title: Understanding Data Science: An Emerging Discipline for Data Intensive Discovery

abstract: Over the past two decades, Data-Intensive Analysis has emerged not only as a basis for the Fourth Paradigm of engineering and scientific discovery but as a basis for discovery in most human endeavors for which data is available. Originating in the 1960s, its recent emergence due to Big Data and massive computing power is leading to widespread deployment, yet it is in its infancy in its application and our understanding of it; hence in its development. Given the potential risks and rewards of Data-Intensive Analysis and its breadth of application, it is imperative that we get this right. The objective of this emerging Fourth Paradigm is more than acquiring data and extracting knowledge. Like its predecessor the scientific method, the objective of the Fourth Paradigm is to investigate phenomena by acquiring new knowledge, and correct and integrate it with previous knowledge. In addition, data science is a body of principles and techniques with which to measure and improve the correctness, completeness, and efficiency of Data-Intensive Analysis. It is now time to identify and understand the fundamentals. In my research, I have analyzed more than 30 very large-scale use cases to understand current practical aspects, to gain insight into the fundamentals, and to address the fourth “V” of Big Data – veracity -the accuracy of the data and the resulting analytics This development may take


Noncooperative Differential Games. A Tutorial


title: The Adaptive Ontology-Based Personalized Recommender System

abstract: Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user's preferences. After acquiring users' preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a real-world case, a movie-recommender system adopting proposed recommendation strategies, is implemented.


title: A Weighted Probabilistic Neural Network

abstract: The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians. In practice, PNN is often an excellent pattern classifier, outperforming other classifiers including backpropagation. However, it is not robust with respect to affine transformations of feature space, and this can lead to poor performance on certain data. We have derived an extension of PNN called Weighted PNN (WPNN) which compensates for this flaw by allowing anisotropic Gaussians, i.e. Gaussians whose covariance is not a multiple of the identity matrix. The covariance is optimized using a genetic algorithm, some interesting features of which are its redundant, logarithmic encoding and large population size. Experimental results validate our claims.


title: Pancultural nostalgia: Prototypical conceptions across cultures.

abstract: Nostalgia is a frequently experienced complex emotion, understood by laypersons in the United Kingdom and United States of America to (a) refer prototypically to fond, self-relevant, social memories and (b) be more pleasant (e.g., happy, warm) than unpleasant (e.g., sad, regretful). This research examined whether people across cultures conceive of nostalgia in the same way. Students in 18 countries across 5 continents (N = 1,704) rated the prototypicality of 35 features of nostalgia. The samples showed high levels of agreement on the rank-order of features. In all countries, participants rated previously identified central (vs. peripheral) features as more prototypical of nostalgia, and showed greater interindividual agreement regarding central (vs. peripheral) features. Cluster analyses revealed subtle variation among groups of countries with respect to the strength of these pancultural patterns. All except African countries manifested the same factor structure of nostalgia features. Additional exemplars generated by participants in an open-ended format did not entail elaboration of the existing set of 35 features. Findings identified key points of cross-cultural agreement regarding conceptions of nostalgia, supporting the notion that nostalgia is a pancultural emotion.


title: Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation

abstract: In this paper, we propose a color-based segmentation method that uses the K-means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space image and then separate the position of tumor objects from other items of an MR image by using K-means clustering and histogram-clustering. Experiments demonstrate that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.


title: Style Transfer from Non-Parallel Text by Cross-Alignment

abstract: This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a broad family of problems including machine translation, decipherment, and sentiment modification. The key challenge is to separate the content from other aspects such as style. We assume a shared latent content distribution across different text corpora, and propose a method that leverages refined alignment of latent representations to perform style transfer. The transferred sentences from one style should match example sentences from the other style as a population. We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.


Accelerated Attributed Network Embedding.


title: A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization

abstract: A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract from their popularities for financial time series forecasting: One limitation of an ARIMA model is that it requires a large amount of historical data to generate an accurate result. Both theoretical and empirical findings suggest that combining different time series models may be an effective method of improving the predictive performances of data especially when the models in the ensemble are quite different. The main purpose of present paper is to combine the ARIMA model with the particle swarm optimization (PSO) model in order to improve and generate more accurate forecasting results. Under small data information, combining the PSO and ARIMA models performs better performance results compared to an ARIMA model itself. The proposed model is robust and it may be used as an alternative forecasting tool in economics areas.


title: Posture based health monitoring and unusual behavior recognition system for elderly using dynamic Bayesian network

abstract: Video surveillance cameras play a vital role in society. Elderly monitoring is one of the major applications of surveillance camera. It is observed that most of the elderly people live alone at homes. They desire aging at homes. Due to aging elderly people may experience some abnormal behaviors like chest pain, headache etc. Since they live alone these abnormal activities are unnoticed. This unnoticed activities cause severe health problems and finally may cause death. So a monitoring system is needed to monitor the behavior and give alerts to the care givers. A computer vision based elderly health care monitoring system using Dynamic Bayesian network (DBN) is developed. Modelling sequential data is an important feature in machine learning domain. The DBN model detects the abnormal activities such as backward fall, chest pain, forward fall, headache, and vomit. Human postures are recognized from silhouettes so that the privacy of the people is preserved and this model is robust to different environmental setup. This DBN model is both generative and discriminative and evaluated with real time video sequences and gives 82% accuracy. This system helps to give immediate attention to the people who are suffering in home alone due to severe health issues.


title: Magic in Pieces: An Analysis of Magic Trick Construction Using Artificial Intelligence as a Design Aid

abstract: Artificial intelligence AI techniques are successfully used for various design and optimization tasks, and have previously been applied to design magic tricks. A magical effect is an event that the observer perceives as being outside the normal physical rules of the world. In this work, we revisit an AI framework that has been used to produce a two-dimensional interlocking piece jigsaw design with two simultaneous solutions, one showing 12 identical shapes, the other only ten, giving rise to the believable illusion of the shapes vanishing. To achieve this effect, the design framework enables the combination of both geometric optimization constraints and constraints derived from empirically determined psychophysical and cognitive investigations. We further discuss the human factors that affect an observer’s perception of a magic trick and how these observations can be used in a computational optimization process. We also provide a more detailed analysis of the algorithms used to generate the magical effects.


title: Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

abstract: Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers. The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.


title: Beyond stereotypes of adolescent risk taking: Placing the adolescent brain in developmental context

abstract: Abstract Recent neuroscience models of adolescent brain development attribute the morbidity and mortality of this period to structural and functional imbalances between more fully developed limbic regions that subserve reward and emotion as opposed to those that enable cognitive control. We challenge this interpretation of adolescent development by distinguishing risk-taking that peaks during adolescence (sensation seeking and impulsive action) from risk taking that declines monotonically from childhood to adulthood (impulsive choice and other decisions under known risk). Sensation seeking is primarily motivated by exploration of the environment under ambiguous risk contexts, while impulsive action, which is likely to be maladaptive, is more characteristic of a subset of youth with weak control over limbic motivation. Risk taking that declines monotonically from childhood to adulthood occurs primarily under conditions of known risks and reflects increases in executive function as well as aversion to risk based on increases in gist-based reasoning. We propose an alternative Life-span Wisdom Model that highlights the importance of experience gained through exploration during adolescence. We propose, therefore, that brain models that recognize the adaptive roles that cognition and experience play during adolescence provide a more complete and helpful picture of this period of development.


title: TEACHING TEXT AND CONTEXT THROUGH MULTIMEDIA 1

abstract: The use of multimedia technology to teach language in its authentic cultural context represents a double challenge for language learners and teachers. On the one hand, the computer gives learners access to authentic video footage and other cultural materials that can help them get a sense of the sociocultural context in which the language is used. On the other hand, CD-ROM multimedia textualizes this context in ways that need to be "read" and interpreted. Learners are thus faced with the double task of (a) observing and choosing culturally relevant features of the context and (b) putting linguistic features in relation to other features to arrive at some understanding of language in use. This paper analyzes the interaction of text and context in a multimedia Quechua language program, and makes suggestions for teaching foreign languages through multimedia technology.


title: Declarative graph querying in practice and theory

abstract: With the recent resurgence of interest in graph data man- agement, there has been a flurry of research on the design and engineering of graph query languages. On the design side, there is a large body of theoretical results that have been obtained regarding graph languages. On the engineer- ing side, many sophisticated scalable solutions for graph query processing have been developed and put into practice. While both areas are focusing on the study of graph query languages, there has been relatively little work bridging the results on both sides. This tutorial will survey the state of the art in this landscape with a particular focus on uncovering and highlighting indicative research issues that are ripe for collaboration and cross-fertilization between the engineering and theoretical studies of graph database systems.


title: A comparison of biological and cultural evolution

abstract: This review begins with a definition of biological evolution and a description of its general principles. This is followed by a presentation of the biological basis of culture, specifically the concept of social selection. Further, conditions for cultural evolution are proposed, including a suggestion for language being the cultural replicator corresponding to the concept of the gene in biological evolution. Principles of cultural evolution are put forward and compared to the principles of biological evolution. Special emphasis is laid on the principle of selection in cultural evolution, including presentation of the concept of cultural fitness. The importance of language as a necessary condition for cultural evolution is stressed. Subsequently, prime differences between biological and cultural evolution are presented, followed by a discussion on interaction of our genome and our culture. The review aims at contributing to the present discussion concerning the modern development of the general theory of evolution, for example by giving a tentative formulation of the necessary and sufficient conditions for cultural evolution, and proposing that human creativity and mind reading or theory of mind are motors specific for it. The paper ends with the notion of the still ongoing coevolution of genes and culture.


title: A survey of performance measures to evaluate ego-lane estimation and a novel sensor-independent measure along with its applications

abstract: Lane estimation plays a central role for Driver Assistance Systems, therefore many approaches have been proposed to measure its performance. However, no commonly agreed metric exists. In this work, we first present a detailed survey of the current measures. Most of them apply pixel-level benchmarks on camera images and require a time-consuming and fault-prone labeling process. Moreover, these metrics cannot be used to assess other sources such as the detected guardrails, curbs or other vehicles. Therefore, we introduce an efficient and sensor-independent metric, which provides an objective and intuitive self-assessment for the entire road estimation process at multiple levels: individual detectors, lane estimation itself, and the target applications (e.g., lane keeping system). Our metric does not require a high labeling effort and can be used both online and offline. By selecting the evaluated points in specific distances, it can be applied to any road model representation. By comparing in 2D vehicle coordinate system, two possibilities exist to generate the ground-truth: the human-driven path or the expensive alternative with DGPS and detailed maps. This paper applies both methods and reveals that the human-driven path also qualifies for this task and it is applicable to scenarios without GPS signal, e.g., tunnel. Although the lateral offset between reference and detection is widely used in the majority of works, this paper shows that another criterion, the angle deviation, is more appropriate. Finally, we compare our metric with other state-of-the-art metrics using real data recordings from different scenarios.


title: Human Pharmacology of Ayahuasca: Subjective and Cardiovascular Effects, Monoamine Metabolite Excretion, and Pharmacokinetics

abstract: The effects of the South American psychotropic beverage ayahuasca on subjective and cardiovascular variables and urine monoamine metabolite excretion were evaluated, together with the drug9s pharmacokinetic profile, in a double-blind placebo-controlled clinical trial. This pharmacologically complex tea, commonly obtained from Banisteriopsis caapi and Psychotria viridis , combines N,N -dimethyltryptamine (DMT), an orally labile psychedelic agent showing 5-hydroxytryptamine 2A agonist activity, with monoamine oxidase (MAO)-inhibiting β-carboline alkaloids (harmine, harmaline, and tetrahydroharmine). Eighteen volunteers with prior experience in the use of psychedelics received single oral doses of encapsulated freeze-dried ayahuasca (0.6 and 0.85 mg of DMT/kg of body weight) and placebo. Ayahuasca produced significant subjective effects, peaking between 1.5 and 2 h, involving perceptual modifications and increases in ratings of positive mood and activation. Diastolic blood pressure showed a significant increase at the high dose (9 mm Hg at 75 min), whereas systolic blood pressure and heart rate were moderately and nonsignificantly increased. C max values for DMT after the low and high ayahuasca doses were 12.14 ng/ml and 17.44 ng/ml, respectively. T max (median) was observed at 1.5 h after both doses. The T max for DMT coincided with the peak of subjective effects. Drug administration increased urinary normetanephrine excretion, but, contrary to the typical MAO-inhibitor effect profile, deaminated monoamine metabolite levels were not decreased. This and the negligible harmine plasma levels found suggest a predominantly peripheral (gastrointestinal and liver) site of action for harmine. MAO inhibition at this level would suffice to prevent first-pass metabolism of DMT and allow its access to systemic circulation and the central nervous system.


title: Performance of Mobility Models with different Routing Protocols by using Simulation Tools for WSN: A Review

abstract: Research on the mobility models for wireless sensor networks has been an interesting area of networking. As a result, researchers over the past few years have measured the performance of mobility models by applying protocols through various simulation tools for wireless sensor networks. Wireless sensor network is a group of sensing devices which are geographically distributed that monitor the condition around it such as physical or environmental which communicates wirelessly to cooperatively pass data to the main location. The goal of the proposed work is to provide researchers with clear idea about the available mobility models for wireless sensor network performance which can be investigated on various available simulation tools with several metrics of sensor networks. Hence, we attempt to provide an overview of the current research status for wireless sensor network mobility models.


title: Automating Large-Scale Data Quality Verification

abstract: Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream. Therefore, a crucial, but tedious task for everyone involved in data processing is to verify the quality of their data. We present a system for automating the verification of data quality at scale, which meets the requirements of production use cases. Our system provides a declarative API, which combines common quality constraints with user-defined validation code, and thereby enables 'unit tests' for data. We efficiently execute the resulting constraint validation workload by translating it to aggregation queries on Apache Spark. Our platform supports the incremental validation of data quality on growing datasets, and leverages machine learning, e.g., for enhancing constraint suggestions, for estimating the 'predictability' of a column, and for detecting anomalies in historic data quality time series. We discuss our design decisions, describe the resulting system architecture, and present an experimental evaluation on various datasets.


title: Machine Learning and Integral Equations.

abstract: As both light transport simulation and reinforcement learning are ruled by the same Fredholm integral equation of the second kind, machine learning techniques can be used for efficient photorealistic image synthesis: Light transport paths are guided by an approximate solution to the integral equation that is learned during rendering. In analogy to recent advances in reinforcement learning for playing games, we investigate the training of neural networks to represent this approximate solution in the context of Monte Carlo and quasi-Monte Carlo methods in order to compute functionals of integral equations.


title: Fully automated neuropsychological assessment for detecting mild cognitive impairment

abstract: We present an end-to-end system for automatically scoring spoken responses to a narrative recall test administered to seniors when screening for cognitive impairment. In Wechsler Logical Memory (WLM) test, a patient listens to a brief narrative, then retells the story once immediately and again after a brief delay. We transcribe the retellings automatically using an ASR system, align the transcripts to the source narrative, extract features that replicate the standard clinical scoring method, and then use the features for automatic assessment using a classifier. On a test corpus of 72 subjects, we empirically evaluate different ASR adaptation strategies and analyze the errors with respect to clinical assessment. Despite imperfect recognition, the system presented here yields classification accuracy comparable to that of manually assigned scores. Our results show that automatic assessment of neuropsychological tests such as the WLM is practical for screening large cohorts.


title: Two-person interaction detection using body-pose features and multiple instance learning

abstract: Human activity recognition has potential to impact a wide range of applications from surveillance to human computer interfaces to content based video retrieval. Recently, the rapid development of inexpensive depth sensors (e.g. Microsoft Kinect) provides adequate accuracy for real-time full-body human tracking for activity recognition applications. In this paper, we create a complex human activity dataset depicting two person interactions, including synchronized video, depth and motion capture data. Moreover, we use our dataset to evaluate various features typically used for indexing and retrieval of motion capture data, in the context of real-time detection of interaction activities via Support Vector Machines (SVMs). Experimentally, we find that the geometric relational features based on distance between all pairs of joints outperforms other feature choices. For whole sequence classification, we also explore techniques related to Multiple Instance Learning (MIL) in which the sequence is represented by a bag of body-pose features. We find that the MIL based classifier outperforms SVMs when the sequences extend temporally around the interaction of interest.


title: Introducing Belbic: Brain Emotional Learning Based Intelligent Controller

abstract: Abstract Modeling emotions has attracted much attention in recent years, both in cognitive psychology and design of artificial systems. Far from being a negative factor in decision making, emotions have shown to be a strong faculty for making fast satisficing decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. We applied the proposed controller (termed BELBIC) for some SISO, MIMO and nonlinear systems. Our results demonstrate excellent control action, disturbance handling and system parameter robustness for BELBIC.


title: FAO/WHO Scientific Update on carbohydrates in human nutrition: conclusions

abstract: The Scientific Update involved consideration of a number of key issues that have arisen since the Joint FAO/WHO Expert Consultation on Carbohydrates in Human Nutrition was held in 1997 (FAO, 1998) or where new data may have altered conclusions drawn some 10 years ago. The Scientific Update enabled some firm conclusions to be drawn and identified a number of areas where more research is required to enable definitive recommendations. The review papers prepared as part of this Scientific Update applied the criteria used by the 2002 WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases to describe strength of evidence for drawing the conclusions of the scientific review (WHO, 2003). The following are agreedupon summaries of both the review papers, and discussions from the authors’ meeting (Geneva, 17–18 July 2006) on each issue area. The experts who participated at the authors meeting were: John H Cummings, Hans Englyst Timothy Key, Simin Liu, Jim Mann (Chairman), Rob van Dam, Bernard Venn, Carolyn Summerbell (Rapporteur), Gabriele Riccardi, Ricardo Uauy, HH Vorster (Rapporteur) and Martin Wiseman. The FAO Secretariat members were Kraisid Tontisirin and Frank Martinez Nocito and the WHO Secretariat members were Chizuru Nishida and Denise Costa Coitinho.


title: Characterizing the torque lookup table of an IPM machine for automotive application

abstract: Motor drive technologies are being incorporated into the EV and HEV automobiles with ever increasing speed. With the output torque to be the main control target, various control algorithms are developed that aim to achieve high torque accuracy while maximizing the machine energy efficiency. Most of such control algorithms entail procedures to characterize the machine under control. This paper documents a detailed, industrially practical procedure of characterizing the torque lookup table for interior permanent magnet (IPM) synchronous machines. The characterized lookup table can be utilized in open-loop torque control algorithms, and is verified to achieve high torque control accuracy and suitable for torque control of IPM in automotive applications.


title: Framework for Rumors Detection in Social Media

abstract: The development of social networks has led the public in general to find easy accessibility for communication with respect to rapid communication to each other at any time. Such services provide the quick transmission of information which is its positive side but its negative side needs to be kept in mind thereby misinformation can spread. Nowadays, in this era of digitalization, the validation of such information has become a real challenge, due to lack of information authentication method. In this paper, we design a framework for the rumors detection from the Facebook events data, which is based on inquiry comments. The proposed Inquiry Comments Detection Model (ICDM) identifies inquiry comments utilizing a rule-based approach which entails regular expressions to categorize the sentences as an inquiry into those starting with an intransitive verb (like is, am, was, will, would and so on) and also those sentences ending with a question mark. We set the threshold value to compare with the ratio of Inquiry to English comments and identify the rumors. We verified the proposed ICDM on labeled data, collected from snopes.com. Our experiments revealed that the proposed method achieved considerably well in comparison to the existing machine learning techniques. The proposed ICDM approach attained better results of 89% precision, 77% recall, and 82% 𝐹-measure. We are of the opinion that our experimental findings of this study will be useful for the worldwide adoption.


title: Redshift in the optical absorption of ZnO single crystals in the presence of an intense midinfrared laser field.

abstract: We report time-resolved electroabsorption of a weak probe in a 500 μm thick zinc-oxide crystal in the presence of a strong midinfrared pump in the tunneling limit. We observe a substantial redshift in the absorption edge that scales with the cube root of intensity up to 1 TW/cm(2) (0.38 eV cm(2/3) TW(-1/3)) after which it increases more slowly to 0.4 eV at a maximum applied intensity of 5 TW/cm(2). The maximum shift corresponds to more than 10% of the band gap. The change in scaling occurs in a regime of nonperturbative high-order harmonic generation where electrons undergo periodic Bragg scattering from the Brillouin zone boundaries. It also coincides with the limit where the electric field becomes comparable to the ratio of the band gap to the lattice spacing.


title: A novel MTTT mutation m.15933G > A revealed in analysis of mitochondrial DNA in patients with suspected mitochondrial disease

abstract: BackgroundMitochondrial diseases present with variable multi-organ symptoms. Common disease-causing mutations in mitochondrial DNA (mtDNA) are regularly screened in diagnostic work-up, but novel mutations may remain unnoticed.MethodsPatients (N = 66) with a clinical suspicion of mitochondrial disease were screened for their mtDNA coding region using conformation sensitive gel electrophoresis and sequencing. Long-PCR was used to detect deletions followed by POLG1 sequencing in patients with multiple deletions.ResultsWe discovered three novel mtDNA variants that included m.8743G > C, m.11322A > G and m.15933G > A. The novel MTTT variant m.15933G > A is suggested to be pathogenic. Analysis revealed also multiple mtDNA deletions in two patients and five nonsynonymous variants that were putatively pathogenic according to in-silico prediction algorithms. In addition, a rare haplogroup H associated m.7585_7586insT variant was discovered.ConclusionsAmong patients with a suspected mitochondrial disease, a novel MTTT variant m.15933G > A was discovered and is suggested to be pathogenic. In addition, several putatively pathogenic nonsynonymous variants and rare variants were found. These findings highlight the importance of coding region mtDNA screening among patients with clinical features suggesting a mitochondrial disease, but who lack the common mitochondrial disease mutations.


title: SUGILITE: Creating Multimodal Smartphone Automation by Demonstration

abstract: SUGILITE is a new programming-by-demonstration (PBD) system that enables users to create automation on smartphones. SUGILITE uses Android's accessibility API to support automating arbitrary tasks in any Android app (or even across multiple apps). When the user gives verbal commands that SUGILITE does not know how to execute, the user can demonstrate by directly manipulating the regular apps' user interface. By leveraging the verbal instructions, the demonstrated procedures, and the apps? UI hierarchy structures, SUGILITE can automatically generalize the script from the recorded actions, so SUGILITE learns how to perform tasks with different variations and parameters from a single demonstration. Extensive error handling and context checking support forking the script when new situations are encountered, and provide robustness if the apps change their user interface. Our lab study suggests that users with little or no programming knowledge can successfully automate smartphone tasks using SUGILITE.


title: Transformer modeling approaches for Frequency Response Analysis

abstract: Transformer modeling is a task nearly as old as transformer manufacturing. The methodology and principles used to build the model depend on its application, that will require specific features regarding aspects like frequency range, degree of accuracy, type of the model parameters and so on. The diagnosis of the transformer based on the Frequency Response Analysis (FRA) technique is one of the latest applications where transformer modeling has demonstrated that could be a useful tool. However, it is a difficult task and extended research is needed. In this paper, the particular features of the FRA oriented models will be discussed and the different and heterogeneous solutions available in the literature will be sorted, classified and analyzed. Problems in FRA oriented transformer modeling are indicated and possible future trends are exposed.


title: Three-dimensional tongue surface shapes of English consonants and vowels.

abstract: This paper presents three-dimensional tongue surfaces reconstructed from multiple coronal cross-sectional slices of the tongue. Surfaces were reconstructed for sustained vocalizations of the American English sounds [symbol: see text]. Electropalatography (EPG) data were also collected for the sounds to compare tongue surface shapes with tongue-palate contact patterns. The study was interested also in whether 3-D surface shapes of the tongue were different for consonants and vowels. Previous research and speculation had found that there were differences in production, acoustics, and linguistic usage between the two groups. The present study found that four classes of tongue shape were adequate to categorize all the sounds measured. These classes were front raising, complete groove, back raising, and two-point displacement. The first and third classes have been documented before in the midsagittal plane [cf. R. Harshman, P. Ladefoged, and L. Goldstein, J. Acoust. Soc. Am. 62, 693-707 (1976)]. The first three classes contained both vowels and consonants, the last only consonants. Electropalatographic patterns of the sounds indicated three categories of tongue-palate contact: bilateral, cross-sectional, and combination of the two. Vowels used only the first pattern, consonants used all three. The EPG data provided an observable distinction in contact pattern between consonants and vowels. The ultrasound tongue surface data did not. The conclusion was that the tongue actually has a limited repertoire of shapes and positions them against the palate in different ways for consonants versus vowels to create narrow channels, divert airflow, and produce sound.


title: Testing lumpability in Markov chains

abstract: The chi-squared test of Markov chain lumpability is shown to operate reliably under a corrected derivation of the degrees of freedom. The test is used to screen out lumping schemes that corrupt the Markov property and give rise to higher order dependence.


title: A Novel Framework for Imputation of Missing Values in Databases

abstract: Many of the industrial and research databases are plagued by the problem of missing values. Some evident examples include databases associated with instrument maintenance, medical applications, and surveys. One of the common ways to cope with missing values is to complete their imputation (filling in). Given the rapid growth of sizes of databases, it becomes imperative to come up with a new imputation methodology along with efficient algorithms. The main objective of this paper is to develop a unified framework supporting a host of imputation methods. In the development of this framework, we require that its usage should (on average) lead to the significant improvement of accuracy of imputation while maintaining the same asymptotic computational complexity of the individual methods. Our intent is to provide a comprehensive review of the representative imputation techniques. It is noticeable that the use of the framework in the case of a low-quality single-imputation method has resulted in the imputation accuracy that is comparable to the one achieved when dealing with some other advanced imputation techniques. We also demonstrate, both theoretically and experimentally, that the application of the proposed framework leads to a linear computational complexity and, therefore, does not affect the asymptotic complexity of the associated imputation method.


title: Wideband bow-tie slot antenna with tuning stubs

abstract: Printed bow-tie and bow-tie slot antennas are planar-type variations of the biconical antenna that has wideband characteristics. We present the effects of adding a tapered metal stub to a bow-tie slot antenna design to enhance the antenna bandwidth for X-band operation. Our study yields a design with 88% bandwidth relative to 10 GHz. The simulation and analysis are performed using the commercial computer software package, Momentum of Agilent Technologies, Advanced Design System (ADS). Verification of the ADS results is performed by using our developed finite difference time domain (FDTD) code and with measurements of the return loss from 7 to 15 GHz.


title: Development and validation of ego-identity status.

abstract: 4 modes of reacting to the late adolescent identity crisis were described, measured, and validated. Criteria for inclusion in 1 of 4 identity statuses were the presence of crisis and commitment in the areas of occupation and ideology. Statuses were determined for 86 college male Ss by means of individual interviews. Performance on a stressful concept-attainment task, patterns of goal setting, authoritarianism, and vulnerability to self-esteem change were dependent variables. Ss higher in ego identity performed best on the conceptattainment task; those in the status characterized by adherence to parental wishes set goals unrealistically high and subscribed significantly more to authoritarian values. Failure of the self-esteem condition to discriminate among the statuses was attributed to unreliability in self-esteem measurement. Ego identity and identity diffusion (Erikson, 19S6, 1963) refer to polar outcomes of the hypothesized psychosocial crisis occurring in late adolescence. Erikson views this phase of the life cycle as a time of growing occupational and ideological commitment. Facing such imminent adult tasks as getting a job and becoming a citizen, the individual is required to synthesize childhood identifications in such a way that he can both establish a reciprocal relationship with his society and maintain a feeling of continuity within himself. Previous studies have attempted to determine the extent of ego-identity achievement by means of an adjustment measure and the semantic differential technique (Bronson, 19S9), a Q-sort measure of real-ideal-se lf discrepancy (Gruen, 1960), a measure of role variability based on adjective ranking (Block,


Prediction of Fake Profiles on Facebook using Supervised Machine Learning Techniques-A Theoretical Model


title: How emotional auditory stimuli modulate time perception.

abstract: Emotional and neutral sounds rated for valence and arousal were used to investigate the influence of emotions on timing in reproduction and verbal estimation tasks with durations from 2 s to 6 s. Results revealed an effect of emotion on temporal judgment, with emotional stimuli judged to be longer than neutral ones for a similar arousal level. Within scalar expectancy theory (J. Gibbon, R. Church, & W. Meck, 1984), this suggests that emotion-induced activation generates an increase in pacemaker rate, leading to a longer perceived duration. A further exploration of self-assessed emotional dimensions showed an effect of valence and arousal. Negative sounds were judged to be longer than positive ones, indicating that negative stimuli generate a greater increase of activation. High-arousing stimuli were perceived to be shorter than low-arousing ones. Consistent with attentional models of timing, this seems to reflect a decrease of attention devoted to time, leading to a shorter perceived duration. These effects, robust across the 2 tasks, are limited to short intervals and overall suggest that both activation and attentional processes modulate the timing of emotional events.


title: Scaling Video Analytics Systems to Large Camera Deployments

abstract: Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.


title: Fast and Scalable Approximate Spectral Matching for Higher Order Graph Matching

abstract: This paper presents a fast and efficient computational approach to higher order spectral graph matching. Exploiting the redundancy in a tensor representing the affinity between feature points, we approximate the affinity tensor with the linear combination of Kronecker products between bases and index tensors. The bases and index tensors are highly compressed representations of the approximated affinity tensor, requiring much smaller memory than in previous methods, which store the full affinity tensor. We compute the principal eigenvector of the approximated affinity tensor using the small bases and index tensors without explicitly storing the approximated tensor. To compensate for the loss of matching accuracy by the approximation, we also adopt and incorporate a marginalization scheme that maps a higher order tensor to matrix as well as a one-to-one mapping constraint into the eigenvector computation process. The experimental results show that the proposed method is faster and requires smaller memory than the existing methods with little or no loss of accuracy.


title: Wireless energy harvesting for the Internet of Things

abstract: Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical ::: objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy ::: harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of ::: implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead.


title: Gerbil-A Novel Software Framework for Visualization and Analysis in the Multispectral Domain

abstract: Multispectral imaging has been gaining popularity and has been gradually applied to many fields besides remote sensing. Multispectral data provides unique information about material classification and reflectance analysis in general. However, due to the high dimensionality of the data, both human observers as well as computers, have difficulty interpreting this wealth of information. We present a new software package that facilitates the visualization of the relationship between spectral and topological information in a novel fashion. It puts emphasis on the spectral gradient, which is shown to provide enhanced information for many reflectance analysis tasks. It also includes a rich toolbox for evaluation of image segmentation and other algorithms in the multispectral domain. We combine the parallel coordinates visualization technique with hashing for a highly interactive visual connection between spectral distribution, spectral gradient and topology. The framework is released as open-source, has a modern cross-platform design and is well integrated into existing established computer vision software (OpenCV).


Effective User Interactions for Visual Analytics Tools


title: A semi-feature learning approach for tampered region localization across multi-format images

abstract: In multimedia security, it is an important task to localize the tampered image regions. In this work, deep learning is used to solve this problem and the approach can be applied to multi-format images. Concretely, we use Stack Autoencoder to obtain the tampered image block features so that the forgery can be identified in a semi-automatic manner. Contextual information of image block is further integrated to improve the localization accuracy. The approach is tested on a benchmark dataset, with a 92.84% localization accuracy and a 0.9375 Area Under Curve (AUC) score. Compared to the state-of-the-art solutions for multi-format images, our solution has an over 40% AUC improvement and 5.7 times F1 improvement. The results also out-perform several approaches which are designed specifically for JPEG images by 41.12%∼63.08% in AUC and with a 4∼8 times better F1.


title: Deep learning based frameworks for image super-resolution and noise-resilient super-resolution

abstract: Our paper is motivated from the advancement in deep learning algorithms for various computer vision problems. We are proposing a novel end-to-end deep learning based framework for image super-resolution. This framework simultaneously calculates the convolutional features of low-resolution (LR) and high-resolution (HR) image patches and learns the non-linear function that maps these convolutional features of LR image patches to their corresponding HR image patches convolutional features. Here, proposed deep learning based image super-resolution architecture is termed as coupled deep convolutional auto-encoder (CDCA) which provides state-of-the-art results. Super-resolution of a noisy/distorted LR images results in noisy/distorted HR images, as super-resolution process gives rise to spatial correlation in the noise, and further, it cannot be de-noised successfully. Traditional noise resilient image super-resolution methods utilize a de-noising algorithm prior to super-resolution but de-noising process gives rise to loss of some high-frequency information (edges and texture details) and super-resolution of the resultant image provides HR image with missing edges and texture information. We are also proposing a novel end-to-end deep learning based framework to obtain noise resilient image super-resolution. Proposed end-to-end deep learning based framework for noise resilient super-resolution simultaneously perform image de-noising and super-resolution as well as preserves textural details. First, stacked sparse de-noising auto-encoder (SSDA) was learned for LR image de-noising and proposed CDCA was learned for image superresolution. Then, both image de-noising and super-resolution networks were cascaded. This cascaded deep learning network was employed as one integral network where pre-trained weights were serving as initial weights. The integral network was end-to-end trained or fine-tuned on a database having noisy, LR image as an input and target as an HR image. In fine-tuning, all layers of the combined end-to-end network was jointly optimized to perform image de-noising and super-resolution simultaneously. Experimental results show that proposed noise resilient super-resolution framework outperforms the conventional and state-of-the-art approaches in terms of PSNR and SSIM metrics.


title: Carbon-Supported Molybdenum-Based Catalysts for the Hydrodeoxygenation of Maize Oil

abstract: The hydrodeoxygenation (HDO) of maize oil was performed in an autoclave with Mo-based catalysts supported by different carbon materials, such as reduced graphene oxide, activated charcoal, graphite, and fullerene. Nanostructured Mo-based catalysts with different phase compositions were prepared by using the carbothermal hydrogen reduction method at temperatures ranging from 500 to 700 °C and characterized by Raman spectroscopy, N2 adsorption isotherms, SEM, TEM, XRD, X-ray photoelectron spectroscopy, and ammonia temperature-programmed desorption. The highest total hydrocarbon yield of 90.32 % was obtained on the reduced graphene oxide-supported molybdenum carbide catalyst at 700 °C. These results are subject to the complicated effect of different factors such as phase composition, defect concentration, and particle size on the catalytic behavior of the materials. Unique structures of different supports also play a significant role in the HDO reaction. On the basis of the results of comprehensive analysis of products and catalysts, an HDO mechanism was proposed. These Mo-based catalysts are a promising system to prepare high-quality diesel fuels from renewable resources.


title: The causes and prognoses of different types of fractures in wild koalas submitted to wildlife hospitals

abstract: Fractures are a major problem in wild koalas of great veterinary and conservation importance as their occurrence in different locations of the body might result in varying healing success. The aim of this study was to determine the fracture types (defined by location of the fracture) occurring in wild koalas, temporal patterns, possible causes and risk factors of fracture types, and the prognosis for successfully releasing kolas with healed fracture types into the wild. Data from a total of 2031 wild koalas submitted to wildlife hospitals in South-East Queensland, Australia, over a period of 13 years were analysed. Approximately 56.7% of koalas experienced head fractures, 13.4% had torso fractures, 14.9% had limb fractures and 15% had combination fractures. A total of 84.1% of fractures were caused by vehicle collisions, 9.1% by dog attacks, 3.3% by falls from trees, 1.3% by train collisions, 0.2% by livestock trampling and 1.8% due to unknown causes. Multinominal logistic regression was used to identify risk factors (cause of fracture, age category, sex, year, three-year admission period and season of fracture event) by fracture type. The type of fracture was associated with both the cause of the fracture and the season when it occurred: for example torso fractures (compared to combination fractures) were associated with dog attacks (OR = 10.98; 95% CI 6.03, 20.01) and falls from trees (OR = 4.79; 95% CI 2.26, 10.19) relative to vehicle collisions. More submissions of koalas with head fractures due to vehicle collisions occurred in spring compared to autumn and winter, coinciding with the breeding season of koalas and increased animal movement. Prognosis for koalas with fractures was poor, with approximately 63.8% of koalas admitted dead on arrival, 34.2% euthanised, and only 2.0% of koalas able to be released. Given this data, further research into mitigation strategies to decrease the risk of fractures and to increase the observed low recovery rate should be considered.


title: An integrated full-bridge Class-DE ultrasound transducer driver for HIFU applications

abstract: This paper presents an integrated MRI-compatible full-bridge Class-DE ultrasound transducer driver design for HIFU applications. This design includes a digital logic unit to individually control the phase and duty ratio of each transducer when the driver is used in an array configuration. This work also shows the performance of the proposed driver with three different ultrasonic transducers. Simulation results predict at least 89% efficiency for the two transducers that support ideal Class-DE operation without matching networks. The proposed driver has been implemented by using AMS AG H35 CMOS process. The total die area of the driver is 3 mm2.


title: Clustered regularly interspaced short palindrome repeats (CRISPRs) have spacers of extrachromosomal origin

abstract: Numerous prokaryote genomes contain structures known as clustered regularly interspaced short palindromic repeats (CRISPRs), composed of 25–50 bp repeats separated by unique sequence spacers of similar length. CRISPR structures are found in the vicinity of four genes named cas1 to cas4. In silico analysis revealed another cluster of three genes associated with CRISPR structures in many bacterial species, named here as cas1B, cas5 and cas6, and also revealed a certain number of spacers that have homology with extant genes, most frequently derived from phages, but also derived from other extrachromosomal elements. Sequence analysis of CRISPR structures from 24 strains of Streptococcus thermophilus and Streptococcus vestibularis confirmed the homology of spacers with extrachromosomal elements. Phage sensitivity of S. thermophilus strains appears to be correlated with the number of spacers in the CRISPR locus the strain carries. The authors suggest that the spacer elements are the traces of past invasions by extrachromosomal elements, and hypothesize that they provide the cell immunity against phage infection, and more generally foreign DNA expression, by coding an anti-sense RNA. The presence of gene fragments in CRISPR structures and the nuclease motifs in cas genes of both cluster types suggests that CRISPR formation involves a DNA degradation step.


title: MOCUS : moving object counting using ultrasonic sensor networks

abstract: Counting the number of moving objects in a given area has many practical applications. By investigating a series of state-of-the-art technologies, we propose a Moving Object Counting approach using Ultrasonic Sensor networks (MOCUS). In MOCUS, we deploy a network of three-node ultrasound sensor clusters, with each cluster having one ultrasound transmitting node and two ultrasound receiving nodes. Such three-node sensor clusters can successfully offset interference problems and accurately detect the direction of moving objects. In order to cover a wide area, MOCUS employs multiple sensor clusters, forming a wireless sensor network. To alleviate the impact of object moving velocity, shape of objects and distinguish closely tied multiple objects, we introduce intra-cluster analysis and inter-cluster cooperation techniques. We deploy a MOCUS prototype in our lab and evaluate the design through extensive experiments.


title: Web usage mining: discovery and applications of usage patterns from Web data

abstract: Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given.


title: Wizard-of-Oz Data Collection for Perception and Interaction in Multi-User Environments

abstract: In this paper we present the setup of an extensive Wizard-of-Oz environment used for the data collection and the development of a dialogue system. The envisioned Perception and Interaction Assistant will act as an independent dialogue partner. Passively observing the dialogue between the two human users with respect to a limited domain, the system should take the initiative and get meaningfully involved in the communication process when required by the conversational situation. The data collection described here involves audio and video data. We aim at building a rich multi-media data corpus to be used as a basis for our research which includes, inter alia, speech and gaze direction recognition, dialogue modelling and proactivity of the system. We further aspire to obtain data with emotional content to perfom research on emotion recognition, psychopysiological and usability analysis.


title: Trends in biotechnological production of fuel ethanol from different feedstocks.

abstract: Present work deals with the biotechnological production of fuel ethanol from different raw materials. The different technologies for producing fuel ethanol from sucrose-containing feedstocks (mainly sugar cane), starchy materials and lignocellulosic biomass are described along with the major research trends for improving them. The complexity of the biomass processing is recognized through the analysis of the different stages involved in the conversion of lignocellulosic complex into fermentable sugars. The features of fermentation processes for the three groups of studied feedstocks are discussed. Comparative indexes for the three major types of feedstocks for fuel ethanol production are presented. Finally, some concluding considerations on current research and future tendencies in the production of fuel ethanol regarding the pretreatment and biological conversion of the feedstocks are presented.


Injection rhinoplasty: non-surgical nasal augmentation and correction of post-rhinoplasty contour asymmetries with hyaluronic acid: how we do it


title: Neural Emoji Recommendation in Dialogue Systems

abstract: Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multi-turn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long short-term memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.


Social Media in Disaster Relief Usage Patterns , Data Mining Tools , and Current Research Directions


title: AI safety via debate

abstract: To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences. One approach to specifying complex goals asks humans to judge during training which agent behaviors are safe and useful, but this approach can fail if the task is too complicated for a human to directly judge. To help address this concern, we propose training agents via self play on a zero sum debate game. Given a question or proposed action, two agents take turns making short statements up to a limit, then a human judges which of the agents gave the most true, useful information. In an analogy to complexity theory, debate with optimal play can answer any question in PSPACE given polynomial time judges (direct judging answers only NP questions). In practice, whether debate works involves empirical questions about humans and the tasks we want AIs to perform, plus theoretical questions about the meaning of AI alignment. We report results on an initial MNIST experiment where agents compete to convince a sparse classifier, boosting the classifier's accuracy from 59.4% to 88.9% given 6 pixels and from 48.2% to 85.2% given 4 pixels. Finally, we discuss theoretical and practical aspects of the debate model, focusing on potential weaknesses as the model scales up, and we propose future human and computer experiments to test these properties.


FireNet: an international network for landscape fire information


Multiple signals in anterior cingulate cortex


title: The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid

abstract: The Internet of Things(IoT) and Smart Grid are of great importance in promoting and guiding development of information technology and economic. At Present, the application of the IoT develops rapidly, but due to the special requirements of some applications, the existing technology can not meet them very good. Much research work is doing to build IoT. WiFi-based Wireless Sensor Network(WSN) has the features of high bandwidth and rate, non-line-transmission ability, large-scale data collection and high cost-effective, and it has the capability of video monitoring, which can not be realized with ZigBee. The research on WiFi-based WSN and its application has high practical significance to the development of the Internet of Things and Smart Grid. Based on the current research work of applications in the Internet of Things and the characteristics of WiFi-based WSN, this paper discusses the application of WiFi-based WSN in Internet of Things, which includes Smart Grid, Smart Agriculture and Intelligent environment protection.


A Planning and Learning Hierarchy using Qualitative Reasoning for the On-Line Acquisition of Robotic Behaviors


title: Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function

abstract: Medial prefrontal cortex (MPFC) is among those brain regions having the highest baseline metabolic activity at rest and one that exhibits decreases from this baseline across a wide variety of goal-directed behaviors in functional imaging studies. This high metabolic rate and this behavior suggest the existence of an organized mode of default brain function, elements of which may be either attenuated or enhanced. Extant data suggest that these MPFC regions may contribute to the neural instantiation of aspects of the multifaceted “self.” We explore this important concept by targeting and manipulating elements of MPFC default state activity. In this functional magnetic resonance imaging (fMRI) study, subjects made two judgments, one self-referential, the other not, in response to affectively normed pictures: pleasant vs. unpleasant (an internally cued condition, ICC) and indoors vs. outdoors (an externally cued condition, ECC). The ICC was preferentially associated with activity increases along the dorsal MPFC. These increases were accompanied by decreases in both active task conditions in ventral MPFC. These results support the view that dorsal and ventral MPFC are differentially influenced by attentiondemanding tasks and explicitly self-referential tasks. The presence of self-referential mental activity appears to be associated with increases from the baseline in dorsal MPFC. Reductions in ventral MPFC occurred consistent with the fact that attention-demanding tasks attenuate emotional processing. We posit that both self-referential mental activity and emotional processing represent elements of the default state as represented by activity in MPFC. We suggest that a useful way to explore the neurobiology of the self is to explore the nature of default state activity.


title: Combination treatment with eldecalcitol (ED-71) and raloxifene improves bone mechanical strength by suppressing bone turnover and increasing bone mineral density in ovariectomized rats.

abstract: The aim of this study was to investigate the effect of combination treatment with eldecalcitol (ELD) and raloxifene (RAL) on bone turnover, bone mineral density (BMD), and bone strength. Eight-month-old rats were ovariectomized (OVX) or sham operated, and divided into five groups (Sham, OVX+vehicle, OVX+RAL, OVX+ELD and OVX+ELD+RAL). ELD (7.5 ng/kg) and RAL (0.3mg/kg) were orally administered alone or in combination daily. Urinary deoxypyridinoline (DPD) levels were measured after 4, 8, and 12 weeks of treatment. After 12 weeks of treatment, BMD and mechanical properties of the lumbar spine and femur were assessed, and bone histomorphometry was performed. Urinary DPD levels in all the treatment groups were significantly decreased compared with the OVX+vehicle group. At 4 weeks of treatment, urinary DPD level of the combination group was significantly lower than that of either monotherapy group. The reduction in the BMD of the lumbar spine and femur by OVX was significantly prevented in all the treatment groups, and the BMD in the combination group was significantly higher than that in either monotherapy group. The ultimate load and work to failure of the fifth lumbar vertebra were significantly improved only by the combination treatment. The femoral midshaft ultimate load was significantly increased in the OVX+ELD group and the combination group, and the femoral midshaft work to failure was increased only in the combination group. Bone histomorphometric analysis using the third lumbar vertebra revealed that osteoblast surface (Ob.S/BS), osteoclast surface (Oc.S/BS) and osteoclast number (N.Oc/BS) significantly decreased in all treatment groups, and osteoid surface (OS/BS) and bone formation rate (BFR/BS) significantly decreased in the ELD-treated and combination groups. The values of Ob.S/BS and OS/BS in the combination group were lower than those in either of the monotherapy groups. The bone formation parameters in the combination group were not reduced to below levels of the sham-operated control, suggesting that the combination therapy with ELD and RAL may not cause oversuppression of bone turnover. These results indicated that the combination treatment with ELD and RAL might be a beneficial therapy with respect to their combined effects of enhancing the mechanical properties of trabecular and cortical bone by suppressing bone turnover and increasing BMD more than either monotherapy.


title: Bug Replication in Code Clones: An Empirical Study

abstract: Code clones are exactly or nearly similar code fragments in the code-base of a software system. Existing studies show that clones are directly related to bugs and inconsistencies in the code-base. Code cloning (making code clones) is suspected to be responsible for replicating bugs in the code fragments. However, there is no study on the possibilities of bug-replication through cloning process. Such a study can help us discover ways of minimizing bug-replication. Focusing on this we conduct an empirical study on the intensities of bug-replication in the code clones of the major clone-types: Type 1, Type 2, and Type 3. According to our investigation on thousands of revisions of six diverse subject systems written in two different programming languages, C and Java, a considerable proportion (i.e., up to 10%) of the code clones can contain replicated bugs. Both Type 2 and Type 3 clones have higher tendencies of having replicated bugs compared to Type 1 clones. Thus, Type 2 and Type 3 clones are more important from clone management perspectives. The extent of bug-replication in the buggy clone classes is generally very high (i.e., 100% in most of the cases). We also find that overall 55% of all the bugs experienced by the code clones can be replicated bugs. Our study shows that replication of bugs through cloning is a common phenomenon. Clone fragments having method-calls and if-conditions should be considered for refactoring with high priorities, because such clone fragments have high possibilities of containing replicated bugs. We believe that our findings are important for better maintenance of software systems, in particular, systems with code clones.


title: A hybrid movie recommender system based on neural networks

abstract: Recommender systems offer a solution to the problem of successful information search in the knowledge reservoirs of the Internet by providing individualized recommendations. Content-based and Collaborative Filtering are usually applied to predict recommendations. A combination of the results of the above techniques is used in this work to construct a system that provides precise recommendations concerning movies. The content filtering part of the system is based on trained neural networks representing individual user preferences. Filtering results are combined using Boolean and fuzzy aggregation operators. The proposed hybrid system was tested on the MovieLens data yielding high accuracy predictions.


title: A new t-type direct AC/AC converter

abstract: In this paper a novel T type scheme for 1-phase direct ac/ac converters is proposed. In this topology, frequency changing takes place directly (without common DC voltage bus) through three converters that are connected in a T structure. Unity power factor and precise output voltage regulation is achieved through appropriate control of these three converters. H-bridge converters are implemented, as the basic unit, in each branches of T to make the proposed converter. The converter is controlled such that the average active power of each H-bridges remains zero. So capacitors are implemented in DC links of H-bridges. In comparison to conventional back-to-back converters, life time of each capacitor increases due to capacitor current ripples reduction. Principle of operation and control scheme is explained. Simulation results verify performance of the converter while operating as a power supply at different frequencies.


title: Software Architecture in Practice (Second Edition)

abstract: The architect is faced with a swarm of competing, if not conflicting, influences and demands, surprisingly few of which are concerned with getting the system to work correctly. The organizational and technical environment brings to bear a weighty set of sometimes implicit demands, and in practice these are as important as any of the explicit requirements for the software even though they are almost never written down.


title: The effects of human body and bending on dipole textile antenna performance and SAR

abstract: In this paper, the interaction between a single band textile antenna with human body is examined. The simulations are performed by means of CST Microwave Studio with a single band 2.4 GHz patch dipole antenna as the radiating source. The effects of bending and different distances from the body are also considered in this study. Results have clearly indicated that the human body has notably shifts the antenna resonant frequency and modifies the radiation pattern at the frequencies investigated. Furthermore, the results obtained show that the SAR values are significantly influenced by the amount of curvature of the antenna and the separation distance between the antenna and the human body. The averaged SAR value is increased up to 92.3 % when the curved textile antenna is bent and placed 1 mm away from the body.


title: The microvascular pattern of the supraspinatus tendon.

abstract: The vascular pattern of the supraspinatus tendon was studied in 18 human anatomic specimens. The ages of the specimens ranged from 26 to 84 years. Selective vascular injection with a silicon-rubber compound allowed visualization of the vascular bed of the rotator cuff and humeral head. The presence of a hypovascular or critical zone close to the insertion of the supraspinatus tendon into the humeral head was confirmed. However, only a uniformly sparse vascular distribution was found at the articular side, as opposed to the well-vascularized bursal side. This was also confirmed with histologic sections of the tendon. The poor vascularity of the tendon in this area could be a significant factor in the pathogenesis of degenerative rotator cuff tears.


title: Personalized, adaptive digital educational games using narrative game-based learning objects

abstract: Based on previous work of the authors, this paper describes the concept and use of Narrative Game-based Learning Objects (NGLOB) for the personalization and adaptation of Story-based Digital Educational Games (DEG). In the first part of the paper, we characterize the potential of personalized, adaptive DEG and provide a brief overview of related work. Then, in section 3 and 4 we describe our concept of NGLOBs combining learner modeling, player modeling and storytelling and a prototypical realization of the concept implemented in the context of the Bat Cave scenario within the EU funded project 80Days. Finally, the main results are summarized and further research activities such as additional evaluation studies are outlined.


title: Compensating procedures for power quality amplification of AC electrified railway systems using FACTS

abstract: The low power factor and the voltage fluctuation are two stability problems in many ac electrified railway systems. This paper surveys the usage of several types of Flexible AC Transmission Systems (FACTS) for power quality improvements of an electrified railway system which has been consisted of phase-controlled thyristor converters to feed the DC motor drives of their locomotives. The section of a typical single-phase 25-kV, 50Hz, electrified railway system is loaded with ac-dc thyristor-based locomotives. It is equipped with either static VAR compensator (SVC) or static synchronous compensator (STATCOM). The capability of this system as power conditioner is confirmed by using detailed simulations in MATLAB Simulink software. At first, stability problems which are created without using of these compensators are considered. Then stability investigations such as voltage profile of feeder station and analyzing of power factor steady state are conducted on the impact of these compensators on some case studies. It was found that the SVC and STATCOM considerably improve the power quality parameters of the system in below: regulation of the voltage profile and power factor improvement.


title: Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction

abstract: Bregman methods introduced in [S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, Multiscale Model. Simul., 4 (2005), pp. 460-489] to image processing are demonstrated to be an efficient optimization method for solving sparse reconstruction with convex functionals, such as the $\ell^1$ norm and total variation [W. Yin, S. Osher, D. Goldfarb, and J. Darbon, SIAM J. Imaging Sci., 1 (2008), pp. 143-168; T. Goldstein and S. Osher, SIAM J. Imaging Sci., 2 (2009), pp. 323-343]. In particular, the efficiency of this method relies on the performance of inner solvers for the resulting subproblems. In this paper, we propose a general algorithm framework for inverse problem regularization with a single forward-backward operator splitting step [P. L. Combettes and V. R. Wajs, Multiscale Model. Simul., 4 (2005), pp. 1168-1200], which is used to solve the subproblems of the Bregman iteration. We prove that the proposed algorithm, namely, Bregmanized operator splitting (BOS), converges without fully solving the subproblems. Furthermore, we apply the BOS algorithm and a preconditioned one for solving inverse problems with nonlocal functionals. Our numerical results on deconvolution and compressive sensing illustrate the performance of nonlocal total variation regularization under the proposed algorithm framework, compared to other regularization techniques such as the standard total variation method and the wavelet-based regularization method.


title: Modeling genetic algorithms with Markov chains

abstract: We model a simple genetic algorithm as a Markov chain. Our method is both complete (selection, mutation, and crossover are incorporated into an explicitly given transition matrix) and exact; no special assumptions are made which restrict populations or population trajectories. We also consider the asymptotics of the steady state distributions as population size increases.


title: Modal Analysis for the Vibration of the Dry-Type Transformer in the Offshore Oil Exploitation Power System

abstract: The transformer windings may be subjected to excessive electromagnetic force during the short-circuit process. Under the electromagnetic force, the transformer windings may be deformed. The transformer structure plays a crucial influence on the deformation degree. In this paper, the modal analysis method is used to verify the rationality of a 35 kV epoxy resin cast dry-type transformer’s structure that is used in an offshore oil exploitation power system in China. The finite element model of the transformer is established. After the appropriate materials are set to the components of the transformer, the various modes of the transformer vibration are obtained based on the simulation analysis. According to the form of electromagnetic force subjected by the windings, both natural frequencies and vibration modes of each transformer component are obtained, and used to evaluate the influences of the electromagnetic force on the windings. Finally, the structure design of the studied transformer is analyzed based on the above analysis and simulation results.


title: Rotman Lens-Based Retrodirective Array

abstract: A new type of broadband retrodirective array, which has been constructed using a microstrip Rotman lens, is presented. Automatic tracking of targets is obtained by exploiting the conjugate phase response of the beamforming network which is exhibited when the input ports are terminated with either open or short circuits. In addition, the true time-delay property of the Rotman lens gives broadband operation of the self-tracking array when used in conjunction with Vivaldi antennas. The simulated and measured bistatic and monostatic radar cross-section (RCS) patterns of a structure consisting of 13 beamports and 12 array ports are presented at frequencies in the range 8-12 GHz. Significantly enhanced RCS within the scan coverage ±40° is demonstrated by comparing the retrodirective behavior of a 12-element Vivaldi array terminated with and without the Rotman lens.


The Use of Information Retrieval Techniques for Intrusion Detection


title: The Role of Nigella sativa and Its Active Constituents in Learning and Memory

abstract: The loss of the ability for learning and memory is a prominent feature of dementia, which affects millions of individuals all over the world, due to either neurodegenerative diseases or brain injury. Although a lot of information is known about the pathology involved, treatment remains elusive at best. The Black Seed of Nigella sativa has been historically and religiously used for thousands of years for preventing and treating many different kinds of diseases. This review article looks at Nigella sativa and its potential role in facilitating learning and memory. The possible use of this seed’s extract or compounds isolated from it, such as thymoquinone, for treating damaged brain neural tissue is discussed. The evidence presented in this paper appears to be supporting the hypothesis that this plant and/or its bioactive constituents can enhance learning and memory in health and disease in animals and humans.


title: A phrase mining framework for recursive construction of a topical hierarchy

abstract: A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from three different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.


title: Threshold voltage distribution in MLC NAND flash memory: characterization, analysis, and modeling

abstract: With continued scaling of NAND flash memory process technology and multiple bits programmed per cell, NAND flash reliability and endurance are degrading. Understanding, characterizing, and modeling the distribution of the threshold voltages across different cells in a modern multi-level cell (MLC) flash memory can enable the design of more effective and efficient error correction mechanisms to combat this degradation. We show the first published experimental measurement-based characterization of the threshold voltage distribution of flash memory. To accomplish this, we develop a testing infrastructure that uses the read retry feature present in some 2Y-nm (i.e., 20-24nm) flash chips. We devise a model of the threshold voltage distributions taking into account program/erase (P/E) cycle effects, analyze the noise in the distributions, and evaluate the accuracy of our model. A key result is that the threshold voltage distribution can be modeled, with more than 95% accuracy, as a Gaussian distribution with additive white noise, which shifts to the right and widens as P/E cycles increase. The novel characterization and models provided in this paper can enable the design of more effective error tolerance mechanisms for future flash memories.


Heavy Metal Contamination and Remediation in Asian Agricultural Land


title: AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets

abstract: The density-based clustering algorithm DBSCAN is a state-of-the-art data clustering technique with numerous applications in many fields. However, its O(n2) time complexity still remains a severe weakness. In this paper, we propose a novel anytime approach to cope with this problem by reducing both the range query and the label propagation time of DBSCAN. Our algorithm, called AnyDBC, compresses the data into smaller density-connected subsets called primitive clusters and labels objects based on connected components of these primitive clusters for reducing the label propagation time. Moreover, instead of passively performing the range query for all objects like existing techniques, AnyDBC iteratively and actively learns the current cluster structure of the data and selects a few most promising objects for refining clusters at each iteration. Thus, in the end, it performs substantially fewer range queries compared to DBSCAN while still guaranteeing the exact final result of DBSCAN. Experiments show speedup factors of orders of magnitude compared to DBSCAN and its fastest variants on very large real and synthetic complex datasets.


title: Palmprint identification using feature-level fusion

abstract: In this paper, we propose a feature-level fusion approach for improving the efficiency of palmprint identification. Multiple elliptical Gabor filters with different orientations are employed to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code. The similarity of two Fusion Codes is measured by their normalized hamming distance. A dynamic threshold is used for the final decisions. A database containing 9599 palmprint images from 488 different palms is used to validate the performance of the proposed method. Comparing our previous non-fusion approach and the proposed method, improvement in verification and identification are ensured.


title: A Feature Terms based Method for Improving Text Summarization with Supervised POS Tagging

abstract: Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user and task. When this is done by means of a computer, i.e. automatically, it calls as Automatic Text Summarization. Summarization can be classified into two approaches: extraction and abstraction. Extraction based summaries are produced by concatenating several sentences taken exactly as they appear in the texts being summarized. Abstraction based summaries are written to convey the main information in the input and may reuse phrases or clauses from it. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentences selection. One of the methods to obtain the sentences is to assign some feature terms of sentences for the summary called ranking sentences and then select the best ones. The first step in summarization by extraction is the identification of important features. In our approach 1000 computer science related research papers are used as test documents. Each document is prepared by preprocessing process: sentence segmentation, tokenization, stop word removal, case folding, lemmatization, and stemming. Then, using important features, sentence filtering features, data compression features and finally calculating score for each sentence. The proposed text summarization is based on HMM tagger to improve the quality of the summary. Here, comparing our results with the existing summarizers which are Copernicus summarizer, Great summarizer and Microsoft Word 2007 summarizers etc. The proposed system is also tested with four types‘ similarities: Cosine, Jaccard, Jarowinkler and Sorenson similarities. The results show that the best quality for the summaries was obtained by feature terms method. General Terms Text Mining, Information Extraction, Automatic Text Summarization, Natural Language Processing, POS Tagging.


title: Approximate SRAMs With Dynamic Energy-Quality Management

abstract: In this paper, approximate SRAMs are explored in the context of error-tolerant applications, in which energy is saved at the cost of the occurrence of read/write errors (i.e., signal quality degradation). This analysis investigates variation-resilient techniques that enable dynamic management of the energy-quality tradeoff down to the bit level. In these techniques, the different impacts of errors on quality at different bit positions are explicitly considered as key enabler of energy savings that are far larger than a simple voltage scaling. The analysis is based on the experimental results in an energy-quality scalable 28-nm SRAM and the extrapolation to a wide range of conditions through the models that combine the individual energy contributions. Results show that the joint adoption of multiple bit-level techniques provides substantially larger energy gains than individual techniques. Compared with the simple voltage scaling at isoquality, the joint adoption of these techniques can provide more than $2\times $ energy reduction at negligible area penalty. Energy savings turn out to be highly sensitive to the choice of joint techniques, thus showing the crucial importance of dynamic energy-quality management in approximate SRAMs.


title: The Psychometric Properties of the Internet Addiction Test

abstract: There is growing concern about excessive Internet use and whether this can amount to an addiction. In researching this topic, a valid and reliable assessment instrument is essential. In her survey of Internet addiction, Young1,2 designed the Internet Addiction Test (IAT), which provides a basis for developments. The IAT has high face validity, but it has not been subjected to systematic psychometric testing. This study sought to replicate and expand Young's1 survey, and to examine the IAT more systematically. A questionnaire that existed as a Web page was devised, consisting of the IAT and 15 other questions regarding the respondents' demographic information and Internet usage. Participants were recruited through the Internet, yielding 86 valid responses (29 males and 57 females). Factor analysis of the IAT revealed six factors—salience, excessive use, neglecting work, anticipation, lack of control, and neglecting social life. These factors showed good internal consistency and concurrent validity, with sa...


title: Comment on ``Experimental violations of the second law of thermodynamics for small systems and short timescales''

abstract: The experimental verification of the Fluctuation Theorem by Wang et al. is not a violation but even a confirmation of the second law, resulting from their observations in a proper interpretation.


title: MobiScore: Towards Universal Credit Scoring from Mobile Phone Data

abstract: Credit is a widely used tool to finance personal and corporate projects. The risk of default has motivated lenders to use a credit scoring system, which helps them make more efficient decisions about whom to extend credit. Credit scores serve as a financial user model, and have been traditionally computed from the user’s past financial history. As a result, people without any prior financial history might be excluded from the credit system. In this paper we present MobiScore, an approach to build a model of the user’s financial risk from mobile phone usage data, which previous work has shown to convey information about e.g. personality and socioeconomic status. MobiScore could replace traditional credit scores when no financial history is available, providing credit access to currently excluded population sectors, or be used as a complementary source of information to improve traditional finance-based scores. We validate the proposed approach using real data from a telecommunications operator and a financial institution in a Latin American country, resulting in an accurate model of default comparable to traditional credit scoring techniques.


title: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

abstract: We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, GradCAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multimodal inputs (e.g. VQA) or reinforcement learning, without any architectural changes or re-training. We combine GradCAM with fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into their failure modes (showing that seemingly unreasonable predictions have reasonable explanations), (b) are robust to adversarial images, (c) outperform previous methods on weakly-supervised localization, (d) are more faithful to the underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, our visualizations show that even non-attention based models can localize inputs. Finally, we conduct human studies to measure if GradCAM explanations help users establish trust in predictions from deep networks and show that GradCAM helps untrained users successfully discern a "stronger" deep network from a "weaker" one. Our code is available at this https URL. A demo and a video of the demo can be found at this http URL and this http URL.


title: Effect of Two Antibacterial Mouth Sprays and Dentifrices on Dental Plaque and Gingivitis in Beagle Dogs

abstract: Plaque inhibition by dilute mouth sprays and dentifrices containing benzethonium chloride and chlorhexidine gluconate was compared in beagle dogs. Agents with chlorhexidine gluconate produced less plaque than their benzethonium chloride or placebo counterparts, but the differences were not significant when compared to mean control scores registered during interexperimental recovery periods.


Cerebral Angiographic Findings of Cosmetic Facial Filler-related Ophthalmic and Retinal Artery Occlusion


title: Economic dispatch for a microgrid considering renewable energy cost functions

abstract: Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.


title: Sentiment analysis of Chinese online reviews using ensemble learning framework

abstract: Unstructured online reviews are undergoing a rather rapid expansion with the development of E-commerce, and they contain sentiment information in which consumers and businesses are very interested. Therefore, effective sentiment classification has become one of the important research topics. Many studies have shown that ensemble learning methods may have great hopeful applicability in sentiment classification tasks. In this paper, we propose a new ensemble learning framework for sentiment classification of Chinese online reviews. First of all, according to the complicated characteristics of Chinese online reviews, we extract Part of Speech Combination Pattern, Frequent Word Sequence Pattern and Order Preserved Submatrix Pattern as the input features. Furthermore, we use the algorithm of Random Subspace based on Information Gain by considering the problem of massive features in the reviews, which can improve the base classifiers simultaneously. Finally, we adopt the algorithm of Constructing Base Classifiers based on Product Attributes to combine the sentiment information of each attribute in a review so as to obtain better performance on sentiment classification. The experimental results show that the proposed ensemble learning framework has significant improvement in sentiment classification of Chinese online reviews.


title: Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

abstract: Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their wide coverage and high mobility. In this paper, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminals (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy trade-off in Ground-to-UAV (G2U) wireless communication. To characterize this trade-off, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT, and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal trade-off between them. Numerical results are provided to corroborate our study.


title: Development of a Contactless Hall Effect Torque Sensor for Electric Power Steering

abstract: In this paper, we will present an innovative torque sensor design which could be preferably used in Electric Power Assisted Steering (EPAS) application. This torque sensor is a non-contact Hall effect design. The specificity of this structure is its ability to measure the shift angle between two rotating shafts linked by a torsion bar. This measurement is done with stationary electronic components. This unique structure generates enough magnetic flux variation to measure angular shifts from +/-1° to +/-8° with low-cost standard Hall ASICs available from various suppliers. This torque sensor has convinced automotive industry due to its good performances, its compact dimension and a price compatible with the market expectation for this application. This paper will explain the basic magnetic principle of this torque sensor, the improvements which have lead to a structure compatible with automotive industry requirements, some measurement results and possible sensor integration in the application.