title: Chinese and Other Asian Modernisms: A Comparative View of Art-Historical Contexts in the Twentieth Century

abstract: Using a comparative analysis approach, this article examines the development, characteristics and issues concerning the discourse of modern Asian art in the twentieth century, with the aim of bringing into picture the place of Asia in the history of modernism. The wide recognition of the Western modernist canon as centre and universal displaces the contribution and significance of the non-Western world in the modern movement. From a cross-cultural perspective, this article demonstrates that modernism in the field of visual arts in Asia, while has had been complex and problematic, nevertheless emerged. Rather than treating Asian art as a generalized subject, this article argues that, with their subtly different notions of culture, identity and nationhood, the modernisms that emerged from various nations in this region are diverse and culturally specific. Through the comparison of various art-historical contexts in this region (namely China, India, Japan and Korea), this article attempts to map out some similarities as well as differences in their pursuit of an autonomous modernist representation. Many countries in Asia are associated with the history of Western imperialism, and most of these nations started to undergo modernization because of colonial contact with Western powers. A term used to denote the process and impact of the more advanced nations, with particular reference to Western Europe, on the less advanced nations, is accordingly coined "Westernization". Although colonized nations may generally share some similarities in the process of colonization, there are as many differences in the specific experiences related to each of their individual histories. Yet, discourses of colonialism and modernization often overlook the specific realities of various colonized countries, and fail to take into account the importance of individual cultural particularity. Some scholars, such as Partha Mitter, have argued that the discourse of colonial studies tends to have a "monocular vision of the destiny of nations and overlooks the specific cultural expressions of Westernization" (Mitter, 1994, p.6). This article argues that, while certain assumptions about colonialism may exist, every nation's individual experience still has to be rooted in its specific socio-cultural and political history. In the field of art, it would be misleading to generalize the diverse responses of artists from various colonized nations in their processes toward achieving modernism. The emergence of modern art practices in Asia - their forms, functions and contents - needs to be studied in relation to specific, complex realities of individual nations and cultures. Not only does it concern aesthetic experience and personal expression, art plays a role in the formation of social existence. Art contributes to the continuity of traditions and unity of peoples, particularly during times of oppression and depravity. The artistic products of colonized peoples can be considered their variegated responses to Westernization. Major styles or important schools of thoughts in art arose in Asia from the late nineteenth century to the early twentieth century as reactions to or as a result of Westernization (Refer to Table 1). These schools of art sought to create new types of artistic expression and many artists incorporated techniques borrowed from Western painting to rejuvenate traditional art forms. However, many of these objectives and artistic contents remained deeply rooted in each individual culture and some were nationalist-inspired. In understanding the richness and complexity of individual Asian cultural and national histories, this article argues for a rethinking of the prejudiced colonial attitude: that the white colonizers were different from and more superior to their subject peoples. The white colonist' perception of difference was intimately linked to imperial political ambitions and embedded in relations of power. This article reveals the tangible existence of autonomous


title: Relationship Between SES and Academic Achievement of Junior High School Students in China: The Mediating Effect of Self-Concept

abstract: Over the past decades, the relationship between family socioeconomic status (SES) and academic achievement in school-age children has been well documented. However, the underlying mechanism of how family SES works on academic achievement remains unclear. In this study, we examine the possible role of self-concept in the relationship between SES and school academic achievement among 345 junior high school students in China. The results showed that both family SES and self-concept were significantly associated with the children’s Chinese and mathematics performance, and family SES was also significantly correlated with self-concept. The mediation analysis showed that self-concept partially mediated the relationship between SES and school academic achievement. These findings suggest that interventions targeting self-concept may be an effective way in which to improve children’s school academic achievement.


title: Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes

abstract: Comunicacio presentada al Third Workshop on Noisy User-generated Text (W-NUT 2017), celebrat el dia 7 de setembre de 2017 a Copenhaguen, Dinamarca.


title: A tutorial introduction to compressed sensing

abstract: Compressed sensing refers to recovering a large but sparse vector, or a large but low rank matrix, from a small number of linear measurements. This paper presents some of the most popular and useful approaches at present.


title: SMA bio-robotic mimesis of tendril-based climbing plants: First results

abstract: Bio-inspired robots often “come” from the animal world while the plant world has not yet been deeply observed and considered. In this work we addressed a special class of climbing plants, that has evolved to gain height while minimizing the energy expenditure, as a new bio-robotic template: the tendril-bearer plants. These are able to grasp and coil around a support and, after that, push the stem towards the grasped element by recovering a spring-like shape from a wire condition. After the biological analysis, the idea of replicating the grasping by coiling and the pushing by shortening has been focused and replicated by Shape Memory Alloy-based proof of concept prototypes. The results show the feasibility of the approach.


title: Surgical navigation by autostereoscopic image overlay of integral videography

abstract: This paper describes an autostereoscopic image overlay technique that is integrated into a surgical navigation system to superimpose a real three-dimensional (3-D) image onto the patient via a half-silvered mirror. The images are created by employing a modified version of integral videography (IV), which is an animated extension of integral photography. IV records and reproduces 3-D images using a microconvex lens array and flat display; it can display geometrically accurate 3-D autostereoscopic images and reproduce motion parallax without the need for special devices. The use of semitransparent display devices makes it appear that the 3-D image is inside the patient's body. This is the first report of applying an autostereoscopic display with an image overlay system in surgical navigation. Experiments demonstrated that the fast IV rendering technique and patient-image registration method produce an average registration accuracy of 1.13 mm. Experiments using a target in phantom agar showed that the system can guide a needle toward a target with an average error of 2.6 mm. Improvement in the quality of the IV display will make this system practical and its use will increase surgical accuracy and reduce invasiveness.


title: Software and Algorithms for Graph Queries on Multithreaded Architectures

abstract: Search-based graph queries, such as finding short paths and isomorphic subgraphs, are dominated by memory latency. If input graphs can be partitioned appropriately, large cluster-based computing platforms can run these queries. However, the lack of compute-bound processing at each vertex of the input graph and the constant need to retrieve neighbors implies low processor utilization. Furthermore, graph classes such as scale-free social networks lack the locality to make partitioning clearly effective. Massive multithreading is an alternative architectural paradigm, in which a large shared memory is combined with processors that have extra hardware to support many thread contexts. The processor speed is typically slower than normal, and there is no data cache. Rather than mitigating memory latency, multithreaded machines tolerate it. This paradigm is well aligned with the problem of graph search, as the high ratio of memory requests to computation can be tolerated via multithreading. In this paper, we introduce the multithreaded graph library (MTGL), generic graph query software for processing semantic graphs on multithreaded computers. This library currently runs on serial machines and the Cray MTA-2, but Sandia is developing a run-time system that will make it possible to run MTGL-based code on symmetric multiprocessors. We also introduce a multithreaded algorithm for connected components and a new heuristic for inexact subgraph isomorphism We explore the performance of these and other basic graph algorithms on large scale-free graphs. We conclude with a performance comparison between the Cray MTA-2 and Blue Gene/Light for s-t connectivity.


title: Object Detection by Spatio-Temporal Analysis and Tracking of the Detected Objects in a Video with Variable Background

abstract: In this paper we propose a novel approach for detecting and tracking objects in videos with variable background i.e. videos captured by moving cameras without any additional sensor. In a video captured by a moving camera, both the background and foreground are changing in each frame of the image sequence. So for these videos, modeling a single background with traditional background modeling methods is infeasible and thus the detection of actual moving object in a variable background is a challenging task. To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter. Then individual blobs, which are parts of one object are merged using Minimum Spanning Tree to form the moving object in the variable background. The height, width and four-bin gray-value histogram of the object are calculated as its features and an object is tracked in each frame using these features to generate the trajectories of the object through the video sequence. In this work, problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of kalman filter. The major advantage of our method over most of the existing tracking algorithms is that, the proposed method does not require initialization in the first frame or training on sample data to perform. Performance of the algorithm has been tested on benchmark videos and very satisfactory result has been achieved. The performance of the algorithm is also comparable and superior with respect to some benchmark algorithms.


title: Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.

abstract: BACKGROUND ::: Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. ::: ::: ::: METHODS AND RESULTS ::: Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. ::: ::: ::: CONCLUSIONS ::: This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.


title: Bayes-Optimal Hierarchical Multilabel Classification

abstract: Hierarchical multilabel classification allows a sample to belong to multiple class labels residing on a hierarchy, which can be a tree or directed acyclic graph (DAG). However, popular hierarchical loss functions, such as the H-loss, can only be defined on tree hierarchies (but not on DAGs), and may also under- or over-penalize misclassifications near the bottom of the hierarchy. Besides, it has been relatively unexplored on how to make use of the loss functions in hierarchical multilabel classification. To overcome these deficiencies, we first propose hierarchical extensions of the Hamming loss and ranking loss which take the mistake at every node of the label hierarchy into consideration. Then, we first train a general learning model, which is independent of the loss function. Next, using Bayesian decision theory, we develop Bayes-optimal predictions that minimize the corresponding risks with the trained model. Computationally, instead of requiring an exhaustive summation and search for the optimal multilabel, the resultant optimization problem can be efficiently solved by a greedy algorithm. Experimental results on a number of real-world data sets show that the proposed Bayes-optimal classifier outperforms state-of-the-art methods.


Stance and influence of Twitter users regarding the Brexit referendum


The Effect of Diagnostic Imaging on Surgical Treatment Planning in Diseases of the Thymus


title: One-dimensional Cutting Stock Problem with Divisible Items

abstract: This paper considers the one-dimensional cutting stock problem with divisible items, which is a new problem in the cutting stock literature. The problem exists in steel industries. In the new problem, each item can be divided into smaller pieces, then they can be recombined again by welding. The objective is to minimize both the trim loss and the number of the welds. We present a mathematical model and a dynamic programming based heuristic for the problem. Furthermore, a software, which is based on the proposed heuristic algorithm, is developed to use in MKA company, and its performance is analyzed by solving real-life problems in the steel industry. The computational experiments show the efficiency of the proposed algorithm.


title: Shadow removal algorithm with shadow area border processing

abstract: This article is devoted to shadow detection and removal algorithm for very high resolution satellite images. The shadows were identified by shadow detection index calculation and thresholding. In this work the Otsu method is applied to determine the optimal threshold of binarization. After obtaining a shadow binary mask, we restore illumination in the shadow areas by using a shadow formation model. As a result, we obtained a shadow-free satellite image. The distortions that appear at the shadow borders significantly reduce the quality of the obtained image. We proposed a method of solving such an important problem as shadow area border processing.


title: Fluorescence Enhancement in Hot Spots of AFM-Designed Gold Nanoparticle Sandwiches

abstract: We observe an enhancement of fluorescence from a single fluorescent sphere, which is sandwiched between two individual gold nanoparticles, forming a hot spot of strong field enhancement. The fluorescence enhancing hot spot is custom-designed by the deliberate assembly of gold nanoparticles with an atomic force microscope cantilever. The fluorescence intensity is monitored while the separation between the two gold nanoparticles is reduced by gradually pushing the gold nanoparticles closer to the fluorescent sphere. The fluorescence enhancement is maximal when the distance between the two gold nanoparticles is smallest, when the excitation polarization is parallel to the axis of the sandwich, and when the fluorescent sphere is positioned exactly on the axis connecting the two gold nanoparticles.


Learning To Route with Deep RL


title: Love at first fright: Partner salience moderates roller coaster–induced excitation transfer

abstract: This study examined the effects of residual nervous system arousal on perceptions of sexual attraction. Researchers approached individuals (males, n = 165; females, n = 135) at amusement parks as they were either waiting to begin or as they had just gotten off a roller-coaster ride. Participants were shown a photograph of an average attractive, opposite-gendered individual and asked to rate the individual on attractiveness and dating desirability. Participants were also asked to rate their seatmates' levels of attractiveness. Consistent with the predictions of excitation transfer theory, for males and females riding with a nonromantic partner, ratings of attractiveness and dating desirability toward the photographed individual were higher among persons exiting than entering the ride. Among persons riding with a romantic partner, there were no significant differences in attractiveness or dating desirability ratings between persons entering and exiting the ride. The findings are discussed in terms of the potential moderator effects of a salient romantic partner on excitation transfer.


title: Agent-based simulation of human movement shaped by the underlying street structure

abstract: Relying on random and purposive moving agents, we simulated human movement in large street networks. We found that aggregate flow, assigned to individual streets, is mainly shaped by the underlying street structure, and that human moving behavior (either random or purposive) has little effect on the aggregate flow. This finding implies that given a street network, the movement patterns generated by purposive walkers (mostly human beings) and by random walkers are the same. Based on the simulation and correlation analysis, we further found that the closeness centrality is not a good indicator for human movement, in contrast to a long-standing view held by space syntax researchers. Instead we suggest that Google's PageRank and its modified version (weighted PageRank), betweenness and degree centralities are all better indicators for predicting aggregate flow.


title: A Lexical Metaschema for the UMLS Semantic Network

abstract: Objective: A metaschema is a high-level abstraction network of the UMLS's semantic network (SN) obtained from a partition of the SN's collection of semantic types. Every metaschema has nodes, called meta-semantic types, each of which denotes a group of semantic types constituting a subject area of the SN. A new kind of metaschema, called the lexical metaschema, is derived from a lexical partition of the SN. The lexical metaschema is compared to previously derived metaschemas, e.g., the cohesive metaschema.Design: A new lexical partitioning methodology is presented based on identical word-usage among the names of semantic types and the definitions of their respective children. The lexical metaschema is derived from the application of the methodology. We compare the constituent meta-semantic types and their underlying semantic-type groups with the previously derived cohesive metaschema. A similar comparison of the lexical partition and a published partition of the SN is also carried out.Results: The lexical partition of the SN has 21 semantic-type groups, each of which represents a subject area. The lexical metaschema thus has 21 meta-semantic types, 19 meta-child-of hierarchical relationships, and 86 meta-relationships. Our comparison shows that 15 out of the 21 meta-semantic types in the lexical metaschema also appear in the cohesive metaschema, and 80 semantic types are covered by identical meta-semantic types or refinements between the two metaschemas. The comparison between the lexical partition and the semantic partition shows that they have very low similarity.Conclusion: The algorithmically derived lexical metaschema serves as an abstraction of the SN and provides views representing different subject areas. It compares favorably with the cohesive metaschema derived via the SN's relationship configuration.


title: Parallel particle swarm optimization clustering algorithm based on MapReduce methodology

abstract: Large scale data sets are difficult to manage. Difficulties include capture, storage, search, analysis, and visualization of large data. In particular, clustering of large scale data has received considerable attention in the last few years and many application areas such as bioinformatics and social networking are in urgent need of scalable approaches. The new techniques need to make use of parallel computing concepts in order to be able to scale with increasing data set sizes. In this paper, we propose a parallel particle swarm optimization clustering (MR-CPSO) algorithm that is based on MapReduce. The experimental results reveal that MR-CPSO scales very well with increasing data set sizes and achieves a very close to the linear speedup while maintaining the clustering quality. The results also demonstrate that the proposed MR-CPSO algorithm can efficiently process large data sets on commodity hardware.


title: Open-Ended Coaxial Dielectric Probe Effective Penetration Depth Determination

abstract: We have performed a series of experiments, which demonstrate the effect of open-ended coaxial diameter on the depth of penetration. We used a two-layer configuration of a liquid and movable cylindrical piece of either Teflon or acrylic. The technique accurately demonstrates the depth in a sample for which a given probe diameter provides a reasonable measure of the bulk dielectric properties for a heterogeneous volume. In addition, we have developed a technique for determining the effective depth for a given probe diameter size. Using a set of simulations mimicking four 50- $\Omega $ coaxial cable diameters, we demonstrate that the penetration depth in both water and saline has a clear dependence on the probe diameter, but is remarkably uniform over frequency and with respect to the intervening liquid permittivity. Two different 50- $ \Omega $ commercial probes were similarly tested and confirm these observations. This result has significant implications to a range of dielectric measurements, most notably in the area of tissue property studies.


The Effects of Using Authentic Materials on Students ’ Achievement towards English Reading Comprehension among Narathiwat Technical College Students


title: Understanding Interobserver Agreement: The Kappa Statistic

abstract: Items such as physical exam findings, radiographic interpretations, or other diagnostic tests often rely on some degree of subjective interpretation by observers. Studies that measure the agreement between two or more observers should include a statistic that takes into account the fact that observers will sometimes agree or disagree simply by chance. The kappa statistic (or kappa coefficient) is the most commonly used statistic for this purpose. A kappa of 1 indicates perfect agreement, whereas a kappa of 0 indicates agreement equivalent to chance. A limitation of kappa is that it is affected by the prevalence of the finding under observation. Methods to overcome this limitation have been described.


title: Three-phase inverter topologies for grid-connected photovoltaic systems

abstract: In this paper, the energy conversion efficiency (ECE) and cost characteristics of three-phase photovoltaic (PV) inverters (3P-PVIs) are studied comprehensively based on the operating principles of topologies and with respect to various performance indicators such as semiconductor device count and utilization factors, semiconductor efficiency (SE), dc-bus capacitor and filter inductor voltage/current ripple factors. The evaluation of topologies are based on datasheet parameters and analytical results are provided for 50 kW, 100 kW, and 200 kW power ratings. Such an evaluation of topologies is beneficial to engineers to wisely choose the appropriate topology for both transformerless and transformer based PV systems and to design the inverter accordingly.


title: The perceptual evaluation of information systems using the construct of user satisfaction: case study of a large french group

abstract: This research work concerns the perceptual evaluation of the performance of information systems (IS) and more particularly, the construct of user satisfaction. Faced with the difficulty of obtaining objective measures for the success of IS, user satisfaction appeared as a substitutive measure of IS performance (DeLone & McLean, 1992). Some researchers have indeed shown that the evaluation of an IS could not happen without an analysis of the feelings and perceptions of individuals who make use of it. Consequently, the concept of satisfaction has been considered as a guarantee of the performance of an IS. Also it has become necessary to ponder the drivers of user satisfaction. The analysis of models and measurement tools for satisfaction as well as the adoption of a contingency perspective has allowed the description of principal dimensions that have a direct or less direct impact on user perceptions The case study of a large French group, carried out through an interpretativist approach conducted by way of 41 semi-structured interviews, allowed the conceptualization of the problematique of perceptual evaluation of IS in a particular field study. This study led us to confirm the impact of certain factors (such as perceived usefulness, participation, the quality of relations with the IS Function and its resources and also the fit of IS with user needs). On the contrary, other dimensions regarded as fundamental do not receive any consideration or see their influence nuanced in the case studied (the properties of IS, the ease of use, the quality of information). Lastly, this study has allowed for the identification of the influence of certain contingency and contextual variables on user satisfaction and, above all, for the description of the importance of interactions between the IS Function and the users


title: A Fast and Simple Graph Kernel for RDF

abstract: In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-of-words feature vectors, which increases performance in two of the four experiments.


title: 300 Faces In-The-Wild Challenge

abstract: Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues. (a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low. (b) Most published works report experimental results using different training/testing sets, different error metrics and, of course, landmark points with semantically different locations. In this paper, we aim to overcome the aforementioned problems by (a) proposing a semi-automatic annotation technique that was employed to re-annotate most existing facial databases under a unified protocol, and (b) presenting the 300 Faces In-The-Wild Challenge (300-W), the first facial landmark localization challenge that was organized twice, in 2013 and 2015. To the best of our knowledge, this is the first effort towards a unified annotation scheme of massive databases and a fair experimental comparison of existing facial landmark localization systems. The images and annotations of the new testing database that was used in the 300-W challenge are available from http://ibug.doc.ic.ac.uk/resources/300-W_IMAVIS/.


title: RRAM-Based Analog Approximate Computing

abstract: Approximate computing is a promising design paradigm for better performance and power efficiency. In this paper, we propose a power efficient framework for analog approximate computing with the emerging metal-oxide resistive switching random-access memory (RRAM) devices. A programmable RRAM-based approximate computing unit (RRAM-ACU ) is introduced first to accelerate approximated computation, and an approximate computing framework with scalability is then proposed on top of the RRAM-ACU. In order to program the RRAM-ACU efficiently, we also present a detailed configuration flow, which includes a customized approximator training scheme, an approximator-parameter-to-RRAM-state mapping algorithm, and an RRAM state tuning scheme. Finally, the proposed RRAM-based computing framework is modeled at system level. A predictive compact model is developed to estimate the configuration overhead of RRAM-ACU and help explore the application scenarios of RRAM-based analog approximate computing. The simulation results on a set of diverse benchmarks demonstrate that, compared with a x86–64 CPU at 2 GHz, the RRAM-ACU is able to achieve 4.06– $196.41 {\times }$ speedup and power efficiency of 24.59–567.98 GFLOPS/W with quality loss of 8.72% on average. And the implementation of hierarchical model and X application demonstrates that the proposed RRAM-based approximate computing framework can achieve >12.8 $\times$ power efficiency than its pure digital implementation counterparts (CPU, graphics processing unit, and field- programmable gate arrays).


title: From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems

abstract: Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adaptation is done on a per-task basis, and retraining is needed when the system configuration changes. The resulting inefficiency in terms of data and training time requirements can be mitigated, if domain knowledge is available, by selecting a suitable model class and learning procedure, collectively known as inductive bias. However, it is generally difficult to encode prior knowledge into an inductive bias, particularly with black-box model classes such as neural networks. Meta-learning provides a way to automatize the selection of an inductive bias. Meta-learning leverages data or active observations from tasks that are expected to be related to future, and a priori unknown, tasks of interest. With a meta-trained inductive bias, training of a machine learning model can be potentially carried out with reduced training data and/or time complexity. This paper provides a high-level introduction to meta-learning with applications to communication systems.


title: Gathering in Dynamic Rings

abstract: The gathering (or multi-agent rendezvous) problem requires a set of mobile agents, arbitrarily positioned at different nodes of a network to group within finite time at the same location, not fixed in advanced.


title: Iris recognition using circular symmetric filters

abstract: Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.


title: Automated Hate Speech Detection and the Problem of Offensive Language

abstract: A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.


title: Synchronous GABAA-receptor-dependent potentials in limbic areas of the in-vitro isolated adult guinea pig brain

abstract: Epileptiform discharges are known to reflect the hypersynchronous glutamatergic activation of cortical neurons. However, experimental evidence has revealed that epileptiform synchronization is also contributed to by population events mediated by GABA A receptors. Here, we analysed the spatial distribution of GABAA-receptor-dependent interictal events in the hippocampal/parahippocampal region of the adult guinea pig brain isolated in vitro. We found that arterial perfusion of this preparation with 4-aminopyridine caused the appearance of glutamatergic-independent interictal potentials that were reversibly abolished by GABA A receptor antagonism. Laminar profiles and current source density analysis performed in different limbic areas demonstrated that these GABAA-receptor-mediated events were independently generated in different areas of the hippocampal/parahippocampal formation (most often in the medial entorhinal cortex) and propagated between interconnected limbic structures of both hemispheres. Finally, intracellular recordings from principal neurons of the medial entorhinal cortex demonstrated that the GABAergic field potential correlated to inhibitory postsynaptic potentials (membrane potential reversal, -68.12 ± 8.01 mV, n = 5) that were interrupted by ectopic spiking. Our findings demonstrate that, in an acute seizure model developed in the adult guinea pig brain, hypersynchronous GABAA-receptor-mediated interictal events are generated from independent sources and propagate within limbic cortices in the absence of excitatory synaptic transmission. As spared or enhanced inhibition was reported in models of epilepsy, our data may support a role of GABA-mediated signaling in ictogenesis and epileptogenesis.


title: Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items

abstract: To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users' decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women's Clothing & Accessories from Amazon, which we use for this demonstration.


title: Analysis and experimental verification of a forth order plant model for manipulator force control

abstract: The analysis and experimental verification of a fourth order model of the arm/sensor/environment system plant, to be used in evaluation of force control strategies are presented. Both the undamped and underdamped eases are investigated. Approximations and qualifications, based on the reality of our experimental system, help reduce the solution to just a few dominant terms. Comparing these terms with experimental data of the system undergoing small oscillations yields approximate values for all of the parameters in the model. To justify the obtained values and thereby the approximations used, two measures are taken. First, a simulation of the fourth order model is performed, and compared against experimental data obtained from the CMU DD Arm II system. Second, a stability analysis of several force control schemes acting on the modelled plant is reviewed and compared against experimental tests of the controllers. In both cases, the simulation and analysis match closely with the experimental results, confirming the validity of the plant model. >


title: Perceived social isolation and cognition

abstract: Social species, from Drosophila melanogaster to Homo sapiens, fare poorly when isolated. Homo sapiens, an irrepressibly meaning-making species, are, in normal circumstances, dramatically affected by perceived social isolation. Research indicates that perceived social isolation (i.e. loneliness) is a risk factor for, and may contribute to, poorer overall cognitive performance, faster cognitive decline, poorer executive functioning, increased negativity and depressive cognition, heightened sensitivity to social threats, a confirmatory bias in social cognition that is self-protective and paradoxically self-defeating, heightened anthropomorphism and contagion that threatens social cohesion. These differences in attention and cognition impact on emotions, decisions, behaviors and interpersonal interactions that can contribute to the association between loneliness and cognitive decline and between loneliness and morbidity more generally.


title: Stochastic Answer Networks for Machine Reading Comprehension

abstract: We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).


title: Content-based image retrieval: approaches and trends of the new age

abstract: The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics.


title: The HEXACO Honesty-Humility, Agreeableness, and Emotionality Factors A Review of Research and Theory

abstract: We review research and theory on the HEXACO personality dimensions of Honesty-Humility (H), Agreeableness (A), and Emotionality (E), with particular attention to the following topics: (1) the origins of the HEXACO model in lexical studies of personality structure, and the content of the H, A, and E factors in those studies; (2) the operationalization of the H, A, and E factors in the HEXACO Personality Inventory–Revised; (3) the construct validity of self-reports on scales measuring the H factor; (4) the theoretical distinction between H and A; (5) similarity and assumed similarity between social partners in personality, with a focus on H and A; (6) the extent to which H (and A and E) variance is represented in instruments assessing the “Five-Factor Model” of personality; and (7) the relative validity of scales assessing the HEXACO and Five-Factor Model dimensions in predicting criteria conceptually relevant to H, A, and E.


title: Novel Control Method for Multimodule PV Microinverter With Multiple Functions

abstract: This paper presents a novel control method for multi-module photovoltaic (PV) micro inverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push-pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode (LIM), i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in stand-alone mode (STM) to supply uninterruptible power to the load. This paper presents a multi-loop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multi-module parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.


Asguard : A Hybrid-Wheel Security and SAR-Robot Using Bio-Inspired Locomotion for Rough Terrain


title: A Wireless Distributed Wall Climbing Robotic System for Reconnaissance Purpose

abstract: The paper proposes a wireless distributed wall climbing robotic system for reconnaissance purpose. Firstly, we introduce the mechanism of the distributed wall climbing robotic system. The mother wall-climbing robot of one single suction cup with two wheels locomotion system enables fast motion and can adapt nearly any kind of vertical wall surface in urban environment. The child wall climbing robot is an inchworm-like biped mechanical structure with the advantages of small size and minimal weight, which enables the robot translate from one surface to another and can avoid detection. Secondly, embedded controllers of wall climbing robotic system are designed. TMS320LF2000trade digital signal processor (DSP) from Texas Instrumentstrade was selected for multi-motor control and sensory confusion since it demonstrates high-speed performance. With the Li-battery energy supply and wireless communication system, robots have the ability to explore the world semi-autonomously. Lastly, experiments were taken to evaluate the efficiency of the distributed robotic system


title: A Characteristics-Based Approach to the Numerical Solution of the Two-Fluid Model

abstract: This paper is concerned with numerical solutions of the two-fluid models of two-phase flow. The two-fluid modeling approach is based on the effective-field description of inter-penetrating continua and uses constitutive laws to account for the inter-field interactions. The effective-field balance equations are derived by a homogenization procedure and known to be non-hyperbolic. Despite their importance and widespread application, predictions by such models have been hampered by numerical pitfalls manifested in the formidable challenge to obtain convergent numerical solutions under computational grid refinement. At the root of the problem is the absence of hyperbolicity in the field equations and the resulting ill-posedness. The aim of the present work is to develop a high-order-accurate numerical scheme that is not subject to such limitations. The main idea is to separate conservative and non-conservative parts, by implementing the latter as part of the source term. The conservative part, being effectively hyperbolic, is treated by a characteristics-based method. The scheme performance is examined on a compressible-incompressible two-fluid model. Convergence of numerical solutions to the analytical one is demonstrated on a benchmark (water faucet) problem.Copyright © 2003 by ASME


Sentiment analysis: A new approach for effective use of linguistic knowledge and exploiting similarities in a set of documents to be classified


Top NewSQL Databases and Features Classification


title: Genetic diversity in strains of Helicobacter pylori from India and their relatedness to strains from other parts of the world.

abstract: Helicobacter pylori is predominantly transmitted within families and infection occurs mostly in early childhood, frequently leading to persistent infection lifelong. In the present study, genetic diversity of Helicobacter pylori among North and South Indian isolates was evaluated. 16S rDNA, cagA, vacA and iceA genes were amplified followed by sequencing of respective amplicons for diversity analysis. Result of PCR assay showed that status of pathogenicity genes varied among strains from Varanasi and Hyderabad. Phylogenetic analysis based on 16S rDNA sequences showed clustering of Hyderabad and Varanasi strains in separate groups, pointing to significant diversity. Additionally, phylogenetic analysis revealed that most of the Varanasi strains shared homology with the strains from Taiwan except for two isolates which matched with an isolate from Brazil. On the other hand majority of the Hyderabad strains showed relatedness with strains from Brazil except one which showed homology with one strain from Taiwan. In conclusion our results show that genetic diversity among H. pylori isolates is widely prevalent regardless of the region from which they are isolated. More interestingly, phylogenetic analysis suggests that the Indian strains of H. pylori show close homology to those from Taiwan and/or Brazil.


title: Secure and constant cost public cloud storage auditing with deduplication

abstract: Data integrity and storage efficiency are two important requirements for cloud storage. Proof of Retrievability (POR) and Proof of Data Possession (PDP) techniques assure data integrity for cloud storage. Proof of Ownership (POW) improves storage efficiency by securely removing unnecessarily duplicated data on the storage server. However, trivial combination of the two techniques, in order to achieve both data integrity and storage efficiency, results in non-trivial duplication of metadata (i.e., authentication tags), which contradicts the objectives of POW. Recent attempts to this problem introduce tremendous computational and communication costs and have also been proven not secure. It calls for a new solution to support efficient and secure data integrity auditing with storage deduplication for cloud storage. In this paper we solve this open problem with a novel scheme based on techniques including polynomial-based authentication tags and homomorphic linear authenticators. Our design allows deduplication of both files and their corresponding authentication tags. Data integrity auditing and storage deduplication are achieved simultaneously. Our proposed scheme is also characterized by constant realtime communication and computational cost on the user side. Public auditing and batch auditing are both supported. Hence, our proposed scheme outperforms existing POR and PDP schemes while providing the additional functionality of deduplication. We prove the security of our proposed scheme based on the Computational Diffie-Hellman problem, the Static Diffie-Hellman problem and the t-Strong Diffie-Hellman problem. Numerical analysis and experimental results on Amazon AWS show that our scheme is efficient and scalable.


title: Vehicle detection based on LiDAR and camera fusion

abstract: Vehicle detection is important for advanced driver assistance systems (ADAS). Both LiDAR and cameras are often used. LiDAR provides excellent range information but with limits to object identification; on the other hand, the camera allows for better recognition but with limits to the high resolution range information. This paper presents a sensor fusion based vehicle detection approach by fusing information from both LiDAR and cameras. The proposed approach is based on two components: a hypothesis generation phase to generate positions that potential represent vehicles and a hypothesis verification phase to classify the corresponding objects. Hypothesis generation is achieved using the stereo camera while verification is achieved using the LiDAR. The main contribution is that the complementary advantages of two sensors are utilized, with the goal of vehicle detection. The proposed approach leads to an enhanced detection performance; in addition, maintains tolerable false alarm rates compared to vision based classifiers. Experimental results suggest a performance which is broadly comparable to the current state of the art, albeit with reduced false alarm rate.


title: The Architecture Of The Festival Speech Synthesis System

abstract: A tufting machine in which a double eccentric is utilized to vary the stroke of the needle bar and in which an adjusting device is associated with the connecting rod and is manipulatable to maintain the bottom dead center position of the needle bar regardless of variation in stroke.


title: Learning Affinity via Spatial Propagation Networks

abstract: In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation model, the spatially varying transformation matrix exactly constitutes an affinity matrix that models dense, global pairwise relationships of an image. Specifically, we develop a three-way connection for the linear propagation model, which (a) formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but (b) results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix. Instead of designing the similarity kernels according to image features of two points, we can directly output all the similarities in a purely data-driven manner. The spatial propagation network is a generic framework that can be applied to many affinity-related tasks, including but not limited to image matting, segmentation and colorization, to name a few. Essentially, the model can learn semantically-aware affinity values for high-level vision tasks due to the powerful learning capability of the deep neural network classifier. We validate the framework on the task of refinement for image segmentation boundaries. Experiments on the HELEN face parsing and PASCAL VOC-2012 semantic segmentation tasks show that the spatial propagation network provides a general, effective and efficient solution for generating high-quality segmentation results.


title: A Review of the Research in Emotionally Focused Therapy for Couples

abstract: Emotionally Focused Therapy for Couples (EFT) is a brief evidence-based couple therapy based in attachment theory. Since the development of EFT, efficacy and effectiveness research has accumulated to address a range of couple concerns. EFT meets or exceeds the guidelines for classification as an evidence-based couple therapy outlined for couple and family research. Furthermore, EFT researchers have examined the process of change and predictors of outcome in EFT. Future research in EFT will continue to examine the process of change in EFT and test the efficacy and effectiveness of EFT in new applications and for couples of diverse backgrounds and concerns.


title: Adversarial Threshold Neural Computer for Molecular de Novo Design

abstract: In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp3-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection...


title: Analytics for smart grid cybersecurity

abstract: Guidelines, directives, and policy statements are usually presented in “linear” text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like, — even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as “data”, transforming text into a structured model, and generate a network views of the text(s), that we then can use for vulnerability mapping, risk assessments and control point analysis. We apply this approach using two NIST reports on cybersecurity of smart grid, more than 600 pages of text. Here we provide a synopsis of approach, methods, and tools. (Elsewhere we consider (a) system-wide level, (b) aviation e-landscape, (c) electric vehicles, and (d) SCADA for smart grid).


Georeferencing: The geographic associations of information


title: Optical Quantum Computing

abstract: In 2001 all-optical quantum computing became feasible with the discovery that scalable quantum computing is possible using only single photon sources, linear optical elements, and single photon detectors. Although it was in principle scalable, the massive resource overhead made the scheme practically daunting. However, several simplifications were followed by proof-of-principle demonstrations, and recent approaches based on cluster states or error encoding have dramatically reduced this worrying resource overhead, making an all-optical architecture a serious contender for the ultimate goal of a large-scale quantum computer. Key challenges will be the realization of high-efficiency sources of indistinguishable single photons, low-loss, scalable optical circuits, high efficiency single photon detectors, and low-loss interfacing of these components.


title: Time series anomaly detection using recessive subsequence

abstract: Time series arise frequently in many sciences and engineering application, including finance, digital audio, motion capture, network security, and transportation. In this work, we propose a technique for discovering anomalies in time series that takes advantages of the Symbolic Aggregate approXimation (SAX) technique and inspiration from a motif discovery algorithm. We use SAX to reduce the dimension of the time series and apply the idea of motif discovery to detect anomalies. We consider recessive sequences instead of frequent sequences similar to motif finding. We evaluate the algorithm on several real-world data from different areas, such as the car speed data, the motion capture data, and the weather data. Experiments demonstrate the effectiveness of the proposed algorithm to discover anomalies in real-world time series.


title: RockJIT: Securing Just-In-Time Compilation Using Modular Control-Flow Integrity

abstract: Managed languages such as JavaScript are popular. For performance, modern implementations of managed languages adopt Just-In-Time (JIT) compilation. The danger to a JIT compiler is that an attacker can often control the input program and use it to trigger a vulnerability in the JIT compiler to launch code injection or JIT spraying attacks. In this paper, we propose a general approach called RockJIT to securing JIT compilers through Control-Flow Integrity (CFI). RockJIT builds a fine-grained control-flow graph from the source code of the JIT compiler and dynamically updates the control-flow policy when new code is generated on the fly. Through evaluation on Google's V8 JavaScript engine, we demonstrate that RockJIT can enforce strong security on a JIT compiler, while incurring only modest performance overhead (14.6% on V8) and requiring a small amount of changes to V8's code. Key contributions of RockJIT are a general architecture for securing JIT compilers and a method for generating fine-grained control-flow graphs from C++ code.


title: Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules

abstract: We present an approach to text simplification based on synchronous dependency grammars. The higher level of abstraction afforded by dependency representations allows for a linguistically sound treatment of complex constructs requiring reordering and morphological change, such as conversion of passive voice to active. We present a synchronous grammar formalism in which it is easy to write rules by hand and also acquire them automatically from dependency parses of aligned English and Simple English sentences. The grammar formalism is optimised for monolingual translation in that it reuses ordering information from the source sentence where appropriate. We demonstrate the superiority of our approach over a leading contemporary system based on quasi-synchronous tree substitution grammars, both in terms of expressivity and performance.


title: Happiness, an inside job?: Turnover prediction using employee likeability, engagement and relative happiness

abstract: In this paper, we describe how to rank employees for risk of turnover by using data obtained from a happiness self-reporting app. Two data sources are used: daily happiness and social interactions. The data spans 2.5 years and 4,356 employees of 34 companies based in Barcelona. For each employee, we build features at three levels: individual, company level and social interaction graph level. We develop various turnover risk models and we compare how different features affect performance prediction. The results show that the top three features that explain turnover risk are: ratio of likes received (likeability), posting frequency (engagement), and relative happiness (employee happiness normalized by company mean). Surprisingly, a priori expected explanatory features such as mean happiness level and the ratio of likes (positivity), were not significant. Precision@50 = 80% out of a test set with 116 churns, sample size N=2k.


title: Development of a novel 10 GHz-band hose-type soft resin waveguide

abstract: We develop a resin waveguide for microwave power transfer and wireless communication in an automobile harness. Resin waveguides are more lightweight, low-cost, and flexible than conventional waveguides. Our prototype resin waveguide is fabricated from an elastomeric material with a very low dielectric loss by a copper-foil forming process. The low emission and loss of the transmission line are confirmed by the transmission characteristic (−0.39 dB/m in the 10 GHz band).


title: RMiner: a tool set for role mining

abstract: Recently, there are many approaches proposed for mining roles using automated technologies. However, it lacks a tool set that can be used to aid the application of role mining approaches and update role states. In this demonstration, we introduce a tool set, RMiner, which is based on the core of WEKA, an open source data mining tool. RMiner implements most of the classic and latest role mining algorithms and provides interactive tools for administrator to update role states. The running examples of RMiner are presented to demonstrate the effectiveness of the tool set.


title: Screen space spherical harmonics occlusion (S3HO) sampling

abstract: One objective of global illumination (GI) models is to solve the lighting integral of the upper hemisphere at a surface point. One particular effect of GI that has become very popular in the past years is Ambient Occlusion (AO), where, independently of the lighting, a coverage factor that indicates the amount of occlusion induced by its local neighboring geometry is calculated for every surface point. Screen Space Ambient Occlusion (SSAO) algorithms allow for the approximation of this occlusion factor in real time. Ritschel et al. [2009] proposed Screen Space Directional Occlusion (SSDO), combining the calculation of occlusion and lighting by simultaneously accumulating the light of unoccluded directions when sampling the occlusion. Contrary to AO this results in colored shadows simulating directional lighting effects. Green [2003] outlined techniques based on Spherical Harmonics (SH) that approximate global lighting models with the basic principle of representing both, the incoming radiance field and the occlusion, as functions defined on the sphere that can be efficiently represented in the spherical Fourier domain. Calculating the spherical occlusion function, however, remains computationally expensive since ray tracing methods have to be applied.


title: Finding traces of self-regulated learning in activity streams

abstract: This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.


title: Availability Modeling and Analysis of a Virtualized System

abstract: Availability is one of the key requirements for modern networked system. Availability of a virtualized system can be modelled and analyzed using stochastic models. In our previous work, availability of a virtualized system was modeled using a hierarchical model to incorporate the detailed behavior of virtual machines (VMs)' failure and recovery with respect to the system behavior. In particular, a truncated continuous time Markov chain model was used for VM mode. In this paper, we propose to construct a stochastic reward nets (SRN) to model and analyze the availability of a virtualized system. Further, we study the effect on the availability when restrictions on the guard functions are relaxed. We use a virtualized data center (VDC) using three hosts with a multiple number of VMs on each hosting servers, and we incorporate (i) failure and recovery of hosting servers and VMs, (ii) VM high availability (HA) and (iii) VM live migration (LM). The VDCs with/without using VM HA and LM are compared in terms of capacity oriented availability (i.e., number of available VMs).


title: The TaToo Semantic Case - Requirements, impacts and applications

abstract: Up today numerous semantic developments, starting from ontologies and leading to semantic applications in various (mostly financial or business) domains have been de veloped and put on the market. Still real suitable systems or application fostering the enrichment of environmental resources in order to make them better understanda ble and/or discoverable for a broader community are missing or have not be thought of yet. TaToo (Tagging Tools b ased on a semantic discovery framework, FP7 project, www.tatoo-project.eu ) intends to demonstrate how the enorm ous amount of environmental information can become better discoverable and understandable for a broad rang e of user, e.g. from environmental experts up to the man on th e street. Clearly upfront to the definition, specification an d implementation of the TaToo framework and tools the requirements with respect to environmental resources handl ed in TaToo (e.g. environmental data, services, app lications etc.) have to be defined. TaToo deals with three scenario s, playing a two folded role. On the one hand they are key providers for challenging requirements but they are al so the evaluation facility to prove (as domain expe rts) the results of project. The proposed paper will describe: a) the main categories of requirements (incl. the a pproach of requirements collection) written down e. g. for tagging, cross-domain annotation and discovery, meta-information, ontologies, etc. b) brief overview on the tools and services for tag ging and discovery provided by the TaToo project c) description of Validation Scenario on Climate Twins Regions and its requirements as well as usage o f TaToo developments d) the expected impact on Europeans Single Information Space and its environmental society/community


Antibiotic growth-promoters in food animals


title: Dynamic Approach for Data Scrubbing Process

abstract: It is very difficult to over-emphasize the benefits of accurate data. Errors in data are generally the most expensive aspect of data entry, costing the users even much more compared to the original data entry. Unfortunately, these costs are intangibles or difficult to measure. If errors are detected at an early stage then it requires little cost to remove the errors. Incorrect and misleading data lead to all sorts of unpleasant and unnecessary expenses. Unluckily, it would be very expensive to correct the errors after the data has been processed, particularly when the processed data has been converted into the knowledge for decision making. No doubt a stitch in time saves nine i.e. a timely effort will prevent more work at later stage. Moreover, time spent in processing errors can also have a significant cost. One of the major problems with automated data entry systems are errors. In this paper we discuss many well known techniques to minimize errors, different cleansing approaches and, suggest how we can improve accuracy rate. Framework available for data cleansing offer the fundamental services such as attribute selection, formation of tokens, selection of clustering algorithms, selection of eliminator functions etc.


title: Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

abstract: In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.


title: Sugar alcohols—their role in the modern world of sweeteners: a review

abstract: Epidemic obesity and diabetes encouraged the changes in population lifestyle and consumers’ food products awareness. Food industry has responded people’s demand by producing a number of energy-reduced products with sugar alcohols as sweeteners. These compounds are usually produced by a catalytic hydrogenation of carbohydrates, but they can be also found in nature in fruits, vegetables or mushrooms as well as in human organism. Due to their properties, sugar alcohols are widely used in food, beverage, confectionery and pharmaceutical industries throughout the world. They have found use as bulk sweeteners that promote dental health and exert prebiotic effect. They are added to foods as alternative sweeteners what might be helpful in the control of calories intake. Consumption of low-calorie foods by the worldwide population has dramatically increased, as well as health concerns associated with the consequent high intake of sweeteners. This review deals with the role of commonly used sugar alcohols such as erythritol, isomalt, lactitol, maltitol, mannitol, sorbitol and xylitol as sugar substitutes in food industry.


title: A 0.4 V driving multi-touch capacitive sensor with the driving signal frequency set to (n+0.5) times the inverse of the LCD VCOM noise period

abstract: The time-periodic property of the LCD VCOM noise is utilized to reduce the effect of the VCOM noise on the mutual-capacitance measuring touch sensor placed on a LCD panel. The amplitude of the touch sensor driving signal (VSTM) can be reduced reliably down to 0.4 V with the reporting rate of 189 Hz, by using the following two methods. (1) The frequency of VSTM is set to (n+0.5)·f ::: N ::: , by synchronizing VSTM to a LCD gate driver signal with a referenceless CDR. fN is the inverse of the LCD VCOM noise period (TN) and n is a positive integer. (2) The reset period of the RX integrator is set to 2·TN.


title: Obesity among adults with disabling conditions.

abstract: CONTEXT ::: Obesity, a leading cause of preventable death and chronic disease, is associated with disability. Little is known about obesity among adults with specific disabilities. ::: ::: ::: OBJECTIVES ::: To determine the prevalence of obesity in adults with physical and sensory limitations and serious mental illness. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: The 1994-1995 National Health Interview Survey of 145 007 US community-dwelling respondents, 25 626 of whom had 1 or more disabilities. ::: ::: ::: MAIN OUTCOME MEASURES ::: Likelihood of being obese, attempting weight loss, and receiving exercise counseling among adults with and without disabilities. ::: ::: ::: RESULTS ::: Among adults with disabilities, 24.9% were obese vs 15.1% of those without disabilities. After adjusting for sociodemographic factors, adults with a disability were more likely to be obese, with an adjusted odds ratio (AOR) of 1.9 (95% confidence interval [CI], 1.8-2.0). The highest risk occurred among adults with some (AOR, 2.4; 95% CI, 2.3-2.5) or severe (AOR, 2.5; 95% CI, 2.3-2.7) lower extremity mobility difficulties. After further adjustment for comorbid conditions, adults with disabilities were as likely to attempt weight loss as those without disabilities, except for adults with severe lower extremity mobility difficulties, who were less likely (AOR, 0.7; 95% CI, 0.5-0.9]), and adults with mental illness, who were more likely (AOR, 1.4; 95% CI, 1.2-1.8). Physician exercise counseling was reported less often among adults with severe lower extremity (AOR, 0.5; 95% CI, 0.4-0.7) and upper extremity (AOR, 0.7; 95% CI, 0.5-1.0) mobility difficulties. ::: ::: ::: CONCLUSION ::: Obesity appears to be more prevalent in adults with sensory, physical, and mental health conditions. Health care practitioners should address weight control and exercise among adults with disabilities.


title: Bit-width reduction and customized register for low cost convolutional neural network accelerator

abstract: This paper presents a low area and energy efficient hardware accelerator for the deep convolutional neural networks (CNNs). Based on the multiply-accumulate (MAC) based architecture, three design techniques are proposed to reduce the hardware cost of the convolutional computations. First, to reduce the computational bit-width of convolutions, an adaptive bit-width reduction scheme is proposed based on differential input method. The bit-width reduction approach can reduce the 37 % of operation bit-width with almost ignorable CNN accuracy degradation. Second, it has been found that adapting bi-directional filtering window in CNN accelerator can considerably reduce the energy for data movement with much smaller number of memory accesses. To expedite the bi-directional filtering operations, we also propose a bidirectional first-input-first-output (bi-FIFO). With SRAM bit-cell layout manner, the proposed bi-FIFO facilitates fast data re-distribution with area and energy efficiency. To verify the effectiveness of the proposed techniques, the AlexNet accelerator has been designed. The numerical results show that the proposed adaptive bit-width reduction scheme achieves 25.9% and 47.3% of area and energy savings, respectively. The bi-FIFO based accelerator also achieves 33 % improved processing time.


title: Comparison of 3-Phase Wide Output Voltage Range PWM Rectifiers

abstract: A three-phase buck boost pulsewidth modulation (PWM) rectifier with a three-switch buck-type rectifier input stage and an integrated dc/dc boost converter output stage, and a three-phase boost buck PWM rectifier system formed by series connection of a boost-type rectifier input stage (Vienna Rectifier) and a dc/dc buck converter output stage are presented and comparatively evaluated. Both systems are characterized by sinusoidal input current and wide output voltage control range. The comparison is for 6 kW rated output power at 400 Vrms line-to-line input and variable output voltage 200 V 600 V and identifies the buck boost approach as significantly superior regarding the overall efficiency, the volume and weight of the passive power components, and the overall system complexity.


title: Sentiment analysis and Twitter: a game proposal

abstract: Pervasive sensing of people's opinions is becoming critical in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. A recent research trend is to understand users' opinions through a sentiment analysis of contents published in the Twitter platform. This approach involves two challenges: the large volume of available data and the large variety of used languages combined with the brevity of texts. The former makes manual analysis unreasonable, whereas the latter complicates any type of automatic analysis. Since sentiment analysis is a difficult process for computers, but it is quite simple for humans, in this article, we transform the sentiment analysis process into a game. Indeed, we consider the game with a purpose approach and we propose a game that involves users in classifying the polarity (e.g., positive, negative, neutral) and the sentiment (e.g., joy, surprise, sadness) of tweets. To evaluate the proposal, we used a dataset of 52,877 tweets, we developed a Web-based game, we invited people to play the game, and we validated the results through two different methods: ground-truth and manual assessment. The obtained results showed that the game approach is effective in measuring people' sentiments and also highlighted that participants liked to play the game.


title: Motivation and incentive preferences of community health officers in Ghana: an economic behavioral experiment approach

abstract: Background ::: Health worker shortage in rural areas is one of the biggest problems of the health sector in Ghana and many developing countries. This may be due to fewer incentives and support systems available to attract and retain health workers at the rural level. This study explored the willingness of community health officers (CHOs) to accept and hold rural and community job postings in Ghana.


title: An integration of urban spatial data with energy simulation to produce X3D city models: the case of Landkreis Ludwigsburg

abstract: In this paper, we describe a chain of steps to produce X3D city models based on transformation and integration of urban 3D CityGML models with DTM tiles, orthophotos and energy simulation outcome of energy demand and photovoltaic potential generated by the SimStadt platform.


title: Why does unsupervised pre-training help deep learning?

abstract: Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, mostly on vision and language data sets. The best results obtained on supervised learning tasks involve an unsupervised learning component, usually in an unsupervised pre-training phase. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. The main question investigated here is the following: how does unsupervised pre-training work? Answering this questions is important if learning in deep architectures is to be further improved. We propose several explanatory hypotheses and test them through extensive simulations. We empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples. The experiments confirm and clarify the advantage of unsupervised pre-training. The results suggest that unsupervised pre-training guides the learning towards basins of attraction of minima that support better generalization from the training data set; the evidence from these results supports a regularization explanation for the effect of pre-training.


StyleCheck : An Automated Stylistic Analysis Tool


title: A Rule-Based Approach For Effective Sentiment Analysis

abstract: The success of Web 2.0 applications has made online social media websites tremendous assets for supporting critical business intelligence applications. The knowledge gained from social media can potentially lead to the development of novel services that are better tailored to users’ needs and at the same time meet the objectives of businesses offering them. Online consumer reviews are one of the critical social media contents. Proper analysis of consumer reviews not only provides valuable information to facilitate the purchase decisions of customers but also helps merchants or product manufacturers better understand general responses of customers on their products for marketing campaign improvement. This study aims at designing an approach for supporting the effective analysis of the huge volume of online consumer reviews and, at the same time, settling the major limitations of existing approaches. Specifically, the proposed rule-based sentiment analysis (R-SA) technique employs the class association rule mining algorithm to automatically discover interesting and effective rules capable of extracting product features or opinion sentences for a specific product feature interested. According to our preliminary evaluation results, the R-SA technique performs well in comparison with its benchmark technique.


title: Married Women's Retirement Behavior

abstract: In this paper we examine the economic and family determinants of married women's retirement behavior. A model of wives' retirement decisions is developed and tested empirically using data on working married women. Estimated response parameters are compared to those obtained previously for male workers. Our findings are directly relevant to policy questions regarding pension and Social Security reform.


title: Bandwidth enhancement and size reduction of microstrip slot antennas

abstract: Reduced size microstrip monopole slot antennas with different slot shapes-straight, L and inverted T, and placed on a small ground plane, are investigated. The ground plane size is 50 mm/spl times/80 mm, which is about the size of a typical PC Wireless card. Detailed simulation and experimental investigations are conducted to understand their behavior and optimize for broadband operation. It is shown that, the variation in the slot shape, from straight to L and T shapes, helps in generating additional resonances, which when coupled to the original resonances of the slot, further increases impedance bandwidths. The bent shapes of the L and T slots reduce their height and provide more space on the ground plane for electronics. A mirror image dual L-slot antenna, placed at two adjacent corners of the ground plane, is also investigated and optimized for the polarization diversity. They provide an impedance bandwidth of 87%, with near orthogonal radiation characteristics. The measured impedance bandwidths (S/sub 11/=-10 dB) of up to 60%, 84%, and 80% are achieved for these straight, L and inverted T slots respectively, by suitably selecting their design parameters. The simulation results are in good agreement with the experimental data considering several practical issues.


title: Predicting unusual right-turn driving behavior at intersection

abstract: This paper proposes a framework for predicting whether a driving behavior that a driver is going to execute is usual or not in that situation for the purpose of preventing accidents caused by human error. The framework is applied to unusual right-turn driving behavior prediction at an intersection and the effectiveness is confirmed with an experiment on a driving simulator.


title: “With-me-ness”: A gaze-measure for students’ attention in MOOCs

abstract: We propose a gaze-based indicator of students’ attention in a MOOC video lecture. We report the results from an eye-tracking study during a MOOC lecture. We define the gaze- based indicator of students’ attention as “with-me-ness”. This answers a question from teachers’ perspective “how much are the students with me?” With-me-ness is defined at two levels: perceptual, following teacher’s deictic acts- and conceptual – following teacher discourse. We conducted an experiment with 40 participants and observed a significant and positive correlation between the two levels of with-me-ness and the posttest scores.


title: An Online Identity and Smart Contract Management System

abstract: In today's online environment, people attend various kinds of activities, exhibit different digital presence, build personal digital reputations, issuing and receiving feedbacks from online communities being involved with. These diverse information sources once aggregated can provide a valuablefuture reference for personal online digital identity and credits check. The primary objective of this paper is to propose a systematic framework for aggregating online identity and reputation information, to provide a holistic approach to personal online behavioral ratings. Major contributions include: An identity aggregation mechanism based on social dependency network is proposed, a smart contract management framework referring to personal online ratings based on the aggregated digital identity, an experiment implementation based on blockchain technology, with illustrative examples and theoretical evaluations to the proposed approach.


Self-Compassion , Wellbeing , and Happiness


title: A Study of Discovery Mechanisms for Peer-to-Peer Applications

abstract: Peer-to-peer applications allow peers to connect or disconnect from a network at any time and are based on a loosely coupled resource distribution model. As a result, robust and efficient discovery mechanisms are central to the efficient functioning of such applications. In this paper we evaluate four discovery mechanisms (flooding and the forward routing algorithms CHORD, Pastry and CAN) against the requirements of three prevalent classes of peer-to-peer applications, and investigate the suitability of these mechanisms for the applications.


title: Semi-supervised feature selection for graph classification

abstract: The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled training graphs. However, in many applications, the labels of graph data are very expensive or difficult to obtain, while there are often copious amounts of unlabeled graph data available. In this paper, we study the problem of semi-supervised feature selection for graph classification and propose a novel solution, called gSSC, to efficiently search for optimal subgraph features with labeled and unlabeled graphs. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform semi-supervised feature selection for graph data in a progressive way together with the subgraph feature mining process. We derive a feature evaluation criterion, named gSemi, to estimate the usefulness of subgraph features based upon both labeled and unlabeled graphs. Then we propose a branch-and-bound algorithm to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space. Empirical studies on several real-world tasks demonstrate that our semi-supervised feature selection approach can effectively boost graph classification performances with semi-supervised feature selection and is very efficient by pruning the subgraph search space using both labeled and unlabeled graphs.


title: A Portable and High-Performance General Matrix-Multiply (GEMM) Library for GPUs and Single-Chip CPU/GPU Systems

abstract: OpenCL is a vendor neutral and portable interface for programming parallel compute devices such as GPUs. Tuning OpenCL implementations of important library functions such as dense general matrix multiply (GEMM) for a particular device is a difficult problem. Further, OpenCL kernels tuned for a particular architecture perform poorly on other architectures. We present a solution to the challenge of writing a portable and high-performance GEMM implementation. We designed and implemented RaijinCL, an OpenCL auto-tuning library for real and complex variants of GEMM that automatically generates tuned kernels for a given architecture. We comprehensively tested our library on a wide variety of architectures and show that the library is competitive with vendor libraries on all tested architectures. We also implemented an autotuner for hybrid CPU+GPU GEMM that takes advantage of both the CPU and GPU on singlechip CPU+GPU platforms such as Intel Ivy Bridge. We show that our solution can outperform CPU-only, GPU-only as well as simple CPU+GPU tuning strategies. In addition to performance results, we provide analysis of architectural limitations as well as OpenCL compiler and runtime issues discovered on various systems, along with guidance on avoiding some of these issues.


A case study on Stuxnet and Flame


title: Improving students ’ help-seeking skills using metacognitive feedback in an intelligent tutoring system

abstract: The present research investigated whether immediate metacognitive feedback on students’ help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an intelligent tutor agent for help seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students’ help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the helpseeking support was no longer in effect. Implications for metacognitive tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.


title: Typefaces and the Perception of Humanness in Natural Language Chatbots

abstract: How much do visual aspects influence the perception of users about whether they are conversing with a human being or a machine in a mobile-chat environment? This paper describes a study on the influence of typefaces using a blind Turing test-inspired approach. The study consisted of two user experiments. First, three different typefaces (OCR, Georgia, Helvetica) and three neutral dialogues between a human and a financial adviser were shown to participants. The second experiment applied the same study design but OCR font was substituted by Bradley font. For each of our two independent experiments, participants were shown three dialogue transcriptions and three typefaces counterbalanced. For each dialogue typeface pair, participants had to classify adviser conversations as human or chatbot-like. The results showed that machine-like typefaces biased users towards perceiving the adviser as machines but, unexpectedly, handwritten-like typefaces had not the opposite effect. Those effects were, however, influenced by the familiarity of the user to artificial intelligence and other participants' characteristics.


title: Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification

abstract: Feature extraction and feature normalization is an important preprocessing technique, usually employed before classification. Feature normalization is a useful step to restrict the values of all features within predetermined ranges. However, appropriate choice of normalization technique and normalization range is an important issue, since, applying normalization on the input could change the structure of data and thereby affecting the outcome of multivariate analysis and calibration used in data mining and pattern recognition problems. This paper investigates and evaluates some popular feature normalization techniques and studies their impact on performance of classifier with application to breast tumor classification using ultrasound images. For evaluating the feature normalization techniques, back-propagation artificial neural network [BPANN] and support vector machine [SVM] classifier models are used. Results show that that normalization of features has significant effect on the classification accuracy. General Terms Pattern Recognition, Medical Image Processing.


title: Software traceability: trends and future directions

abstract: Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.


title: The Impact of Indoor Lighting on Students' Learning Performance in Learning Environments: A knowledge internalization perspective

abstract: The purpose of this study is to identify the influence of indoor lighting on students’ learning performance within learning environments from knowledge internalization perspective. This study is a comprehensive review of literatures base on the influence of indoor lighting on people’s productivity and performance especially students’ learning performance. The result that comes from this study shows that it is essential to improve lighting in learning environments to enhance students’ learning performance and also motivate them to learn more. In this study the researchers utilized Pulay (2010) survey and measured the influence of lighting on students’ learning performance. Utilizing survey data collected from 150 students from Alpha course in Malaysia. This study found significant impact between lighting quality and students’ learning performance this finding is also supported by interview from two experts.


title: Twitter for Sentiment Analysis: When Language Resources are Not Available

abstract: Affective lexicons are a useful tool for emotion studies as well as for opinion mining and sentiment analysis. Such lexicons contain lists of words annotated with their emotional assessments. There exist a number of affective lexicons for English, Spanish, German and other languages. However, only a few of such resources are available for French. A lot of human efforts are needed to build and extend an affective lexicon. In our research, we propose to use Twitter, the most popular microblogging platform nowadays, to collect a dataset of emotional texts in French. Using the collected dataset, we estimated affective norms of words to construct an affective lexicon, which we use for polarity classification of video game reviews. Experimental results show that our method performs comparably to classic supervised learning methods.


title: The third Dialog State Tracking Challenge

abstract: In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. This task is challenging because of speech recognition and language understanding errors, yet good dialog state tracking is crucial to the performance of spoken dialog systems. This paper presents results from the third Dialog State Tracking Challenge, a research community challenge task based on a corpus of annotated logs of human-computer dialogs, with a blind test set evaluation. The main new feature of this challenge is that it studied the ability of trackers to generalize to new entities - i.e. new slots and values not present in the training data. This challenge received 28 entries from 7 research teams. About half the teams substantially exceeded the performance of a competitive rule-based baseline, illustrating not only the merits of statistical methods for dialog state tracking but also the difficulty of the problem.


title: Design of synchronous reluctance motor with multilayer AC winding

abstract: This paper presents a new synchronous reluctance motor (SynRM) design with multilayer AC winding to take the advantages of shorter end-winding length and lower stator MMF harmonics compared to the conventional distributed winding. The multilayer winding concept is described along with a discussion on the winding configuration selection. A multi-barrier rotor is designed for the SynRM using finite element analysis (FEA) based multidimensional optimization. Machine rated performances are compared with a commercial squirrel cage induction motor (SCIM) and a conventionally wound SynRM. Significant efficiency gains were achieved with SynRMs compared to the benchmark SCIM. Compared to conventional wound SynRM, the designed SynRM with multilayer winding yields higher torque output with the same copper loss, lower torque ripple, and better power factor, thereby improving the overall SynRM performance. A prototype SynRM with multilayer winding has been built and FEA results have been experimentally verified.


title: A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms

abstract: Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.