Low-Voltage Super class AB CMOS OTA cells with very high slew rate and power efficiency

A simple technique to achieve low-voltage power-efficient class AB operational transconductance amplifiers (OTAs) is presented. It is based on the combination of class AB differential input stages and local common-mode feedback (LCMFB) which provides additional dynamic current boosting, increased gain-bandwidth product (GBW), and near-optimal current efficiency. LCMFB is applied to various class AB differential input stages, leading to different class AB OTA topologies. Three OTA realizations based on this technique have been fabricated in a 0.5-/spl mu/m CMOS technology. For an 80-pF load they show enhancement factors of slew rate and GBW of up to 280 and 3.6, respectively, compared to a conventional class A OTA with the same 10-/spl mu/A quiescent currents and /spl plusmn/1-V supply voltages. In addition, the overhead in terms of common-mode input range, output swing, silicon area, noise, and static power consumption, is minimal.


Trapped in the net: will internet addiction become a 21st-century epidemic?


Human gut microbiota in obesity and after gastric bypass.

Recent evidence suggests that the microbial community in the human intestine may play an important role in the pathogenesis of obesity. We examined 184,094 sequences of microbial 16S rRNA genes from PCR amplicons by using the 454 pyrosequencing technology to compare the microbial community structures of 9 individuals, 3 in each of the categories of normal weight, morbidly obese, and post-gastric-bypass surgery. Phylogenetic analysis demonstrated that although the Bacteria in the human intestinal community were highly diverse, they fell mainly into 6 bacterial divisions that had distinct differences in the 3 study groups. Specifically, Firmicutes were dominant in normal-weight and obese individuals but significantly decreased in post-gastric-bypass individuals, who had a proportional increase of Gammaproteobacteria. Numbers of the H(2)-producing Prevotellaceae were highly enriched in the obese individuals. Unlike the highly diverse Bacteria, the Archaea comprised mainly members of the order Methanobacteriales, which are H(2)-oxidizing methanogens. Using real-time PCR, we detected significantly higher numbers of H(2)-utilizing methanogenic Archaea in obese individuals than in normal-weight or post-gastric-bypass individuals. The coexistence of H(2)-producing bacteria with relatively high numbers of H(2)-utilizing methanogenic Archaea in the gastrointestinal tract of obese individuals leads to the hypothesis that interspecies H(2) transfer between bacterial and archaeal species is an important mechanism for increasing energy uptake by the human large intestine in obese persons. The large bacterial population shift seen in the post-gastric-bypass individuals may reflect the double impact of the gut alteration caused by the surgical procedure and the consequent changes in food ingestion and digestion.


Sizing equation and Finite Element Analysis optimum design of axial-flux permanent-magnet motor for electric vehicle direct drive

This paper presents the design process of a slotted TORUS axial-flux permanent-magnet motor suitable for direct drive of an electric vehicle through sizing equation and Finite Element Analysis. AFPM motor is a high torque density motor which can be easily and compactly mounted into the automobile's wheel fitting the rim perfectly. A double-sided slotted AFPM motor with 6 rotor poles for high torque density and stable rotation is the preliminary design. In order to determine the design requirements, a simple vehicle dynamic model evaluating the vehicle performance considering an automobile's typical-trip cursing scenario is considered. Axial-flux permanent-magnet machine's fundamental theory and sizing equation are applied to obtain the initial design parameters of the motor with the highest possible torque. The FEA of designed motor was conducted via commercial Vector Field Opera-3D 14.0 software for evaluation and accuracy enhancement of the design parameters. FEA simulation results are compared with those results obtained from sizing equation showing a good agreement of flux density values in various parts of the designed motor at no-load condition. The motor meets all the requirements and limitations of the electric vehicle fitting the shape and the size of a classical rim of the vehicle wheel.


Generic and unified model of Switched Capacitor Converters

A generic modeling methodology that analyzes the losses in Switched Capacitors Converters (SCC) was developed and verified by simulation and experiments. The proposed analytical approach is unified, covering both hard and soft switched SCC topologies. The major advantage of the proposed model is that it expresses the losses as a function of the currents passing through each flying capacitor. Since these currents are linearly proportional to the output current, the model is also applicable to SCC with multiple capacitors. The proposed model provides an insight into the expected losses in SCC and the effects of their operational conditions such as duty cycle. As such, the model can help in the optimization of SCC systems and their control to achieve desired regulations.


Technical Committees Switched-Capacitor Power Electronics Circuits By

One of the main orientations in power electronics in the last decade has been the development of switching-mode converters without inductors and transformers. Light weight, small size and high power density are the result of using only switches and capacitors in the power stage of these converters. Thus, they serve as ideal power supplies for mobile electronic systems (e.g. cellular phones, personal digital assistants, and so forth). Switched-capacitor (SC) converters, with their large voltage conversion ratio, promise to be a response to such challenges of the 21st century as high-efficiency converters with low EMI emissions and the ability to realize steep step-down of the voltage (to 3V or even a smaller supply voltage for integrated circuits) or steep step-up of the voltage for automotive industry or internet services in the telecom industry. This paper is a tutorial of the main results in SC-converter research and design. Switched-Capacitor Converters— A Typical Power Electronics Contribution of the CAS Society From all of the research subjects in the Power Systems and Power Electronics Circuits Technical Committee—the stability of power systems, analysis of power electronics circuits (which are time-variable circuits with internally-controlled switches), chaos, modeling and simulation of converters, hardswitching and soft-switching converters, and so forth—the SCconverter is probably the most pertinent contribution of the CAS Society to power electronics. In fact, most of the researchers in this field belong to our Society’s technical committee and most of the breakthrough contributions on this subject have appeared in CAS publications. The reason for this is clear. Pursuing small size filters in the 1950’s, the circuit theory community found a solution in the elimination of bulky inductors from the structure of a passive filter. Active and then switched-capacitor filters showed the possibility of implementing the filtering function without using magnetic devices. The power electronics designer faced a similar challenge: the request for miniaturization of the power supply. This could be achieved only by eliminating the inductors and transformers. In a converter, the inductor serves the two purposes of processing energy and filtering the output voltage. The use of a switched-capacitor circuit for processing energy seemed doomed to failure because it is known that charging a capacitor from zero is achieved at a 50% efficiency. Ten years of research were needed to overcome the efficiency dilemma and to develop high-efficiency switched-capacitor energy-processing circuits. First SC Converters and Basic Principles The primary goal of any switching-mode power converter is to provide a constant (DC or AC) output voltage at its load, despite variations in the input voltage or load. A control element, therefore, has to be introduced in the process of transmitting energy so that the converter (power stage) changes its topology cyclically, and the durations of the switching topologies are adjusted for regulation purposes. The first SC converters were developed by a group of researchers from Kumamoto, Japan, who processed a DC unregulated voltage toward a DC regulated voltage [1, 2, 4]. These DC-DC converters were soon followed by AC-DC converters [3], DC-AC inverters [5] and AC-AC transformers [6]. By avoiding magnetic elements, these circuits, realized in a hybrid technology, featured a high power density (23W/inch). Figure 1(a). Basic SC step-down DC-DC converter. *A. Ioinovici is with the Department of Electrical and Electronics Engineering, Holon Academic Institute of Technology, Israel. R V S


Unified analysis of switched-capacitor resonant converters

A family of switched-capacitor resonant circuits using only two transistors is presented. The circuit operates under zero-current switching and, therefore, the switching loss is zero. It also offers a wide choice of voltage conversions including fractional as well as multiple and inverted voltage conversion ratios.


Monte Carlo Strength Evaluation: Fast and Reliable Password Checking

Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices and password strength evaluators failed to keep up: they are generally based on heuristic rules designed to defend against obsolete brute force attacks. Many passwords can only be guessed with significant effort, and motivated attackers may be willing to invest resources to obtain valuable passwords. However, it is eminently impractical for the defender to simulate expensive attacks against each user to accurately characterize their password strength. This paper proposes a novel method to estimate the number of guesses needed to find a password using modern attacks. The proposed method requires little resources, applies to a wide set of probabilistic models, and is characterised by highly desirable convergence properties.
 The experiments demonstrate the scalability and generality of the proposal. In particular, the experimental analysis reports evaluations on a wide range of password strengths, and of state-of-the-art attacks on very large datasets, including attacks that would have been prohibitively expensive to handle with existing simulation-based approaches.


Shape from focus system


Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus

Remembering experiences that lead to reward is essential for survival. The hippocampus is required for forming and storing memories of events and places, but the mechanisms that associate specific experiences with rewarding outcomes are not understood. Event memory storage is thought to depend on the reactivation of previous experiences during hippocampal sharp wave ripples (SWRs). We used a sequence switching task that allowed us to examine the interaction between SWRs and reward. We compared SWR activity after animals traversed spatial trajectories and either received or did not receive a reward. Here, we show that rat hippocampal CA3 principal cells are significantly more active during SWRs following receipt of reward. This SWR activity was further enhanced during learning and reactivated coherent elements of the paths associated with the reward location. This enhanced reactivation in response to reward could be a mechanism to bind rewarding outcomes to the experiences that precede them.


An unsupervised user identification algorithm using network embedding and scalable nearest neighbour

Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users.


An information theoretic approach to improve semantic similarity assessments across multiple ontologies

Semantic similarity has become, in recent years, the backbone of numerous knowledge-based applications dealing with textual data. From the different methods and paradigms proposed to assess semantic similarity, ontology-based measures and, more specifically, those based on quantifying the Information Content (IC) of concepts are the most widespread solutions due to their high accuracy. However, these measures were designed to exploit a single ontology. They thus cannot be leveraged in many contexts in which multiple knowledge bases are considered. In this paper, we propose a new approach to achieve accurate IC-based similarity assessments for concept pairs spread throughout several ontologies. Based on Information Theory, our method defines a strategy to accurately measure the degree of commonality between concepts belonging to different ontologies—this is the cornerstone for estimating their semantic similarity. Our approach therefore enables classic IC-based measures to be directly applied in a multiple ontology setting. An empirical evaluation, based on well-established benchmarks and ontologies related to the bio-medical domain, illustrates the accuracy of our approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works. Semantic similarity is a pillar of text understanding since it quantifies the degree of resemblance between the meanings of textual terms. In recent years, due to the marked increase in electronically available textual data, considerable effort has been focused on defining semantic measures, which have been extensively applied in various contexts such as information retrieval [54], information extraction [56], word sense disambiguation [32], data clustering [4,27], data privacy [6,45–47] and biomedicine (e.g. protein classification and interaction [19,57], chemical entity identification [18], identification of the communalities between brain data and linguistic data [15], etc.) to cite a few. Different knowledge sources have been used to facilitate the semantic similarity calculus, including: measures relying solely on textual corpora to estimate similarity from the degree of co-occurrence of terms [11], and measures involving structured knowledge bases such as ontologies [36,37,44], whereby the similarity calculus is based on the analysis of semantic relationships modelled between concepts. Compared to corpora-based approaches, ontology-based measures have a dual benefit: concept meanings can be


How to Remember What to Remember: Exploring Possibilities for Digital Reminder Systems

Digital reminder systems typically use time and place as triggers to remind people to perform activities. In this paper, we investigate how digital reminder systems could better support the process of remembering in a wider range of situations. We report findings from a survey and one-week diary study, which reveal that people want to remember to perform a broad spectrum of activities in the future, many of which cannot be supported by simple time- and location-based reminders. In addition to these examples of prospective memory, or ‘remembering intentions’ [53], we also find that people want support in ‘retrieving’ [53] information and details, especially those encountered through social interactions or intended for use in conversations with others. Drawing on our analysis of what people want to remember and how they try to support this, we draw implications for the design of intelligent reminder systems such as digital assistants (e.g. Microsoft’s Cortana) and smart speaker systems (e.g. Amazon Echo), and highlight the possibilities afforded by drawing on conversation and giving material form to digital reminders.


Automatic emotional speech classification

Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The sequential forward selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the cross-validated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdf are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% /spl plusmn/ 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two-class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.


Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity and scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tend to limit the range of users. This paper presents an approach that combines the advantages of these two kinds of approaches by joining the two methods. Firstly, it employs memory-based CF to fill the vacant ratings of the user-item matrix. Then, it uses the item-based CF as model-based to form the nearest neighbors of every item. At last, it produces prediction of the target user to the target item at real time. The collaborative filtering recommendation method combining memory-based CF and model-based CF can provide better recommendation than traditional collaborative filtering.


Taming verification hardness: an efficient algorithm for testing subgraph isomorphism

Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed. While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.


How Data Breaches Ruin Firm Reputation on Social Media! - Insights from a Sentiment-based Event Study

Data breach events are heavily discussed in social media. Data breaches, which imply the loss of personal sensitive data, have negative consequences on the affected firms such as loss of market value, loss of customers and reputational damage. In the digital era, wherein ensuring information security is extremely demanding and the dissemination of information occurs at a very high speed, protecting corporate reputation has become a challenging task. While several studies have provided empirical evidence of the financial consequences of data breaches, little attention has been dedicated to the link between data breaches and reputational risk. To address this research gap, we have measured the reputational effect of data breaches based on social media content by applying a novel approach, the sentiment-based event study. The empirical results provide strong evidence that data breach events deteriorate the reputation of companies involved in such incidents.


What Is Seen Is Who You Are: Are Cues in Selfie Pictures Related to Personality Characteristics?

Developments and innovation in the areas of mobile information technology, digital media and social networks foster new reflections on computer-mediated communication research, especially in the field of self-presentation. In this context, the selfie as a self-portrait photo is interesting, because as a meaningful gesture, it actively and directly relates the content of the photo to the author of the picture. From the perspective of the selfie as an image and the impression it forms, in the first part of the research we explored the distinctive characteristics of selfie pictures; moreover, from the perspective of the potential reflection of a selfie image on the personality of its author, in the second part we related the characteristics of selfie pictures to various personality constructs (e.g., Big Five personality traits narcissism and femininity-masculinity). Important aspects of selfies especially in relation to gender include the tilt of the head, the side of the face exhibited, mood and head position, later related also to the context of the selfie picture. We found no significant relations between selfie cues and personality constructs. The face-ism index was related to entitlement, and selfie availability to neuroticism.


Tagging Sentence Boundaries

In this paper we tackle sentence boundary disambiguation through a part-of-speech (POS) tagging framework. We describe necessary changes in text tokenization and the implementat ion of a POS tagger and provide results of an evaluation of this system on two corpora. We also describe an extension of the traditional POS tagging by combining it with the document-centered approach to proper name identification and abbreviation handling. This made the resulting system robust to domain and topic shifts. 1 I n t r o d u c t i o n Sentence boundary disambiguation (SBD) is an important aspect in developing virtually any practical text processing application syntactic parsing, Information Extraction, Machine Translation, Text Alignment, Document Summarization, etc. Segmenting text into sentences in most cases is a simple m a t t e r a period, an exclamation mark or a question mark usually signal a sentence boundary. However, there are cases when a period denotes a decimal point or is a part of an abbreviation and thus it does not signal a sentence break. Furthermore, an abbreviation itself can be the last token in a sentence, in which case its period acts at the same time as part of this abbreviation and as the end-of-sentence indicator (fullstop). The first large class of sentence boundary disambiguators uses manually built rules which are usually encoded in terms of regular expression grammars supplemented with lists of abbreviations, common words, proper names, etc. For instance, the Alembic workbench (Aberdeen et al., 1995) contains a sentence splitting module which employs over 100 regular-expression rules written in Flex. To put together a few rules which do a job is fast and easy, but to develop a good rule-based system is quite a labour consuming enterprise. Another potential shortcoming is that such systems are usually closely tailored to a particular corpus and are not easily portable across domains. Automatically trainable software is generally seen as a way of producing systems quickly re-trainable for a new corpus, domain or even for another language. Thus, the second class of SBD systems employs machine learning techniques such as decision tree classifiers (Riley, 1989), maximum entropy modeling (MAXTERMINATOR) (Reynar and Ratnaparkhi, 1997), neural networks (SATZ) (Palmer and Hearst , 1997), etc.. Machine learning systems treat the SBD task as a classification problem, using features such as word spelling, capitalization, suffix, word class, etc., found in the local context of potent im sentence breaking punctuation. There is, however, one catch all machine learning approaches to the SBD task known to us require labeled examples for training. This implies an investment in the annotat ion phase. There are two corpora normally used for evaluation and development in a number of text processing tasks and in the SBD task in particular: the Brown Corpus and the Wall Street Journal (WSJ) corpus both par t of the Penn Treebank (Marcus, Marcinkiewicz, and Santorini, 1993). Words in both these corpora are annota ted with part-ofspeech (POS) information and the text is split into documents, paragraphs and sentences. This gives all necessary information for the development of an SBD system and its evaluation. State-of-theart machine-learning and rule-based SBD systems achieve the error rate of about 0.8-1.5% measured on the Brown Corpus and the WSJ. The best performance on the WSJ was achieved by a combination of the SATZ system with the Alembic system 0.5% error rate. The best performance on the Brown Corpus, 0.2% error rate, was reported by (Riley, 1989), who trained a decision tree classifier on a 25 million word corpus. 1.1 W o r d b a s e d vs . S y n t a c t i c M e t h o d s The first source of ambiguity in end-of-sentence marking is introduced by abbreviations: if we know tha t the word which precedes a period is n o t an abbreviation, then almost certainly this period denotes a sentence break. However, if this word is an abbreviation, then it is not tha t easy to make a clear decision. The second major source of information


Information leakage from optical emanations

A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required; the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack.


Recurrent neural networks for time-series prediction.

Recurrent neural networks have been used for time-s eri s prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and li near regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns and with this in mind we evaluate if we c an get more accurate predictions if we only use the campaign segments when modeling the data. Throughout the entire project the KDD process have been used to give a st ructured work-process. The results showed that recurrent network is not better han the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also showed that we c ould get more accurate predictions when only using information from campaign segments.


Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks

Power consumption is a critical issue in many wireless sensor network scenarios where network life expectancy is measured in months or years. Communication protocols typically rely on duty-cycle mechanisms to reduce the power usage at the cost of decreased network responsiveness and increased communication latency. A low-power radio-triggered device can be used to continuously monitor the channel and activate the node for incoming communications, allowing purely asynchronous operations. To be effective, the power consumption of this wake-up receiver must be on the order of tens of microwatts since this device is always active. This paper presents the ongoing efforts to design such a low-power receiver. Initial results indicate an average power consumption below 20 uW.


Guidance for the evaluation and treatment of hereditary and acquired thrombophilia

Thrombophilias are hereditary and/or acquired conditions that predispose patients to thrombosis. Testing for thrombophilia is commonly performed in patients with venous thrombosis and their relatives; however such testing usually does not provide information that impacts management and may result in harm. This manuscript, initiated by the Anticoagulation Forum, provides clinical guidance for thrombophilia testing in five clinical situations: following 1) provoked venous thromboembolism, 2) unprovoked venous thromboembolism; 3) in relatives of patients with thrombosis, 4) in female relatives of patients with thrombosis considering estrogen use; and 5) in female relatives of patients with thrombosis who are considering pregnancy. Additionally, guidance is provided regarding the timing of thrombophilia testing. The role of thrombophilia testing in arterial thrombosis and for evaluation of recurrent pregnancy loss is not addressed. Statements are based on existing guidelines and consensus expert opinion where guidelines are lacking. We recommend that thrombophilia testing not be performed in most situations. When performed, it should be used in a highly selective manner, and only in circumstances where the information obtained will influence a decision important to the patient, and outweigh the potential risks of testing. Testing should not be performed during acute thrombosis or during the initial (3-month) period of anticoagulation.


Understanding payment card fraud through knowledge extraction from neural networks using large-scale datasets


A whispered Mandarin corpus for speech technology applications

Whispered speech is a natural mode of speech in which voicing is absent – its acoustics differ significantly from normally spoken speech or so-called neutral speech, such that it is challenging to use only neutral speech to build speech processing and automatic recognition systems that can deal effectively with whisper. At the same time, humans can naturally produce and perceive whispered speech without explicit training. Tonal languages such as Mandarin present an interesting dilemma – tone is primarily encoded by pitch tracks which are absent during whispered speech, but humans can still tell tones apart. How humans manage to process whispered speech well without explicit training on it, whereas machine algorithms fail, is presently an unresolved question which could prove fruitful with study. This, however, is hindered by the lack of suitable, systematically collected corpora. We present iWhisper-Mandarin, a 25-hour parallel corpus of neutral and whispered Mandarin, designed to support research in linguistics and speech technology. We demonstrate and verify that earlier techniques applied to whispered speech from non-tonal languages also work with Mandarin, and present some preliminary studies on voice activity detection and whispered Mandarin speech recognition.


Deploying Deep Neural Networks in the Embedded Space

Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.


Smart manufacturing, manufacturing intelligence and demand-dynamic performance

Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. It responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance; demand-driven supply chain services; and broad-based workforce involvement. ITenabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.


Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition

Recurrent neural network language models RNNLMs are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units GPUs and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure F-RNNLM is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.


Learning under Feature Drifts in Textual Streams

Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.


Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.


Modeling Biological Processes for Reading Comprehension

Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.


Attention-over-Attention Neural Networks for Reading Comprehension

Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces “attended attention” for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attentionover-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children’s Book Test datasets.


The transition to high school as a developmental process among multiethnic urban youth.

The high school transition was examined in an ethnically diverse, urban sample of 1,979 adolescents, followed from 7th to 10th grade (M(age) = 14.6, SD = .37 in 7th grade). Twice annually, data were gathered on adolescents' perceptions of school climate, psychological functioning, and academic behaviors. Piecewise growth modeling results indicate that adolescents were doing well before the transition but experienced transition disruptions in psychological functioning and grades, and many continued to struggle across high school. The immediate experience of the transition appeared to be particularly challenging for African American and Latino students when the numerical representation of their ethnic groups declined significantly from middle to high school. Findings highlight the value of examining the transition in a larger developmental context and the importance of implementing transition support.


Smart Kitchen : A User Centric Cooking Support System

Several cooking support systems have been studied to give users instructions based on the recipes stepby-step, using multi-media contents. These systems usually disturb users’ cooking process forcing them to provide information to the system in order to give them beneficial information. In this sense, these systems are considered to be ”system centric”. We propose a system called ”Smart Kitchen” considered to be in ”user centric”, in which a user can cook normally without being concerned about the system. Smart Kitchen can understand cooking processes, in other words, what the user is doing. In this paper, we discuss the design of the Smart Kitchen system and explain three essential modules of tracking food, recognizing food material, and recognizing cooking action.


Ally Friendly Jamming: How to Jam Your Enemy and Maintain Your Own Wireless Connectivity at the Same Time

This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.


Radar Cross-Section Analysis of Backscattering RFID Tags

In this letter, we present a graphical method based on Green's analysis of scattering antennas to optimize the load impedances for achieving maximum modulated radar cross-section (RCS) of backscattering radio-frequency identification (RFID) tags. A planar tag antenna specifically designed for working in the ultra-high-frequency band is used to experimentally validate the method. The measured RCS of the loaded tag antenna shows acceptable agreement with the theory and simulated results. This method is especially valuable to the design of semipassive RFID tags when the communication range is solely determined by the backscattering modulation efficiency of tags.


The impact of emotional intelligence on work engagement of registered nurses: the mediating role of organisational justice.

AIMS AND OBJECTIVES
To explore the impact of emotional intelligence and organisational justice on work engagement in Chinese nurses and to examine the mediating role of organisational justice to provide implications for promoting clinical nurses' work engagement.

BACKGROUND
The importance of work engagement on nurses' well-being and quality of care has been well documented. Work engagement is significantly predicted by job resources. However, little research has concentrated simultaneously on the influence of both personal and organisational resources on nurses' work engagement.

DESIGN
A descriptive, cross-sectional design was employed.

METHODS
A total of 511 nurses from four public hospitals were enrolled by multistage sampling. Data collection was undertaken using the Wong and Law Emotional Intelligence Scale, the Organizational Justice questionnaire and the Utrecht Work Engagement Scale-9. We analysed the data using structural equation modelling.

RESULTS
Emotional intelligence and organisational justice were significant predictors and they accounted for 44% of the variance in nurses' work engagement. Bootstrap estimation confirmed an indirect effect of emotional intelligence on work engagement via organisational justice.

CONCLUSIONS
Emotional intelligence and organisational justice positively predict work engagement and organisational justice partially mediates the relationship between emotional intelligence and work engagement.

RELEVANCE TO CLINICAL PRACTICE
Our study supports the idea that enhancing organisational justice can increase the impact of emotional intelligence. Managers should take into account the importance of emotional intelligence and perceptions of organisational justice in human resources management and apply targeted interventions to foster work engagement.


Design and Analysis of Low-Power 11- Transistor Full Adder

Full adders are exigent components in applications such as digital signal processors (DSP) architectures and microprocessors. In this paper, we propose a technique to build a new 11-transistor FA .We have done HSPICE simulation runs the new design 11-T full adders .In CMOS integrated circuit design there is a tradeoff between static power consumption and technology scaling. Static power dissipation is a challenge for the circuit designer. So we reduce the static power dissipation. In order to achieve lower static power consumption, one has to scarifies design area and circuit performance. In this paper we propose a new circuit of 11-Transistor full adder in CMOS VLSI circuit.


Phishing counter measures and their effectiveness – literature review

Purpose – Phishing is essentially a social engineering crime on the Web, whose rampant occurrences and technique advancements are posing big challenges for researchers in both academia and the industry. The purpose of this study is to examine the available phishing literatures and phishing countermeasures, to determine how research has evolved and advanced in terms of quantity, content and publication outlets. In addition to that, this paper aims to identify the important trends in phishing and its countermeasures and provides a view of the research gap that is still prevailing in this field of study. Design/methodology/approach – This paper is a comprehensive literature review prepared after analysing 16 doctoral theses and 358 papers in this field of research. The papers were analyzed based on their research focus, empirical basis on phishing and proposed countermeasures. Findings – The findings reveal that the current anti-phishing approaches that have seen significant deployments over the internet can be classified into eight categories. Also, the different approaches proposed so far are all preventive in nature. A Phisher will mainly target the innocent consumers who happen to be the weakest link in the security chain and it was found through various usability studies that neither server-side security indicators nor client-side toolbars and warnings are successful in preventing vulnerable users from being deceived. Originality/value – Educating the internet users about phishing, as well as the implementation and proper application of anti-phishing measures, are critical steps in protecting the identities of online consumers against phishing attacks. Further research is required to evaluate the effectiveness of the available countermeasures against fresh phishing attacks. Also there is the need to find out the factors which influence internet user’s ability to correctly identify phishing websites.


Automotive big data: Applications, workloads and infrastructures

Data is increasingly affecting the automotive industry, from vehicle development, to manufacturing and service processes, to online services centered around the connected vehicle. Connected, mobile and Internet of Things devices and machines generate immense amounts of sensor data. The ability to process and analyze this data to extract insights and knowledge that enable intelligent services, new ways to understand business problems, improvements of processes and decisions, is a critical capability. Hadoop is a scalable platform for compute and storage and emerged as de-facto standard for Big Data processing at Internet companies and in the scientific community. However, there is a lack of understanding of how and for what use cases these new Hadoop capabilities can be efficiently used to augment automotive applications and systems. This paper surveys use cases and applications for deploying Hadoop in the automotive industry. Over the years a rich ecosystem emerged around Hadoop comprising tools for parallel, in-memory and stream processing (most notable MapReduce and Spark), SQL and NOSQL engines (Hive, HBase), and machine learning (Mahout, MLlib). It is critical to develop an understanding of automotive applications and their characteristics and requirements for data discovery, integration, exploration and analytics. We then map these requirements to a confined technical architecture consisting of core Hadoop services and libraries for data ingest, processing and analytics. The objective of this paper is to address questions, such as: What applications and datasets are suitable for Hadoop? How can a diverse set of frameworks and tools be managed on multi-tenant Hadoop cluster? How do these tools integrate with existing relational data management systems? How can enterprise security requirements be addressed? What are the performance characteristics of these tools for real-world automotive applications? To address the last question, we utilize a standard benchmark (TPCx-HS), and two application benchmarks (SQL and machine learning) that operate on a dataset of multiple Terabytes and billions of rows.


Online payment system using steganography and visual cryptography

A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.


Algorithmic motion planning in robotics

A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning.<<ETX>>


Elliptic operator for shape analysis

Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.


R: A language and environment for statistical computing.

Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.


A Neural Network Approch to Efficient Valuation of Large Portfolios of Variable Annuities

Managing and hedging the risks associated with Variable Annuity (VA) products requires intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation has compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.


Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy

Throughout life, new neurons are continuously added to the dentate gyrus. As this continuous addition remodels hippocampal circuits, computational models predict that neurogenesis leads to degradation or forgetting of established memories. Consistent with this, increasing neurogenesis after the formation of a memory was sufficient to induce forgetting in adult mice. By contrast, during infancy, when hippocampal neurogenesis levels are high and freshly generated memories tend to be rapidly forgotten (infantile amnesia), decreasing neurogenesis after memory formation mitigated forgetting. In precocial species, including guinea pigs and degus, most granule cells are generated prenatally. Consistent with reduced levels of postnatal hippocampal neurogenesis, infant guinea pigs and degus did not exhibit forgetting. However, increasing neurogenesis after memory formation induced infantile amnesia in these species.


Learning to detect unseen object classes by between-class attribute transfer

We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.


Zero-Shot Learning Through Cross-Modal Transfer

This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semantic basis for understanding what objects look like. Most previous zero-shot learning models can only differentiate between unseen classes. In contrast, our model can both obtain state of the art performance on classes that have thousands of training images and obtain reasonable performance on unseen classes. This is achieved by first using outlier detection in the semantic space and then two separate recognition models. Furthermore, our model does not require any manually defined semantic features for either words or images.


Siamese Neural Networks for One-Shot Image Recognition

The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class. In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks. Humans exhibit a strong ability to acquire and recognize new patterns. In particular, we observe that when presented with stimuli, people seem to be able to understand new concepts quickly and then recognize variations on these concepts in future percepts (Lake et al., 2011). Machine learning has been successfully used to achieve state-ofthe-art performance in a variety of applications such as web search, spam detection, caption generation, and speech and image recognition. However, these algorithms often break down when forced to make predictions about data for which little supervised information is available. We desire to generalize to these unfamiliar categories without necessitating extensive retraining which may be either expensive or impossible due to limited data or in an online prediction setting, such as web retrieval. Proceedings of the 32 International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copyright 2015 by the author(s). Figure 1. Example of a 20-way one-shot classification task using the Omniglot dataset. The lone test image is shown above the grid of 20 images representing the possible unseen classes that we can choose for the test image. These 20 images are our only known examples of each of those classes. One particularly interesting task is classification under the restriction that we may only observe a single example of each possible class before making a prediction about a test instance. This is called one-shot learning and it is the primary focus of our model presented in this work (Fei-Fei et al., 2006; Lake et al., 2011). This should be distinguished from zero-shot learning, in which the model cannot look at any examples from the target classes (Palatucci et al., 2009). One-shot learning can be directly addressed by developing domain-specific features or inference procedures which possess highly discriminative properties for the target task. As a result, systems which incorporate these methods tend to excel at similar instances but fail to offer robust solutions that may be applied to other types of problems. In this paper, we present a novel approach which limits assumptions on the structure of the inputs while automatically acquiring features which enable the model to generalize successfully from few examples. We build upon the deep learnSiamese Neural Networks for One-shot Image Recognition Figure 2. Our general strategy. 1) Train a model to discriminate between a collection of same/different pairs. 2) Generalize to evaluate new categories based on learned feature mappings for


Developing an Intelligent Chat-bot Tool to Assist High School Students for Learning General Knowledge Subjects

UPDATED—11 December 2017. Artificial Intelligent Chatbots are used in industries such as Banking systems, Customer services, Education. There are many intelligent tutoring systems currently in practice, but few of them are known to assist high school students for learning their general knowledge subjects. This project paper proposes an artificial intelligent chat-bot tool developed to assist high school students for learning their general knowledge subjects. The proposed intelligent chat-bot tool is a web based intelligent tutoring system which can be accessed by large number of students globally, the users are not required to pay licensing fees. The intelligent chat-bot tool will be available 24 hours a day at schools or public libraries or any other location which has internet access. The intelligent chatbot tool will make use of Natural Language Processing techniques to answer the queries by high school students. The intelligent chat-bot tool will be trained on a knowledge base comprising of general knowledge questions and answers. The intelligent chat-bot tool will have ability to participate in small talks with its learners.


MRG-OHTC Database for Online Handwritten Tibetan Character Recognition

A handwritten Tibetan database, MRG-OHTC, is presented to facilitate the research of online handwritten Tibetan character recognition. The database contains 910 Tibetan character classes written by 130 persons from Tibetan ethnic minority. These characters are selected from basic set and extension set A of Tibetan coded character set. The current version of this database is collected using electronic pen on digital tablet. We investigate some characteristic of writing style from different writers. We evaluate MRG-OHTC database using existing algorithms as a baseline. Experimental results reveal a big challenge to higher recognition performance. To our knowledge, MRG-OHTC is the first publicly available database for handwritten Tibetan research. It provides a basic database to compare empirically different algorithms for handwritten Tibetan character recognition.


Security Without Identification: Transaction Systems to Make Big Brother Obsolete

The large-scale automated transaction systems of the near future can be designed to protect the privacy and maintain the security of both individuals and organizations.


Interactive Freeform Design of Tensegrity

In this paper, we propose a novel interactive method for flexibly designing tensegrity structures under valid force equilibriums. Unlike previous form-finding techniques that aim to obtain a unique solution by fixing some parameters such as the force densities and lengths of elements, our method provides a design system that allows a user to continuously interact with the form within a multidimensional solution space. First, a valid initial form is generated by converting a given polygon mesh surface into a strut-and-cable network that follows the original surface, and the form is then perturbed to attain an equilibrium state through a two-step optimization of both node coordinates and force densities. Then, the form can be freely transformed by using a standard 2D input device while the system updates the form on the fly based on the orthogonal projection of a deformation mode into the solution space. The system provides a flexible platform for designing a tensegrity form for use as a static architectural structure or a kinetic deployable system.


The Promise of Sustainable Happiness

Reference: Boehm, J. K., & Lyubomirsky, S. (in press). The promise of sustainable happiness. In S. J. Lopez (Ed.), Handbook of positive psychology (2 ed.). Oxford: Oxford University Press. The Promise of Sustainable Happiness Julia K. Boehm and Sonja Lyubomirsky Department of Psychology University of California, Riverside Abstract From ancient history to recent times, philosophers, writers, self-help gurus, and now scientists have taken up the challenge of how to foster greater happiness. This chapter discusses why some people are happier than others, focusing on the distinctive ways that happy and unhappy individuals construe themselves and others, respond to social comparisons, make decisions, and self-reflect. We suggest that, despite several barriers to increased well-being, less happy people can strive successfully to be happier by learning a variety of effortful strategies and practicing them with determination and commitment. The sustainable happiness model (Lyubomirsky, Sheldon, & Schkade, 2005) provides a theoretical framework for experimental intervention research on how to increase and maintain happiness. According to this model, three factors contribute to an individual’s chronic happiness level – 1) the set point, 2) life circumstances, and 3) intentional activities, or effortful acts that are naturally variable and episodic. Such activities, which include committing acts of kindness, expressing gratitude or optimism, and savoring joyful life events, represent the most promising route to sustaining enhanced happiness. We describe a half dozen randomized controlled interventions testing the efficacy of each of these activities in raising and maintaining well-being, as well as the mediators and moderators underlying their effects. Future researchers must endeavor not only to learn which particular practices make people happier, but how and why they do so.


Comparative Study of Cloud Computing Data Security Methods

Cloud computing is the concept implemented to decipher the Daily Computing Problems. Cloud computing is basically virtual pool of resources and it provides these resources to users via internet. Cloud computing is the internet based development and used in computer technology. The prevalent problem associated with cloud computing is data privacy, security, anonymity and reliability etc. But the most important between them is security and how cloud provider assures it. The work plan here is to eliminate the concerns regarding data privacy using encryption algorithms to enhance the security in cloud. Have discussed about cloud computing security mechanisms and presented the comparative study of several algorithms.


Determining the informational, navigational, and transactional intent of Web queries

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching. 2007 Elsevier Ltd. All rights reserved.


A Kernel Approach to Comparing Distributions

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.


Beliefs and biases in web search

People's beliefs, and unconscious biases that arise from those beliefs, influence their judgment, decision making, and actions, as is commonly accepted among psychologists. Biases can be observed in information retrieval in situations where searchers seek or are presented with information that significantly deviates from the truth. There is little understanding of the impact of such biases in search. In this paper we study search-related biases via multiple probes: an exploratory retrospective survey, human labeling of the captions and results returned by a Web search engine, and a large-scale log analysis of search behavior on that engine. Targeting yes-no questions in the critical domain of health search, we show that Web searchers exhibit their own biases and are also subject to bias from the search engine. We clearly observe searchers favoring positive information over negative and more than expected given base rates based on consensus answers from physicians. We also show that search engines strongly favor a particular, usually positive, perspective, irrespective of the truth. Importantly, we show that these biases can be counterproductive and affect search outcomes; in our study, around half of the answers that searchers settled on were actually incorrect. Our findings have implications for search engine design, including the development of ranking algorithms that con-sider the desire to satisfy searchers (by validating their beliefs) and providing accurate answers and properly considering base rates. Incorporating likelihood information into search is particularly important for consequential tasks, such as those with a medical focus.


Emotional intelligence as a standard intelligence.

The authors have claimed that emotional intelligence (EI) meets traditional standards for an intelligence (J. D. Mayer, D. R. Caruso, & P. Salovey, 1999). R. D. Roberts, M. Zeidner, and G. Matthews (2001) questioned whether that claim was warranted. The central issue raised by Roberts et al. concerning Mayer et al. (1999) is whether there are correct answers to questions on tests purporting to measure EI as a set of abilities. To address this issue (and others), the present authors briefly restate their view of intelligence, emotion, and EI. They then present arguments for the reasonableness of measuring EI as an ability, indicate that correct answers exist, and summarize recent data suggesting that such measures are, indeed, reliable.


Architecting for DevOps and Continuous Deployment

Development and Operations (DevOps) in the context of Continuous Deployment (CD) have emerged as an attractive software development movement, which tries to establish a strong connection between development and operations teams. CD is defined as the ability to quickly put new releases into production. We believe that DevOps/CD brings new challenges for architects, which considerably impacts both on their (architectural) design decisions and their organizational responsibilities. We assert that there is an important and urgent need of sufficient research work to gain a deep understanding of how DevOps/CD adoption can influence architecting, architectural decision-making processes and their outcomes in an organization. This PhD research is aimed at understanding and addressing new challenges for designing architectures for supporting DevOps in the context of CD.


SASI: a generic texture descriptor for image retrieval

In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.


Iterative Integration of Visual Insights during Scalable Patent Search and Analysis

Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz”, a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.


Compact four-port MIMO antenna system at 3.5 GHz

This work proposed a compact multiple-input multiple-output (MIMO) antenna system having four antennas each fed by coplanar waveguide (CPW) operating for 3.5GHz band (3400–3600) MHz. The proposed MIMO antenna system is realized with two groups of printed Inverted L-monopole of size 5×4 mm2 placed orthogonally to each other to attain the diversity capability of polarization and pattern at the edge of non-ground portion of size 15 × 15 mm2 of mobile handset. The proposed MIMO antenna system consists of four monopole inverted-L shape radiator each with parasitic shorted stripe extending from the ground plane having same dimensions. The parasitic inverted-L shorted striped served as tuning element by reducing the resonating frequency of the monopole radiator and improving the impedance matching. The mutual coupling between the closely packed antennas is addressed by the neutralization line by feeding anti-phase current to the neighbor antenna. The simulation result showed that that the impedance bandwidth at S11 ≤ −10 dB is 200 MHz enough for 5G network communication channel. The diversity parameters of interest are envelop correlation coefficient and mean effective gain are also calculated.


PostBL: Post-mesh Boundary Layer Mesh Generation Tool

A boundary layer mesh is a mesh with dense element distribution in the normal direction along specific boundaries. PostBL is a utility to generate boundary layers elements on an already existing mesh model. PostBL supports creation of hexahedral, prism, quad and tri boundary layer elements. It is a part of MeshKit, which is an open-source library for mesh generation functionalities. Generally, boundary layer mesh generation is a pre-meshing process; in this effort, we start from a model that has already been meshed. Boundary layers elements can be generated along the entire skin, selected exterior or internal surface boundaries. MeshKit uses graph-based approach for representing meshing operations; PostBL is one such meshing operation. It can be coupled to other meshing operations like Jaal, NetGen, TetGen, CAMAL and custom meshing tools like RGG. Simple examples demonstrating generation of boundary layers on different mesh types and OECD Vattenfall T-Junction benchmark hexahedral-mesh are presented.


Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media

The web is a huge source of valuable information. However, in recent times, there is an increasing trend towards false claims in social media, other web-sources, and even in news. Thus, factchecking websites have become increasingly popular to identify such misinformation based on manual analysis. Recent research proposed methods to assess the credibility of claims automatically. However, there are major limitations: most works assume claims to be in a structured form, and a few deal with textual claims but require that sources of evidence or counter-evidence are easily retrieved from the web. None of these works can cope with newly emerging claims, and no prior method can give user-interpretable explanations for its verdict on the claim’s credibility. This paper overcomes these limitations by automatically assessing the credibility of emerging claims, with sparse presence in web-sources, and generating suitable explanations from judiciously selected sources. To this end, we retrieve diverse articles about the claim, and model the mutual interaction between: the stance (i.e., support or refute) of the sources, the language style of the articles, the reliability of the sources, and the claim’s temporal footprint on the web. Extensive experiments demonstrate the viability of our method and its superiority over prior works. We show that our methods work well for early detection of emerging claims, as well as for claims with limited presence on the web and social media.


Functional Specialization for Semantic and Phonological Processing in the Left Inferior Prefrontal Cortex

Neuroimaging and neuropsychological studies have implicated left inferior prefrontal cortex (LIPC) in both semantic and phonological processing. In this study, functional magnetic resonance imaging was used to examine whether separate LIPC regions participate in each of these types of processing. Performance of a semantic decision task resulted in extensive LIPC activation compared to a perceptual control task. Phonological processing of words and pseudowords in a syllable-counting task resulted in activation of the dorsal aspect of the left inferior frontal gyrus near the inferior frontal sulcus (BA 44/45) compared to a perceptual control task, with greater activation for nonwords compared to words. In a direct comparison of semantic and phonological tasks, semantic processing preferentially activated the ventral aspect of the left inferior frontal gyrus (BA 47/45). A review of the literature demonstrated a similar distinction between left prefrontal regions involved in semantic processing and phonological/lexical processing. The results suggest that a distinct region in the left inferior frontal cortex is involved in semantic processing, whereas other regions may subserve phonological processes engaged during both semantic and phonological tasks.


Color spaces for computer graphics

Normal human color perception is a product of three independent sensory systems. By mirroring this mechanism, full-color display devices create colors as mixtures of three primaries. Any displayable color can be described by the corresponding values of these primaries. Frequently it is more convenient to define various other color spaces, or coordinate systems, for color representation or manipulation. Several such color spaces are presented which are suitable for applications involving user specification of color, along with the defining equations and illustrations. The use of special color spaces for particular kinds of color computations is discussed.


Using CMMI together with agile software development: A systematic review

http://dx.doi.org/10.1016/j.infsof.2014.09.012 0950-5849/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Center of Informatics, Federal University of Pernambuco (CIn/UFPE), Av. Jornalista Anibal Fernandes, S/N, Cidade Universitária, 50. Recife, PE, Brazil. Tel.: +55 81 8250 0470. E-mail addresses: fss4@cin.ufpe.br (F. Selleri Silva), fsfs@cin.ufpe.br (F.S.F. Soares), alp5@cin.ufpe.br (A.L. Peres), ima2@cin.ufpe.br (I.M.d. Azevedo), aplfv@ci (A.P.L.F. Vasconcelos), fkk@cin.ufpe.br (F.K. Kamei), srlm@cin.ufpe.br (S.R.d.L. Meira). Fernando Selleri Silva a,b,⇑, Felipe Santana Furtado Soares , Angela Lima Peres , Ivanildo Monteiro de Azevedo , Ana Paula L.F. Vasconcelos , Fernando Kenji Kamei , Silvio Romero de Lemos Meira a,c


Embedding Temporal Network via Neighborhood Formation

Given the rich real-life applications of network mining as well as the surge of representation learning in recent years, network embedding has become the focal point of increasing research interests in both academic and industrial domains. Nevertheless, the complete temporal formation process of networks characterized by sequential interactive events between nodes has yet seldom been modeled in the existing studies, which calls for further research on the so-called temporal network embedding problem. In light of this, in this paper, we introduce the concept of neighborhood formation sequence to describe the evolution of a node, where temporal excitation effects exist between neighbors in the sequence, and thus we propose a Hawkes process based Temporal Network Embedding (HTNE) method. HTNE well integrates the Hawkes process into network embedding so as to capture the influence of historical neighbors on the current neighbors. In particular, the interactions of low-dimensional vectors are fed into the Hawkes process as base rate and temporal influence, respectively. In addition, attention mechanism is also integrated into HTNE to better determine the influence of historical neighbors on current neighbors of a node. Experiments on three large-scale real-life networks demonstrate that the embeddings learned from the proposed HTNE model achieve better performance than state-of-the-art methods in various tasks including node classification, link prediction, and embedding visualization. In particular, temporal recommendation based on arrival rate inferred from node embeddings shows excellent predictive power of the proposed model.


Survey of semantic annotation platforms

The realization of the Semantic Web requires the widespread availability of semantic annotations for existing and new documents on the Web. Semantic annotations are to tag ontology class instance data and map it into ontology classes. The fully automatic creation of semantic annotations is an unsolved problem. Instead, current systems focus on the semi-automatic creation of annotations. The Semantic Web also requires facilities for the storage of annotations and ontologies, user interfaces, access APIs, and other features to fully support annotation usage. This paper examines current Semantic Web annotation platforms that provide annotation and related services, and reviews their architecture, approaches and performance.


The String-to-String Correction Problem

The string-to-string correction problem is to determine the distance between two strings as measured by the minimum cost sequence of “edit operations” needed to change the one string into the other. The edit operations investigated allow changing one symbol of a string into another single symbol, deleting one symbol from a string, or inserting a single symbol into a string. An algorithm is presented which solves this problem in time proportional to the product of the lengths of the two strings. Possible applications are to the problems of automatic spelling correction and determining the longest subsequence of characters common to two strings.


Ontology Learning for the Semantic Web

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-ToOnto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. Ontologies for the Semantic Web Conceptual structures that define an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly defined and machine processable semantics. By defining shared and common domain theories, ontologies help both people and machines to communicate concisely,


Design and optimization on ESD self-protection schemes for 700V LDMOS in high voltage power IC

This paper presents an ESD self-protection scheme for a 700V high-voltage laterally diffused metal-oxide-semiconductor (LDMOS) field effect transistor. The safe operating area (SOA) and breakdown failure mechanism of 700V LDMOS are analyzed using simulations and experimental results. The scalability of thermal failure current with LDMOS width is also demonstrated.


Battery Pack Modeling , Simulation , and Deployment on a Multicore Real Time Target

Battery Management System (BMS) design is a complex task requiring sophisticated models that mimic the electrochemical behavior of the battery cell under a variety of operating conditions. Equivalent circuits are well-suited for this task because they offer a balance between fidelity and simulation speed, their parameters reflect direct experimental observations, and they are scalable. Scalability is particularly important at the real time simulation stage, where a model of the battery pack runs on a real-time simulator that is physically connected to the peripheral hardware in charge of monitoring and control. With modern battery systems comprising hundreds of cells, it is important to employ a modeling and simulation approach that is capable of handling numerous simultaneous instances of the basic unit cell while maintaining real time performance. In previous publications we presented a technique for the creation of a battery cell model that contains the electrochemical fingerprints of a battery cell based on equivalent circuit model fitting to experimental data. In this work we extend our previous model to represent a battery pack, featuring cell creation, placement, and connection using automation scripts, thus facilitating the design of packs of arbitrary size and electrical topology. In addition, we present an assessment of model partitioning schemes for real time execution on multicore targets to ensure efficient use of hardware resources, a balanced computational load, and a study of the potential impact of the calculation latencies inherent to distributed systems on solver accuracy. Prior to C code generation for real time execution, a model profiler assesses the model partitioning and helps determine the multicore configuration that results in the lowest average turnaround time, the time elapsed between task start and finish. The resulting model is useful in the generation of multiple operating scenarios of interest in the design of charging, balancing, and safety related procedures. CITATION: Gazzarri, J., Shrivastava, N., Jackey, R., and Borghesani, C., "Battery Pack Modeling, Simulation, and Deployment on a Multicore Real Time Target," SAE Int. J. Aerosp. 7(2):2014, doi:10.4271/2014-01-2217. 2014-01-2217 Published 09/16/2014 Copyright © 2014 The MathWorks, Inc. doi:10.4271/2014-01-2217 saeaero.saejournals.org A second goal is to provide a scalable methodology capable of supporting any number of battery cell components configured in series or parallel. A MATLAB script creates, places, and connects each model component, including battery cell blocks and electrical components for the battery pack load, and partitions the model.


Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.


Using Object Information for Spotting Text

Text spotting, also called text detection, is a challenging computer vision task because of cluttered backgrounds, diverse imaging environments, various text sizes and similarity between some objects and characters, e.g., tyre and ’o’. However, text spotting is a vital step in numerous AI and computer vision systems, such as autonomous robots and systems for visually impaired. Due to its potential applications and commercial values, researchers have proposed various deep architectures and methods for text spotting. These methods and architectures concentrate only on text in images, but neglect other information related to text. There exists a strong relationship between certain objects and the presence of text, such as signboards or the absence of text, such as trees. In this paper, a text spotting algorithm based on text and object dependency is proposed. The proposed algorithm consists of two subconvolutional neural networks and three training stages. For this study, a new NTU-UTOI dataset containing over 22k non-synthetic images with 277k bounding boxes for text and 42 text-related object classes is established. According to our best knowledge, it is the second largest nonsynthetic text image database. Experimental results on three benchmark datasets with clutter backgrounds, COCO-Text, MSRA-TD500 and SVT show that the proposed algorithm provides comparable performance to state-of-the-art text spotting methods. Experiments are also performed on our newly established dataset to investigate the effectiveness of object information for text spotting. The experimental results indicate that the object information contributes significantly on the performance gain.


A Mobile Application for Smart House Remote Control System

At the start of the second decade of 21th century, the time has come to make the Smart Houses a reallity for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connectiing to the server application and also the usecases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and soloutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses. Keywords—Smart House, Mobile Application, Remote Control, Automated Home, Windows Mobile.


Adaptive energy-aware free viewpoint video transmission over wireless networks

As consumer-level cameras are becoming cheaper and cheaper, the scene of interest can now be captured by multiple cameras simultaneously from different viewpoints. By transmitting texture and depth maps of the views captured using two adjacent cameras, users can synthesize any view in between using depth image based rendering (DIBR) technique such as 3D-warping. This provides users with continuous view angles and is key technique for Free Viewpoint TV (FTV), etc. In this scenario, multiple views must be delivered through bandwidth-limited wireless networks, how to adaptive reduce the source coding rate to fit the bandwidth becomes an important issue. The standard multi-view video codec H.264 MVC explores the inter-view and intra-view correlation to reduce source encoding rate. But all views are required at the client side for decoding any single view, which is apparently not transmission efficient. A better solution is using H.264 to differentially encode the two adjacent views separately, but still this requires double-folder bandwidth comparing to the traditional video streaming. Multiple description coding (MDC) encodes one view into multiple descriptions for robust transmission. This inspires another option: sending only part of each view. This can greatly reduce the source encoding rate, but interpolating skipped frames before synthesizing the middle view consumes mobile devices' precious battery life. Whether to send at a lower source encoding rate but with extra energy consumption or send at a high source encoding rate is nontrivial, and has not been studied in any formal way. In this paper, we investigated both source encoding rate reduction and energy consumption with the bandwidth constraint. We formulated the problem into an optimization problem, i.e., maximizing the system utility, which is defined as the received video quality minus the weighted energy consumption. Simulation results showed that our proposed scheme provides a significant advantage over competing schemes in typical network conditions.


Integral Channel Features – Addendum

This document is meant to serve as an addendum to [1], published at BMVC 2009. The purpose of this addendum is twofold: (1) to respond to feedback we’ve received since publication and (2) to describe a number of changes, especially to the non-maximal suppression, that further improve performance. The performance of our updated detection increases 5% to over 91% detection rate at 1 false positive per image on the INRIA dataset, and similarly on the Caltech Pedestrian Dataset, while overall system runtime for multiscale detection decreases by 1/3 to just under 1.5s per 640×480 image. We begin by rectifying an important omission to the related work. Levi and Weiss had an innovative application of integral images to multiple image channels quite early on, demonstrating good results on face detection from few training examples [4]. This work appears to be the earliest such use of integral images, indeed the authors even describe a precursor to integral histograms. Many thanks to Mark Everingham for sending us this reference.