title: Dynamic Reasoning Systems

abstract: A {\it dynamic reasoning system} (DRS) is an adaptation of a conventional formal logical system that explicitly portrays reasoning as a temporal activity, with each extralogical input to the system and each inference rule application being viewed as occurring at a distinct time step. Every DRS incorporates some well-defined logic together with a controller that serves to guide the reasoning process in response to user inputs. Logics are generic, whereas controllers are application-specific. Every controller does, nonetheless, provide an algorithm for nonmonotonic belief revision. The general notion of a DRS comprises a framework within which one can formulate the logic and algorithms for a given application and prove that the algorithms are correct, i.e., that they serve to (i) derive all salient information and (ii) preserve the consistency of the belief set. This paper illustrates the idea with ordinary first-order predicate calculus, suitably modified for the present purpose, and two examples. The latter example revisits some classic nonmonotonic reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be resolved in the context of a DRS, using an expanded version of first-order logic that incorporates typed predicate symbols. All concepts are rigorously defined and effectively computable, thereby providing the foundation for a future software implementation.


title: Design of a Wideband Horizontally Polarized Omnidirectional Printed Loop Antenna

abstract: This letter presents the design of a novel wideband horizontally polarized omnidirectional printed loop antenna. The proposed antenna consists of a loop with periodical capacitive loading and a parallel stripline as an impedance transformer. Periodical capacitive loading is realized by adding interlaced coupling lines at the end of each section. Similarly to mu-zero resonance (MZR) antennas, the periodical capacitive loaded loop antenna proposed in this letter allows current along the loop to remain in phase and uniform. Therefore, it can achieve a horizontally polarized omnidirectional pattern in the far field, like a magnetic dipole antenna, even though the perimeter of the loop is comparable to the operating wavelength. Furthermore, the periodical capacitive loading is also useful to achieve a wide impedance bandwidth. A prototype of the proposed periodical capacitive loaded loop antenna is fabricated and measured. It can provide a wide impedance bandwidth of about 800 MHz (2170-2970 MHz, 31.2%) and a horizontally polarized omnidirectional pattern in the azimuth plane.


Petri Nets : Properties , Analysis and Appl ka t ions


title: Service Quality Delivery Through Web Sites: A Critical Review of Extant Knowledge

abstract: Evidence exists that service quality delivery through Web sites is an essential strategy to success, possibly more important than low price and Web presence. To deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. Information on this topic is beginning to emerge from both academic and practitioner sources, but this information has not yet been examined as a whole. The goals of this article are to review and synthesize the literature about service quality delivery through Web sites, describe what is known about the topic, and develop an agenda for needed research.


title: Controlled-Channel Attacks: Deterministic Side Channels for Untrusted Operating Systems

abstract: The presence of large numbers of security vulnerabilities in popular feature-rich commodity operating systems has inspired a long line of work on excluding these operating systems from the trusted computing base of applications, while retaining many of their benefits. Legacy applications continue to run on the untrusted operating system, while a small hyper visor or trusted hardware prevents the operating system from accessing the applications' memory. In this paper, we introduce controlled-channel attacks, a new type of side-channel attack that allows an untrusted operating system to extract large amounts of sensitive information from protected applications on systems like Overshadow, Ink Tag or Haven. We implement the attacks on Haven and Ink Tag and demonstrate their power by extracting complete text documents and outlines of JPEG images from widely deployed application libraries. Given these attacks, it is unclear if Over shadow's vision of protecting unmodified legacy applications from legacy operating systems running on off-the-shelf hardware is still tenable.


title: Fully-Trainable Deep Matching

abstract: Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this paper, we remove this limitation by rewriting the complete DM algorithm as a convolutional neural network. This results in a novel deep architecture for image matching that involves a number of new layer types and that, similar to recent networks for image segmentation, has a U-topology. We demonstrate the utility of the approach by improving the performance of DM by learning it end-to-end on an image matching task.


title: Influence of heart rate on the BOLD signal: The cardiac response function

abstract: Abstract It has previously been shown that low-frequency fluctuations in both respiratory volume and cardiac rate can induce changes in the blood-oxygen level dependent (BOLD) signal. Such physiological noise can obscure the detection of neural activation using fMRI, and it is therefore important to model and remove the effects of this noise. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described [Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008b. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40, 644–654.], no such mapping for heart rate (HR) has been proposed. In the current study, the effects of RV and HR are simultaneously deconvolved from resting state fMRI. It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes our subject population. It is observed that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and that its form is adequately modeled by Birn et al.'s previously-described respiration response function. Furthermore, it is shown that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.


title: Application and challenges of power electronics for variable frequency electric power system of more electric aircraft

abstract: The use of electric power is increasing in more electric aircraft (MEA). On-board constant frequency (CF) AC electric power system (EPS) can not provide enough power for MEA increasing electric loads. As a result, EPS of MEA is moving from CF AC to variable frequency (VF) AC systems. Typical CF and VF EPS architectures are presented and compared. The application of power electronics in aircraft EPS is presented. New requirements and challenges for the design of power electronics equipments on VF EPS of MEA are also discussed.


title: A wall climbing robot for tank inspection. An autonomous prototype

abstract: This paper describes a solution to a mobile climbing robot on magnetic wheels, designed for inspecting exterior oil tank surfaces made of metal sheets. A mechanical design has been developed which presents a practical solution without an umbilical cord. The inspection system has been developed based on client/server architecture. The robot runs a client application and a remote PC executes the server functions. They will both allow any necessary inspections to be performed simultaneously by more than one robot. A sensorial system and a data fusion strategy to estimate the absolute robot position is proposed to allow the robot to navigate autonomously. The graphical monitoring of the robot position in the remote PC (server application) provides the operator with the possibility of controlling the robot, even in situations in which the operator visibility of an area tank is very low or inexistent. Previous experiments have demonstrated the mechanical system's robustness. These experiments consist of robot trajectory measurements and the comparison to a motion kinematic model.


title: Fred West: Bio-Psycho-Social Investigation of Psychopathic Sexual Serial Killer

abstract: This paper looks briefly at the case study of psychopathic sexual serial killer Frederick Walter Stephen West. His criminal behaviour and other behavioural problems are often assumed to be rooted in the home, inadequate discipline, or poor role models. However, based on research arguments presented in this paper, it is obvious that the answer to what significantly contributed to the development of this complex distorted personality and subsequent violent behaviour is far more multifaceted. It seems to be a result of a highly complex interaction of biological, psychological and sociological factors.


title: Automatic optic disc and optic cup detection in retinal images acquired by mobile phone

abstract: Medical image processing plays an important role in the automatic detection of certain eye diseases, disease documentation, treatment monitoring and educational purposes. The continuous improvement of the hardware of mobile phones gives us the ability to capture images like this. Interesting question can be: are these images suitable for automatic image processing, or more specifically, how much information can be obtained from this kind of images? In this paper, we would like to investigate whether a mobile device suitable to use as a medical diagnostic tool. We present a comparison between images made by a mobile device and a clinical eye examination one. We perform optic disc and optic cup detection from which we calculate cup to disc ratio. Our results show that images acquired by mobile phone are suitable for automatic image processing. Beyond these, a database of images were set up to allow researchers to test their algorithms and methods for image processing.


title: Alternative implementations of hybrid branch predictors

abstract: Very accurate branch prediction is an important requirement for achieving high performance on deeply pipelined, superscalar processors. To improve on the prediction accuracy of current single-scheme branch predictors, hybrid (multiple-scheme) branch predictors have been proposed (McFarling (1993), Chang et al. (1994)). These predictors combined multiple single-scheme predictors into a single predictor. They use a selection mechanism to decide for each branch, which single-scheme predictor to use. The performance of a hybrid predictor depends on its single-scheme predictor components and its selection mechanism. Using known single-scheme predictors and selection mechanisms, this paper identifies the most effective hybrid predictor implementation. In addition, it introduces a new selection mechanism, the 2-level selector, which further improves the performance of the hybrid branch predictor.


title: A finite difference scheme for option pricing in jump diffusion and exponential Lévy models

abstract: We present a finite difference method for solving parabolic partial integro-differential equations with possibly singular kernels which arise in option pricing theory when the random evolution of the underlying asset is driven by a Levy process or, more generally, a time-inhomogeneous jump-diffusion process. We discuss localization to a finite domain and provide an estimate for the localization error under an integrability condition on the Levy measure. We propose an explicit-implicit finite difference scheme which can be used to price European and barrier options in such models. We study stability and convergence of the scheme proposed and, under additional conditions, provide estimates on the rate of convergence. Numerical tests are performed with smooth and nonsmooth initial conditions.


title: Rational Thinking and Cognitive Sophistication: Development, Cognitive Abilities, and Thinking Dispositions.

abstract: We studied developmental trends in 5 important reasoning tasks that are critical components of the operational definition of rational thinking. The tasks measured denominator neglect, belief bias, base rate sensitivity, resistance to framing, and the tendency toward otherside thinking. In addition to age, we examined 2 other individual difference domains that index cognitive sophistication: cognitive ability (intelligence and executive functioning) and thinking dispositions (actively open-minded thinking, superstitious thinking, and need for cognition). All 5 reasoning domains were consistently related to cognitive sophistication regardless of how it was indexed (age, cognitive ability, thinking dispositions). The implications of these findings for taxonomies of developmental trends in rational thinking tasks are discussed.


title: Eye-motion detection system for mnd patients

abstract: This paper aims to develop an eye-motion based communication system for motor neuron disease (MND) patients to contact with care providers any time they want when they lie on the bed. This eye-motion detection system involves technical modules of eye-blink detection, gaze estimation and head pose estimation on MND patients. The system comprises a rotating arm with a camera, an infrared light source and a speaker. The arm with the camera will autonomously rotate to track the face of the patient with arbitrary body directions so that necessary eye detections can be properly conducted when it is called. When the patient needs to call out, only designated blinking and eye motions will trigger the call out action for the need of finding the care provider.


title: Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election

abstract: We present an analysis of traffic to websites known for publishing fake news in the months preceding the 2016 US presidential election. The study is based on the combined instrumentation data from two popular desktop web browsers: Internet Explorer 11 and Edge. We find that social media was the primary outlet for the circulation of fake news stories and that aggregate voting patterns were strongly correlated with the average daily fraction of users visiting websites serving fake news. This correlation was observed both at the state level and at the county level, and remained stable throughout the main election season. We propose a simple model based on homophily in social networks to explain the linear association. Finally, we highlight examples of different types of fake news stories: while certain stories continue to circulate in the population, others are short-lived and die out in a few days.


title: The Impact of Social Media on Student Academic Life in Higher Education

abstract: Social media is rapidly changing the communication setting of todayâ€™s social world. The emergent of social media is significantly influencing the academic life of students. Institutions and academician are continually trying with social media technologies hoping to excite critical thinking skills, collaboration, and knowledge construction. Today social media has been accepted by higher institution making it a platform where students connect with their instructors, fellow students and other higher authorities across the board. This therefore called for the study to explore and examine how social media has impacted on studentsâ€™ academic life. The study implored a qualitative approach in assessing these impacts. Ten (10) participants were conveniently sampled and interviewed with a period of two weeks. After studying the phenomena that were of interest to the study, and transcribing the various responses of the participants the results reveals that social media is widely used by students of higher institution and that participant are in support of the idea that social media contribute a significant quota to the development of their academic life.


title: Pseudo labels for imbalanced multi-label learning

abstract: The classification with instances which can be tagged with any of the 2L possible subsets from the predefined L labels is called multi-label classification. Multi-label classification is commonly applied in domains, such as multimedia, text, web and biological data analysis. The main challenge lying in multi-label classification is the dilemma of optimising label correlations over exponentially large label powerset and the ignorance of label correlations using binary relevance strategy (1-vs-all heuristic). The classification with label powerset usually encounters with highly skewed data distribution, called imbalanced problem. While binary relevance strategy reduces the problem from exponential to linear, it totally neglects the label correlations. In this artical, we propose a novel strategy of introducing Balanced Pseudo-Labels (BPL) which build more robust classifiers for imbalanced multi-label classification, which embeds imbalanced data in the problems innately. By incorporating the new balanced labels we aim to increase the average distances among the distinct label vectors. In this way, we also code the label correlation implicitly in the algorithm. Another advantage of the proposed method is that it can combined with any classifier and it is proportional to linear label transformation. In the experiment, we choose five multi-label benchmark data sets and compare our algorithm with the most state-of-art algorithms. Our algorithm outperforms them in standard multi-label evaluation in most scenarios.


title: A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

abstract: The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.


title: CESAR: A Testbed Infrastructure to Evaluate the Efficiency of Wireless Automotive Software Updates

abstract: Connected vehicles allow to update the software (SW) running on their integrated electronic control units (ECUs) over-the-air. Such updates are complex procedures that involve several steps, such as the authentication with a remote device, the secure and reliable wireless transfer of the new binary, as well as its installation and verification on the target ECU. Each of these aspects affects the efficiency of the entire SW update process, and it is important to evaluate the impact of different solutions on the functionality of a vehicle and to compare their performance on real hardware. In this paper we present CESAR, a configurable testbed infrastructure that allows to evaluate the efficiency of an automotive SW update system in a highly automated way. CESAR allows to specify different update mechanisms, security configurations, wireless protocols used for the data transfer, and to carefully define the scenario of interest (i.e., pin down the number of wireless vehicle interfaces, the network topology, and the target ECU). Furthermore, CESAR can be used to measure the efficiency of a SW update on real hardware, and to derive insights about the weaknesses of a system under test or about the interaction of a specific SW with a given ECU.


title: A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning

abstract: In order for people to be able to trust and take advantage of the results of advanced machine learning and artificial intelligence solutions for real decision making, people need to be able to understand the machine rationale for given output. Research in explain artificial intelligence (XAI) addresses the aim, but there is a need for evaluation of human relevance and understandability of explanations. Our work contributes a novel methodology for evaluating the quality or human interpretability of explanations for machine learning models. We present an evaluation benchmark for instance explanations from text and image classifiers. The explanation meta-data in this benchmark is generated from user annotations of image and text samples. We describe the benchmark and demonstrate its utility by a quantitative evaluation on explanations generated from a recent machine learning algorithm. This research demonstrates how human-grounded evaluation could be used as a measure to qualify local machine-learning explanations.


The Dynamics of Collaborative Knowledge Production.


title: Effects of Awareness on the Control of Attention

abstract: Previous studies show that it is possible to attend to a stimulus without awareness of it. Whether attention and awareness are independent or have a specific relationship, however, remains debated. Here, we tested three aspects of visual attention with and without awareness of the visual stimulus. Metacontrast masking rendered participants either subjectively aware or not aware of the stimulus. Attention drawn to the stimulus was measured by using the stimulus as a cue in a spatial attention task. We found that attention was drawn to the stimulus regardless of whether or not people were aware of it. However, attention changed significantly in the absence of awareness in at least three ways. First, attention to a task-relevant stimulus was less stable over time. Second, inhibition of return, the automatic suppression of attention to a task-irrelevant stimulus, was reduced. Third, attention was more driven by the luminance contrast of the stimulus. These findings add to the growing information on the behavior of attention with and without awareness. The findings are also consistent with our recently proposed account of the relationship between attention and awareness. In the attention schema theory, awareness is the internal model of attention. Just as the brain contains a body schema that models the body and helps control the body, so it contains an attention schema that helps control attention. In that theory, in the absence of awareness, the control of attention should suffer in basic ways predictable from dynamical systems theory. The present results confirm some of those predictions.


title: Buck-boost PWM converters having two independently controlled switches

abstract: Single-switch step-up/step-down converters, such as the buck-boost, SEPIC and Cuk, have relatively high voltage and current stresses on components compared to the buck or the boost converter. A buck-boost converter with two independently controlled switches can work as a boost or as a buck converter depending on input-output conditions, and thus achieves lower stresses on components. Using the converter synthesis method from D. Zhou (1995), families of two-switch buck-boost converters are generated, including several new converter topologies. The two-switch buck-boost converters are evaluated and compared in terms of component stresses in universal-input power-factor-corrector applications. Among them, one new two-switch converter is identified that has low inductor conduction losses (50% of the boost converter), low inductor volt-seconds (72% of the boost converter), and about the same switch conduction losses and voltage stresses as the boost converter.


title: Monoidal cut strengthening and generalized mixed-integer rounding for disjunctions and complementarity constraints

abstract: Abstract In the early 1980s, Balas and Jeroslow presented monoidal disjunctive cuts exploiting the integrality of variables. This article investigates the relation of monoidal cut strengthening to other classes of cutting planes for general two-term disjunctions. We introduce a generalization of mixed-integer rounding cuts and show equivalence to monoidal disjunctive cuts. Moreover, we demonstrate the effectiveness of these cuts via computational experiments on instances involving complementarity constraints. Finally, we present an adaptation of the mixed-integer rounding approach for mixed-complementarity problems.


title: A Biologically Inspired Automatic System for Media Quality Assessment

abstract: Photo aesthetic quality evaluation is a challenging task in artificial intelligence systems. In this paper, we propose a biologically inspired aesthetic descriptor that mimicks humans sequentially perceiving visually/semantically salient In general, visually salient regions are perceived by low-level visual features, such as the high contrast between the foreground and the background objects; while semantically salient regions are perceived by high-level visual features such as human faces. regions in a photo. In particular, a weakly supervised learning paradigm is developed to project the local image descriptors into a low-dimensional semantic space. Then, each graphlet can be described by multiple types of visual features, both in low-level and in high-level. Since humans usually perceive only a few salient regions in a photo, a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues. Top-ranked graphlets are those visually/semantically prominent local aesthetic descriptors in a photo. They are sequentially linked into a path that simulates humans actively viewing process. Finally, we learn a probabilistic aesthetic measure based on such actively viewing paths (AVPs) from the training photos. Experimental results show that: 1) the AVPs are 87.65% consistent with real human gaze shifting paths, as verified by the eye-tracking data and 2) our aesthetic measure outperforms many of its competitors.


title: Large-Scale cost-based abduction in full-fledged first-order predicate logic with cutting plane inference

abstract: Abduction is inference to the best explanation. Abduction has long been studied intensively in a wide range of contexts, from artificial intelligence research to cognitive science. While recent advances in large-scale knowledge acquisition warrant applying abduction with large knowledge bases to real-life problems, as of yet no existing approach to abduction has achieved both the efficiency and formal expressiveness necessary to be a practical solution for large-scale reasoning on real-life problems. The contributions of our work are the following: (i) we reformulate abduction as an Integer Linear Programming (ILP) optimization problem, providing full support for first-order predicate logic (FOPL); (ii) we employ Cutting Plane Inference, which is an iterative optimization strategy developed in Operations Research for making abductive reasoning in full-fledged FOPL tractable, showing its efficiency on a real-life dataset; (iii) the abductive inference engine presented in this paper is made publicly available.


title: Parameter estimation, nonlinearity and Occam's razor

abstract: Nonlinear systems are capable of displaying complex behavior even if this is the result of a small number of interacting time scales. A widely studied case is when complex dynamics emerges out of a nonlinear system being forced by a simple harmonic function. In order to identify if a recorded time series is the result of a nonlinear system responding to a simpler forcing, we develop a discrete nonlinear transformation for time series based on synchronization techniques. This allows a parameter estimation procedure which simultaneously searches for a good fit of the recorded data, and small complexity of a fluctuating driving parameter. We illustrate this procedure using data from respiratory patterns during birdsong production.


title: An Efficient File Hierarchy Attribute-Based Encryption Scheme in Cloud Computing

abstract: Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption technology to solve the challenging problem of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, particularly in the area of healthcare and the military. However, the hierarchy structure of shared files has not been explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme is proposed in cloud computing. The layered access structures are integrated into a single access structure, and then, the hierarchical files are encrypted with the integrated access structure. The ciphertext components related to attributes could be shared by the files. Therefore, both ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is proved to be secure under the standard assumption. Experimental simulation shows that the proposed scheme is highly efficient in terms of encryption and decryption. With the number of the files increasing, the advantages of our scheme become more and more conspicuous.


title: Semantic similarity in the biomedical domain: an evaluation across knowledge sources

abstract: BACKGROUND ::: Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity; these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods; but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks. ::: ::: ::: RESULTS ::: We evaluated knowledge based and corpus IC based semantic similarity measures derived from SNOMED CT, MeSH, and the UMLS on recently developed semantic similarity benchmarks. Semantic similarity measures based on the UMLS, which contains SNOMED CT and MeSH, significantly outperformed those based solely on SNOMED CT or MeSH across evaluations. Intrinsic IC based measures significantly outperformed path-based and distributional measures. We released all code required to reproduce our results and all tools developed as part of this study as open source, available under http://code.google.com/p/ytex. We provide a publicly-accessible web service to compute semantic similarity, available under http://informatics.med.yale.edu/ytex.web/. ::: ::: ::: CONCLUSIONS ::: Knowledge based semantic similarity measures are more practical to compute than distributional measures, as they do not require an external corpus. Furthermore, knowledge based measures significantly and meaningfully outperformed distributional measures on large semantic similarity benchmarks, suggesting that they are a practical alternative to distributional measures. Future evaluations of semantic similarity measures should utilize benchmarks powered to detect significant differences in measure accuracy.


title: A Compact Rijndael Hardware Architecture with S-Box Optimization

abstract: Compact and high-speed hardware architectures and logic optimization methods for the AES algorithm Rijndael are described. Encryption and decryption data paths are combined and all arithmetic components are reused. By introducing a new composite field, the S-Box structure is also optimized. An extremely small size of 5.4 Kgates is obtained for a 128-bit key Rijndael circuit using a 0.11-µm CMOS standard cell library. It requires only 0.052 mm2 of area to support both encryption and decryption with 311 Mbps throughput. By making effective use of the SPN parallel feature, the throughput can be boosted up to 2.6 Gbps for a high-speed implementation whose size is 21.3 Kgates.


Embeddings as a Means of Domain Adaptation for the Machine Translation


title: Automatic access of the meanings of ambiguous words in context: some limitations of knowledge-based processing

abstract: Abstract Five experiments are described on the processing of ambiguous words in sentences. Two classes of ambiguous words (noun-noun and noun-verb) and two types of context (priming and nonpriming) were investigated using a variable stimulus onset asynchrony (SOA) priming paradigm. Noun-noun ambiguities have two semantically unrelated readings that are nouns (e.g., PEN, ORGAN); noun-verb ambiguities have both noun and verb readings that are unrelated (e.g., TIRE, WATCH). Priming contexts contain a word highly semantically or associatively related to one meaning of the ambiguous word; nonpriming contexts favor one meaning of the word through other types of information (e.g., syntactic or pragmatic). In nonpriming contexts, subjects consistently access multiple meanings of words and select one reading within 200 msec. Lexical priming differentially affects the processing of subsequent noun-noun and noun-verb ambiguities, yielding selective access of meaning only in the former case. The results suggest that meaning access is an automatic process which is unaffected by knowledge-based (“top-down”) processing. Whether selective or multiple access of meaning is observed largely depends on the structure of the ambiguous word, not the nature of the context.


title: Context-Aware Neural Machine Translation Learns Anaphora Resolution

abstract: Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).


title: Filtering Antenna With Two-Octave Harmonic Suppression

abstract: A novel second-order antenna with filtering performance and two-octave harmonic suppression is proposed. In order to reduce the effects of the harmonics of the antenna, two types of antennas [planar inverted-F antenna (PIFA) and patch] with different resonant characteristics are integrated into the design. Compared to the traditional patch antennas, this integrated work can not only eliminate the high-order harmonics of the antenna but also improve the in-band bandwidth and frequency selectivity. The second- and fourth-order harmonics of the patch are suppressed because of the detuned harmonic performance of the PIFA and patch. The third-order harmonic is eliminated by integrating notch resonators in the PIFA. A prototype that works at 2.4 GHz is developed to demonstrate the PIFA-patch integration scheme. Measured and simulated results of antennas agree well with each other, demonstrating good performance of bandwidth, second-order filtering, radiation, and wideband harmonic suppression (up to 11 GHz).


Using Kibana and ElasticSearch for the Recommendation of Job Offers to Students


title: Design of Neuromorphic Architectures with Memristors

abstract: Next-generation high-performance computing processors have a pressing need to expand their computation capabilities to support massively parallel applications. Moreover, conventional Von Neumann based architectures cannot meet these complex computational demands. Neuromorphic architectures, which improve the efficiency and robustness of complex computations by emulating the behavior of biological processes in hardware, offer a viable alternative solution. In this chapter, we discuss the design criteria and challenges to realize such architectures using emerging memristor technology. In particular, the memristor models, synapse circuits, fundamental processing units (neural logic blocks) for the neuromorphic architectures, and hybrid CMOS/memristor neural network (CMHNN) topologies using supervised learning will be presented for different benchmarks.


title: A survey of hardware Trojan threat and defense

abstract: Hardware Trojans (HTs) can be implanted in security-weak parts of a chip with various means to steal the internal sensitive data or modify original functionality, which may lead to huge economic losses and great harm to society. Therefore, it is very important to analyze the specific HT threats existing in the whole life cycle of integrated circuits (ICs), and perform protection against hardware Trojans. In this paper, we elaborate an IC market model to illustrate the potential HT threats faced by the parties involved in the model. Then we categorize the recent research advances in the countermeasures against HT attacks. Finally, the challenges and prospects for HT defense are illuminated.


title: Optimizing K-means text document clustering using latent semantic indexing and pillar algorithm

abstract: Document clustering is an important tool to help managing the vast amount of digital text document. This paper introduces a new approach to cluster text document. First, text is preprocessed and indexed using inverted index. Then the index is trimmed using TF-DF thresholding. After that, Term Document Matrix is built based on TF-IDF. Next step uses Latent Semantic Indexing to extract important feature from Term Document Matrix. The following process is selecting seeds via Pillar algorithm. Based on determined seeds, K-Means clustering is performed. Experiment result proves that this approach outperforms standard K-Means document clustering.


title: Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition

abstract: The proliferation of smartphones has significantly facilitated people’s daily life, and diverse and powerful embedded sensors make smartphone a ubiquitous platform to acquire and analyze data, which may also provide great potential for efficient human activity recognition. This paper presents a systematic performance analysis of motion-sensor behavior for human activity recognition via smartphones. Sensory data sequences are collected via smartphones, when participants perform typical and daily human activities. A cycle detection algorithm is applied to segment the data sequence for obtaining the activity unit, which is then characterized by time-, frequency-, and wavelet-domain features. Then both personalized and generalized model using diverse classification algorithms are developed and implemented to perform activity recognition. Analyses are conducted using 27 681 sensory samples from 10 subjects, and the performance is measured in the form of F-score under various placement settings, and in terms of sensitivity to user space, stability to combination of motion sensors, and impact of data imbalance. Extensive results show that each individual has its own specific and discriminative movement patterns, and the F-score for personalized model and generalized model can reach 95.95% and 96.26%, respectively, which indicates our approach is accurate and efficient for practical implementation.


title: DendroPy: a Python library for phylogenetic computing

abstract: Summary: DendroPy is a cross-platform library for the Python programming language that provides for object-oriented reading, writing, simulation and manipulation of phylogenetic data, with an emphasis on phylogenetic tree operations. DendroPy uses a splits-hash mapping to perform rapid calculations of tree distances, similarities and shape under various metrics. It contains rich simulation routines to generate trees under a number of different phylogenetic and coalescent models. DendroPy’s data simulation and manipulation facilities, in conjunction with its support of a broad range of phylogenetic data formats (NEXUS, Newick, PHYLIP, FASTA, NeXML, etc.), allow it to serve a useful role in various phyloinformatics and phylogeographic pipelines. Availability: The stable release of the library is available for download and automated installation through the Python Package Index site (http://pypi.python.org/pypi/DendroPy), while the active development source code repository is available to the public from GitHub (http://github.com/jeetsukumaran/DendroPy).


title: Improved pre-training of Deep Belief Networks using Sparse Encoding Symmetric Machines

abstract: Restricted Boltzmann Machines (RBM) continue to be a popular methodology to pre-train weights of Deep Belief Networks (DBNs). However, the RBM objective function cannot be maximized directly. Therefore, it is not clear what function to monitor when deciding to stop the training, leading to a challenge in managing the computational costs. The Sparse Encoding Symmetric Machine (SESM) has been suggested as an alternative method for pre-training. By placing a sparseness term on the NN output codebook, SESM allows the objective function to be optimized directly and reliably be monitored as an indicator to stop the training. In this paper, we explore SESM to pre-train DBNs and apply this the first time to speech recognition. First, we provide a detailed analysis comparing the behavior of SESM and RBM. Second, we compare the performance of SESM pre-trained and RBM pre-trained DBNs on TIMIT and a 50 hour English Broadcast News task. Results indicate that pre-trained DBNs using SESM and RBMs achieve comparable performance and outperform randomly initialized DBNs with SESM providing a much easier stopping criterion relative to RBM.


title: Evidence for the potential influence of cyclic nucleotides on maintenance of or reactivation from latency of herpes simplex virus in trigeminal ganglionic neurons.

abstract: Herpes simplex virus infections are epidemic throughout developed countries, and recurrent herpes simplex keratitis is the most common cause of corneal blindness in these countries, NIH (1973). No available antiviral agent is capable of eradicating the state of viral ganglionic latency, and hence no effective treatment currently exists for prevention of viral re-activation from latency, with resultant recurrent infectious viral clinical manifestations. Putative triggers of re-activation include stress, sunburn, menses, trauma, and fever. These 'triggers' seem to share at least one common characteristic: the potential ability to influence intracellular cyclic nucleotide levels through the action of such first order messengers as catacolamines (stress, trauma) and/or arachadonic acid metabolites (sunburn, fever, trauma, and menses). We exploited an in vitro model of HSV ganglionic latency, and developed a model of in vitro organ culture ganglionic viral reactivation from latency. We then examined the effect of a variety of agents on this model. We found that agents which have been shown to elevate cyclic AMP levels consistently produce increased viral shedding (compared to control, spontaneous reactivation rate) in our model of viral reactivation from latency. In contrast, agents which have been shown to depress c-AMP levels and/or to elevate c-GMP levels inhibit viral reactivation from latency in this in vitro model. We conclude that intracellular cyclic nucleotide levels may influence events which control herpes simplex genome transcription.


title: Enabling Force Sensing During Ground Locomotion: A Bio-Inspired, Multi-Axis, Composite Force Sensor Using Discrete Pressure Mapping

abstract: This paper presents a new force sensor design approach that maps the local sampling of pressure inside a composite polymeric footpad to forces in three axes, designed for running robots. Conventional multiaxis force sensors made of heavy metallic materials tend to be too bulky and heavy to be fitted in the feet of legged robots, and vulnerable to inertial noise upon high acceleration. To satisfy the requirements for high speed running, which include mitigating high impact forces, protecting the sensors from ground collision, and enhancing traction, these stiff sensors should be paired with additional layers of durable, soft materials; but this also degrades the integrity of the foot structure. The proposed foot sensor is manufactured as a monolithic, composite structure composed of an array of barometric pressure sensors completely embedded in a protective polyurethane rubber layer. This composite architecture allows the layers to provide compliance and traction for foot collision while the deformation and the sampled pressure distribution of the structure can be mapped into three axis force measurement. Normal and shear forces can be measured upon contact with the ground, which causes the footpad to deform and change the readings of the individual pressure sensors in the array. A one-time training process using an artificial neural network is all that is necessary to relate the normal and shear forces with the multiaxis foot sensor output. The results show that the sensor can predict normal forces in the Z-axis up to 300 N with a root mean squared error of 0.66% and up to 80 N in the X- and Y-axis. The experiment results demonstrates a proof-of-concept for a lightweight, low cost, yet robust footpad sensor suitable for use in legged robots undergoing ground locomotion.


title: Learning Biological Processes with Global Constraints

abstract: Biological processes are complex phenomena involving a series of events that are related to one another through various relationships. Systems that can understand and reason over biological processes would dramatically improve the performance of semantic applications involving inference such as question answering (QA) ‐ specifically “How?” and “Why?” questions. In this paper, we present the task of process extraction, in which events within a process and the relations between the events are automatically extracted from text. We represent processes by graphs whose edges describe a set of temporal, causal and co-reference event-event relations, and characterize the structural properties of these graphs (e.g., the graphs are connected). Then, we present a method for extracting relations between the events, which exploits these structural properties by performing joint inference over the set of extracted relations. On a novel dataset containing 148 descriptions of biological processes (released with this paper), we show significant improvement comparing to baselines that disregard process structure.


title: Precise Determination of the Refractive Index of Samples Showing Low Transmission Bands by THz Time-Domain Spectroscopy

abstract: In this paper, we propose a method to determine the refractive index and the absorption coefficient of samples showing spectral bands of very low transmission. The method, takes advantages of both transmission and reflection terahertz time-domain spectroscopy (THz-TDS) to obtain more precise optical constants than when only reflection measurement is used. It has been then validated using pellets made of maltose, which exhibits resonant lines within the THz spectral range.


title: Observation-Level Interaction with Clustering and Dimension Reduction Algorithms

abstract: Observation-Level Interaction (OLI) is a sensemaking technique relying upon the interactive semantic exploration of data. By manipulating data items within a visualization, users provide feedback to an underlying mathematical model that projects multidimensional data into a meaningful two-dimensional representation. In this work, we propose, implement, and evaluate an OLI model which explicitly defines clusters within this data projection. These clusters provide targets against which data values can be manipulated. The result is a cooperative framework in which the layout of the data affects the clusters, while user-driven interactions with the clusters affect the layout of the data points. Additionally, this model addresses the OLI "with respect to what" problem by providing a clear set of clusters against which interaction targets are judged and computed.


title: Text analytics of unstructured textual data: A study on military peacekeeping document using R text mining package

abstract: This paper describes a technique of text analytics on peacekeeping documents to discover significant text patterns exist in the documents. These documents are considered as unstructured textual data.The paper proposes a framework that consists of 3 stages (i) data collection (ii) document preprocessing and (iii) text analytics and visualization.The technique ::: is developed using R text mining package for text analytics experiments.


title: Numerical analysis of contact plastic bodies made by aluminium alloy with taking account of micro-roughness surfaces

abstract: The following article describes selected aspects of numerical modeling of the process of ::: bonding metal alloys with consideration for micro-roughness. Plastic contact between two ::: deformable bodies is studied within a DEFROM FEM environment. The paper presents ::: selected numerical analysis results for an aluminum alloy. The mathematical model of surface ::: roughness has been created on the basis of the surface real profile. The dependence between ::: the tool lathe angle and the feed has been used to build a numerical model of roughness after ::: completion of the turning process. The article investigates the impact of wave roughness in ::: respect to the size effect and the possibility of cold welding as well as the simplification ::: process of real surface roughness.


Intrusion Detection in Wireless Ad-Hoc Networks


title: Noisy training for deep neural networks in speech recognition

abstract: Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting moderate noises into the training data intentionally and randomly, more generalizable DNN models can be learned. This ‘noise injection’ technique, although known to the neural computation community already, has not been studied with DNNs which involve a highly complex objective function. The experiments presented in this paper confirm that the noisy training approach works well for the DNN model and can provide substantial performance improvement for DNN-based speech recognition.


title: CDroid: towards a cloud-integrated mobile operating system

abstract: Current offloading mechanisms for mobile energy- hungry apps consider the cloud as a separate remote support to the mobile devices. We take a different approach: We present CDroid, a system residing partially on the device and partially on a cloud software clone coupled with the device, and uses the cloud-side as just-another-resource of the real device. It enhances the user-experience by improving web navigation, compressing and caching web-pages, blocking unwanted ads, and protects user data by virus scanning apps on the cloud-side prior installation on the real-device. CDroid puts the first steps towards a hybrid cloud-integrated mobile system of the future. I. INTRODUCTION The proliferation of portable devices and high-speed mobile connectivity has attracted the attention of many developers. Every day are build new cool apps that allow us to do complex things on our smartphones, but that also strain their already limited energy resources. In this scenario, cloud computing is seen as a promising technology that can offer many benefits


An 8 b / 10 b Encoding Serializer / Deserializer ( SerDes ) Circuit for High Speed Communication Applications Using a DC Balanced , Partitioned-Block , 8 b / 10 b Transmission Code


Tweet Sarcasm Detection Using Deep Neural Network


title: Evolving grounded communication for robots

abstract: Abstract The computational and robotic synthesis of language evolution is emerging as a new exciting field of research. The objective is to come up with precise operational models of how communities of agents, equipped with a cognitive apparatus, a sensori-motor system, and a body, can arrive at shared grounded communication systems. Such systems may have similar characteristics to animal communication or human language. Apart from its technological interest in building novel applications in the domain of human–robot or robot–robot interaction, this research is of interest to the many disciplines concerned with the origins and evolution of language and communication.


title: An Accurate Arabic Root-Based Lemmatizer for Information Retrieval Purposes

abstract: In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma level analysis and generation does not yet focused in Arabic NLP literatures. In the current research, we propose the first non-statistical accurate Arabic lemmatizer algorithm that is suitable for information retrieval (IR) systems. The proposed lemmatizer makes use of different Arabic language knowledge resources to generate accurate lemma form and its relevant features that support IR purposes. As a POS tagger, the experimental results show that, the proposed algorithm achieves a maximum accuracy of 94.8%. For first seen documents, an accuracy of 89.15% is achieved, compared to 76.7% of up to date Stanford accurate Arabic model, for the same, dataset.


title: Sodium thiosulfate prevents cisplatin-induced hypomagnesemia.

abstract: Clearance studies were performed in four groups of male Wistar rats to assess the protective effect of sodium thiosulfate on cisplatin-induced hypomagnesemia. In group I, sodium thiosulfate (400 mg/kg) was injected intraperitoneally once weekly for 3 consecutive weeks. In group II, only cisplatin (2.5 mg/kg) was administered. In group III, both cisplatin (2.5 mg/kg) and sodium thiosulfate (400 mg/kg) were injected via the intraperitoneal route. When both drugs were administered together, they were injected into different parts of the peritoneal cavity. In group IV cisplatin was administered intraperitoneally and sodium thiosulfate intravenously. Sodium thiosulfate prevented a rise in plasma creatinine. The overall glomerular filtration rates of groups III and IV were the same as in group I. Hypomagnesemia was noted in group II, whereas in groups I, III, and IV the plasma magnesium level remained unchanged. The fractional excretion of magnesium was also higher in group II than in groups I, III, and IV. These differences persisted for the duration of the study. These results suggest that concurrent injections of sodium thiosulfate intraperitoneally or intravenously prevented the hypomagnesemic and the nephrotoxic effects of cisplatin and can be of clinical significance.


title: The social role of social media: the case of Chennai rains-2015

abstract: Social media has altered the way individuals communicate in present scenario. Individuals feel more connected on Facebook and Twitter with greater communication freedom to chat, share pictures, and videos. Hence, social media is widely employed by various companies to promote their product and services and establish better customer relationships. Owing to the increasing popularity of these social media platforms, their usage is also expanding significantly. Various studies have discussed the importance of social media in the corporate world for effective marketing communication, customer relationships, and firm performance, but no studies have focused on the social role of social media, i.e., in disaster resilience in India. Various academicians and practitioners have advocated the importance and use of social media in disaster resilience. This article focuses on the role that social media can play during the time of natural disasters, with the help of the recent case of Chennai floods in India. This study provides a better understanding about the role social media can play in natural disaster resilience in Indian context.


title: Variable-forgetting factor RLS for stereophonic acoustic echo cancellation with widely linear model

abstract: A widely linear (WL) model was recently proposed for stereophonic acoustic echo cancellation (SAEC). In this framework, the classical two-input/two-output SAEC scheme with real random variables is recasted as a single-input/single-output system with complex random variables. The main advantage of this approach is that instead of handling two (real) output signals separately, we only handle one (complex) output signal. In general, due to their good convergence features, recursive least-squares (RLS) algorithms are preferable for SAEC applications. However, the performance of RLS-based algorithms is governed by the forgetting factor, whose value leads to a compromise between convergence rate/tracking capabilities on the one hand and misadjustment/stability on the other hand. In this paper, we develop a variable-forgetting factor RLS (VFF-RLS) algorithm for SAEC with the WL model. Simulation results indicate the good performance (in terms of tracking and robustness) of the proposed algorithm.


title: Undergraduate Students' Failure in Programming Courses in Institutions of Higher Education in Developing Countries: A Nigerian Perspective

abstract: Universities of developing countries are faced with increased dropout rate from computing degrees due to the high failure rate of introductory programing courses. In this paper, we apply a grounded theory approach to investigate why student’s fail introductory programming courses in a Nigeria University. A qualitative method of data collection was adopted to examine the students’ views about the factors that contribute to student’s failure in introductory programming courses. The findings identified lack of intrinsic motivation, lack of future expectation, anxiety, peer influences, and poor lecturer skills and behavior as the challenges resulting in a high failure rate in introductory programming courses. The study contributes to the research on computer education in developing countries by proposing a model to understand the factors that affect IS student learning process when undertaking introductory programming courses. We conclude with some recommendations following recommendations that could help students to overcome the challenges in learning computer programming.


title: Exercises to prevent lower limb injuries in youth sports: cluster randomised controlled trial

abstract: Abstract Objective To investigate the effect of a structured warm-up programme designed to reduce the incidence of knee and ankle injuries in young people participating in sports. Design Cluster randomised controlled trial with clubs as the unit of randomisation. Setting 120 team handball clubs from central and eastern Norway (61 clubs in the intervention group, 59 in the control group) followed for one league season (eight months). Participants 1837 players aged 15-17 years; 958 players (808 female and 150 male) in the intervention group; 879 players (778 female and 101 male) in the control group. Intervention A structured warm-up programme to improve running, cutting, and landing technique as well as neuromuscular control, balance, and strength. Main outcome measure The rate of acute injuries to the knee or ankle. Results During the season, 129 acute knee or ankle injuries occurred, 81 injuries in the control group (0.9 (SE 0.09) injuries per 1000 player hours; 0.3 (SE 0.17) in training v 5.3 (SE 0.06) during matches) and 48 injuries in the intervention group (0.5 (SE 0.11) injuries per 1000 player hours; 0.2 (SE 0.18) in training v 2.5 (SE 0.06) during matches). Fewer injured players were in the intervention group than in the control group (46 (4.8%) v (76 (8.6%); relative risk intervention group v control group 0.53, 95% confidence interval 0.35 to 0.81). Conclusion A structured programme of warm-up exercises can prevent knee and ankle injuries in young people playing sports. Preventive training should therefore be introduced as an integral part of youth sports programmes.


title: Analyzing the business model for mobile payment from banks' perspective : An empirical study

abstract: The increasing number of smart phones presents a significant opportunity for the development of m-payment services. Despite the predicted success of m-payment, the market remains immature in most countries. This can be explained by the lack of agreement on standards and business models for all stakeholders in m-payment ecosystem. In this paper, the STOF business model framework is employed to analyze m-payment services from the point of view of one of the key players in the ecosystem i.e., banks. We apply Analytic Hierarchy Process (AHP) method to analyze the critical design issues for four domains of the STOF model. The results of the analysis show that service domain is the most important, followed by technology, organization and finance domains. Security related issues are found to be the most important by bank representatives. The future research can be extended to the m-payment ecosystem by collecting data from different actors from the ecosystem.


title: A CMOS 77-GHz Receiver Front-End for Automotive Radar

abstract: This paper presents the design of a receiver (Rx) front-end for automotive radar application operating at 76-77 GHz. The Rx employs a double conversion architecture, which consists of a five-stage low-noise amplifier (LNA), a sub-harmonic mixer (SHM), and a double-balanced passive mixer (PSM). By adopting this architecture, millimeter-wave frequency synthesizer design can be relaxed. In the LNA layout, the output of each stage is positioned close to the input of the follow stage, thus creating a LC resonance load. As a result, complex interstage matching networks is simplified. The SHM driven by a 38-GHz local oscillator (LO) is adopted to avoid push/pull effect and power consumption of the voltage-controlled oscillator. A PSM is utilized for the second conversion since it consumes no dc current and has low flickering noise. To connect the singled-ended LNA and SHM, a 77-GHz balun is designed; and for driving the SHM, two 38-GHz baluns and an in-phase/quadrature coupler to provide quadrature 38-GHz LO are designed. The proposed Rx is implemented in a 65-nm CMOS technology and measurement results show 16-dB voltage gain and 13-dB calculated noise figure while dissipating 23.5 mA from a black 1.2-V supply.


title: Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

abstract: Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.


title: A MULTILEVEL MODEL OF RESISTANCE TO INFORMATION TECHNOLOGY IMPLEMENTATION

abstract: To better explain resistance to information technology implementation, we used a multilevel, longitudinal approach. We first assessed extant models of resistance to IT. Using semantic analysis, we identified five basic components of resistance: behaviors, object, subject, threats, and initial conditions. We further examined extant models to (1) carry out a preliminary specification of the nature of the relationships between these components and (2) refine our understanding of the multilevel nature of the phenomenon. Using analytic induction, we examined data from three case studies of clinical information systems implementations in hospital settings, focusing on physicians' resistance behaviors. The resulting mixed-determinants model suggests that group resistance behaviors vary during implementation. When a system is introduced, users in a group will first assess it in terms of the interplay between its features and individual and/or organizational-level initial conditions. They then make projections about the consequences of its use. If expected consequences are threatening, resistance behaviors will result. During implementation, should some trigger occur to either modify or activate an initial condition involving the balance of power between the group and other user groups, it will also modify the object of resistance, from system to system significance. If the relevant initial conditions pertain to the power of the resisting group vis-a-vis the system advocates, the object of resistance will also be modified, from system significance to system advocates. Resistance behaviors will follow if threats are perceived from the interaction between the object of resistance and initial conditions. We also found that the bottom-up process by which group resistance behaviors emerge from individual behaviors is not the same in early versus late implementation. In early implementation, the emergence process is one of compilation, described as a combination of independent, individual behaviors. In later stages of implementation, if group level initial conditions have become active, the emergence process is one of composition, described as the convergence of individual behaviors.


title: Recognizing creative needs in user interface design

abstract: The creative process requires experimentation, the exploration of variations, and the continual evaluation of one's progress. While these processes are frequently non-linear and iterative, modern user interfaces do not explicitly support these practices, and instead impose a linear progression through tasks that is a poor fit for creative pursuits. In this paper we use data from three case studies, and draw upon Sch$#246;n's theory of reflection-in-action to illustrate specific deficiencies in current user interfaces when used in creative endeavors. We then develop a set of guidelines for user interface design and demonstrate their application in three designs intended to support tasks in the domain of image manipulation.


title: Edge Attention-based Multi-Relational Graph Convolutional Networks

abstract: Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, compound structures are often represented by the hydrogen-depleted molecular graph where nodes correspond to atoms and edges correspond to chemical bonds. Multiple attributes can be important to characterize chemical bonds, such as atom pair (the types of atoms that a bond connects), aromaticity, and whether a bond is in a ring. The different attributes lead to different graph representations for the same molecule. There is growing interests in both chemistry and machine learning fields to directly learn molecular properties of compounds from the molecular graph, instead of from fingerprints predefined by chemists. The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution. For each bond attribute, a real-valued attention matrix is used to replace the binary adjacency matrix. By designing a dictionary for the edge attention, and forming the attention matrix of each molecule by looking up the dictionary, the EAGCN exploits correspondence between bonds in different molecules. The prediction of compound properties is based on the aggregated node features, which is independent of the varying molecule (graph) size. We demonstrate the efficacy of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and Lipophilicity, and interpret the resultant attention weights.


title: Imagenet classification with deep convolutional neural networks

abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.


A systematic literature review of green software metrics


title: Neural network studies. 1. Comparison of overfitting and overtraining

abstract: The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equivalent of multiple linear regression (MLR) has been examined using artificial structured data sets and real literature data. The predictive ability of the networks has been estimated using a training/ test set protocol. The results have shown advantages of ANN over MLR analysis. The ANNs do not require high order terms or indicator variables to establish complex structure-activity relationships. Overfitting does not have any influence on network prediction ability when overtraining is avoided by cross-validation. Application of ANN ensembles has allowed the avoidance of chance correlations and satisfactory predictions of new data have been obtained for a wide range of numbers of neurons in the hidden layer.


title: Explaining Contradictory Relations Between Risk Perception and Risk Taking

abstract: Different studies have documented opposite relations between perceived risk and behavior. The present study tested a theoretical explanation that reconciles these conflicting results. Adolescents (N = 596) completed alternative measures of risk perception that differed in cue specificity and response format. As predicted by fuzzy-trace theory, measures that emphasized verbatim retrieval and quantitative processing produced positive correlations between perceived risk and risky behavior; risk perceptions reflected the extent to which adolescents engaged in risky behavior. In contrast, measures that assessed global, gist-based judgments of risk produced negative correlations; higher risk perceptions were associated with less risk taking, a protective rather than reflective relation. Endorsement of simple values and principles provided the greatest protection against risk taking. Results support a dual-processes interpretation of the relation between risk perception and risk taking according to which observe...


title: Review of Security Issues in Industrial Networks

abstract: Although awareness is constantly rising, that industrial computer networks (in a very broad sense) can be exposed to serious cyber threats, many people still think that the same countermeasures, developed to protect general-purpose computer networks, can be effectively adopted also in those situations where a physical system is managed/controlled through some distributed Information and Communication Technology (ICT) infrastructure. Unfortunately, this is not the case, as several examples of successful attacks carried out in the last decade, and more frequently in the very recent past, have dramatically shown. Experts in this area know very well that often the peculiarities of industrial networks prevent the adoption of classical approaches to their security and, in particular, of those popular solutions that are mainly based on a detect and patch philosophy. This paper is a contribution, from the security point of view, to the assessment of the current situation of a wide class of industrial distributed computing systems. In particular, the analysis presented in this paper takes into account the process of ensuring a satisfactory degree of security for a distributed industrial system, with respect to some key elements such as the system characteristics, the current state of the art of standardization and the adoption of suitable controls (countermeasures) that can help in lowering the security risks below a predefined, acceptable threshold.


title: Hybrid classification of Bayesian and extreme learning machine for heartbeat classification of arrhythmia detection

abstract: A cardiac circumstance affected through irregular electrical action of the heart is called an arrhythmia. A noninvasive method called Electrocardiogram (ECG) is used to diagnosis arrhythmias or irregularities of the heart. The difficulty encountered by doctors in the analysis of heartbeat irregularities id due to the non-stationary of ECG signal, the existence of noise and the abnormality of the heartbeat. The computer-assisted study of ECG signal supports doctors to diagnoses diseases of cardiovascular. The major limitations of all the ECG signal analysis of arrhythmia detection are because to the non-stationary behavior of the ECG signals and unobserved information existent in the ECG signals. In addition, detection based on Extreme learning machine (ELM) has become a common technique in machine learning. However, it easily suffers from overfitting. This paper proposes a hybrid classification technique using Bayesian and Extreme Learning Machine (B-ELM) technique for heartbeat recognition of arrhythmia detection AD. The proposed technique is capable of detecting arrhythmia classes with a maximum accuracy of (98.09%) and less computational time about 2.5s.


title: Gather customer concerns from online product reviews - A text summarization approach

abstract: Product reviews possess critical information regarding customers' concerns and their experience with the product. Such information is considered essential to firms' business intelligence which can be utilized for the purpose of conceptual design, personalization, product recommendation, better customer understanding, and finally attract more loyal customers. Previous studies of deriving useful information from customer reviews focused mainly on numerical and categorical data. Textual data have been somewhat ignored although they are deemed valuable. Existing methods of opinion mining in processing customer reviews concentrates on counting positive and negative comments of review writers, which is not enough to cover all important topics and concerns across different review articles. Instead, we propose an automatic summarization approach based on the analysis of review articles' internal topic structure to assemble customer concerns. Different from the existing summarization approaches centered on sentence ranking and clustering, our approach discovers and extracts salient topics from a set of online reviews and further ranks these topics. The final summary is then generated based on the ranked topics. The experimental study and evaluation show that the proposed approach outperforms the peer approaches, i.e. opinion mining and clustering-summarization, in terms of users' responsiveness and its ability to discover the most important topics.


Robust i-vector extraction for neural network adaptation in noisy environment


title: Cloud-based web application with NFC for employee attendance management system

abstract: Efficient employee attendance management leads any organizations to increase overall corporate performance and accomplish specific goals. Accurate employee attendance records are importantly used to control working discipline and increase worker's productivity. Manual attendance-time checking makes increasingly the expense of time-consuming and paper work of the companies. Human actions i.e. mistake at work, and fraudulent time keeping are additional hidden expenses which affect the productivity of the organization. Variation of the attendance policies set up in different companies make more complicated in evaluation of employee working hours. Hence, automated time-attendance management system is the key operational variables for enhancing the performance, and profitability. The attendance management system captures time-attendance data and serves the management of the employee working hour records. However, some existing time attendance systems have limitations in terms of identification speed, cost of system devices, real-time attendance monitoring, and flexibility of database storage size. In this paper, we introduce a cloud-based employee attendance management system using NFC technology. The proposed application provides several important operations such as captured attendance records using NFC, automatic time calculation, leave and overtime checking, working hours evaluation, real-time updated information access, and generating reports. The proposed system also offers online portal which allows multiple company user accounts, requires no special software to install, and provides more flexible data storage. The evaluation of user satisfaction shows that our proposed system is practically used and satisfied.


title: COSMOS - A Representation Scheme for 3D Free-Form Objects

abstract: We address the problem of representing and recognizing 3D free-form objects when (1) the object viewpoint is arbitrary, (2) the objects may vary in shape and complexity, and (3) no restrictive assumptions are made about the types of surfaces on the object. We assume that a range image of a scene is available, containing a view of a rigid 3D object without occlusion. We propose a new and general surface representation scheme for recognizing objects with free-form (sculpted) surfaces. In this scheme, an object is described concisely in terms of maximal surface patches of constant shape index. The maximal patches that represent the object are mapped onto the unit sphere via their orientations, and aggregated via shape spectral functions. Properties such as surface area, curvedness, and connectivity, which are required to capture local and global information, are also built into the representation. The scheme yields a meaningful and rich description useful for object recognition. A novel concept, the shape spectrum of an object is also introduced within the framework of COSMOS for object view grouping and matching. We demonstrate the generality and the effectiveness of our scheme using real range images of complex objects.


title: Adolescence-limited and life-course-persistent antisocial behavior: Adevelopmental taxonomy

abstract: A dual taxonomy is presented to reconcile 2 incongruous facts about antisocial behavior: (a) It shows impressive continuity over age, but (b) its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence. This article suggests that delinquency conceals 2 distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of 1 sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating in a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive. Language: en


title: An Introduction to Variable and Feature Selection

abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.


title: Pain Catastrophizing and Its Relationship with Health Outcomes: Does Pain Intensity Matter?

abstract: Pain catastrophizing is known to contribute to physical and mental functioning, even when controlling for the effect of pain intensity. However, research has yet to explore whether the strength of the relationship between pain catastrophizing and pain-related outcomes varies across pain intensity levels (i.e., moderation). If this was the case, it would have important implications for existing models of pain and current interventions. The present investigation explored whether pain intensity moderates the relationship between pain catastrophizing and pain-related outcomes. Participants were 254 patients (62% women) with heterogeneous chronic pain. Patients completed a measure of pain intensity, pain interference, pain catastrophizing, and physical and mental health. Pain intensity moderated the relationship between pain catastrophizing and pain interference and between pain catastrophizing and physical health status. Specifically, the strength of the correlation between pain catastrophizing and these outcomes decreased considerably as pain intensity increased. In contrast, pain intensity did not moderate the relationship between pain catastrophizing and mental health. Study findings provide a new insight into the role of pain intensity (i.e., moderator) in the relationship between pain catastrophizing and various pain-related outcomes, which might help develop existent models of pain. Clinical implications are discussed in the context of personalized therapy.


title: Evolution of bitcoin and security risk in bitcoin wallets

abstract: This paper identifies trust factor and rewarding nature of bitcoin system, and analyzes bitcoin features which may facilitate bitcoin to emerge as a universal currency. Paper presents the gap between proposed theoretical-architecture and current practical-implementation of bitcoin system in terms of achieving decentralization, anonymity of users, and consensus. Paper presents three different ways in which a user can manage bitcoins. We attempt to identify the security risk and feasible attacks on these configurations of bitcoin management. We have shown that not all bitcoin wallets are safe against all possible types of attacks. Bitcoin core is only safest mode of operating bitcoin till date as it is secure against all feasible attacks, and is vulnerable only against block-chain rewriting.


title: Directional Texture Feature Extraction using Context Labels of Significance Coding for JPEG2000 Compressed Domain Image Retrieval

abstract: Wavelet transforms is a widely used technique for analysing and describing texture patterns of image. Another advantage of wavelet transform is that one can analyse textures with multiresolution and can classify directional texture pattern information into each directional subband. Where as, HL subband implies horizontal frequency information, LH subband implies vertical frequency information and HH subband implies diagonal frequency. Nevertheless, many wavelet-based image retrieval approaches are not good tool to use directional subband information, obtained by wavelet transforms, for efficiently directional texture pattern classification of retrieved images. This paper proposes a novel image retrieval technique in JPEG2000 compressed domain using image significant map to compute an image context in order to construct image index. Experimental results indicate that the proposed method can effectively differentiate and categorize images with different texture directional information as compared to other known methods.


title: The big data system, components, tools, and technologies: a survey

abstract: The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches. However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data. This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data. In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process. Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models. The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML. Further, we classify analytics based on the type of data, domain, and application. We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment. Furthermore, we systematically investigate big data tools and technologies (Hadoop 3.0, Spark 2.3) including distributed/cloud-based stream processing tools in a comparative approach. Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters. Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data. Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications.


title: Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013

abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.


Expectation-Conjugate Gradient: An Alternative to EM


title: The Discrete Basis Problem

abstract: Matrix decomposition methods represent a data matrix as a product of two smaller matrices: one containing basis vectors that represent meaningful concepts in the data, and another describing how the observed data can be expressed as combinations of the basis vectors. Decomposition methods have been studied extensively, but many methods return real-valued matrices. If the original data is binary, the interpretation of the basis vectors is hard. We describe a matrix decomposition formulation, the Discrete Basis Problem. The problem seeks for a Boolean decomposition of a binary matrix, thus allowing the user to easily interpret the basis vectors. We show that the problem is computationally difficult and give a simple greedy algorithm for solving it. We present experimental results for the algorithm. The method gives intuitively appealing basis vectors. On the other hand, the continuous decomposition methods often give better reconstruction accuracies. We discuss the reasons for this behavior.


On Securing Multi-Clouds : Survey on Advances and Current Challenges


EVIDENCE BASED DENTISTRY IN PROSTHODONTICS


title: RGB-D Sensor-Based Computer Vision Assistive Technology for Visually Impaired Persons

abstract: A computer vision-based wayfinding and navigation aid can improve the mobility of blind and visually impaired people to travel independently. In this chapter, we focus on RGB-D sensor-based computer vision technologies in application to assist blind and visually impaired persons. We first briefly review the existing computer vision based assistive technology for the visually impaired. Then we provide a detailed description of the recent RGB-D sensor based assistive technology to help blind or visually impaired people. Next, we present the prototype system to detect and recognize stairs and pedestrian crosswalks based on RGB-D images. Since both stairs and pedestrian crosswalks are featured by a group of parallel lines, Hough transform is applied to extract the concurrent parallel lines based on the RGB (Red, Green, and Blue) channels. Then, the Depth channel is employed to recognize pedestrian crosswalks and stairs. The detected stairs are further identified as stairs going up (upstairs) and stairs going down (downstairs). The distance between the camera and stairs is also estimated for blind users. The detection and recognition results on our collected datasets demonstrate the effectiveness and efficiency of our developed prototype. We conclude the chapter by the discussion of the future directions.


title: Understanding the IoT Connectivity Landscape: A Contemporary M2M Radio Technology Roadmap

abstract: This article addresses the market-changing phenomenon of the Internet of Things (IoT), which relies on the underlying paradigm of machine-to-machine (M2M) communications to integrate a plethora of various sensors, actuators, and smart meters across a wide spectrum of businesses. Today the M2M landscape features an extreme diversity of available connectivity solutions which, due to the enormous economic promise of the IoT, need to be harmonized across multiple industries. To this end, we comprehensively review the most prominent existing and novel M2M radio technologies, as well as share our first-hand real-world deployment experiences, with the goal to provide a unified insight into enabling M2M architectures, unique technology features, expected performance, and related standardization developments. We pay particular attention to the cellular M2M sector employing 3GPP LTE technology. This work is a systematic recollection of our many recent research, industrial, entrepreneurial, and standardization efforts within the contemporary M2M ecosystem.


title: Face and eye detection on hard datasets

abstract: Face and eye detection algorithms are deployed in a wide variety of applications. Unfortunately, there has been no quantitative comparison of how these detectors perform under difficult circumstances. We created a dataset of low light and long distance images which possess some of the problems encountered by face and eye detectors solving real world problems. The dataset we created is composed of reimaged images (photohead) and semi-synthetic heads imaged under varying conditions of low light, atmospheric blur, and distances of 3m, 50m, 80m, and 200m. This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.


title: DESIGN AND IMPLEMENTATION OF A VIRTUAL CLASSROOM SYSTEM

abstract: In the last few decades, education has witnessed some advances in technologies involving computeraided learning that promises to drastically change the methods of teaching and learning. The World Wide Web has played a major role in information storage and dissemination in the educational community. Conventional classroom based teaching involves the delivery of course materials by the lecturer in a particular place at a defined time. Hence it imposes a constraint of time and place on both the instructor and the student. Due to human factor arising from the traditional classroom method, the lecturer may not always be able to put in optimum effort towards preparing and delivering course materials. There may also be inconsistencies in the pedagogy and learning style due to repetitive nature of teaching/learning. The objective of this paper is to develop a virtual classroom system to enhance learning on campus. The system was developed using PHP and MySQL as server side programming and database respectively. The web-based virtual classroom provides a web enabled interactive model for e-learning in which the course material is presented using multimedia and hypermedia.


title: Applications of Blockchain in Healthcare: Current Landscape&Challenges

abstract: Several problems in healthcare stem from the complex network of intermediaries and the lack of traceability of transactions. To mention a few: healthcare data is fragmented across several silos negatively affecting research and services, about half of the clinical trials are never reported, the cost of drug discovery is ever increasing, and substandard and fake medicines are still a huge problem. Blockchain has the potential to solve these problems as it provides trust without any intermediaries, has traceability as a default feature, and promises new business models by enabling novel incentive structures. Due to its potential, blockchain has gathered significant interest in the healthcare industry. In this paper, we review major use cases of blockchain in healthcare: patient data management, pharmaceutical research, supply chain management of medical goods, prescription management, billing claims management, analytics, and telemedicine alongside the related projects. We found that most of the blockchain projects are limited as white-papers, proof of concepts, and products with a limited user base. However, we observed that the quantity, quality, and maturity of the projects are increasing. We also discuss technical, regulatory, and business challenges to the adoption of blockchain in the healthcare industry