title: Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

text: The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.


title: Learning Discriminative Latent Attributes for Zero-Shot Classification

text: Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity. However, few works study whether the human-designed semantic attributes are discriminative enough to recognize different classes. Moreover, attributes are often correlated with each other, which makes it less desirable to learn each attribute independently. In this paper, we propose to learn a latent attribute space, which is not only discriminative but also semantic-preserving, to perform the ZSL task. Specifically, a dictionary learning framework is exploited to connect the latent attribute space with attribute space and similarity space. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.


title: Epistemological perspectives on IS research: a framework for analysing and systematizing epistemological assumptions

text: Over the last three decades, a methodological pluralism has developed within information systems (IS) research. Various disciplines and many research communities as well, contribute to this discussion. However, working on the same research topic or studying the same phenomenon does not necessarily ensure mutual understanding. Especially within this multidisciplinary and international context, the epistemological assumptions made by different researchers may vary fundamentally. These assumptions exert a substantial impact on how concepts like validity, reliability, quality and rigour of research are understood. Thus, the extensive publication of epistemological assumptions is, in effect, almost mandatory. Hence, the aim of this paper is to develop an epistemological framework which can be used for systematically analysing the epistemological assumptions in IS research. Rather than attempting to identify and classify IS research paradigms, this research aims at a comprehensive discussion of epistemology within the context of IS. It seeks to contribute to building the basis for identifying similarities as well as differences between distinct IS approaches and methods. In order to demonstrate the epistemological framework, the consensus-oriented interpretivist approach to conceptual modelling is used as an example.


title: Learning physical parameters from dynamic scenes

text: Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.


title: Architectures for Natural Language Generation: Problems and Perspectives

text: Current research in natural language generation is situated in a computational linguistics tradition that was founded several decades ago. We critically analyse some of the architectural assumptions underlying existing systems and point out some problems in the domains of text planning and lexicalization. Guided by the identification of major generation challenges viewed from the angles of knowledge-based systems and cognitive psychology, we sketch some new directions for future research.


title: The Second Dialog State Tracking Challenge

text: A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system’s actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites. This paper presents a second challenge, which continues this tradition and introduces some additional features – a new domain, changing user goals and a richer dialog state. The challenge received 31 entries from 9 research groups. The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions. An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.


title: Attention Is All You Need

text: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.2 after training for 4.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.


title: One Model To Learn Them All

text: Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.


title: Incremental LSTM-based dialog state tracker

text: A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.


title: Application of neural networks to inverse lens distortion modelling

text: The accurate and quick modelling of inverse lens distortion to rectify images or predict the image coordinates of real world objects has long been a challenge. This work investigates the use of artificial neural networks to perform this modelling. Several architectures are investigated and multiple methods of training the networks are used to achieve the best results. The error is expressed as a physical displacement on the imaging chip so that a fair comparison can be made between other published results which are based on cameras with different resolutions. It is shown that the novel application of locally optimised neural networks to residual lens calibration data yields an inverse distortion modelling that is highly competitive with prior published results.


title: Quantitative function for community detection.

text: We propose a quantitative function for community partition -- i.e., modularity density or D value. We demonstrate that this quantitative function is superior to the widely used modularity Q and also prove its equivalence with the objective function of the kernel k means. Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.


title: Learning Rapid-Temporal Adaptations

text: A hallmark of human intelligence and cognition is its flexibility. One of the longstanding goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation – the ability to adapt quickly to new environments or tasks – that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.


title: Exemplary Automotive Attack Scenarios : Trojan Horses for Electronic Throttle Control System ( ETC ) and Replay Attacks on the Power Window System

text: The consideration of targeted security attacks is not yet common in the automotive domain where primarily safety requirements are considered. Hence this survey addresses the relatively new field of IT-security in automotive technology in order to motivate further research. With the emergence of automotive technologies like car-to-car (c2c) communication, the challenges increase. In order to show the necessity for such research we describe how in theory, a potential attack on the Electronic Throttle Control (ETC) could succeed, if no countermeasures are taken, with potentially drastic consequences on the safety of the vehicle, its occupants and its environment as a result. Also an attack on the power window system is shown using the implementation techniques already employed in the design of automotive components. Our goal is to show, that such exploited security vulnerabilities are likely to have serious consequences with respect to the vehicles safety. Therefore such attacks need to be considered and suitable countermeasures have to be identified. In order to achieve this, the connection of the two disciplines Safety and Security for automotive IT components needs to be discussed. We introduce two exemplary attacks, the Trojan Horse targeting at the Electronic Throttle Control System (ETC) and a replay attack on the electric window lift. The first attack is mainly discussed in theory, the second is elaborated in a practical simulation scenario. Finally we introduce a general investigation of potential ways of intrusion and present some first practical results from tests with current automotive hardware.


title: Real-time object detection, localization and verification for fast robotic depalletizing

text: Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times. In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).


title: Tagging Patient Notes With ICD-9 Codes

text: There is substantial growth in the amount of medical data being generated in hospitals. With over 96% adoption rate [1], Electronic Medical/Health Records are used to store most of this medical data. If harnessed correctly, this medium provides a very convenient platform for secondary data analysis of these records to improve medical and patient care. One crucial feature of the information stored in these systems are ICD9-diagnosis codes, which are used for billing purposes and integration to other databases. These codes are assigned to medical text and require expert annotators with experience and training. In this paper we formulate this problem as a multi-label classification problem and propose a deep learning framework to classify the ICD-9 codes a patient is assigned at the end of a visit. We demonstrate that a simple LSTM model with a single layer of non-linearity can learn to classify patient notes with their corresponding ICD-9 labels moderately well.


title: Visualization of patent analysis for emerging technology

text: Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future. 2007 Elsevier Ltd. All rights reserved.


title: Perceptions of Sex, Gender, and Puberty Suppression: A Qualitative Analysis of Transgender Youth

text: International guidelines recommend the use of Gonadotropin-Releasing Hormone (GnRH) agonists in adolescents with gender dysphoria (GD) to suppress puberty. Little is known about the way gender dysphoric adolescents themselves think about this early medical intervention. The purpose of the present study was (1) to explicate the considerations of gender dysphoric adolescents in the Netherlands concerning the use of puberty suppression; (2) to explore whether the considerations of gender dysphoric adolescents differ from those of professionals working in treatment teams, and if so in what sense. This was a qualitative study designed to identify considerations of gender dysphoric adolescents regarding early treatment. All 13 adolescents, except for one, were treated with puberty suppression; five adolescents were trans girls and eight were trans boys. Their ages ranged between 13 and 18 years, with an average age of 16 years and 11 months, and a median age of 17 years and 4 months. Subsequently, the considerations of the adolescents were compared with views of clinicians treating youth with GD. From the interviews with the gender dysphoric adolescents, three themes emerged: (1) the difficulty of determining what is an appropriate lower age limit for starting puberty suppression. Most adolescents found it difficult to define an appropriate age limit and saw it as a dilemma; (2) the lack of data on the long-term effects of puberty suppression. Most adolescents stated that the lack of long-term data did not and would not stop them from wanting puberty suppression; (3) the role of the social context, for which there were two subthemes: (a) increased media-attention, on television, and on the Internet; (b) an imposed stereotype. Some adolescents were positive about the role of the social context, but others raised doubts about it. Compared to clinicians, adolescents were often more cautious in their treatment views. It is important to give voice to gender dysphoric adolescents when discussing the use of puberty suppression in GD. Otherwise, professionals might act based on assumptions about adolescents' opinions instead of their actual considerations. We encourage gathering more qualitative research data from gender dysphoric adolescents in other countries.


title: Good genes, complementary genes and human mate preferences

text: The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.


title: Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts

text: The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .


title: Internet addiction: a systematic review of epidemiological research for the last decade.

text: In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.


title: Analysis and Characterisation of Spoof Surface Plasmon Polaritons based Wideband Bandpass Filter at Microwave Frequency

text: This paper presents the wideband bandpass filter (BPF) in the microwave frequency domain. The realisation approach is based on spoof surface plasmon polaritons (SSPPs) phenomenon using plasmonic metamaterial. A novel unit cell is designed for filter design using an LC resonator concept. Then SSPPs BPF is realised using an optimised mode converter and five unit cells. This paper includes a brief design detail of the proposed novel unit cell. The passband of BPF is achieved at approximately 1.20 5.80 GHz, 3dB bandwidth is tentatively 4.60 GHz and the insertion loss is less than 2 dB approximately over the passband. The overall dimension of fabricated filter is (90 x 45) mm. A basic schematic of transmission line representation is also proposed to evaluate the BPF structure.


title: Hybrid Electric Vehicles: Architecture and Motor Drives

text: Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and polluting emissions. Among several technologies, hybrid electric vehicle (HEV) traction is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions, and long operating range. Moreover, the technologies of all the component hardware are technically and markedly available. At present, almost all the major automotive manufacturers are developing hybrid electric vehicles, and some of them have marketed their productions, such as Toyota and Honda. This paper reviews the present technologies of HEVs in the range of drivetrain configuration, electric motor drives, and energy storages


title: Learning from other subjects helps reducing Brain-Computer Interface calibration time

text: A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.


title: Location Segmentation, Inference and Prediction for Anticipatory Computing

text: This paper presents an analysis of continuous cellular tower data representing five months of movement from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validated using data from Bluetooth beacons placed in the homes of the subjects. We then use these inferred locations as states within several dynamic Bayesian networks to predict each subject’s subsequent movements with over 90% accuracy. We also introduce the X-Factor model, a DBN with a latent variable corresponding to abnormal behavior. We conclude with a description of extensions for this model, such as incorporating additional contextual and temporal variables already being logged by the


title: A Novel Self-Reference Technique for STT-RAM Read and Write Reliability Enhancement

text: Spin-transfer torque random access memory (STT-RAM) has demonstrated great potential in embedded and stand-alone applications. However, process variations and thermal fluctuations greatly influence the operation reliability of STT-RAM and limit its scalability. In this paper, we propose a new field-assisted access scheme to improve the read/write reliability and performance of STT-RAM. During read operations, an external magnetic field is applied to a magnetic tunneling junction (MTJ) device, generating a resistive sense signal without referring to other devices. Such a self-reference scheme offers a very promising alternative approach to overcome the severe cell-to-cell variations at highly scaled technology node. Furthermore, the external magnetic field can be used to assist the MTJ switching during write operations without introducing extra hardware overhead.


title: Tendinous insertion of semimembranosus muscle into the lateral meniscus

text: Forty-two cadaver knees were used for morphologic and MRI observations of the tendinous distal expansions of the semimembranosus m. and the posterior capsular structures of the knee. A tendinous branch of the semimembranosus m. inserting into the posterior horn of the lateral meniscus was found in 43.2% of the knees dissected, besides five already known insertional branches; capsular, direct, anterior and inferior, as well as the oblique popliteal ligament. The tendon had three morphologic types; thin, broad and round. All three types moved the lateral meniscus posteriorly when pulled on. Thus, the semimembranosus m. may also have a protective function for the lateral meniscus as well as the already well established function of protecting the medial meniscus in knee flexion. When a semimembranosus tendon attachment to the posterior horn of the lateral meniscus is present, its normal insertion is difficult to differentiate from a lateral meniscus tear in MRI and this may cause misdiagnosis.


title: Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex

text: The cortical local field potential (LFP) is a common measure of population activity, but its relationship to synaptic activity in individual neurons is not fully established. This relationship has been typically studied during anesthesia and is obscured by shared slow fluctuations. Here, we used patch-clamp recordings in visual cortex of anesthetized and awake mice to measure intracellular activity; we then applied a simple method to reveal its coupling to the simultaneously recorded LFP. LFP predicted membrane potential as accurately as synaptic currents, indicating a major role for synaptic currents in the relationship between cortical LFP and intracellular activity. During anesthesia, cortical LFP predicted excitation far better than inhibition; during wakefulness, it predicted them equally well, and visual stimulation further enhanced predictions of inhibition. These findings reveal a central role for synaptic currents, and especially inhibition, in the relationship between the subthreshold activity of individual neurons and the cortical LFP during wakefulness.


title: The underlying structure of visuospatial working memory in children with mathematical learning disability.

text: This study examined visual, spatial-sequential, and spatial-simultaneous working memory (WM) performance in children with mathematical learning disability (MLD) and low mathematics achievement (LMA) compared with typically developing (TD) children. Groups were matched on reading decoding performance and verbal intelligence. Besides statistical significance testing, we used bootstrap confidence interval estimation and computed effect sizes. Children were individually tested with six computerized tasks, two for each visuospatial WM subcomponent. We found that both MLD and LMA children had low visuospatial WM function in both spatial-simultaneous and spatial-sequential WM tasks. The WM deficit was most expressed in MLD children and less in LMA children. This suggests that WM scores are distributed along a continuum with TD children achieving top scores and MLD children achieving low scores. The theoretical and practical significance of findings is discussed. Statement of Contribution What is already known on this subject? Working memory plays an important role in mathematical achievement. Children with mathematical learning disability (MLD) usually have low working memory resources. Conflicting results have been reported concerning the role of VSWM in individuals with MLD. What the present study adds? Children with different degree of impairment in math achievement and typically developing children were tested. Visual, spatial-sequential, and spatial-simultaneous working memory tasks were examined. Only spatial-sequential and spatial-simultaneous working memory tasks discriminated the two impairments groups.


title: The neuroscience of social decision-making.

text: Given that we live in highly complex social environments, many of our most important decisions are made in the context of social interactions. Simple but sophisticated tasks from a branch of experimental economics known as game theory have been used to study social decision-making in the laboratory setting, and a variety of neuroscience methods have been used to probe the underlying neural systems. This approach is informing our knowledge of the neural mechanisms that support decisions about trust, reciprocity, altruism, fairness, revenge, social punishment, social norm conformity, social learning, and competition. Neural systems involved in reward and reinforcement, pain and punishment, mentalizing, delaying gratification, and emotion regulation are commonly recruited for social decisions. This review also highlights the role of the prefrontal cortex in prudent social decision-making, at least when social environments are relatively stable. In addition, recent progress has been made in understanding the neural bases of individual variation in social decision-making.


title: Audit Log Management in MongoDB

text: In the past few years, web-based applications and their data management needs have changed dramatically. Relational databases are often being replaced by other viable alternatives, such as NoSQL databases, for reasons of scalability and heterogeneity. MongoDB, a NoSQL database, is an agile database built for scalability, performance and high availability. It can be deployed in single server environment and also on complex multi-site architectures. MongoDB provides high performance for read and write operations by leveraging in-memory computing. Although researchers have motivated the need for MongoDB, not much appears in the area of log management. Efficient log management techniques are needed for various reasons including security, accountability, and improving the performance of the system. Towards this end, we analyze the different logging methods offered by MongoDB and compare them to the NIST standard. Our analysis indicates that profiling and mongosniff are useful for log management and we present a simple model that combines the two techniques.


title: Bayesian Information Extraction Network

text: Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential of DBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain. 1 Information Extraction Information extraction ( IE) is the task of filling in template information from previously unseen text which belongs to a pre-defined domain. The resulting database is suited for formal queries and filtering.IE systems generally work by detecting patterns in the text that help identify significant information. Researchers have shown [Freitag and McCallum, 1999; Ray and Craven, 2001 ] that a probabilistic approach allows the construction of robust and well-performing systems. However, the existing probabilistic systems are generally based on Hidden Markov Models ( HMMs). Due to this relatively impoverished representation, they are unable to take advantage of the wide array of linguistic information used by many non-probabilistic IE systems. In addition, existing HMM -based systems model each target category separately, failing to capture relational information, such as typical target order, or the fact that each element only belongs to a single category. This paper shows how to incorporate a wide array of knowledge into a probabilistic IE system, based on dynamic Bayesian networks ( DBN)—a rich probabilistic representation that generalizes HMMs. Let us illustrateIE by describing seminar announcements which got established as one of the most popular benchmark domains in the field[Califf and Mooney, 1999; Freitag and McCallum, 1999; Soderland, 1999; Roth and Yih, 2001; Ciravegna, 2001 ]. People receive dozens of seminar announcements weekly and need to manually extract information and paste it into personal organizers. The goal of an IE system is to automatically identify target fields such as location and topic of a seminar, date and starting time, ending time and speaker. Announcements come in many formats, but usually follow some pattern. We often find a header with a gist in the form “ PostedBy: john@host.domain; Who: Dr. Steals; When: 1 am; ” and so forth. Also in the body of the message, the speaker usually precedes both location and starting time, which in turn precedes ending time as in: ‘‘Dr. Steals presents in Dean Hall at one am.’’ The task is complicated since some fields may be missing or may contain multiple values. This kind of data falls into the so-called semi-structured text category. Instances obey certain structure and usually contain information for most of the expected fields in some order. There are two other categories: free text and structured text. Instructured text , the positions of the information fields are fixed and values are limited to pre-defined set. Consequently, theIE systems focus on specifying the delimiters and order associated with each field. At the opposite end lies the task of extracting information fromfree textwhich, although unstructured, is assumed to be grammatical. Here IE systems rely more on syntactic, semantic and discourse knowledge in order to assemble relevant information potentially scattered all over a large document. IE algorithms face different challenges depending on the extraction targets and the kind of the text they are embedded in. In some cases, the target is uniquely identifiable (singleslot), while in others, the targets are linked together in multislot association frames. For example, a conference schedule has several slots for related speaker, topic and time of the presentation, while a seminar announcement usually refers to a unique event. Sometimes it is necessary to identify each word in a target slot, while some benefit may be reaped from partial identification of the target, such as labeling the beginning or end of the slot separately. Many applications involve processing of domain-specific jargon like Internetese —a style of writing prevalent in news groups, e-mail messages, bulletin boards and online chat rooms. Such documents do not follow a good grammar, spelling or literary style. Often these are more like a stream-of-consciousness ranting in which asciiart and pseudo-graphic sketches are used and emphasis is provided by all-capitals, or using multiple exclamation signs. As we exemplify below, syntactic analysers easily fail on such Position 1 2 3 4 5 6 7 8 9 10 Tag <speaker > <location> <s-time> Phrase Doctor Steals Presents in Dean Hall at 1 am . Lemma Dr. present in hall at am PoS NNP NNP(VB) VB(NNS) IN NNP NN(NNP) IN CD NN(RB) . Syn.segm. NP NP (VP) VP PP NP NP PP NP NP(VP) Semantic Title LstName LstName Location Time Length 3 6 8 2 4 4 2 1 2 1 Case Upper Upper Upper lower Upper Upper lower lower Table 1: Sample phrase and its representation in multiple feature values for ten tokens.


title: A Framework for Highly-Available Composed Real-Time Internet Services Bhaskaran Raman Qualifying Examination

text: Applicationservicesfor theend-userareall importantin today’scommunicationnetworks,andcoulddictatethesuccessor failureof technologyor serviceproviders[39]. It is importantto develop and deploy applicationfunctionality quickly [18]. The ability to compose servicesfrom independentprovidersprovidesa flexible way to quickly build new end-to-endfunctionality. Suchcompositionof services acrossthe network and acrossdifferent serviceproviders becomesespeciallyimportant in the context of growing popularityandheterogeneityin 3G+ accessnetworks and devices[25]. In this work, we provide a framework for constructing such composed services on the Internet. Robustnessandhigh-availability arecrucial for Internet services.While cluster-basedmodelsfor resilienceto failureshave beenbuilt for web-servers[27] aswell asproxy services[11, 4], theseareinadequatein thecontext of composedservices.This is especiallyso whenthe application sessionis long-lived, andfailureshaveto behandledduring asession. In the context of composed services, we address the important and challenging issues of resilience to failures, and adapting to changes in overall performance during longlived sessions. Our framework is basedon a connection-orientedoverlay network of compute-clusterson theInternet.Theoverlay network provides the context for composingservices over the wide-area,and monitoring for livenessand performanceof a session. We have performedinitial analysesof the feasibility of network failure detectionover the wide-areaInternet. And we have a preliminaryevaluation of the overheadassociatedwith suchan overlay network. We presentour plansfor furtherevaluationandrefinement of thearchitecture;andfor examiningissuesrelatedto the creationof theoverlaytopology. 1 Intr oduction Application functionality for the end-useris thedriving force behinddevelopmentof technologyandits adoption. It is importantto be ableto developanddeploy new functionality quickly [18]. Compositionof existing servicesto achieve new functionalityenablessuchquick development throughreuseof alreadydeployed components.Consider for example,thescenarioshown in Figure1. Video-on-demand server Replicated instances Service Provider A


title: Neural Message Passing for Quantum Chemistry

text: Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.


title: Substrate integrated waveguide (SIW) power amplifier using CBCPW-to-SIW transition for matching network

text: In this paper, for the first time a novel substrate integrated waveguide (SIW)-based 10W power amplifier (PA), designed with conductor-backed coplanar waveguide (CBCPW)-to-SIW transition matching network (MN), is presented. Transition between CBCPW and SIW is employed for both input and output MN designs, the proposed SIW PA can be easily connected with any microstrip or SIW-based circuits. Asymmetrical and symmetrical types of CBCPW-to-SIW transition MN are proposed. Output SIW-based MN is designed with asymmetrical structure by using one inductive metalized post and input SIW-based MN is designed with symmetrical structure by using two identical inductive metalized posts. One SIW-based 10W PA using GaN HEMT at 3.62 GHz is designed, fabricated, and measured. Measured results show that the maximum power added efficiency (PAE) is 54.24 % with 39.74 dBm output power and the maximum gain is 13.31 dB. At the design frequency of 3.6 GHz, the size of proposed SIW-based PA is comparable with other microstrip-based PAs.


title: High Performance Single-Chip FPGA Rijndael Algorithm Implementations

text: This paper describes high performance single-chip FPGA implementations of the new Advanced Encryption Standard (AES) algorithm, Rijndael. The designs are implemented on the Virtex-E FPGA family of devices. FPGAs have proven to be very effective in implementing encryption algorithms. They provide more flexibility than ASIC implementations and produce higher data-rates than equivalent software implementations. A novel, generic, parameterisable Rijndael encryptor core capable of supporting varying key sizes is presented. The 192-bit key and 256-bit key designs run at data rates of 5.8 Gbits/sec and 5.1 Gbits/sec respectively. The 128-bit key encryptor core has a throughput of 7 Gbits/sec which is 3.5 times faster than similar existing hardware designs and 21 times faster than known software implementations, making it the fastest single-chip FPGA Rijndael encryptor core reported to date. A fully pipelined single-chip 128-bit key Rijndael encryptor/decryptor core is also presented. This design runs at a data rate of 3.2 Gbits/sec on a Xilinx Virtex-E XCV3200E-8-CG1156 FPGA device. There are no known singlechip FPGA implementations of an encryptor/decryptor Rijndael design.


AES Proposal : Rijndael


title: Very Compact FPGA Implementation of the AES Algorithm

text: In this paper a compact FPGA architecture for the AES algorithm with 128-bit key targeted for low-cost embedded applications is presented. Encryption, decryption and key schedule are all implemented using small resources of only 222 Slices and 3 Block RAMs. This implementation easily fits in a low-cost Xilinx Spartan II XC2S30 FPGA. This implementation can encrypt and decrypt data streams of 150 Mbps, which satisfies the needs of most embedded applications, including wireless communication. Specific features of Spartan II FPGAs enabling compact logic implementation are explored, and a new way of implementing MixColumns and InvMixColumns transformations using shared logic resources is presented.


title: A compact FPGA implementation of the hash function whirlpool

text: Recent breakthroughs in cryptanalysis of standard hash functions like SHA-1 and MD5 raise the need for alternatives. A credible alternative to for instance SHA-1 or the SHA-2 family of hash functions is Whirlpool. Whirlpool is a hash function that has been evaluated and approved by NESSIE and is standardized by ISO/IEC. To the best of our knowledge only one FPGA implementation of Whirlpool has been published to date. This implementation is designed for high throughput rates requiring a considerable amount of hardware resources. In this article we present a compact hardware implementation of the hash function Whirlpool. The proposed architecture uses an innovative state representation that makes it possible to reduce the required hardware resources remarkably. The complete implementation requires 1456 CLB-slices and, most notably, no block RAMs.


title: House Price Prediction : Hedonic Price Model vs . Artificial Neural Network

text: The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.


title: Search mode and purchase intention in online shopping behavior

text: This study focuses on the effect of website visitors' goal-oriented search mode on purchase intention in online environments. In a study of 874 respondents recruited from 13 online shops representing a diversity of product categories and customer segments, the effect of visitors' goal-oriented search mode on purchase intention is found to be moderated by product involvement and product risk. Furthermore, product involvement, product risk, product knowledge and Internet experience are found to have positive effects on the degree of goaloriented search mode of the visitors. Also, product knowledge and Internet experience are reported to have direct positive effects on purchase intention. The results point to the importance of understanding the characteristics of website visitors, and to customize the support and search services offered on the website to the characteristics and preferences of the individual visitor. Customization of this kind may partly be based on immediate visitor history (referrer) and may be used to increase purchase intention, and eventually online sales.


title: Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry

text: User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.


title: Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks

text: Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.


title: 5.2 Distributed system of digitally controlled microregulators enabling per-core DVFS for the POWER8TM microprocessor

text: Integrated voltage regulator modules (iVRMs) [1] provide a cost-effective path to realizing per-core dynamic voltage and frequency scaling (DVFS), which can be used to optimize the performance of a power-constrained multi-core processor. This paper presents an iVRM system developed for the POWER8™ microprocessor, which functions as a very fast, accurate low-dropout regulator (LDO), with 90.5% peak power efficiency (only 3.1% worse than an ideal LDO). At low output voltages, efficiency is reduced but still sufficient to realize beneficial energy savings with DVFS. Each iVRM features a bypass mode so that some of the cores can be operated at maximum performance with no regulator loss. With the iVRM area including the input decoupling capacitance (DCAP) (but not the output DCAP inherent to the cores), the iVRMs achieve a power density of 34.5W/mm2, which exceeds that of inductor-based or SC converters by at least 3.4× [2].


title: New Word Detection in Ancient Chinese Literature

text: Mining Ancient Chinese corpus is not as convenient as modern Chinese, because there is no complete dictionary of ancient Chinese words which leads to the bad performance of tokenizers. So finding new words in ancient Chinese texts is significant. In this paper, the Apriori algorithm is improved and used to produce candidate character sequences. And a long short-term memory (LSTM) neural network is used to identify the boundaries of the word. Furthermore, we design word confidence feature to measure the confidence score of new words. The experimental results demonstrate that the improved Apriori-like algorithm can greatly improve the recall rate of valid candidate character sequences, and the average accuracy of our method on new word detection raise to 89.7%.


title: Attacking Deterministic Signature Schemes Using Fault Attacks

text: Many digital signature schemes rely on random numbers that are unique and non-predictable per signature. Failures of random number generators may have catastrophic effects such as compromising private signature keys. In recent years, many widely-used cryptographic technologies adopted deterministic signature schemes because they are presumed to be safer to implement. In this paper, we analyze the security of deterministic ECDSA and EdDSA signature schemes and show that the elimination of random number generators in these schemes enables new kinds of fault attacks. We formalize these attacks and introduce practical attack scenarios against EdDSA using the Rowhammer fault attack. EdDSA is used in many widely used protocols such as TLS, SSH, and IPSec, and we show that these protocols are not vulnerable to our attack. We formalize the necessary requirements of protocols using these deterministic signature schemes to be vulnerable, and discuss mitigation strategies and their effect on fault attacks against deterministic signature schemes.


title: Development of a FACS-verified set of basic and self-conscious emotion expressions.

text: In 2 studies, the authors developed and validated of a new set of standardized emotion expressions, which they referred to as the University of California, Davis, Set of Emotion Expressions (UCDSEE). The precise components of each expression were verified using the Facial Action Coding System (FACS). The UCDSEE is the first FACS-verified set to include the three "self-conscious" emotions known to have recognizable expressions (embarrassment, pride, and shame), as well as the 6 previously established "basic" emotions (anger, disgust, fear, happiness, sadness, and surprise), all posed by the same 4 expressers (African and White males and females). This new set has numerous potential applications in future research on emotion and related topics.


title: The use of Social Networking Sites (SNSs) by the faculty members of the School of Library & Information Science, PAAET, Kuwait

text: Purpose – The purpose of this study is to describe the usage of Social Networking Sites (SNSs) by the faculty members of the School of Library and Information Science (SLIS), at the College of Basic Education, the Public Authority for Applied Education and Training (PAAET), Kuwait. Design/methodology/approach – A survey conducted to collect data from 33 faculty members of whom only 21 members were using SNSs, representing 63.6 per cent of the total sample, and 12 members were not using SNSs, representing 36.4 per cent of the total sample. This study revealed that SNSs are used moderately by the faculty members. Findings – This study showed that faculty members who were using SNSs tend to be males, aged between 41 and 50 years, PhD holders, ranked as assistant professors, full-time members, specialized in information technologies with a relatively new experience of teaching ranged from one to five years, and most of the faculty members who were not using SNSs tended to be also males, aged between 41 and 60 years, PhD holders, ranked as lecturers, full-time members specialized in organization of information with a teaching experience ranged from 16 to 20 years. More than half of the faculty members were using SNSs for three years to less than six years, and a large number of them were using SNSs several times a week and were accessing these sites more from their school office, home and school laboratory. There are no any statistical significant differences between the demographic data of participants (gender, age and education level) and either their use or non-use of SNSs. There are no significant differences between the academic rank, teaching status and teaching experience of faculty and their use of SNSs. However, there is a significant relation between the faculty’s area of teaching and their use of SNSs. Faculty members were interested in the use of SNSs. YouTube, Twitter, Facebook and blogs respectively were used mostly by faculty members, but Twitter, Facebook and YouTube were the most famous SNSs they have profiles on. Faculty members have adopted SNSs mainly for the purpose of communicating with others, finding and sharing information with peers and students as well. Tasks on SNSs made by faculty members were mostly to make communication, send/receive messages and find general and specific information. Faculty members’ profiles on SNSs were mostly on Twitter, Facebook, YouTube, blogs, wikis and podcasting respectively. Faculty members confirmed that the use of YouTube, Facebook, blogs, Twitter, wikis and podcasting respectively was at least effective and the use of YouTube, Facebook, Twitter, Blogs and Wikis respectively was at least fairly useful fairly easy to them. Faculty members are in general agreement about the effectiveness of SNSs especially for disseminating and sharing information, communication and informal collaboration. The study showed The researcher would like to thank the Public Authority for Applied Education and Training (PAAET), the state of Kuwait, for supporting this paper (BE-11-10). The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0264-0473.htm


title: The Bluetooth radio system

text: A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.


title: Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

text: The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods.


title: Baby Talk : Understanding and Generating Image Descriptions

text: We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.


title: Single camera lane detection and tracking

text: In this paper we present a method to detect and track straight lane boundaries, using a forward-looking single camera mounted on-board. The method proved to be robust, even under varying lighting conditions, and in the presence of heavy shadowing cast by vegetation, vehicles, bridges, etc. Moreover, false positive hardly occur. The lane markings are being continuously detected even when the vehicle is performing maneuvers such as excursion or lane change. The performance is achieved by a novel combination of methods, namely, by first finding vanishing point candidates for the lane boundaries, and later by the careful selection of line segments given a vanishing point, and finally by using a smart heuristics to select the whole set of four lane boundaries. Suppression of false positives is further achieved by tracking the vanishing point, and by constraining the lane width based on recent history.


title: CLAMS: Bringing Quality to Data Lakes

text:
With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable.
 We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs.
 CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.


title: Distributed Detection of Sensor Worms Using Sequential Analysis and Remote Software Attestations

text: Recent work has demonstrated that self-propagating worms are a real threat to sensor networks. Since worms can enable an adversary to quickly compromise an entire sensor network, they must be detected and stopped as quickly as possible. To meet this need, we propose a worm propagation detection scheme for sensor networks. The proposed scheme applies a sequential analysis to detect worm propagation by leveraging the intuition that a worm’s communication pattern is different from benign traffic. In particular, a worm in a sensor network requires a long sequence of packets propagating hop-by-hop to each new infected node in turn. We thus have detectors that observe communication patterns in the network, a worm spreading hop-by-hop will quickly create chains of connections that would not be seen in normal traffic. Once detector nodes identify the worm propagation pattern, they initiate remote software attestations to detect infected nodes. Through analysis and simulation, we demonstrate that the proposed scheme effectively and efficiently detects worm propagation. In particular, it blocks worm propagation while restricting the fraction of infected nodes to at most 13.5% with an overhead of at most 0.63 remote attestations per node per time slot.


title: “Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot

text: Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.


title: A 20 Gb/s 0.4 pJ/b Energy-Efficient Transmitter Driver Utilizing Constant- ${\rm G}_{\rm m}$  Bias

text: This paper describes a transmitter driver based on a CMOS inverter with a resistive feedback. By employing the proposed driver topology, the pre-driver can be greatly simplified, resulting in a remarkable reduction of the overall driver power consumption. It also offers another advantage that the implementation of equalization is straightforward, compared with a conventional voltage-mode driver. Furthermore, the output impedance remains relatively constant while the data is being transmitted, resulting in good signal integrity. For evaluation of the driver performance, a fully functional 20 Gb/s transmitter is implemented, including a PRBS generator, a serializer, and a half-rate clock generator. In order to enhance the overall speed of the digital circuits for 20 Gb/s data transmission, the resistive feedback is applied to the time-critical inverters, which enables shorter rise/fall times. The prototype chip is fabricated in a 65 nm CMOS technology. The implemented driver circuit operates up to the data rate of 20 Gb/s, exhibiting an energy efficiency of 0.4 pJ/b for the output swing of 250 mVpp,diff.


title: A Technique for Data Deduplication using Q-Gram Concept with Support Vector Machine

text: Several systems that rely on consistent data to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi-replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations in developing methods for removing replicas from its data repositories.In this paper, we have proposed accordingly. In the previous work, duplicate record detection was done using three different similarity measures and neural network. In the previous work, we have generated feature vector based on similarity measures and then, neural network was used to find the duplicate records. In this paper, we have developed Q-gram concept with support vector machine for deduplication process. The similarity function, which we are used Dice coefficient,Damerau–Levenshtein distance,Tversky index for similarity measurement. Finally, support vector machine is used for testing whether data record is duplicate or not. A set of data generated from some similarity measures are used as the input to the proposed system. There are two processes which characterize the proposed deduplication technique, the training phase and the testing phase the experimental results showed that the proposed deduplication technique has higher accuracy than the existing method. The accuracy obtained for the proposed deduplication 88%.


title: A New Travelling Wave Antenna in Microstrip

text: The radiation characteristics of the first higher order mode of microstrip lines are investigated. As a result, a simple travelling wave antenna element is scribed, having a larger bandwidth compared with resonator antennas. A method to excite the first higher order mode is shown. A single antenna element is treated theoretically and experimentally, and an array of four antenna elements is demonstrated.


title: The pathogenesis of Campylobacter jejuni-mediated enteritis.

text: Campylobacter jejuni, a gram-negative spiral shaped bacterium, is a frequent cause of gastrointestinal food-borne illness in humans throughout the world. Illness with C. jejuni ranges from mild to severe diarrheal disease. This article focuses on Campylobacter virulence determinants and their potential role in the development of C. jejuni-mediated enteritis. A model is presented that diagrams the interactions of C. jejuni with the intestinal epithelium. Additional work to identify and characterize C. jejuni virulence determinants is certain to provide novel insights into the diversity of strategies employed by bacterial pathogens to cause disease.


title: Real-Time Systems - Design Principles for Distributed Embedded Applications

text: Now, we come to offer you the right catalogues of book to open. real time systems design principles for distributed embedded applications is one of the literary work in this world in suitable to be reading material. That's not only this book gives reference, but also it will show you the amazing benefits of reading a book. Developing your countless minds is needed; moreover you are kind of people with great curiosity. So, the book is very appropriate for you.


title: Performance evaluation of synchronous rectification in front-end full-bridge rectifiers

text: In this paper, performance evaluation of synchronous rectification in front-end full-bridge rectifiers is presented. Specifically, the implementation of the full-bridge rectifier with the two diode rectifiers connected to the negative output terminal replaced by two synchronous rectifiers (SRs) and the implementation with all four diode rectifiers replaced by four SRs are considered. In both implementations, the SRs are N-channel MOSFETs controlled by sensing the line voltage. Two methods of line-voltage sensing are presented. First, direct line-voltage sensing, and second, indirect line-voltage sensing, i.e., sensing the line voltage between an input terminal and the negative output terminal. The proposed implementations also include a protection method of preventing accidental short circuit between the input or output terminals. In addition, SR power-management methods such as turning off the SRs at light loads and at no load are provided. The protection of SRs at large inrush currents is also discussed. Experimental results obtained on a 90-W (19.5-V/4.6-A) laptop adapter for the universal line voltage range (90-264 Vrms) are given. In the experimental circuit with four SRs, the efficiency improvement at 90-Vrms line voltage and full load is 1.31%.


A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a number of steps bounded by a polynomial in the length of the input. Many problems with wide applicability – e.g., set cover, knapsack, hitting set, max cut, and satisfiability – lack a polynomial algorithm for solving them, but also lack a proof that no such polynomial algorithm exists. Hence, they remain “open problems.” This paper references the recent work, “On the Reducibility of Combinatorial Problems” [1]. BODY A large class of open problems are mutually convertible via poly-time reductions. Hence, either all can be solved in poly-time, or none can. REFERENCES [1] R. Karp. Reducibility Among Combinatorial Problems. In Complexity of Computer Computations, 1972. ∗With apologies to Professor Richard Karp. Volume X of Tiny Transactions on Computer Science This content is released under the Creative Commons Attribution-NonCommercial ShareAlike License. Permission to make digital or hard copies of all or part of this work is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. CC BY-NC-SA 3.0: http://creativecommons.org/licenses/by-nc-sa/3.0/.


title: Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks

text: Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform the problem from a series of grapheme-to-phoneme conversions to a word-to-pronunciation conversion. Training joint-sequence based G2P require explicit grapheme-to-phoneme alignments which are not straightforward since graphemes and phonemes don't correspond one-to-one. The LSTM based approach forgoes the need for such explicit alignments. We experiment with unidirectional LSTM (ULSTM) with different kinds of output delays and deep bidirectional LSTM (DBLSTM) with a connectionist temporal classification (CTC) layer. The DBLSTM-CTC model achieves a word error rate (WER) of 25.8% on the public CMU dataset for US English. Combining the DBLSTM-CTC model with a joint n-gram model results in a WER of 21.3%, which is a 9% relative improvement compared to the previous best WER of 23.4% from a hybrid system.


Text-To-Speech Synthesis


title: Speech recognition with deep recurrent neural networks

text: Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.


title: A comparative analysis of data center network architectures

text: Advances in data intensive computing and high performance computing facilitate rapid scaling of data center networks, resulting in a growing body of research exploring new network architectures that enhance scalability, cost effectiveness and performance. Understanding the tradeoffs between these different network architectures could not only help data center operators improve deployments, but also assist system designers to optimize applications running on top of them. In this paper, we present a comparative analysis of several well known data center network architectures using important metrics, and present our results on different network topologies. We show the tradeoffs between these topologies and present implications on practical data center implementations.


title: Tuning metaheuristics: A data mining based approach for particle swarm optimization

text: The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic’s efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models. 2011 Elsevier Ltd. All rights reserved.


title: A Unified Approach to the Change of Resolution: Space and Gray-Level

text: Multiple resolution analysis of images is a current trend in computer vision. In most cases, only spatial resolution has been considered. However, image resolution has an additional aspect: gray level, or color, resolution. Color resolution has traditionally been considered in the area of computer graphics. By defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tools as changes in color resolution. A gray tone image, for example, can be compared to a halftone image having only two colors (black and white), but of higher spatial resolution. An important application can be in pyramids, one of the most commonly used multiple (spatial) resolution schemes, where this approach provides a tool to change the color resolution as well. Increasing color resolution while reducing spatial resolution to retain more image details and prevent aliasing is an example of the possibility to find optimal combinations of resolution reduction, spatial and color, to best fit an application.


title: PaperVis: Literature Review Made Easy

text: Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.


title: Front-End Factor Analysis for Speaker Verification

text: This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.


title: PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector

text: In this paper, we address the task of instance-level semantic boundary detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including instances from both foreground objects and different types of background, and call it the PASCAL Boundaries dataset. This is a timely introduction of the dataset since results on the existing standard dataset for measuring edge detection performance, i.e. BSDS500, have started to saturate. In addition to generating a new dataset, we propose a deep network-based multi-scale semantic boundary detector and name it Multi-scale Deep Semantic Boundary Detector (M-DSBD). We evaluate M-DSBD on PASCAL boundaries and compare it to baselines. We test the transfer capabilities of our model by evaluating on MS-COCO and BSDS500 and show that our model transfers well.


title: Service-Oriented Architecture for Big Data Analytics in Smart Cities

text: A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends on employing a wide range of advanced technologies to improve the performance of various services and activities such as transportation, energy, healthcare, and education, while at the same time improve the city's resources utilization and initiate new business opportunities. One of the promising technologies to support such efforts is the big data technology. Effective and intelligent use of big data accumulated over time in various sectors can offer many advantages to enhance decision making in smart cities. In this paper we identify the different types of decision making processes involved in smart cities. Then we propose a service-oriented architecture to support big data analytics for decision making in smart cities. This architecture allows for integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations needed to effectively utilize available big data. It provides different functions and capabilities to use big data and provide smart capabilities as services that the architecture supports. As a result, different big data applications will be able to access and use these services for varying proposes within the smart city.


title: Automatic C-to-CUDA Code Generation for Affine Programs

text: Graphics Processing Units (GPUs) offer tremendous computational power. CUDA (Compute Unified Device Architecture) provides a multi-threaded parallel programming model, facilitating high performance implementations of general-purpose computations. However, the explicitly managed memory hierarchy and multi-level parallel view make manual development of high-performance CUDA code rather complicated. Hence the automatic transformation of sequential input programs into efficient parallel CUDA programs is of considerable interest. This paper describes an automatic code transformation system that generates parallel CUDA code from input sequential C code, for regular (affine) programs. Using and adapting publicly available tools that have made polyhedral compiler optimization practically effective, we develop a C-to-CUDA transformation system that generates two-level parallel CUDA code that is optimized for efficient data access. The performance of automatically generated code is compared with manually optimized CUDA code for a number of benchmarks. The performance of the automatically generated CUDA code is quite close to hand-optimized CUDA code and considerably better than the benchmarks’ performance on a multicore CPU.


title: An Experimental Evaluation of Apple Siri and Google Speech Recognition

text: We perform an experimental evaluation of two popular cloud-based speech recognition systems. Cloudbased speech recognition systems enhances Web surfing, transportation, health care, etc. Using voice commands helps drivers stay connected to the Internet by avoiding traffic safety risks. The performance of these type of applications should be robust under difficult network conditions. User frustration with network traffic problems can affect the usability of these applications. We evaluate the performance of two popular cloud-based speech recognition applications, Apple Siri and Google Speech Recognition (GSR) under various network conditions. We evaluate transcription delay and accuracy of transcription of each application under different packet loss and jitter values. Results of our study show that performance of cloud-based speech recognition systems can be affected by jitter and packet loss; which are commonly occurring over WiFi and cellular network connections. keywords: Cloud Speech Recognition, Quality of Experience, Software Measurement, Streaming Media, Real-time Systems.


title: Interactive evolution for the procedural generation of tracks in a high-end racing game

text: We present a framework for the procedural generation of tracks for a high-end car racing game (TORCS) using interactive evolution. The framework maintains multiple populations and allow users to work both on their own population (in single-user mode) or to collaborate with other users on a shared population. Our architecture comprises a web frontend and an evolutionary backend. The former manages the interaction with users (e.g., logs registered and anonymous users, collects evaluations, provides access to all the evolved populations) and maintains the database server that stores all the present/past populations. The latter runs all the tasks related to evolution (selection, recombination and mutation) and all the tasks related to the target racing game (e.g., the track generation). We performed two sets of experiments involving five human subjects to evolve racing tracks alone (in a single-user mode) or cooperatively. Our preliminary results on five human subjects show that, in all the experiments, there is an increase of users' satisfaction as the evolution proceeds. Users stated that they perceived improvements in the quality of the individuals between subsequent populations and that, at the end, the process produced interesting tracks.


title: Bandwidth-Enhanced Low-Profile Cavity-Backed Slot Antenna by Using Hybrid SIW Cavity Modes

text: A bandwidth enhanced method of a low-profile substrate integrated waveguide (SIW) cavity-backed slot antenna is presented in this paper. Bandwidth enhancement is achieved by simultaneously exciting two hybrid modes in the SIW-backed cavity and merging them within the required frequency range. These two hybrid modes, whose dominant fields are located in different half parts of the SIW cavity, are two different combinations of the and resonances. This design method has been validated by experiments. Compared with those of a previously presented SIW cavity-backed slot antenna, fractional impedance bandwidth of the proposed antenna is enhanced from 1.4% to 6.3%, its gain and radiation efficiency are also slightly improved to 6.0 dBi and 90%, and its SIW cavity size is reduced about 30%. The proposed antenna exhibits low cross polarization level and high front to back ratio. It still retains advantages of low-profile, low fabrication cost, and easy integration with planar circuits.


title: Ontology semantic approach to extraction of knowledge from holy quran

text: With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology consists of noun concepts identified in al-Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query.


title: A survey on text mining techniques

text: text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques of text categorization and classification is recently developed. Some of them are supervised and some of them unsupervised manner of document arrangement. This presented paper discusses different method of text categorization and cluster analysis for text documents. In addition of that a new text mining technique is proposed for future implementation. Keywords— text mining, classification, cluster analysis, survey


title: Bullying among young adolescents: the strong, the weak, and the troubled.

text:
OBJECTIVES
Bullying and being bullied have been recognized as health problems for children because of their association with adjustment problems, including poor mental health and more extreme violent behavior. It is therefore important to understand how bullying and being bullied affect the well-being and adaptive functioning of youth. We sought to use multiple data sources to better understand the psychological and social problems exhibited by bullies, victims, and bully-victims.

DESIGN, SETTING, AND PARTICIPANTS
Analysis of data from a community sample of 1985 mostly Latino and black 6th graders from 11 schools in predominantly low socioeconomic status urban communities (with a 79% response rate).

MAIN OUTCOME MEASURES
Peer reports of who bullies and who is victimized, self-reports of psychological distress, and peer and teacher reports of a range of adjustment problems.

RESULTS
Twenty-two percent of the sample was classified as involved in bullying as perpetrators (7%), victims (9%), or both (6%). Compared with other students, these groups displayed school problems and difficulties getting along with classmates. Despite increased conduct problems, bullies were psychologically strongest and enjoyed high social standing among their classmates. In contrast, victims were emotionally distressed and socially marginalized among their classmates. Bully-victims were the most troubled group, displaying the highest level of conduct, school, and peer relationship problems.

CONCLUSIONS
To be able to intervene with bullying, it is important to recognize the unique problems of bullies, victims, and bully-victims. In addition to addressing these issues directly with their patients, pediatricians can recommend school-wide antibullying approaches that aim to change peer dynamics that support and maintain bullying.