title: Idiopathic isolated clitoromegaly: A report of two cases

text: BACKGROUND: Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare. METHODS: Two acquired clitoromegaly cases treated in Ataturk Training Hospital, Izmir, Turkey are presented. RESULTS: History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly. CONCLUSION: The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.


title: On the design and implementation of linear differential microphone arrays.

text: Differential microphone array (DMA), a particular kind of sensor array that is responsive to the differential sound pressure field, has a broad range of applications in sound recording, noise reduction, signal separation, dereverberation, etc. Traditionally, an Nth-order DMA is formed by combining, in a linear manner, the outputs of a number of DMAs up to (including) the order of N - 1. This method, though simple and easy to implement, suffers from a number of drawbacks and practical limitations. This paper presents an approach to the design of linear DMAs. The proposed technique first transforms the microphone array signals into the short-time Fourier transform (STFT) domain and then converts the DMA beamforming design to simple linear systems to solve. It is shown that this approach is much more flexible as compared to the traditional methods in the design of different directivity patterns. Methods are also presented to deal with the white noise amplification problem that is considered to be the biggest hurdle for DMAs, particularly higher-order implementations.


title: Data-free parameter pruning for Deep Neural Networks

text: Deep Neural nets (NNs) with millions of parameters are at the heart of many stateof-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85% of the total parameters in an MNIST-trained network, and about 35% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.


title: An 82.4% efficiency package-bondwire-based four-phase fully integrated buck converter with flying capacitor for area reduction

text: Multi-phase converters have become a topic of great interest due to the high output power capacity and output ripple cancellation effect. They are even more beneficial to nowadays high-frequency fully integrated converters with output capacitor integrated on-chip. As one of the dominant chip area consumers, reducing the size of the on-chip decoupling capacitors directly leads to cost down. It is reported that a 5× capacitor area reduction can be achieved with a four-phase converter compared to a single-phase one [1]. However, the penalty is obvious. Every extra phase comes with an inductor, which is also counted as cost and becomes more dominant with increase in the number of phases.


title: Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model

text: In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.


title: Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation

text: Aiming at simultaneous detection and segmentation (SD-S), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper.


title: Learning to simplify: fully convolutional networks for rough sketch cleanup

text: In this paper, we present a novel technique to simplify sketch drawings based on learning a series of convolution operators. In contrast to existing approaches that require vector images as input, we allow the more general and challenging input of rough raster sketches such as those obtained from scanning pencil sketches. We convert the rough sketch into a simplified version which is then amendable for vectorization. This is all done in a fully automatic way without user intervention. Our model consists of a fully convolutional neural network which, unlike most existing convolutional neural networks, is able to process images of any dimensions and aspect ratio as input, and outputs a simplified sketch which has the same dimensions as the input image. In order to teach our model to simplify, we present a new dataset of pairs of rough and simplified sketch drawings. By leveraging convolution operators in combination with efficient use of our proposed dataset, we are able to train our sketch simplification model. Our approach naturally overcomes the limitations of existing methods, e.g., vector images as input and long computation time; and we show that meaningful simplifications can be obtained for many different test cases. Finally, we validate our results with a user study in which we greatly outperform similar approaches and establish the state of the art in sketch simplification of raster images.


title: Perceptual Losses for Real-Time Style Transfer and Super-Resolution

text: We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.


Fully convolutional networks for semantic segmentation


title: Scientific Table Search Using Keyword Queries

text: Tables are common and important in scientific documents, yet most text-based document search systems do not capture structures and semantics specific to tables. How to bridge different types of mismatch between keywords queries and scientific tables and what influences ranking quality needs to be carefully investigated. This paper considers the structure of tables and gives different emphasis to table components. On the query side, thanks to external knowledge such as knowledge bases and ontologies, key concepts are extracted and used to build structured queries, and target quantity types are identified and used to expand original queries. A probabilistic framework is proposed to incorporate structural and semantic information from both query and table sides. We also construct and release TableArXiv, a high quality dataset with 105 queries and corresponding relevance judgements for scientific table search. Experiments demonstrate significantly higher accuracy overall and at the top of the rankings than several baseline methods.


title: Decision support for Cybersecurity risk planning

text: a r t i c l e i n f o Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions. Assuring a secure information technology (IT) environment for the transaction of commerce is a major concern. The magnitude of the task is growing yearly, as attackers become more knowledgeable, more determined, and bolder in their efforts. According to a lead security expert at International Data Corporation, a global provider of market intelligence and advisory services for the IT community, " Emerging new attack vectors, more targeted campaigns, compliance, and the layering of new technologies on the corporate infrastructure are all factors having a tremendous impact on the overall risk to an organization " [21]. Given that corporate expenditures are usually a good indicator of the level of concern about an issue, security is obviously near the top of many IT executives' lists. According to IDC, the global market for providers of information security services is projected to exceed $32.5 billion in 2010 [21]. The New York Times recently noted that " the intrusion into Google's computers and related attacks from within China on some thirty other companies point to the rising sophistication of such assaults and the vulnerability of even the best defenses " [25]. The Times further states that, according to the Computer Security Institute, malware infections are up from one-half to almost two-thirds of companies surveyed last year, at an average cost of $235,000 for each organization. Finally, the newspaper observes, malware is being exploited in new terrains with malicious code that turns on cellphone microphones and cameras for the purpose of industrial spying [25]. Because of the variety of methods attackers use to try to infiltrate and disrupt …


title: Memory reduction method for deep neural network training

text: Training deep neural networks requires a large amount of memory, making very deep neural networks difficult to fit on accelerator memories. In order to overcome this limitation, we present a method to reduce the amount of memory for training a deep neural network. The method enables to suppress memory increase during the backward pass, by reusing the memory regions allocated for the forward pass. Experimental results exhibit our method reduced the occupied memory size in training by 44.7% on VGGNet with no accuracy affection. Our method also enabled training speedup by increasing the mini batch size up to double.


title: MATLAB based code for 3 D joint inversion of Magnetotelluric and Direct Current resistivity imaging data

text: 24 th EM Induction Workshop, Helsingør, Denmark, August 12-19, 2018 1 /4 MATLAB based code for 3D joint inversion of Magnetotelluric and Direct Current resistivity imaging data M. Israil 1 , A. Singh 1 Anita Devi 1 , and Pravin K.Gupta 1 1 Indian Institute of Technology, Roorkee, 247667, India, mohammad.israil@gmail.com


title: An Efficient Privacy-Preserving Ranked Keyword Search Method

text: Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.


title: Distributed Denial of Service: Taxonomies of Attacks, Tools, and Countermeasures

text: Distributed Denial of Service (DDoS) attacks have become a large problem for users of computer systems connected to the Internet. DDoS attackers hijack secondary victim systems using them to wage a coordinated large-scale attack against primary victim systems. As new countermeasures are developed to prevent or mitigate DDoS attacks, attackers are constantly developing new methods to circumvent these new countermeasures. In this paper, we describe DDoS attack models and propose taxonomies to characterize the scope of DDoS attacks, the characteristics of the software attack tools used, and the countermeasures available. These taxonomies illustrate similarities and patterns in different DDoS attacks and tools, to assist in the development of more generalized solutions to countering DDoS attacks, including new derivative attacks.


title: Meta-Analytic Review of Leader-Member Exchange Theory : Correlates and Construct Issues

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title: Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet wavelets & Common Spatial Pattern algorithms

text: EEG-based Brain Computer Interfaces (BCIs) are quite noisy brain signals recorded from the scalp (electroencephalography, EEG) to translate the user's intent into action. This is usually achieved by looking at the pattern of brain activity across many trials while the subject is imagining the performance of an instructed action - the process known as motor imagery. Nevertheless, existing motor imagery classification algorithms do not always achieve good performances because of the noisy and non-stationary nature of the EEG signal and inter-subject variability. Thus, current EEG BCI takes a considerable upfront toll on patients, who have to submit to lengthy training sessions before even being able to use the BCI. In this study, we developed a data-efficient classifier for left/right hand motor imagery by combining in our pattern recognition both the oscillation frequency range and the scalp location. We achieve this by using a combination of Morlet wavelet and Common Spatial Pattern theory to deal with nonstationarity and noise. The system achieves an average accuracy of 88% across subjects and was trained by about a dozen training (10-15) examples per class reducing the size of the training pool by up to a 100-fold, making it very data-efficient way for EEG BCI.


title: Cluster-Based Scalable Network Services

text: We identify three fundamental requirements for scalable network services: incremental scalability and overflow growth provisioning, 24x7 availability through fault masking, and costeffectiveness. We argue that clusters of commodity workstations interconnected by a high-speed SAN are exceptionally well-suited to meeting these challenges for Internet-server workloads, provided the software infrastructure for managing partial failures and administering a large cluster does not have to be reinvented for each new service. To this end, we propose a general, layered architecture for building cluster-based scalable network services that encapsulates the above requirements for reuse, and a service-programming model based on composable workers that perform transformation, aggregation, caching, and customization (TACC) of Internet content. For both performance and implementation simplicity, the architecture and TACC programming model exploit BASE, a weaker-than-ACID data semantics that results from trading consistency for availability and relying on soft state for robustness in failure management. Our architecture can be used as an “off the shelf” infrastructural platform for creating new network services, allowing authors to focus on the “content” of the service (by composing TACC building blocks) rather than its implementation. We discuss two real implementations of services based on this architecture: TranSend, a Web distillation proxy deployed to the UC Berkeley dialup IP population, and HotBot, the commercial implementation of the Inktomi search engine. We present detailed measurements of TranSend’s performance based on substantial client traces, as well as anecdotal evidence from the TranSend and HotBot experience, to support the claims made for the architecture.


title: Multi-scale CNN Stereo and Pattern Removal Technique for Underwater Active Stereo System

text: Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reﬂections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artiﬁcial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by ﬂuctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patterns, etc., those noises and patterns should be removed from captured images to recover original texture. To solve these problems, we propose three approaches; a depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method and CNN-based texture recovery method. A depth-dependent calibration I sour analysis to ﬁnd the acceptable depth range for approximation by center projection to ﬁnd the certain target depth for calibration. In terms of CNN stereo, unlike common CNN based stereo methods which do not consider strong disturbances like refraction or bubbles, we designed a novel CNN architecture for stereo matching using multi-scale information, which is intended to be robust against such disturbances. Finally, we propose a multi-scale method for bubble and a projected-pattern removal method using CNNs to recover original textures. Experimental results are shown to prove the effectiveness of our method compared with the state of the art techniques. Furthermore, reconstruction of a live swimming ﬁsh is demonstrated to conﬁrm the feasibility of our techniques.


title: Advances in Digital Forensics XIII

text: In digital forensics, examinations are carried out to explain events and demonstrate the root cause from a number of plausible causes. Yin’s approach to case study research offers a systematic process for investigating occurrences in their real-world contexts. The approach is well suited to examining isolated events and also addresses questions about causality and the reliability of findings. The techniques that make Yin’s approach suitable for research also apply to digital forensic examinations. The merits of case study research are highlighted in previous work that established the suitability of the case study research method for conducting digital forensic examinations. This research extends the previous work by demonstrating the practicality of Yin’s case study method in examining digital events. The research examines the relationship between digital evidence – the effect – and its plausible causes, and how patterns can be identified and applied to explain the events. Establishing these patterns supports the findings of a forensic examination. Analytic strategies and techniques inherent in Yin’s case study method are applied to identify and analyze patterns in order to establish the findings of a digital forensic examination.


title: A Machine Learning Approach for SQL Queries Response Time Estimation in the Cloud

text: Cloud computing provides on-demand services with pay-as-you-go model. In this sense, customers expect quality from providers where QoS control over DBMSs services demands decision making, usually based on performance aspects. It is essential that the system be able to assimilate the characteristics of the application in order to determine the execution time for its queries' execution. Machine learning techniques are a promising option in this context, since they o er a wide range of algorithms that build predictive models for this purpose based on the past observations of the demand and on the DBMS system behavior. In this work, we propose and evaluate the use of regression methods for modeling the SQL query execution time in the cloud. This process uses di erent techniques, such as bag-of-words, statistical lter and genetic algorithms. We use K-Fold Cross-Validation for measuring the quality of the models in each step. Experiments were carried out based on data from database systems in the cloud generated by the TPC-C benchmark.


title: Ontology based context modeling and reasoning using OWL

text: Here we propose an OWL encoded context ontology (CONON) for modeling context in pervasive computing environments, and for supporting logic-based context reasoning. CONON provides an upper context ontology that captures general concepts about basic context, and also provides extensibility for adding domain-specific ontology in a hierarchical manner. Based on this context ontology, we have studied the use of logic reasoning to check the consistency of context information, and to reason over low-level, explicit context to derive high-level, implicit context. By giving a performance study for our prototype, we quantitatively evaluate the feasibility of logic based context reasoning for nontime-critical applications in pervasive computing environments, where we always have to deal carefully with the limitation of computational resources.


title: OPINE: Extracting Product Features and Opinions from Reviews

text: Consumers have to often wade through a large number of on-line reviews in order to make an informed product choice. We introduce OPINE, an unsupervised, high-precision information extraction system which mines product reviews in order to build a model of product features and their evaluation by reviewers.


title: Latent Variable PixelCNNs for Natural Image Modeling

text: We study probabilistic models of natural images and extend the autoregressive family of PixelCNN models by incorporating latent variables. Subsequently, we describe two new generative image models that exploit different image transformations as latent variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of our LatentPixelCNN models, in particular showing that they produce much more realistically looking image samples than previous stateof-the-art probabilistic models.


title: Top-down control of visual attention in object detection

text: Current computational models of visual attention focus on bottom-up information and ignore scene context. However, studies in visual cognition show that humans use context to facilitate object detection in natural scenes by directing their attention or eyes to diagnostic regions. Here we propose a model of attention guidance based on global scene config uration. We show that the statistics of low-level features across the scene image determine where a specific object (e.g. a person) should be located. Human eye movements show that regions chosen by the top-down model agree with regions scrutinized by human observers performing a visual search task for people. The results validate the proposition that top-down information from visual context modulates the saliency of image regions during the task of object de tection. Contextual information provides a shortcut for effi cient object detection systems.


title: Continuous software engineering: A roadmap and agenda

text: Throughout its short history, software development has been characterized by harmful disconnects between important activities such as planning, development and implementation. The problem is further exacerbated by the episodic and infrequent performance of activities such as planning, testing, integration and releases. Several emerging phenomena reflect attempts to address these problems. For example, Continuous Integration is a practice which has emerged to eliminate discontinuities between development and deployment. In a similar vein, the recent emphasis on DevOps recognizes that the integration between software development and its operational deployment needs to be a continuous one. We argue a similar continuity is required between business strategy and development, BizDev being the term we coin for this. These disconnects are even more problematic given the need for reliability and resilience in the complex and data-intensive systems being developed today. We identify a number of continuous activities which together we label as ‘Continuous ∗’ (i.e. Continuous Star) which we present as part of an overall roadmap for Continuous Software engineering. We argue for a continuous (but not necessarily rapid) software engineering delivery pipeline. We conclude the paper with a research agenda. © 2015 Elsevier Inc. All rights reserved.


title: Stable Marriage with Ties and Unacceptable Partners

text: An instance of the classical stable marriage problem involves n men and n women, and each person ranks all n members of the opposite sex in strict order of preference. The effect of allowing ties in the preference lists has been investigated previously, and three natural definitions of stability arise. In this paper, we extend this study by allowing a preference list to involve ties and/or be incomplete. We show that, under the weakest notion of stability, the stable matchings need not be all of the same cardinality, and the decision problem related to finding a maximum cardinality stable matching is NP-complete, even if the ties occur in the preference lists of one sex only. This result has important implications for practical matching schemes such as the well-known National Resident Matching Program [9]. In the cases of the other two notions of stability, Irving [5] has described algorithms for testing whether a stable matching exists, and for constructing such a matching if one does exist, where a preference list is complete but may involve ties. We demonstrate how to extend these algorithms to the case where a preference list may be incomplete and/or involve ties.


title: Speech recognition with primarily temporal cues.

text: Nearly perfect speech recognition was observed under conditions of greatly reduced spectral information. Temporal envelopes of speech were extracted from broad frequency bands and were used to modulate noises of the same bandwidths. This manipulation preserved temporal envelope cues in each band but restricted the listener to severely degraded information on the distribution of spectral energy. The identification of consonants, vowels, and words in simple sentences improved markedly as the number of bands increased; high speech recognition performance was obtained with only three bands of modulated noise. Thus, the presentation of a dynamic temporal pattern in only a few broad spectral regions is sufficient for the recognition of speech.


title: Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks

text: Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images.


title: Satisfaction, trust and online purchase intention: A study of consumer perceptions

text: This paper tries to do the research of analyzing the relation of the customer trust, the customer satisfaction and purchase intention under the electronic commerce shopping environment. Drawing on the theory of customer trust, customer satisfaction and purchase intention, based on a survey of 102 respondents from the University in China, the United Kingdom, and the United States, the results showed that there is significant correlation between satisfaction and trust, satisfaction and purchase intention, trust and purchase intention, and more than 50% of respondents chose Taobao and Tmall, and the second is Amazon when they shopping onling. It could be expected that this kind of study is able to provide suggestion and guide for internet merchant.


title: Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

text: Social media has become one of the most popular marketing channels for many companies, which aims at maximizing their influence by various marketing campaigns conducted from their official accounts on social networks. However, most of these marketing accounts merely focus on the contents of their tweets. Less effort has been made on understanding tweeting time, which is a major contributing factor in terms of attracting customers' attention and maximizing the influence of a social marketing campaign. To that end, in this paper, we provide a focused study of temporal retweeting patterns and their influence on social media marketing campaigns. Specifically, we investigate the users' retweeting patterns by modeling their retweeting behaviors as a generative process, which considers temporal, social, and topical factors. Moreover, we validate the predictive power of the model on the dataset collected from Sina Weibo, the most popular micro blog platform in China. By discovering the temporal retweeting patterns, we analyze the temporal popular topics and recommend tweets to users in a time-aware manner. Finally, experimental results show that the proposed algorithm outperforms other baseline methods. This model is applicable for companies to conduct their marketing campaigns at the right time on social media.


title: From learning models of natural image patches to whole image restoration

text: Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently. This raises three questions - do priors that give high likelihood to the data also lead to good performance in restoration? Can we use such patch based priors to restore a full image? Can we learn better patch priors? In this work we answer these questions. We compare the likelihood of several patch models and show that priors that give high likelihood to data perform better in patch restoration. Motivated by this result, we propose a generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated. We show how to derive an appropriate cost function, how to optimize it and how to use it to restore whole images. Finally, we present a generic, surprisingly simple Gaussian Mixture prior, learned from a set of natural images. When used with the proposed framework, this Gaussian Mixture Model outperforms all other generic prior methods for image denoising, deblurring and inpainting.


title: Note-Taking With Computers : Exploring Alternative Strategies for Improved Recall

text: Three experiments examined note-taking strategies and their relation to recall. In Experiment 1, participants were instructed either to take organized lecture notes or to try and transcribe the lecture, and they either took their notes by hand or typed them into a computer. Those instructed to transcribe the lecture using a computer showed the best recall on immediate tests, and the subsequent experiments focused on note-taking using computers. Experiment 2 showed that taking organized notes produced the best recall on delayed tests. In Experiment 3, however, when participants were given the opportunity to study their notes, those who had tried to transcribe the lecture showed better recall on delayed tests than those who had taken organized notes. Correlational analyses of data from all 3 experiments revealed that for those who took organized notes, working memory predicted note-quantity, which predicted recall on both immediate and delayed tests. For those who tried to transcribe the lecture, in contrast, only note-quantity was a consistent predictor of recall. These results suggest that individuals who have poor working memory (an ability traditionally thought to be important for note-taking) can still take effective notes if they use a note-taking strategy (transcribing using a computer) that can help level the playing field for students of diverse cognitive abilities.


title: Analytical Tools for Blockchain: Review, Taxonomy and Open Challenges

text: Bitcoin has introduced a new concept that could feasibly revolutionise the entire Internet as it exists, and positively impact on many types of industries including, but not limited to, banking, public sector and supply chain. This innovation is grounded on pseudo-anonymity and strives on its innovative decentralised architecture based on the blockchain technology. Blockchain is pushing forward a race of transaction-based applications with trust establishment without the need for a centralised authority, promoting accountability and transparency within the business process. However, a blockchain ledger (e.g., Bitcoin) tend to become very complex and specialised tools, collectively called “Blockchain Analytics”, are required to allow individuals, law enforcement agencies and service providers to search, explore and visualise it. Over the last years, several analytical tools have been developed with capabilities that allow, e.g., to map relationships, examine flow of transactions and filter crime instances as a way to enhance forensic investigations. This paper discusses the current state of blockchain analytical tools and presents a thematic taxonomy model based on their applications. It also examines open challenges for future development and research.


title: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers

text: In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for different languages, the feature transformations can be shared across languages. In this paper we propose a shared-hidden-layer multilingual DNN (SHL-MDNN), in which the hidden layers are made common across many languages while the softmax layers are made language dependent. We demonstrate that the SHL-MDNN can reduce errors by 3-5%, relatively, for all the languages decodable with the SHL-MDNN, over the monolingual DNNs trained using only the language specific data. Further, we show that the learned hidden layers sharing across languages can be transferred to improve recognition accuracy of new languages, with relative error reductions ranging from 6% to 28% against DNNs trained without exploiting the transferred hidden layers. It is particularly interesting that the error reduction can be achieved for the target language that is in different families of the languages used to learn the hidden layers.


title: Communication-Efficient Learning of Deep Networks from Decentralized Data

text: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10–100× as compared to synchronized stochastic gradient descent.


title: Federated Meta-Learning for Recommendation

text: Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user privacy. In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous approaches. In this framework, user-specific recommendation models are locally trained by a shared parameterized algorithm, which preserves user privacy and at the same time utilizes information from other users to help model training. Interestingly, the model thus trained exhibits a high capacity at a small scale, which is energyand communicationefficient. Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale. For example, on a production dataset, a shared model under Google Federated Learning (McMahan et al., 2017) with 900,000 parameters has prediction accuracy 76.72%, while a shared algorithm under federated meta-learning with less than 30,000 parameters achieves accuracy of 86.23%.


title: Neural networks and principal component analysis: Learning from examples without local minima

text: We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding to the projection onto the subspace generated by the first principal vectors of a covariance matrix associated with the training patterns. All the additional critical points of E are saddle points (corresponding to projections onto subspaces generated by higher order vectors). The auto-associative case is examined in detail. Extensions and implications for the learning algorithms are discussed.


title: A CORDIC processor for FFT computation and its implementation using gallium arsenide technology

text: In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplicationand-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations. Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor. The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.


title: A current-starved inverter-based differential amplifier design for ultra-low power applications

text: As silicon feature sizes decrease, more complex circui try arrays can now be contrived on a single die. This increase in the number of on-chip devices per unit area results in increased power dissipation per unit area. In order to meet certain power and operating temperature specifications, circuit design necessitates a focus on power efficiency, which is especially important in systems employing hundreds or thousands of instances of the same device. In large arrays, a slight increase in the power efficiency of a single component is heightened by the number of instances of the device in the system. This paper proposes a fully differential, low-power current-starving inverter-based amplifier topology designed in a commercial 0.18μm process. This design achieves 46dB DC gain and a 464 kHz uni ty gain frequency with a power consumption of only 145.32nW at 700mV power supply vol tage for ultra-low power, low bandwidth applications. Higher bandwidth designs are also proposed, including a 48dB DC gain, 2.4 MHz unity-gain frequency amplifier operating at 900mV wi th only 3.74μW power consumption.


title: Conditional connectivity

text: For a noncomplete graph G , the traditional definition of its connectivity is the minimum number of points whose removal results in a disconnected subgraph with components H , , . . . , H k . The conditional connectivity of G with respect to some graphtheoretic property P is the smallest cardinality of a set S of points, if any, such that every component Hi of the disconnected graph G S has property P. A survey of promising properties P is presented. Questions for various P-connectivities are listed in analogy with known results on connectivity and line-connectivity.


title: A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects

text: From half a million hectares at the turn of the century, Philippine mangroves have declined to only 120,000 ha while fish/shrimp culture ponds have increased to 232,000 ha. Mangrove replanting programs have thus been popular, from community initiatives (1930s–1950s) to government-sponsored projects (1970s) to large-scale international development assistance programs (1980s to present). Planting costs escalated from less than US$100 to over $500/ha, with half of the latter amount allocated to administration, supervision and project management. Despite heavy funds for massive rehabilitation of mangrove forests over the last two decades, the long-term survival rates of mangroves are generally low at 10–20%. Poor survival can be mainly traced to two factors: inappropriate species and site selection. The favored but unsuitable Rhizophora are planted in sandy substrates of exposed coastlines instead of the natural colonizers Avicennia and Sonneratia. More significantly, planting sites are generally in the lower intertidal to subtidal zones where mangroves do not thrive rather than the optimal middle to upper intertidal levels, for a simple reason. Such ideal sites have long been converted to brackishwater fishponds whereas the former are open access areas with no ownership problems. The issue of pond ownership may be complex and difficult, but such should not outweigh ecological requirements: mangroves should be planted where fishponds are, not on seagrass beds and tidal flats where they never existed. This paper reviews eight mangrove initiatives in the Philippines and evaluates the biophysical and institutional factors behind success or failure. The authors recommend specific protocols (among them pushing for a 4:1 mangrove to pond ratio recommended for a healthy ecosystem) and wider policy directions to make mangrove rehabilitation in the country more effective.


title: What characterizes a shadow boundary under the sun and sky?

text: Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.


title: 3D distance fields: a survey of techniques and applications

text: A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?.


title: Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization

text: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.


title: Noise radar using random phase and frequency modulation

text: Pulse compression radar is used in a great number of applications. Excellent range resolution and high electronic counter-countermeasures performance is achieved by wideband long pulses, which spread out the transmitted energy in frequency and time. By using a random noise waveform, the range ambiguity is suppressed as well. In most applications, the random signal is transmitted directly from a noise-generating microwave source. A sine wave, which is phase or frequency modulated by random noise, is an alternative, and in this paper, the ambiguity function and the statistical characteristics of the correlation output for the latter configuration are further analyzed. Range resolution is then improved because the noise bandwidth of the modulated carrier is wider than that of the modulating signal, and the range sidelobes are also further suppressed. Random biphase modulation gives a 4-dB (/spl pi//sup 2//4) improvement, but much higher sidelobe suppression could be achieved using continuous phase/frequency modulation. Due to the randomness of the waveform, the output correlation integral is accompanied by a noise floor, which limits the possible sidelobe suppression as determined by the time-bandwidth product. In synthetic aperture radar (SAR) applications with distributed targets, this product should be large compared with the number of resolution elements inside the antenna main beam. The advantages of low range sidelobes and enhanced range resolution make frequency/phase-modulated noise radar attractive for many applications, including SAR mapping, surveillance, altimetry, and scatterometry. Computer algorithms for reference signal delay and compression are discussed as replacements for the classical delay line implementation.


title: Collusive Piracy Prevention in P2P Content Delivery Networks

text: Collusive piracy is the main source of intellectual property violations within the boundary of a P2P network. Paid clients (colluders) may illegally share copyrighted content files with unpaid clients (pirates). Such online piracy has hindered the use of open P2P networks for commercial content delivery. We propose a proactive content poisoning scheme to stop colluders and pirates from alleged copyright infringements in P2P file sharing. The basic idea is to detect pirates timely with identity-based signatures and time-stamped tokens. The scheme stops collusive piracy without hurting legitimate P2P clients by targeting poisoning on detected violators, exclusively. We developed a new peer authorization protocol (PAP) to distinguish pirates from legitimate clients. Detected pirates will receive poisoned chunks in their repeated attempts. Pirates are thus severely penalized with no chance to download successfully in tolerable time. Based on simulation results, we find 99.9 percent prevention rate in Gnutella, KaZaA, and Freenet. We achieved 85-98 percent prevention rate on eMule, eDonkey, Morpheus, etc. The scheme is shown less effective in protecting some poison-resilient networks like BitTorrent and Azureus. Our work opens up the low-cost P2P technology for copyrighted content delivery. The advantage lies mainly in minimum delivery cost, higher content availability, and copyright compliance in exploring P2P network resources.


title: RanDroid: Android malware detection using random machine learning classifiers

text: The growing polularity of Android based smartphone attracted the distribution of malicious applications developed by attackers which resulted the need for sophisticated malware detection techniques. Several techniques are proposed which use static and/or dynamic features extracted from android application to detect malware. The use of machine learning is adapted in various malware detection techniques to overcome the mannual updation overhead. Machine learning classifiers are widely used to model Android malware patterns based on their static features and dynamic behaviour. To address the problem of malware detection, in this paper we have proposed a machine learning-based malware detection system for Android platform. Our proposed system utilizes the features of collected random samples of goodware and malware apps to train the classifiers. The system extracts requested permissions, vulnerable API calls along with the existence of app's key information such as; dynamic code, reflection code, native code, cryptographic code and database from applications, which was missing in previous proposed solutions and uses them as features in various machine learning classifiers to build classification model. To validate the performance of proposed system, "RanDroid" various experiments have been carriedout, which show that the RanDroid is capable to achieve a high classification accuracy of 97.7 percent.


title: Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation

text: In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size. Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor. We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.


title: A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

text: Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.


title: Robust Cross-lingual Hypernymy Detection using Dependency Context

text: Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.1


title: Broadband and compact impedance transformers for microwave circuits

text: Most microwave circuits and antennas use impedance transformers. In a conventional power amplifier, large number of inductors and capacitor or transmission line sections are use to realize an impedance transformer. This article presents an asymmetric broadside-coupled microstrip TLT (transmission line transformer) on the GaAs substrate. This matching network can be easily implemented using any monolithic microwave integrated circuit (MMIC) process supporting multilevel metallization


title: Seeing Behind the Camera: Identifying the Authorship of a Photograph

text: We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.


title: Participatory Air Pollution Monitoring Using Smartphones

text: Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on off-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the effect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.


title: Bluetooth based home automation system

text: The past decade has seen significant advancement in the field of consumer electronics. Various ‘intelligent’ appliances such as cellular phones, air-conditioners, home security devices, home theatres, etc. are set to realize the concept of a smart home. They have given rise to a Personal Area Network in home environment, where all these appliances can be interconnected and monitored using a single controller. Busy families and individuals with physical limitation represent an attractive market for home automation and networking. A wireless home network that does not incur additional costs of wiring would be desirable. Bluetooth technology, which has emerged in late 1990s, is an ideal solution for this purpose. This paper describes an application of Bluetooth technology in home automation and networking environment. It proposes a network, which contains a remote, mobile host controller and several client modules (home appliances). The client modules communicate with the host controller through Bluetooth devices. q 2002 Elsevier Science B.V. All rights reserved.


title: OCNet: Object Context Network for Scene Parsing

text: In this paper, we address the problem of scene parsing with deep learning and focus on the context aggregation strategy for robust segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we introduce an object context pooling (OCP) scheme, which represents each pixel by exploiting the set of pixels that belong to the same object category with such a pixel, and we call the set of pixels as object context. Our implementation, inspired by the self-attention approach, consists of two steps: (i) compute the similarities between each pixel and all the pixels, forming a socalled object context map for each pixel served as a surrogate for the true object context, and (ii) represent the pixel by aggregating the features of all the pixels weighted by the similarities. The resulting representation is more robust compared to existing context aggregation schemes, e.g., pyramid pooling modules (PPM) in PSPNet and atrous spatial pyramid pooling (ASPP), which do not differentiate the context pixels belonging to the same object category or not, making the reliability of contextually aggregated representations limited. We empirically demonstrate our approach and two pyramid extensions with state-ofthe-art performance on three semantic segmentation benchmarks: Cityscapes, ADE20K and LIP. Code has been made available at: https://github.com/PkuRainBow/ OCNet.pytorch.


title: RETOS: Resilient, Expandable, and Threaded Operating System for Wireless Sensor Networks

text: This paper presents the design principles, implementation, and evaluation of the RETOS operating system which is specifically developed for micro sensor nodes. RETOS has four distinct objectives, which are to provide (1) a multithreaded programming interface, (2) system resiliency, (3) kernel extensibility with dynamic reconfiguration, and (4) WSN-oriented network abstraction. RETOS is a multithreaded operating system, hence it provides the commonly used thread model of programming interface to developers. We have used various implementation techniques to optimize the performance and resource usage of multithreading. RETOS also provides software solutions to separate kernel from user applications, and supports their robust execution on MMU-less hardware. The RETOS kernel can be dynamically reconfigured, via loadable kernel framework, so a application-optimized and resource-efficient kernel is constructed. Finally, the networking architecture in RETOS is designed with a layering concept to provide WSN-specific network abstraction. RETOS currently supports Atmel ATmega128, TI MSP430, and Chipcon CC2430 family of microcontrollers. Several real-world WSN applications are developed for RETOS and the overall evaluation of the systems is described in the paper.


title: MRTouch: Adding Touch Input to Head-Mounted Mixed Reality

text: We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.


title: Aneka Cloud Application Platform and Its Integration with Windows Azure

text: Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.


title: Distributional Semantics Resources for Biomedical Text Processing

text: The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.


title: Optimization algorithm using scrum process

text: Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.


Evolving a Heuristic Function for the Game of Tetris


title: Relations Among Loneliness, Social Anxiety, and Problematic Internet Use

text: The model of problematic Internet use advanced and tested in the current study proposes that individuals' psychosocial well-being, along with their beliefs about interpersonal communication (both face-to-face and online) are important cognitive predictors of negative outcomes arising from Internet use. The study examined the extent to which social anxiety explains results previously attributed to loneliness as a predictor of preference for online social interaction and problematic Internet use. The results support the hypothesis that the relationship between loneliness and preference for online social interaction is spurious, and that social anxiety is the confounding variable.


title: The comorbid psychiatric symptoms of Internet addiction: attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility.

text:
PURPOSE
To: (1) determine the association between Internet addiction and depression, self-reported symptoms of attention deficit and hyperactivity disorder (ADHD), social phobia, and hostility for adolescents; and (2) evaluate the sex differences of association between Internet addiction and the above-mentioned psychiatric symptoms among adolescents.

METHODS
A total of 2114 students (1204 male and 910 female) were recruited for the study. Internet addiction, symptoms of ADHD, depression, social phobia, and hostility were evaluated by the self-report questionnaire.

RESULTS
The results demonstrated that adolescents with Internet addiction had higher ADHD symptoms, depression, social phobia, and hostility. Higher ADHD symptoms, depression, and hostility are associated with Internet addiction in male adolescents, and only higher ADHD symptoms and depression are associated with Internet addiction in female students.

CONCLUSION
These results suggest that Internet addiction is associated with symptoms of ADHD and depressive disorders. However, hostility was associated with Internet addiction only in males. Effective evaluation of, and treatment for ADHD and depressive disorders are required for adolescents with Internet addiction. More attention should be paid to male adolescents with high hostility in intervention of Internet addiction.


title: Internet Addiction: The Emergence of a New Clinical Disorder

text: Anecdotal reports indicated that some on-line users were becoming addicted to the Internet in much that same way that others became addicted to drugs or alcohol which resulted in academic, social, and occupational impairment. However, research among sociologists, psychologists, or psychiatrists has not formally identified addictive use of the Internet as a problematic behavior. This study investigated the existence of Internet addiction and the extent of problems caused by such potential misuse. This study utilized an adapted version of the criteria for pathological gambling defined by the DSM-IV (APA, 1994). On the basis of this criteria, case studies of 396 dependent Internet users (Dependents) and a control group of 100 non-dependent Internet users (Non-Dependents) were classified. Qualitative analyses suggests significant behavioral and functional usage differences between the two groups. Clinical and social implications of pathological Internet use and future directions for research are discussed.


title: Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey.

text: This study examined the relationship of Internet addiction to depression and suicidal ideation in Korean adolescents. The participants were 1573 high-school students living in a city who completed the self-reported measures of the Internet Addiction Scale, the Korean version of the Diagnostic Interview Schedule for Children-Major Depression Disorder-Simple Questionnaire, and the Suicidal Ideation Questionnaire-Junior. A correlational survey design was employed. Among the samples, 1.6% was diagnosed as Internet addicts, while 38.0% was classified as possible Internet addicts. The prevalence of Internet addiction did not vary with gender. The levels of depression and suicide ideation were highest in the Internet-addicts group. Future studies should investigate the direct relationship between psychological health problems and Internet dependency.


title: Internet addiction among Chinese adolescents: prevalence and psychological features.

text:
BACKGROUND
To investigate the prevalence of Internet addiction among Chinese adolescents and to explore the psychological features associated with Internet addiction.

METHODS
A total of 2620 high school students from four high schools in Changsha City were surveyed using Diagnostic Questionnaire for Internet Addiction (YDQ), Eysenck Personality Questionnaire (the edition for children, EPQ), Time Management Disposition Scale (TMDS) and Strengths and Difficulties Questionnaire (SDQ). The mean age of whole sample was 15.19 years (ranging from 12 years to 18 years). According to the modified YDQ criteria by Beard, 64 students who were diagnosed as Internet addiction (the mean age: 14.59 years) and 64 who were diagnosed as being normal in Internet usage (the mean age: 14.81 years) were included in a case-control study.

RESULTS
The rate of Internet use among the surveyed adolescents was 88%, among which the incidence rate of Internet addiction was 2.4%. The Internet addiction group had significantly higher scores on the EPQ subscales of neuroticism, psychoticism, and lie than the control group (P < 0.05). The Internet addiction group scored lower than the control group on the TMDS subscales of sense of control over time, sense of value of time, and sense of time efficacy (P < 0.05). Compared with the control group, the Internet addiction group had also significantly higher scores on the SDQ subscales of emotional symptoms, conduct problems, hyperactivity, total difficulties and lower scores on the subscale of prosocial behaviours (P < 0.05).

CONCLUSIONS
The present study suggests that Internet addiction is not rare among Chinese adolescents. In addition, adolescents with Internet addiction possess different psychological features when compared with those who use the Internet less frequently.


title: Hardware resource estimation for heterogeneous FPGA-based SoCs

text: The increasing complexity of recent System-on-Chip (SoC) designs introduces new challenges for design space exploration tools. In addition to the time-to-market challenge, designers need to estimate rapidly and accurately both performance and area occupation of complex and diverse applications. High-Level Synthesis (HLS) has been emerged as an attractive solution for designers to address these challenges in order to explore a large number of SoC configurations. In this paper, we target hybrid CPU-FPGA based SoCs. We propose a high-level area estimation tool based on an analytic model without requiring register-transfer level (RTL) implementations. This technique allows to estimate the required FPGA resources at the source code level to map an application to a hybrid CPU-FPGA system. The proposed model also enables a fast design exploration with different trade-offs through HLS optimization pragmas. Experimental results show that the proposed area analytic model provides an accurate estimation with a negligible error (less than 5+) compared to RTL implementations.


title: No double-dissociation between optic ataxia and visual agnosia: Multiple sub-streams for multiple visuo-manual integrations

text: The current dominant view of the visual system is marked by the functional and anatomical dissociation between a ventral stream specialised for perception and a dorsal stream specialised for action. The "double-dissociation" between visual agnosia (VA), a deficit of visual recognition, and optic ataxia (OA), a deficit of visuo-manual guidance, considered as consecutive to ventral and dorsal damage, respectively, has provided the main argument for this dichotomic view. In the first part of this paper, we show that the currently available empirical data do not suffice to support a double-dissociation between OA and VA. In the second part, we review evidence coming from human neuropsychology and monkey data, which cast further doubts on the validity of a simple double-dissociation between perception and action because they argue for a far more complex organisation with multiple parallel visual-to-motor connections: 1. A dorso-dorsal pathway (involving the most dorsal part of the parietal and pre-motor cortices): for immediate visuo-motor control--with OA as typical disturbance. The latest research about OA is reviewed, showing how these patients exhibit deficits restricted to the most direct and fast visuo-motor transformations. We also propose that mild mirror ataxia, consisting of misreaching errors when the controlesional hand is guided to a visual goal though a mirror, could correspond to OA with an isolated "hand effect". 2. A ventral stream-prefrontal pathway (connections from the ventral visual stream to pre-frontal areas, by-passing the parietal areas): for "mediate" control (involving spatial or temporal transpositions [Rossetti, Y., & Pisella, L. (2003). Mediate responses as direct evidence for intention: Neuropsychology of Not to-, Not now- and Not there-tasks. In S. Johnson (Ed.), Cognitive Neuroscience perspectives on the problem of intentional action (pp. 67-105). MIT Press.])--with VA as typical disturbance. Preserved visuo-manual guidance in patients with VA is restricted to immediate goal-directed guidance, they exhibit deficits for delayed or pantomimed actions. 3. A ventro-dorsal pathway (involving the more ventral part of the parietal lobe and the pre-motor and pre-frontal areas): for complex planning and programming relying on high representational levels with a more bilateral organisation or an hemispheric lateralisation--with mirror apraxia, limb apraxia and spatial neglect as representatives. Mirror apraxia is a deficit that affects both hands after unilateral inferior parietal lesion with the patients reaching systematically and repeatedly toward the virtual image in the mirror. Limb apraxia is localized on a more advanced conceptual level of object-related actions and results from deficient integrative, computational and "working memory" capacities of the left inferior parietal lobule. A component of spatial working memory has recently been revealed also in spatial neglect consecutive to lesion involving the network of the right inferior parietal lobule and the right frontal areas. We conclude by pointing to the differential temporal constraints and integrative capabilities of these parallel visuo-motor pathways as keys to interpret the neuropsychological deficits.


title: Mechanisms of decompensation and organ failure in cirrhosis: From peripheral arterial vasodilation to systemic inflammation hypothesis.

text: The peripheral arterial vasodilation hypothesis has been most influential in the field of cirrhosis and its complications. It has given rise to hundreds of pathophysiological studies in experimental and human cirrhosis and is the theoretical basis of life-saving treatments. It is undisputed that splanchnic arterial vasodilation contributes to portal hypertension and is the basis for manifestations such as ascites and hepatorenal syndrome, but the body of research generated by the hypothesis has revealed gaps in the original pathophysiological interpretation of these complications. The expansion of our knowledge on the mechanisms regulating vascular tone, inflammation and the host-microbiota interaction require a broader approach to advanced cirrhosis encompassing the whole spectrum of its manifestations. Indeed, multiorgan dysfunction and failure likely result from a complex interplay where the systemic spread of bacterial products represents the primary event. The consequent activation of the host innate immune response triggers endothelial molecular mechanisms responsible for arterial vasodilation, and also jeopardizes organ integrity with a storm of pro-inflammatory cytokines and reactive oxygen and nitrogen species. Thus, the picture of advanced cirrhosis could be seen as the result of an inflammatory syndrome in contradiction with a simple hemodynamic disturbance.


title: Single Image Shadow Removal via Neighbor-Based Region Relighting

text: In this paper we present a novel method for shadow removal in single images. For each shadow region we use a trained classifier to identify a neighboring lit region of the same material. Given a pair of lit-shadow regions we perform a region relighting transformation based on histogram matching of luminance values between the shadow region and the lit region. Then, we adjust the CIELAB a and b channels of the shadow region by adding constant offsets based on the difference of the median shadow and lit pixel values. We demonstrate that our approach produces results that outperform the state of the art by evaluating our method using a publicly available benchmark dataset.


title: Electric grid state estimators for distribution systems with microgrids

text: In the development of smart grid, state estimation in distribution systems will likely face more challenges than that at the transmission level. This paper addresses one of such challenges, namely, the absence of topology information, by developing a forecast-aided topology change detection method and an event-triggered recursive Bayesian estimator to identify the correct topology. Simulation studies with microgrid induced changes are presented to illustrate the effectiveness of the proposed algorithm.


title: Thumb Motor Performance Varies by Movement Orientation, Direction, and Device Size During Single-Handed Mobile Phone Use

text:
OBJECTIVE
The aim of this study was to determine if thumb motor performance metrics varied by movement orientation, direction, and device size during single-handed use of a mobile phone device.

BACKGROUND
With the increased use of mobile phones, understanding how design factors affect and improve performance can provide better design guidelines.

METHOD
A repeated measures laboratory experiment of 20 right-handed participants measured the thumb tip's 3-D position relative to a phone during reciprocal tapping tasks across four phone designs and four thumb tip movement orientations. Each movement orientation included two movement directions: an "outward" direction consisting in CMC (carpometacarpal) joint flexion or abduction movements and an "inward" direction consisting in CMC joint extension or adduction movements. Calculated metrics of the thumb's motor performance were Fitts' effective width and index of performance.

RESULTS
Index of performance varied significantly across phones, with performance being generally better for the smaller devices. Performance was also significantly higher for adduction-abduction movement orientations compared to flexion-extension, and for "outward" compared to "inward" movement directions.

CONCLUSION
For single-handed device use, adduction-abduction-type movements on smaller phones lead to better thumb performance.

APPLICATION
The results from this study can be used to design new mobile phone devices and keypad interfaces that optimize specific thumb motions to improve the user-interface experience during single-handed use.


title: Elliptic Curve Cryptosystems

text: The application of elliptic curves to the field of cryptography has been relatively recent. It has opened up a wealth of possibilities in terms of security, encryption, and real-world applications. In particular, we are interested in public-key cryptosystems that use the elliptic curve discrete logarithm problem to establish security. The objective of this thesis is to assemble the most important facts and findings into a broad, unified overview of this field. To illustrate certain points, we also discuss a sample implementation of the elliptic curve analogue of the El


title: Electronic Payments of Small Amounts

text: This note considers the application of electronic cash to transactions in which many small amounts must be paid to the same payee and in which it is not possible to just pay the total amount afterwards. The most notable example of such a transaction is payment for phone calls. If currently published electronic cash systems are used and a full payment protocol is executed for each of the small amounts, the overall complexity of the system will be prohibitively large (time, storage and communication). This note describes how such payments can be handled in a wide class of payment systems. The solution is very easy to adapt as it only in uences the payment and deposit transactions involving such payments. Furthermore, making and verifying each small payment requires very little computation and communication, and the total complexity of both transactions is comparable to that of a payment of a xed amount.


title: A 25-kHz 3rd-order continuous-time Delta-Sigma modulator using tri-level quantizer

text: This paper presents a 3rd order continuous-time Delta-Sigma modulator with a tri-level quantizer, which provides 3-dB reduction of quantization noise without dynamic element matching (DEM). The tri-level DAC linearity is analyzed and it shows that a highly linear tri-level DAC can be realized in fully-differential active-RC Delta-Sigma modulator. The performance of the tri-level continuous-time Delta-Sigma modulator has been verified through simulations using a standard 0.18-μm CMOS process. It achieves 81-dB SNDR at 3.2-MS/s sampling rate and consumes 1.14-μW power with ideal amplifier.


title: Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks

text: Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pheromone update by particle swarm optimization. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm. Keywordssocial network analysis; clique problem; ACO; PSO.