it was found that most feature extraction parameters did not have a large effect on recognition accuracy and that the SVM-based recognisers outperform both ANN- and HMM-based recognisers.


title: Instructional design of a programming course: a learning theoretic approach

abstract: We present a brief overview of a model for the human cognitive architecture and three learning theories based on this model: cognitive load theory, cognitive apprenticeship, and worked examples (a key area of cognitive skill acquisition). Based on this brief overview we argue how an introductory object-oriented programming course is designed according to results of cognitive science and educational psychology in general and cognitive load theory and cognitive skill acquisition in particular; the principal techniques applied are: worked examples, scaffolding, faded guidance, cognitive apprenticeship, and emphasis of patterns to aid schema creation and improve learning. As part of the presentation of the course, we provide a characterization of model-driven programming---the approach we have adopted in the introductory programming course. The result is an introductory programming course emphasizing a pattern-based approach to programming and schema acquisition in order to improve learning.


title: You want to measure coping but your protocol’ too long: Consider the brief cope

abstract: Studies of coping in applied settings often confront the need to minimize time demands on participants. The problem of participant response burden is exacerbated further by the fact that these studies typically are designed to test multiple hypotheses with the same sample, a strategy that entails the use of many time-consuming measures. Such research would benefit from a brief measure of coping assessing several responses known to be relevant to effective and ineffective coping. This article presents such a brief form of a previously published measure called the COPE inventory (Carver, Scheier, & Wcintraub, 1989), which has proven to be useful in health-related research. The Brief COPE omits two scales of the full COPE, reduces others to two items per scale, and adds one scale. Psychometric properties of the Brief COPE arc reported, derived from a sample of adults participating in a study of the process of recovery after Hurricane Andrew.


title: Complex Document Classification and Localization Application on Identity Document Images

abstract: This paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc. First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%.