title: Semantic texton forests for image categorization and segmentation

abstract: We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors. The nodes in the trees provide (i) an implicit hierarchical clustering into semantic textons, and (ii) an explicit local classification estimate. Our second contribution, the bag of semantic textons, combines a histogram of semantic textons over an image region with a region prior category distribution. The bag of semantic textons is computed over the whole image for categorization, and over local rectangular regions for segmentation. Including both histogram and region prior allows our segmentation algorithm to exploit both textural and semantic context. Our third contribution is an image-level prior for segmentation that emphasizes those categories that the automatic categorization believes to be present. We evaluate on two datasets including the very challenging VOC 2007 segmentation dataset. Our results significantly advance the state-of-the-art in segmentation accuracy, and furthermore, our use of efficient decision forests gives at least a five-fold increase in execution speed.


title: Three control flow obfuscation methods for Java software

abstract: Three novel control computation (control flow) obfuscation methods are described for protecting Java class files. They are basic block fission obfuscation, intersecting loop obfuscation and replacing goto obfuscation. The basic block fission obfuscation splits some chosen basic block(s) into more basic blocks, in which opaque predicates and goto instructions are inserted to make decompiling unsuccessful. The intersecting loop obfuscation intersects two loops and then adds this pattern into programs. The intersecting loop structure should not appear in a Java source program or a class file. The replacing goto obfuscation replaces goto instructions with conditional branch instructions. The new methods were tested against 16 decompilers. The study also implemented multi-level exit obfuscation and single-level exit obfuscation for comparison. Both the intersecting loop obfuscation and the replacing goto obfuscation successfully defeated all the decompilers.


Notes On Stenopus Scutellatus Rankin and Neopontoni-Des Beaufort'Ensis ( Borradaile ) From the Northern Gulf of Mexico


title: FABRICATION AND MICROSTRUCTURAL CHARACTERIZATION OF NANO-CRYSTALLINE ZrO 2 -BASED COMPOSITE

abstract: Nanocrystalline ZrO 2 -spinel powders consisting of nano-particles of ≤10 nm can suc- cessfully be synthesized using high-energy ball-milling process. Using spark-plasma-sintering technique, the nanocrystalline powders can be consolidated into dense nanocrystalline com- posite with an average grain size of <100 nm. The mechanical properties of ZrO 2 -spinel compos- ite can significantly be improved by the nano-crystalization. Flexural strength σ f was heightened by a factor of 2.0-2.5: the maximum σ f of the nanocrystalline composite reached ≈2.4 GPa. Flow stress of the nanocrystalline composite can be lowered by about 50% than that of submicrometer- sized composite.


title: Beyond the Dolev-Yao Model: Realistic Application-Specific Attacker Models for Applications Using Vehicular Communication

abstract: In recent time, the standards for Vehicular Ad-hoc Networks (VANETs) and Intelligent Transportation Systems (ITSs) matured and scientific and industry interest is high especially as autonomous driving gets a lot of media attention. Autonomous driving and other assistance systems for cars make heavy use of VANETs to exchange information.They may provide more comfort, security and safety for drivers. However, it is of crucial importance for the user's trust in these assistance systems that they could not be influenced by malicious users. VANETs are likely attack vectors for such malicious users, hence application-specific security requirements must be considered during the design of applications using VANETs. In literature, many attacks on vehicular communication have been described but attacks on specific vehicular networking applications are often missing. This paper fills in this gap by describing standardized vehicular networking applications, defining and extending previous attacker models, and using the resulting new models to characterize the possible attackers interested in the specific vehicular networking application. The attacker models presented in this paper hopefully provide great benefit for the scientific community and industry as they allow to compare security evaluations of different works, characterize attackers, their intentions and help to plan application-specific security controls for vehicular networking applications.


title: Broadcast yourself: understanding YouTube uploaders

abstract: YouTube uploaders are the central agents in the YouTube phenomenon. We conduct extensive measurement and analysis of YouTube uploaders. We estimate YouTube scale and examine the uploading behavior of YouTube users. We demonstrate the positive reinforcement between on-line social behavior and uploading behavior. Furthermore, we examine whether YouTube users are truly broadcasting themselves, via characterizing and classifying videos as either user generated or user copied.


title: Security Analysis of FHSS-type Drone Controller

abstract: Unmanned Aerial Vehicles UAVs, or drones, have attracted a considerable amount of attentions due to their utility to civilian as well as military applications. However, the security issues involved in UAV technology have not been extensively discussed in the literature. As a first step toward analyzing these security issuxes, we investigate security in drone controllers, especially controllers that adopt Frequency Hopping Spread Spectrum FHSS. In order to affect an FHSS-type controller, an attacker first has to access its physical layer. This is difficult because of the pseudorandomness of the hopping sequence and the rapidly changing channels. However, these difficulties can be relaxed when the attacker acquires the hopping sequence and when the hopping speed of the target system is not significant. In this paper, we propose a general scheme to extract the hopping sequence of FHSS-type controllers using a software-defined radio SDR. We also propose a method to address the issue of the limited bandwidth of the SDR. We implemented our scheme on a Universal Software Radio Peripheral USRP, successfully extracted the hopping sequence of the target system, and exposed the baseband signal.


title: Deep Image Prior

abstract: Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity (Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior).


title: Event Extraction as Frame-Semantic Parsing

abstract: Based on the hypothesis that frame-semantic parsing and event extraction are structurally identical tasks, we retrain SEMAFOR, a stateof-the-art frame-semantic parsing system to predict event triggers and arguments. We describe how we change SEMAFOR to be better suited for the new task and show that it performs comparable to one of the best systems in event extraction. We also describe a bias in one of its models and propose a feature factorization which is better suited for this model.


title: Applying Morphology Generation Models to Machine Translation

abstract: We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic information from both the source and target languages. Our inflection generation models are trained independently of the SMT system. We investigate different ways of combining the inflection prediction component with the SMT system by training the base MT system on fully inflected forms or on word stems. We applied our inflection generation models in translating English into two morphologically complex languages, Russian and Arabic, and show that our model improves the quality of SMT over both phrasal and syntax-based SMT systems according to BLEU and human judgements.


title: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

abstract: This paper presents a review of - and classification scheme for - the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject has yet to be carried out. This paper thus represents the first systematic, identifiable and comprehensive academic literature review of the data mining techniques that have been applied to FFD. 49 journal articles on the subject published between 1997 and 2008 was analyzed and classified into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud, and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that data mining techniques have been applied most extensively to the detection of insurance fraud, although corporate fraud and credit card fraud have also attracted a great deal of attention in recent years. In contrast, we find a distinct lack of research on mortgage fraud, money laundering, and securities and commodities fraud. The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data. This paper also addresses the gaps between FFD and the needs of the industry to encourage additional research on neglected topics, and concludes with several suggestions for further FFD research.


title: On critical points of proper holomorphic maps on the unit disk

abstract: We prove that a proper holomorphic map on the unit disk in the complex plane is uniquely determined up to post-composition with a Moebius transformation by its critical points.


title: The global research trend of porcine reproductive and respiratory syndrome virus (PRRSV): A mini review

abstract: The Porcine reproductive and respiratory syndrome (PRRS) is a significantly harmful illness in swine causing respiratory problems and reproductive failure in sows, gilts, and pneumonia in young pigs. PRRS is often diagnosed with available test methods to identify disease agent or to detect the immune response of virus. In spite of many extensive researches, there are still no specific treatments and many unknown relationships between this virus and other diseases in swine. Virus isolation and molecular techniques are possible to identify PRRS virus or its acid nucleic but the high genetic diversity of virus results to unwanted false-negative results. Although PRRS has spreads throughout the world, many researches still focus on updating the methods to quickly diagnose PRRSV and to generate an effective fast treatment. This review highlights the current global PRRS research trend, its detection and diagnosis.


title: CubeSVD: a novel approach to personalized Web search

abstract: As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.


title: A review on routing protocols for application in wireless sensor networks

abstract: Wireless sensor networks are harshly restricted by storage capacity, energy and computing power. So it is essential to design effective and energy aware protocol in order to enhance the network lifetime. In this paper, a review on routing protocol in WSNs is carried out which are classified as data-centric, hierarchical and location based depending on the network structure. Then some of the multipath routing protocols which are widely used in WSNs to improve network performance are also discussed. Advantages and disadvantages of each routing algorithm are discussed thereafter. Furthermore, this paper compares and summarizes the performances of routing protocols.


title: Social collaborative filtering for cold-start recommendations

abstract: We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.


CD-Tree: A clustering-based dynamic indexing and retrieval approach


title: A 0.13/spl mu/m CMOS T/R switch design for ultrawideband wireless applications

abstract: A CMOS transmit-receive (T/R) switch design for ultrawideband (UWB) wireless applications is presented. A shunt inductor is adopted in the T/R switch signal path to improve the insertion loss (IL) and work as electrostatic discharge (ESD) protection device. Two bypassing switches at the gate of the main switching transistors provide additional isolation. Control voltage as low as 1.2V is applied to handle the T/R switch operation. Implemented in UMC 0.13/spl mu/m MMRF CMOS technology, the measured results show an IL of 0.78-0.99dB in the receive (RX) mode and an isolation from TX port to RX port higher than 30.8dB in the transmit (TX) mode in the 3.1-4.8GHz frequency range. The measured ESD rating is 3kV HBM. The T/R switch is designed for monolithic integration with UWB RF front-end building blocks. The on-chip inductor eliminates the need of an off-chip one for the wideband matching network.


title: Setting a Research Agenda for IT Project Management Offices

abstract: This paper recommends a number of research directions and themes to IS researchers concerning the planning, implementation, operations, and delivery of business value from IT Project Management Offices (IT PMOs). The recommendations are based on a review and analysis of academic literature on PMOs in which the authors noted that there were many challenges and tensions regarding PMOs that seemed unresolved. These formed the basis for a recommended research agenda that is presented in this paper. The research agenda is organized under three main themes: Up front planning and preparation for an IT PMO implementation, implementing and operating an IT PMO, and finally, delivering value from an IT PMO. These themes were then discussed with IT PMO managers in the context of their organizational environment, to establish whether they represented issues and challenges that were of genuine concern. It is intended that the research agenda will motivate and guide future research into IT PMOs.


title: An unsupervised long short-term memory neural network for event detection in cell videos

abstract: We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.


title: Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

abstract: The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.


title: Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study

abstract: This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learning rate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.


title: The Effect of Individual Factors on Health Behaviors Among College Students: The Mediating Effects of eHealth Literacy

abstract: Background: College students’ health behavior is a topic that deserves attention. Individual factors and eHealth literacy may affect an individual’s health behaviors. The integrative model of eHealth use (IMeHU) provides a parsimonious account of the connections among the digital divide, health care disparities, and the unequal distribution and use of communication technologies. However, few studies have explored the associations among individual factors, eHealth literacy, and health behaviors, and IMeHU has not been empirically investigated. Objective: This study examines the associations among individual factors, eHealth literacy, and health behaviors using IMeHU. Methods: The Health Behavior Scale is a 12-item instrument developed to measure college students’ eating, exercise, and sleep behaviors. The eHealth Literacy Scale is a 12-item instrument designed to measure college students’ functional, interactive, and critical eHealth literacy. A nationally representative sample of 525 valid college students in Taiwan was surveyed. A questionnaire was administered to collect background information about participants’ health status, degree of health concern, major, and the frequency with which they engaged in health-related discussions. This study used Amos 6.0 to conduct a confirmatory factor analysis to identify the best measurement models for the eHealth Literacy Scale and the Health Behavior Scale. We then conducted a multiple regression analysis to examine the associations among individual factors, eHealth literacy, and health behaviors. Additionally, causal steps approach was used to explore indirect (mediating) effects and Sobel tests were used to test the significance of the mediating effects. Results: The study found that perceptions of better health status (t 520 =2.14-6.12, P 520 =2.58-6.95, P P <.001-.03). Specifically, higher levels of critical eHealth literacy promoted students’ health status and their practice of multiple positive health behaviors, including eating, exercise, and sleep behaviors. Conclusions: Because this study showed that eHealth literacy mediates the association between individual factors and health behaviors, schools should aim to enhance students’ eHealth literacy and promote their health behaviors to help them achieve high levels of critical eHealth literacy. Although some of the study’s hypotheses were not supported in this study, the factors that influence health behaviors are complex and interdependent. Therefore, a follow-up study should be conducted to further explore how these factors influence one another. [J Med Internet Res 2014;16(12):e287]


title: Longitudinal changes in adolescent risk-taking: a comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior.

abstract: Prior studies have highlighted adolescence as a period of increased risk-taking, which is postulated to result from an overactive reward system in the brain. Longitudinal studies are pivotal for testing these brain-behavior relations because individual slopes are more sensitive for detecting change. The aim of the current study was twofold: (1) to test patterns of age-related change (i.e., linear, quadratic, and cubic) in activity in the nucleus accumbens, a key reward region in the brain, in relation to change in puberty (self-report and testosterone levels), laboratory risk-taking and self-reported risk-taking tendency; and (2) to test whether individual differences in pubertal development and risk-taking behavior were contributors to longitudinal change in nucleus accumbens activity. We included 299 human participants at the first time point and 254 participants at the second time point, ranging between ages 8–27 years, time points were separated by a 2 year interval. Neural responses to rewards, pubertal development (self-report and testosterone levels), laboratory risk-taking (balloon analog risk task; BART), and self-reported risk-taking tendency (Behavior Inhibition System/Behavior Activation System questionnaire) were collected at both time points. The longitudinal analyses confirmed the quadratic age pattern for nucleus accumbens activity to rewards (peaking in adolescence), and the same quadratic pattern was found for laboratory risk-taking (BART). Nucleus accumbens activity change was further related to change in testosterone and self-reported reward-sensitivity (BAS Drive). Thus, this longitudinal analysis provides new insight in risk-taking and reward sensitivity in adolescence: (1) confirming an adolescent peak in nucleus accumbens activity, and (2) underlining a critical role for pubertal hormones and individual differences in risk-taking tendency.


title: A 6.6kW SiC bidirectional on-board charger

abstract: This paper proposes a bidirectional architecture to implement an on-board charger (OBC) including an interleaved totem bridgeless PFC and Mode-Switched (MS) DC-DC converter. In the charging mode with the relay on, the MS DC-DC converter behave as a high efficiency Pulse Frequency Modulation (PFM) LLC converter since the charging mode is the most majority application scenario for the OBC. The PFC output voltage is regulated between 360 V to 400 V feeding into the DC-DC converter, so that 900 V SiC MOSFETs can be used both in the AC-DC and DC-DC stage to realize high frequency and high efficiency owing to the fast switching speed and high voltage rating with low on-resistance. The synchronous rectification (SR) is applied on the secondary side MOSFETs of the LLC converter to further improve the efficiency taking advantage of the bidirectional full-bridge topology. In the discharging mode with the relay off, the MS DC-DC stage is a two-stage converter. The reverse operation of the LLC converter operates as a DC-DC transformer (DCX) at the resonant point, and its output voltage is regulated to the bus voltage by a boost converter because the reverse gain of the LLC converter is lower than 1. A 6.6 kW SiC MS bidirectional OBC of DC-DC stage is built. In the charging mode, the peak efficiency is 96.37% at 6.6 kW with 380 V input voltage and 336 V output voltage. In the discharging mode, the peak efficiency is 96.87% at 3.3 kW with 346 V input voltage and 380 V output voltage.


Automatic Segmentation of Data Sequences


title: Selective Exposure in the Age of Social Media Endorsements Trump Partisan Source Affiliation When Selecting News Online

abstract: Much of the literature on polarization and selective exposure presumes that the internet exacerbates the fragmentation of the media and the citizenry. Yet this ignores how the widespread use of social media changes news consumption. Social media provide readers a choice of stories from different sources that come recommended from politically heterogeneous individuals, in a context that emphasizes social value over partisan affiliation. Building on existing models of news selectivity to emphasize information utility, we hypothesize that social media’s distinctive feature, social endorsements, trigger several decision heuristics that suggest utility. In two experiments, we demonstrate that stronger social endorsements increase the probability that people select content and that their presence reduces partisan selective exposure to levels indistinguishable from chance.


title: Towards a Formal Foundation of Web Security

abstract: We propose a formal model of web security based on an abstraction of the web platform and use this model to analyze the security of several sample web mechanisms and applications. We identify three distinct threat models that can be used to analyze web applications, ranging from a web attacker who controls malicious web sites and clients, to stronger attackers who can control the network and/or leverage sites designed to display user-supplied content. We propose two broadly applicable security goals and study five security mechanisms. In our case studies, which include HTML5 forms, Referer validation, and a single sign-on solution, we use a SAT-based model-checking tool to find two previously known vulnerabilities and three new vulnerabilities. Our case study of a Kerberos-based single sign-on system illustrates the differences between a secure network protocol using custom client software and a similar but vulnerable web protocol that uses cookies, redirects, and embedded links instead.


title: Sampling-Based Robot Motion Planning: A Review

abstract: Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.


title: Attribute-based data access control in mobile cloud computing

abstract: With the thriving growth of the cloud computing, the security and privacy concerns of outsourcing data have been increasing dramatically. However, because of delegating the management of data to an untrusted cloud server in data outsourcing process, the data access control has been recognized as a challenging issue in cloud storage systems. One of the preeminent technologies to control data access in cloud computing is Attribute-based Encryption (ABE) as a cryptographic primitive, which establishes the decryption ability on the basis of a users attributes. This paper provides a comprehensive survey on attribute-based access control schemes and compares each schemes functionality and characteristic. We also present a thematic taxonomy of attribute-based approaches based on significant parameters, such as access control mode, architecture, revocation mode, revocation method, revocation issue, and revocation controller. The paper reviews the state-of-the-art ABE methods and categorizes them into three main classes, such as centralized, decentralized, and hierarchal, based on their architectures. We also analyzed the different ABE techniques to ascertain the advantages and disadvantages, the significance and requirements, and identifies the research gaps. Finally, the paper presents open issues and challenges for further investigations. Studying the fundamental of ABE cryptosystem based on different criteria.Taxonomizing the ABE access control in cloud computing.Analysing the existing techniques critically based on taxonomy to identify the gaps.Identifying open issues and challenges of the existing ABE access control techniques.


title: Exosomes: new molecular targets of diseases

abstract: Extracellular vesicles (EVs) comprise apoptotic bodies, microvesicles and exosomes, and they perform as key regulators in cell-to-cell communication in normal as well as diseased states. EVs contain natural cargo molecules, such as miRNA, mRNA and proteins, and transfer these functional cargos to neighboring cells or more distant cells through circulation. These functionally active molecules then affect distinct signaling cascades. The message conveyed to the recipient cells is dependent upon the composition of the EV, which is determined by the parent cell and the EV biogenesis. Because of their properties such as increased stability in circulation, biocompatibility, low immunogenicity and toxicity, EVs have drawn attention as attractive delivery systems for therapeutics. This review focuses on the functional use of exosomes in therapy and the potential advantages and challenges in using exosomes for therapeutic purposes.


title: 3D cartoon face rigging from sparse examples

abstract: We present a data-driven method for automatically constructing cartoonized 3D blendshapes of a subject’s face. Given a pre-defined blendshape template of the real facial expressions and corresponding cartoonized blendshape template created by an artist, we represent the blendshapes of an identity in the real and cartoon face spaces with the deformations of the blendshape template in each space and learn a mapping between the deformations in the two spaces. To this end, our method decomposes the deformations in each space into two parts: an identity-independent part that is represented with the deformation gradient of the blendshape template, and an identity-dependent part that is modeled by a low-rank linear model. We regress the linear model for the real expressions from a 3D facial expression dataset. An algorithm is then introduced to regress the mapping between the linear models in the two spaces from a small set of real expressions and their cartoonized counterparts. At run time, given the blendshapes of a subject’s real face and her 3D cartoon neutral face, our method automatically constructs the cartoonized blendshapes of the subject with the help of the cartoonized blendshape template and the learned mapping. Our method is user-independent and only requires a small set of 3D cartoonized expressions modeled by the artist for cartoon face rigging. We evaluate our method by creating cartoonized 3D facial animations for variant identities in two different artistic styles. The rigging results demonstrate that our method successfully preserves both artistic styles and personalized expressions of different identities.


title: Generating Ambiguous Figure-Ground Images

abstract: Ambiguous figure-ground images, mostly represented as binary images, are fascinating as they present viewers a visual phenomena of perceiving multiple interpretations from a single image. In one possible interpretation, the white region is seen as a foreground figure while the black region is treated as shapeless background. Such perception can reverse instantly at any moment. In this paper, we investigate the theory behind this ambiguous perception and present an automatic algorithm to generate such images. We model the problem as a binary image composition using two object contours and approach it through a three-stage pipeline. The algorithm first performs a partial shape matching to find a good partial contour matching between objects. This matching is based on a content-aware shape matching metric, which captures features of ambiguous figure-ground images. Then we combine matched contours into a compound contour using an adaptive contour deformation, followed by computing an optimal cropping window and image binarization for the compound contour that maximize the completeness of object contours in the final composition. We have tested our system using a wide range of input objects and generated a large number of convincing examples with or without user guidance. The efficiency of our system and quality of results are verified through an extensive experimental study.


title: Acoustic modelling for speech recognition in Indian languages in an agricultural commodities task domain

abstract: In developing speech recognition based services for any task domain, it is necessary to account for the support of an increasing number of languages over the life of the service. This paper considers a small vocabulary speech recognition task in multiple Indian languages. To configure a multi-lingual system in this task domain, an experimental study is presented using data from two linguistically similar languages - Hindi and Marathi. We do so by training a subspace Gaussian mixture model (SGMM) (Povey et al., 2011; Rose et al., 2011) under a multi-lingual scenario (Burget et al., 2010; Mohan et al., 2012a). Speech data was collected from the targeted user population to develop spoken dialogue systems in an agricultural commodities task domain for this experimental study. It is well known that acoustic, channel and environmental mismatch between data sets from multiple languages is an issue while building multi-lingual systems of this nature. As a result, we use a cross-corpus acoustic normalization procedure which is a variant of speaker adaptive training (SAT) (Mohan et al., 2012a). The resulting multi-lingual system provides the best speech recognition performance for both languages. Further, the effect of sharing ''similar'' context-dependent states from the Marathi language on the Hindi speech recognition performance is presented.


title: SINNET: Social Interaction Network Extractor from Text

abstract: In this paper we present a demo of our system: Social Interaction Network Extractor from Text (SINNET). SINNET is able to extract a social network from unstructured text. Nodes in the network are people and links are social events.


title: Machine Learning for Galaxy Morphology Classification

abstract: In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue.


title: Characterization and Performance Evaluation of the Superjunction RB-IGBT in Matrix Converter

abstract: In this paper, we investigate the performance of the three-phase ac–ac matrix converter (MC) utilizing the novel superjunction (SJ) reverse-blocking (RB)-insulated gate bipolar transistor (IGBT) as switch elements for the first time. The SJ RB-IGBT offers bidirectional blocking capability >1200 V by introducing a shorted-collector trench and n2-layer. The SJ RB-IGBT exhibits bipolar conducting mode in the on-state and the unipolar mode during turn-off. Therefore, both a low on-state voltage ( V on) and a low turnoff energy loss ( E off) can be obtained. The SJ RB-IGBT delivers the E off 80% lower than state-of-the-art RB-IGBTs without penalty in the V on. These merits make the SJ RB-IGBT an attractive candidate for the MC. To address the short circuit arising in commutation of the MC, the split gate is adopted for a low Miller capacitance. The power loss, efficiency, and loss distribution of the three-phase MC with the SJ RB-IGBT and other RB-IGBTs are compared by simulation and calculation. Compared with the MC using conventional nonpunch-through RB-IGBT, the MC with SJ RB-IGBT achieves 85%, 87%, and 56% reductions in the turnoff, reverse recovery, and conduction losses, respectively, yielding 56% reduction in the total loss. The low-power loss and superior dynamic performance of the SJ RB-IGBT enables the MC to operate at higher frequency and higher power density.


title: Low-Voltage Bipolar-Type DC Microgrid for Super High Quality Distribution

abstract: Microgrid is one of the new conceptual power systems for smooth installation of many distributed generations (DGs). While most of the microgrids adopt ac distribution as well as conventional power systems, dc microgrids are proposed and researched for the good connection with dc output type sources such as photovoltaic (PV) system, fuel cell, and secondary battery. Moreover, if loads in the system are supplied with dc power, the conversion losses from sources to loads are reduced compared with ac microgrid. As one of the dc microgrids, we propose “low-voltage bipolar-type dc microgrid,” which can supply super high quality power with three-wire dc distribution line. In this paper, one system for a residential complex is presented as an instance of the dc microgrid. In this system, each house has a cogeneration system (CGS) such as gas engine and fuel cell. The output electric power is shared among the houses, and the total power can be controlled by changing the running number of CGSs. Super capacitors are chosen as main energy storage. To confirm the fundamental characteristics and system operations, we experimented with a laboratory scale system. The results showed that the proposed system could supply high-quality power under several conditions.


title: Zero Shot Recognition with Unreliable Attributes

abstract: In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it possesses—even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.


title: Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers

abstract: The OECD’s Brain and Learning project (2002) emphasized that many misconceptions about the brain exist among professionals in the field of education. Though these so-called “neuromyths” are loosely based on scientific facts, they may have adverse effects on educational practice. The present study investigated the prevalence and predictors of neuromyths among teachers in selected regions in the United Kingdom and the Netherlands. A large observational survey design was used to assess general knowledge of the brain and neuromyths. The sample comprised 242 primary and secondary school teachers who were interested in the neuroscience of learning. It would be of concern if neuromyths were found in this sample, as these teachers may want to use these incorrect interpretations of neuroscience findings in their teaching practice. Participants completed an online survey containing 32 statements about the brain and its influence on learning, of which 15 were neuromyths. Additional data was collected regarding background variables (e.g., age, sex, school type). Results showed that on average, teachers believed 49% of the neuromyths, particularly myths related to commercialized educational programs. Around 70% of the general knowledge statements were answered correctly. Teachers who read popular science magazines achieved higher scores on general knowledge questions. More general knowledge also predicted an increased belief in neuromyths. These findings suggest that teachers who are enthusiastic about the possible application of neuroscience findings in the classroom find it difficult to distinguish pseudoscience from scientific facts. Possessing greater general knowledge about the brain does not appear to protect teachers from believing in neuromyths. This demonstrates the need for enhanced interdisciplinary communication to reduce such misunderstandings in the future and establish a successful collaboration between neuroscience and education.


title: Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation

abstract: We present a novel inference algorithm for arbitrary, binary, undirected graphs. Unlike loopy belief propagation, which iterates fixed point equations, we directly descend on the Bethe free energy. The algorithm consists of two phases, first we update the pairwise probabilities, given the marginal probabilities at each unit, using an analytic expression. Next, we update the marginal probabilities, by following the negative gradient of the Bethe free energy. Both steps are guaranteed to decrease the Bethe free energy, and since it is lower bounded, the algorithm is guaranteed to converge to a local minimum. We also show that the Bethe free energy is equal to the TAP free energy up to second order in the weights. In experiments we confirm that when belief propagation converges it usually finds identical solutions as our belief optimization method. The stable nature of belief optimization makes it ideally suited for learning graphical models from data.


title: Secure Federated Transfer Learning

abstract: Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.


title: On the Impossibility of Basing Identity Based Encryption on Trapdoor Permutations

abstract: We ask whether an identity based encryption (IBE) system can be built from simpler public-key primitives. We show that there is no black-box construction of IBE from trapdoor permutations (TDP) or even from chosen ciphertext secure public key encryption (CCA-PKE). These black-box separation results are based on an essential property of IBE, namely that an IBE system is able to compress exponentially many public-keys into a short public parameters string.


title: EA Based Dynamic Key Generation in RC4 Ciphering Applied to CMS

abstract: RC4 is the most widely used stream cipher algorithm. It is used to protect valuable electronic information. However, RC4 encryption algorithm suers from a secret key generation as a seed problems. This paper proposes an intelligent dynamic secret key generation as a seed by employing an Evolutionary Algorithm (EA). The proposed RC4-EA method tends to enhance the RC4 encryption algorithm with a high degree of a seed key randomness. The main advantage of the proposed RC4-EA method is that the generation of this secret key is done dynamically and randomly; this adds more strength of the RC4 encryption algorithm against breaking this cryptosystems. Several experiments on the proposed RC4-EA method are conducted. Where, the results of the experiments show the improvement of the encryption time and the throughput of the proposed encryption RC4-EA method. Also, the proposed validated RC4-EA encryption method is applied for data ciphering in Content Management System (CMS).


title: SLANGZY: a fuzzy logic-based algorithm for English slang meaning selection

abstract: The text present on online forums and social media platforms conventionally does not follow a standard sentence structure and uses words that are commonly termed as slang or Internet language. Online text mining involves a surfeit of slang words; however, there is a distinct lack of reliable resources available to find accurate meanings of these words. We aim to bridge this gap by introducing SLANGZY, a fuzzy logic-based algorithm for English slang meaning selection which uses a mathematical factor termed as “slang factor” to judge the accuracy of slang word definitions found in Urban Dictionary, the largest Slang Dictionary on the Internet. This slang factor is used to rank definitions of English slang words retrieved from over 4 million unique words on popular social media platforms such as Twitter, YouTube and Reddit. We investigate the usefulness of SLANGZY over Urban Dictionary to find meanings of slang words in social media text and achieve encouraging results due to recognizing the importance of multiple criteria in the calculation of slang factor in the algorithm over successive experiments. The performance of SLANGZY with optimum weights for each criterion is further assessed using the accuracy, error rate, F-Score as well as a difference factor for English slang word definitions. To further illustrate the results, a web portal is created to display the contents of the Slang Dictionary consisting of definitions ranked according to the calculated slang factors.


title: MEMS based hair flow-sensors as model systems for acoustic perception studies

abstract: Arrays of MEMS fabricated flow sensors inspired by the acoustic flow-sensitive hairs found on the cerci of crickets, have been designed, fabricated and characterized. The hairs consist of up to 1 mm long SU-8 structures mounted on suspended membranes with normal translational and rotational degrees of freedom. Electrodes on the membrane and on the substrate form variable capacitors allowing for capacitive read-out. Capacitance versus voltage, frequency dependency and directional sensitivity measurements have been successfully carried out on fabricated sensor arrays, showing the viability of the concept. The sensors form a model-system allowing for investigations on sensory acoustics by their arrayed nature, their adaptivity via electrostatic interaction (frequency tuning and parametric amplifica- tion) and their susceptibility to noise (stochastic resonance)


title: Towards modelling and reasoning support for early-phase requirements engineering

abstract: Requirements are usually understood as stating what a system is supposed to do, as apposed to how it should do it. However, understanding the organizational context and rationales (the "Whys") that lead up to systems requirements can be just as important for the ongoing success of the system. Requirements modelling techniques can be used to help deal with the knowledge and reasoning needed in this earlier phase of requirements engineering. However most existing requirements techniques are intended more for the later phase of requirements engineering, which focuses on completeness, consistency, and automated verification of requirements. In contrast, the early phase aims to model and analyze stakeholder interests and how they might be addressed, or compromised, by various system-and-environment alternatives. This paper argues, therefore, that a different kind of modelling and reasoning support is needed for the early phase. An outline of the i* framework is given as an example of a step in this direction. Meeting scheduling is used as a domain example.


Prerequisite Relation Learning for Concepts in MOOCs.


title: Design of interval type-2 fuzzy logic controller for mobile wheeled inverted pendulum

abstract: In this paper, a novel interval type-2 fuzzy logic controller (IT2FLC) is proposed for controlling a mobile wheeled inverted pendulum(MWIP) with model uncertainties and external disturbances. The MWIP is a typical underactuated system which is generally composed of two subsystems, an invert pendulum and a mobile robot system. The IT2FLC proposed in this paper is composed of a balancing controller, a velocity controller and a yaw steering controller. The proposed integrated IT2FLC is linear combination of the three individual IT2FLCs which uses simplified type reduce method. The proposed controller has simple structure and requires less computation, also it is expected to have robustness to uncertainties and external disturbances in the practical implementation. Considering the model uncertainties and external disturbances, simulation results proved the effectiveness and robustness of the proposed IT2FLC compared with type-1 fuzzy logic controller (T1FLC).


title: EVENODD: an efficient scheme for tolerating double disk failures in RAID architectures

abstract: We present a novel method, that we call EVENODD, for tolerating up to two disk failures in RAID architectures. EVENODD employs the addition of only two redundant disks and consists of simple exclusive-OR computations. This redundant storage is optimal, in the sense that two failed disks cannot be retrieved with less than two redundant disks. A major advantage of EVENODD is that it only requires parity hardware, which is typically present in standard RAID-5 controllers. Hence, EVENODD can be implemented on standard RAID-5 controllers without any hardware changes. The most commonly used scheme that employes optimal redundant storage (i.e., two extra disks) is based on Reed-Solomon (RS) error-correcting codes. This scheme requires computation over finite fields and results in a more complex implementation. For example, we show that the complexity of implementing EVENODD in a disk array with 15 disks is about 50% of the one required when using the RS scheme. The new scheme is not limited to RAID architectures: it can be used in any system requiring large symbols and relatively short codes, for instance, in multitrack magnetic recording. To this end, we also present a decoding algorithm for one column (track) in error. >


title: Revisiting Distributed Synchronous SGD

abstract: Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the synchronous approach is often thought to be impractical due to idle time wasted on waiting for straggling workers. We revisit these conventional beliefs in this paper, and examine the weaknesses of both approaches. We demonstrate that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers. Our approach is empirically validated and shown to converge faster and to better test accuracies.


title: The role of priors in Bayesian models of perception

abstract: In a recent opinion article, Pellicano and Burr (2012) speculate about how a Bayesian architecture might explain many features of autism ranging from stereotypical movement to atypical phenomenological experience. We share the view of other commentators on this paper (Brock, 2012; Friston et al., 2013; Van Boxtel and Lu, 2013) that applying computational methods to psychiatric disorders is valuable (Montague et al., 2012). However, we argue that in this instance there are fundamental technical and conceptual problems which must be addressed if such a perspective is to become useful. ::: ::: Based on the Bayesian observer model (Figure 1), Pellicano and Burr speculate that perceptual abnormalities in autism can be explained by differences in how beliefs about the world are formed, or combined with sensory information, and that sensory processing itself is unaffected (although, confusingly, they also speak of sensory atypicalities in autism). In computational terms, the authors are suggesting that the likelihood function is unaltered in autism and that the posterior is atypical either because of differences in the prior, or because of the way in which prior and likelihood are combined. The latter statement is problematic because within the framework of probability theory, the combination of these two components is fixed as determined by Bayes' theorem: they are multiplied. Put simply, a mathematically consistent Bayesian model cannot accommodate a perceptual abnormality in autism that is due to the way in which belief and sensory information, i.e., prior and likelihood, are combined. Furthermore, if sensory processing is mathematically represented as a likelihood function (as it typically is within Bayesian models), then changes in the prior cannot lead to changes in sensation, as the authors claim (they can only lead to changes in perception).


title: Anonymous and Traceable Group Data Sharing in Cloud Computing

abstract: Group data sharing in cloud environments has become a hot topic in recent decades. With the popularity of cloud computing, how to achieve secure and efficient data sharing in cloud environments is an urgent problem to be solved. In addition, how to achieve both anonymity and traceability is also a challenge in the cloud for data sharing. This paper focuses on enabling data sharing and storage for the same group in the cloud with high security and efficiency in an anonymous manner. By leveraging the key agreement and the group signature, a novel traceable group data sharing scheme is proposed to support anonymous multiple users in public clouds. On the one hand, group members can communicate anonymously with respect to the group signature, and the real identities of members can be traced if necessary. On the other hand, a common conference key is derived based on the key agreement to enable group members to share and store their data securely. Note that a symmetric balanced incomplete block design is utilized for key generation, which substantially reduces the burden on members to derive a common conference key. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.


title: TFLMS: Large Model Support in TensorFlow by Graph Rewriting

abstract: While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. We propose an approach to tackle this problem by rewriting the computational graph of a neural network, in which swap-out and swap-in operations are inserted to temporarily store intermediate results on CPU memory. In particular, we first revise the concept of a computational graph by defining a concrete semantics for variables in a graph. We then formally show how to derive swap-out and swap-in operations from an existing graph and present rules to optimize the graph. To realize our approach, we developed a module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the TensorFlow repository for contributing to the TensorFlow community. With TFLMS, we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size, respectively. In particular, we were able to train 3DUNet using images of size of $192^3$ for image segmentation, which, without TFLMS, had been done only by dividing the images to smaller images, which affects the accuracy.


title: K-plet Recurrent Neural Networks for Sequential Recommendation

abstract: Recurrent Neural Networks have been successful in learning meaningful representations from sequence data, such as text and speech. However, recurrent neural networks attempt to model only the overall structure of each sequence independently, which is unsuitable for recommendations. In recommendation system, an optimal model should not only capture the global structure, but also the localized relationships. This poses a great challenge in the application of recurrent neural networks to the sequence prediction problem. To tackle this challenge, we incorporate the neighbor sequences into recurrent neural networks to help detect local relationships. Thus we propose a K -plet R ecurrent Neural Network (Kr Network for short) to accommodate multiple sequences jointly, and then introduce two ways to model their interactions between sequences. Experimental results on benchmark datasets show that our proposed architecture Kr Network outperforms state-of-the-art baseline methods in terms of generalization, short-term and long term prediction accuracy.


title: Cooperation Among Competitors: The Economics of Payment Card Associations

abstract: We analyze platforms in two-sided markets with network externalities, using the specific context of a payment card association. We study the cooperative determination of the interchange fee by member banks. The interchange fee is the ``access charge'' paid by the merchants' banks (the acquirers) to cardholders' banks (the issuers). We develop a framework in which banks and merchants may have market power and consumers and merchants decide rationally on whether to buy or accept a payment card. After drawing the welfare implications of a cooperative determination of the interchange fee, we describe in detail the factors affecting merchant resistance, compare cooperative and for-profit business models, and make a first cut in the analysis of system competition.


title: A Realistic Power Consumption Model for Wireless Sensor Network Devices

abstract: A realistic power consumption model of wireless communication subsystems typically used in many sensor network node devices is presented. Simple power consumption models for major components are individually identified, and the effective transmission range of a sensor node is modeled by the output power of the transmitting power amplifier, sensitivity of the receiving low noise amplifier, and RF environment. Using this basic model, conditions for minimum sensor network power consumption are derived for communication of sensor data from a source device to a destination node. Power consumption model parameters are extracted for two types of wireless sensor nodes that are widely used and commercially available. For typical hardware configurations and RF environments, it is shown that whenever single hop routing is possible it is almost always more power efficient than multi-hop routing. Further consideration of communication protocol overhead also shows that single hop routing will be more power efficient compared to multi-hop routing under realistic circumstances. This power consumption model can be used to guide design choices at many different layers of the design space including, topology design, node placement, energy efficient routing schemes, power management and the hardware design of future wireless sensor network devices


title: Theory and implementation of path planning by negotiation for decentralized agents

abstract: This paper presents a cooperative decentralized path-planning algorithm for a group of autonomous agents that provides guaranteed collision-free trajectories in real-time. The algorithm is robust with respect to arbitrary delays in the wireless traffic, possible sources being transmission time and error correction. Agents move on reserved areas which are guaranteed not to intersect, therefore ensuring safety. A handshaking procedure guarantees recent information states for the agents. Conflicts between agents are resolved by a cost-based negotiation process. The basic algorithm is augmented by the introduction of waypoints, which increase performance at the cost of additional wireless traffic. An implementation of the algorithm is tested in simulation and successfully applied to a real system of autonomous robots. The results are presented and discussed.


title: The Neglected Intrinsic Resistome of Bacterial Pathogens

abstract: Bacteria with intrinsic resistance to antibiotics are a worrisome health problem. It is widely believed that intrinsic antibiotic resistance of bacterial pathogens is mainly the consequence of cellular impermeability and activity of efflux pumps. However, the analysis of transposon-tagged Pseudomonas aeruginosa mutants presented in this article shows that this phenotype emerges from the action of numerous proteins from all functional categories. Mutations in some genes make P. aeruginosa more susceptible to antibiotics and thereby represent new targets. Mutations in other genes make P. aeruginosa more resistant and therefore define novel mechanisms for mutation-driven acquisition of antibiotic resistance, opening a new research field based in the prediction of resistance before it emerges in clinical environments. Antibiotics are not just weapons against bacterial competitors, but also natural signalling molecules. Our results demonstrate that antibiotic resistance genes are not merely protective shields and offer a more comprehensive view of the role of antibiotic resistance genes in the clinic and in nature.


title: Secure Surfing: Privacy-Preserving Speeded-Up Robust Feature Extractor

abstract: Large-scale multimedia data are being exponentially generated, stored and processed continuously nowadays. Along with the data explosion, the data owner is highly motivated to outsource his/her huge amount of data storage and computation-expensive processing jobs to the cloud by leveraging its abundant resources for cost reduction and flexibility. Despite the fascinating advantages, security and privacy concerns are the primary obstacles that prevent the wide adoption of this promising information technology paradigm. In this work, we aim at outsourcing Speeded-up Robust Features (SURF), a widely-used feature extraction algorithm, to the intrinsically untrusted cloud, while protecting data owner's private information on the outsourced image data. We, for the first time, propose a practical privacy-preserving outsoucing scheme for SURF that can preserve its key characteristics in terms of distinctiveness and robustness. By randomly splitting the original image data and distributing the encrypted data shares to two independent cloud servers, we first design two novel efficient protocols for secure multiplication and comparison by leveraging somewhat homomorphic encryption (SHE) and single-instruction multiple-data (SIMD). We then carefully tune every step of the original SURF to adapt it to the ciphertext domain. A thorough theoretical analysis of effectiveness and security shows that our scheme is practically secure and approximates well the performance of the original SURF executed in the plaintext domain. Extensive experiments are also conducted over real-world image datasets to show that our scheme outperforms the existing solution and indeed performs comparably to the original SURF in preserving its various characteristics including image scale invariance, rotation invariance and robust matching across 3D viewpoint change etc.


title: Non-Bovine Caseins: Quantitative Variability and Molecular Diversity

abstract: Caseins are phosphoproteins synthesised by mammary epithelial cells under multi-hormonal control as more or less large and stable particles, referred to as casein micelles and which appear like raspberries in electron micrographs. These spherical particles seem to be the result of aggregation of smaller discrete subunits or submicelles (Schmidt, 1982; Walstra, 1990), cemented by a calcium phosphate salt (colloidal calcium phosphate). Although this casein micelle model is widely accepted, it remains, in some circumstances, a topic of discussion and controversial debate. This issue will be dealt with in detail elsewhere in this book (Chapters 1 and 5). Casein micelles are present in the milk of all mammals and have a statistically broad distribution in size (Holt, 1985, 1992). In bovine milk, the most thoroughly studied milk to date, the casein micelle is considered to be comprised of submicelles, made of several casein molecules (15 to 20; Schmidt, 1982), arising from the expression of four single-copy autosomal genes which encode four distinct polypeptide chains (αs1-, β-, αs2- and κ-caseins).


title: Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

abstract: Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations in its two components: (1) in anchor graph construction, the estimation of the local weights between each datapoint and its neighboring anchors could be biased and relatively slow; and (2) in anchor graph regularization, the adjacency matrix that estimates the relationship between datapoints, is not sufficiently effective. In this paper, we develop an Efficient Anchor Graph Regularization (EAGR) by tackling these issues. First, we propose a fast local anchor embedding method, which reformulates the optimization of local weights and obtains an analytical solution. We show that this method better reconstructs datapoints with anchors and speeds up the optimizing process. Second, we propose a new adjacency matrix among anchors by considering the commonly linked datapoints, which leads to a more effective normalized graph Laplacian over anchors. We show that, with the novel local weight estimation and normalized graph Laplacian, EAGR is able to achieve better classification accuracy with much less computational costs. Experimental results on several publicly available datasets demonstrate the effectiveness of our approach.


title: A User-centered Music Recommendation Approach for Daily Activities

abstract: The number of songs available on the Internet has grown steadily over the last decade, with the recent growth being due mainly to streaming services. As a consequence, it is extremely dicult for users to find the appropriate music that suit their needs, in particular, while using systems that do not have any previous information about them. This is further exacerbated while selecting appropriate songs for daily activities, like shopping, running or sleeping. In this paper we describe Improvise, a personalized music recommendation solution for daily activities, whose approach associates music content (acoustic features) with activities (context). Each activity is characterized by determining intervals for each content feature, which are then used to filter out songs to be suggested to users. While the initial intervals are generic enough to provide recommendations for di↵erent activities without having previous knowledge about the user’s tastes, our approach also considers users’ feedback to personalize the recommendations for each user and activity. This is done by adapting the intervals according to the feedback from users. Preliminary evaluation shows that we are on the good path to achieve the goal of developing a solution to e↵ectively recommend songs for daily activities, and able to adjust to individual user’s tastes, increasing their satisfaction.


title: A Rotating Synthetic Aperture Radar Imaging Concept for Robot Navigation

abstract: This paper introduces a concept for robot navigation based on a rotating synthetic aperture short-range radar scanner. It uses an innovative broadband holographic reconstruction algorithm, which overcomes the typical problem of residual phase errors caused by an imprecisely measured aperture position and moving targets. Thus, it is no longer necessary to know the exact trajectory of the synthetic aperture radar to get a high-resolution image, which is a major advantage over the classical holographic reconstruction algorithm. However, the developed algorithm is not only used to compute a high-resolution 360 ° 2-D image after each turn of the radar platform while the robot is moving, but also to calculate the relative residual radial velocity between the moving radar scanner system and all targets in the environment. This allows us to determine the exact velocity of the robotic system on which the radar scanner is mounted, and thus to obtain the exact radar trajectory, if there are stationary targets like walls in the environment.


title: The problem of noise in classification: Past, current and future work

abstract: Data have been accumulated to wait for being analyzed in real world. But the imperfection of data complicates the analysis process. According to “garbage in, garbage out”, model built on such data will mislead the following study. Multiple empirical studies have showed that noise in dataset dramatically decrease the classification accuracy and increase the complexity of classification. Therefore, the problem of noise in classification is always the focus in machine learning and data mining. At the same time, noise is uncertain, so the problem is also a difficult and open problem. For systematically studying the problem, we summarize and analyze the main researches from the aspects of noise model, method of handling noise and algorithms of handling noise. Based on the past and current work, we discuss some new directions in solving the problem.


title: A new species of the Gekko japonicus group (Squamata: Gekkonidae) from central Laos

abstract: A new species of the Gekko japonicus group is described from Khammouane Province, central Laos, based on distinct morphological and molecular features. Gekko thakhekensis sp. nov. is distinguished from the remaining congeners by a combination of the following characters: size moderate (SVL 67.6–79.2 mm); nares in contact with rostral; internasals absent; postmentals enlarged; interorbital scales between anterior corners of the eyes 22–26; dorsal tubercles absent; ventral scales between mental and cloacal slit 165–174; midbody scale rows 110–116; ventral scale rows 32–40; subdigital lamellae on first toe 11–13, on fourth toe 14–15; finger and toe webbing present at base, about one fifth of length of digits; tubercles on upper surface of fore and hind limbs absent; precloacal pores 1–5 in males; postcloacal tubercles two; tubercles absent on dorsal surface of tail base; subcaudals enlarged; dorsal surface of body with greyish brown blotches. In molecular analyses, the new species is recovered as a sister taxon to G. scientiadventura, but the two species are separated by approximately 12% divergence as shown by the partial mitochondrial ND2 gene.


A Protocol for Interledger Payments


title: A Novel Transverse-Flux PM Linear Machine With Double Ω-Hoop Stator

abstract: Transverse flux permanent magnet machine (TFPMM) has outstanding characteristics of large torque and arbitrarily set pole-number. Multifarious TFPMM topologies have been proposed to enhance the space utilization and the flux density. This paper presents a TFPMM with double Ω-hoop stator, which is laminated by traditional steel sheets. The construction of the machine has all space occupied by efficacious materials, which stretches far beyond double-side TFPMMs. The electromagnetic characteristics and operation principles are analysed. Benefited from the compact structure this machine is suitable for wind power and electric vehicle applications.


title: Incremental clustering for dynamic information processing

abstract: Clustering of very large document databases is useful for both searching and browsing. The periodic updating of clusters is required due to the dynamic nature of databases. An algorithm for incremental clustering is introduced. The complexity and cost analysis of the algorithm together with an investigation of its expected behavior are presented. Through empirical testing it is shown that the algorithm achieves cost effectiveness and generates statistically valid clusters that are compatible with those of reclustering. The experimental evidence shows that the algorithm creates an effective and efficient retrieval environment.


title: Future railway services-oriented mobile communications network

abstract: The future development of the railway is highly desired to evolve into a new era where infrastructure, trains, travelers, and goods will be increasingly interconnected to provide high comfort, with optimized door-to-door mobility at higher safety. For this vision, it is required to realize seamless high data rate wireless connectivity for railways. To improve the safety and comfort of future railways, wireless communications for railways are required to evolve from only voice and traditional train control signaling services to various high data rate services including critical high-definition (HD) video and other more bandwidth-intensive passenger services, such as onboard and wayside HD video surveillance, onboard real-time high data rate services, train multimedia dispatching video streaming, railway mobile ticketing, and the Internet of Things for railways. Corresponding mobile communications network architecture under various railway scenarios including inter-car, intra-car, inside station, train-to-infrastructure and infrastructure- to-infrastructure are proposed in this article. Wireless coverage based on massive MIMO for railway stations and train cars is proposed to fulfill the requirement of high-data-rate and high spectrum efficiency. The technical challenges brought by the massive MIMO technique are discussed as well.


title: Cross-Domain Perceptual Reward Functions

abstract: In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agents state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.


title: Detecting Passive Cheats in Online Games via Performance-Skillfulness Inconsistency

abstract: As the most commonly used bots in first-person shooter (FPS) online games, aimbots are notoriously difficult to detect because they are completely passive and resemble excellent honest players in many aspects. In this paper, we conduct the first field measurement study to understand the status quo of aimbots and how they play in the wild. For data collection purpose, we devise a novel and generic technique called baittarget to accurately capture existing aimbots from the two most popular FPS games. Our measurement reveals that cheaters who use aimbots cannot play as skillful as excellent honest players in all aspects even though aimbots can help them to achieve very high shooting performance. To characterize the unskillful and blatant nature of cheaters, we identify seven features, of which six are novel, and these features cannot be easily mimicked by aimbots. Leveraging this set of features, we propose an accurate and robust server-side aimbot detector called AimDetect. The core of AimDetect is a cascaded classifier that detects the inconsistency between performance and skillfulness of aimbots. We evaluate the efficacy and generality of AimDetect using the real game traces. Our results show that AimDetect can capture almost all of the aimbots with very few false positives and minor overhead.


title: Browsing, Posting, and Liking on Instagram: The Reciprocal Relationships Between Different Types of Instagram Use and Adolescents' Depressed Mood

abstract: Abstract Although studies have shown that Instagram use and young adults' mental health are cross-sectionally associated, longitudinal evidence is lacking. In addition, no study thus far examined this association, or the reverse, among adolescents. To address these gaps, we set up a longitudinal panel study among 12- to 19-year-old Flemish adolescents to investigate the reciprocal relationships between different types of Instagram use and depressed mood. Self-report data from 671 adolescent Instagram users (61% girls; MAge = 14.96; SD = 1.29) were used to examine our research question and test our hypotheses. Structural equation modeling showed that Instagram browsing at Time 1 was related to increases in adolescents' depressed mood at Time 2. In addition, adolescents' depressed mood at Time 1 was related to increases in Instagram posting at Time 2. These relationships were similar among boys and girls. Potential explanations for the study findings and suggestions for future research are discussed.


title: Foreign Language Attrition.

abstract: Compared to research on language acquisition, research on the maintenance and loss of language skills is a relatively recent development. There is, of course, a longstanding sociolinguistic tradition of research on language shift, but work in the field of attrition really started in the US in the late 1970s (Lambert and Freed 1982). Since then, research on language attrition has grown rapidly in different countries, as is witnessed by special issues devoted to the topic in the journals Applied Psycholinguistics in 1986, ITL-Review of Applied Linguistics in 1989 and Studies in Second Language Acquisition in 1989, as well as by three recent volumes: Weltens, de Bot and van Els (1986), Weltens (1989), and Seliger and Vago (1991).


title: Biobehavioral responses to stress in females: Tend-and-befriend, not fight-or-flight

abstract: The human stress response has been characterized, both physiologically and behaviorally, as "fight-orflight." Although fight-or-flight may characterize the primary physiological responses to stress for both males and females, we propose that, behaviorally, females' responses are more marked by a pattern of "tend-and-befriend." Tending involves nurturant activities designed to protect the self and offspring that promote safety and reduce distress; befriending is the creation and maintenance of social networks that may aid in this process. The biobehavioral mechanism that underlies the tend-and-befriend pattern appears to draw on the attachment-caregiving system, and neuroendocrine evidence from animal and human studies suggests that oxytocin, in conjunction with female reproductive hormones and endogenous opioid peptide mechanisms, may be at its core. This previously unexplored stress regulatory system has manifold implications for the study of stress. Survival depends on the ability to mount a successful response to threat. The human stress response has been characterized as fight-or-flight (Cannon, 1932) and has been represented as an essential mechanism in the survival process. We propose that human female responses to stress (as well as those of some animal species) are not well characterized by fight-or-flig ht, as research has implicitly assumed, but rather are more typically characterized by a pattern we term "tend-and-befriend." Specifically, we suggest that, by virtue of differential parental investment, female stress responses have selectively evolved to maximize the survival of self and offspring. We suggest that females respond to stress by nurturing offspring, exhibiting behaviors that protect them from harm and reduce neuroendocrine responses that may compromise offspring health (the tending pattern), and by befriending, namely,


title: Requirements Prioritization Techniques Review and Analysis

abstract: Requirements prioritization is considered as one of the most important activities in the requirement engineering process. This paper gives an overview of the requirements prioritization activities and techniques. It also presents how data mining and machine learning techniques have been used to prioritize the software project requirements. A comparison between these techniques is also presented.


title: Building Structured Databases of Factual Knowledge from Massive Text Corpora

abstract: In today's computerized and information-based society, people are inundated with vast amounts of text data, ranging from news articles, social media post, scientific publications, to a wide range of textual information from various domains (corporate reports, advertisements, legal acts, medical reports). To turn such massive unstructured text data into structured, actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations) in the text. In this tutorial, we introduce data-driven methods on mining structured facts (i.e., entities and their relations/attributes for types of interest) from massive text corpora, to construct structured databases of factual knowledge (called StructDBs). State-of-the-art information extraction systems have strong reliance on large amounts of task/corpus-specific labeled data (usually created by domain experts). In practice, the scale and efficiency of such a manual annotation process are rather limited, especially when dealing with text corpora of various kinds (domains, languages, genres). We focus on methods that are minimally-supervised, domain-independent, and language-independent for timely StructDB construction across various application domains (news, social media, biomedical, business), and demonstrate on real datasets how these StructDBs aid in data exploration and knowledge discovery.


title: Resonanant enhancement of molecular excitation intensity in inelastic electron scattering spectrum owing to interaction with plasmons in metallic nanoshell

abstract: A quantum-mechanical model to calculate the electron energy-loss spectra (EELS) for the system of a closely located metallic nanoshell and a molecule has been developed. At the resonance between the molecular excitation and plasmon modes in the nanoshell, which can be provided by a proper choice of the ratio of the inner and outer nanoshell radii, the cross-section of inelastic electron scattering at the molecular excitation energy is shown to grow significantly, because the molecular transition borrows the oscillator strength from a plasmon. The enhancement of the inelastic electron scattering by the molecule makes it possible to observe molecular transitions with an electron microscope. The dependences of the EEL spectra on the relative arrangement of the molecule and the nanoshell, the ratio between the inner and outer nanoshell radii, and the scattering angle are plotted and analyzed.


title: www.mpi-magdeburg.mpg.de/preprints Matrix Inversion on CPU-GPU Platforms with Applications in Control Theory ∗

abstract: In this paper we tackle the inversion of large-scale dense matrices via conventional matrix factorizations (LU, Cholesky, LDL T ) and the Gauss-Jordan method on hybrid platforms consisting of a multi-core CPU and a many-core graphics processor (GPU). Specifically, we introduce the different matrix inversion algorithms using a unified framework based on the notation from the FLAME project; we develop hybrid implementations for those matrix operations underlying the algorithms, alternative to those in existing libraries for singleGPU systems; and we perform an extensive experimental study on a platform equipped with state-of-the-art general-purpose architectures from Intel and a “Fermi” GPU from NVIDIA that exposes the efficiency of the different inversion approaches. Our study and experimental results show the simplicity and performance advantage of the GJE-based inversion methods, and the difficulties associated with the symmetric indefinite case.


title: Computation of cyclic redundancy checks via table look-up

abstract: Cyclic Redundancy Check (CRC) codes provide a simple yet powerful method of error detection during digital data transmission. Use of a table look-up in computing the CRC bits will efficiently implement these codes in software.


title: 300-GHz LTCC horn antennas based on antenna-in-package technology

abstract: This paper presents 300-GHz horn antennas aiming at their integration in low-temperature co-fired ceramic (LTCC) packages. Using substrate integrated waveguide technology, we fabricated vertical horn antennas with an LTCC multi-layer process. A cavity inside the multi-layer LTCC substrate and a surrounding via fence are used to form a feeding hollow waveguide and horn structure. The prototype of the LTCC horn antenna exhibits peak gain of 16 dBi and 58-GHz bandwidth with more than 10-dB return loss. The size of the horn antenna is only 5 mm×5 mm×2.7 mm, which makes it to easy to integrate it in a compact LTCC package with an MMIC chip.


Adaptive Moving Window Regression ( AMWR ) Predicted data Kafka Event Collector Complex Event Engine Event


title: Dualing GANs

abstract: Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its saddle point formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the generator and the discriminator of this ‘dualing GAN’ act in concert. We then demonstrate how to extend this intuition to non-linear formulations. For GANs with linear discriminators our approach is able to remove the instability in training, while for GANs with nonlinear discriminators our approach provides an alternative to the commonly used GAN training algorithm.


title: Joint Time-Frequency Scattering for Audio Classification

abstract: We introduce the joint time-frequency scattering transform, a time shift invariant descriptor of time-frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time-frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time-frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.


title: One-Shot Adaptation of Supervised Deep Convolutional Models

abstract: Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-of-the-art deep model on a new domain requires a significant amount of data, which for many applications is simply not available. Transfer of models directly to new domains without adaptation has historically led to poor recognition performance. In this paper, we pose the following question: is a single image dataset, much larger than previously explored for adaptation, comprehensive enough to learn general deep models that may be effectively applied to new image domains? In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be? We show that a generic supervised deep CNN model trained on a large dataset reduces, but does not remove, dataset bias. Furthermore, we propose several methods for adaptation with deep models that are able to operate with little (one example per category) or no labeled domain specific data. Our experiments show that adaptation of deep models on benchmark visual domain adaptation datasets can provide a significant performance boost.


title: Minimizing faulty executions of distributed systems

abstract: When troubleshooting buggy executions of distributed systems, developers typically start by manually separating out events that are responsible for triggering the bug (signal) from those that are extraneous (noise). We present DEMi, a tool for automatically performing this minimization. We apply DEMi to buggy executions of two very different distributed systems, Raft and Spark, and find that it produces minimized executions that are between 1X and 4.6X the size of optimal executions.


title: Investigation of a biomimetic fingertip's ability to discriminate fabrics based on surface textures

abstract: Tactile sensing is an important ability for a humanoid robot which interacts in an unstructured environment. Such a system needs to sense and evaluate surface properties of objects that it interacts with. Among those properties, surface texture identification is a compulsory ability for certain kind of robot systems such as service robots, medical robots and exploratory robots. Therefore, the tactile system of above type of robots should have the ability to identify and discriminate textures with acceptable accuracy. A biomimetic fingertip that can be used in above kinds of robot tactile systems is introduced. The fingertip has the ability to detect force and vibration modalities. This paper reports the ability of the fingertip system to discriminate multiple materials (six fabrics and aluminium plate) by comparing the differences in their surface texture. The materials were classified using features: variance and power of the accelerometer signal. Moreover an Artificial Neural Network (ANN) classifier was evaluated by using the first 300 Fourier coefficients of the accelerometer signal as features. Above two methods used the raw signals of the accelerometers nearest to the contact area. Finally, an input signal was computed by calculating the covariance of two adjacent accelerometers. This new signal was used to calculate features for the ANN classifier. The results showed that the use of a convoluted signal improved the success rate of discriminating the seven textures.


title: Advanced Grid Synchronization System for Power Converters under Unbalanced and Distorted Operating Conditions

abstract: This paper proposes a new technique for grid synchronization under unbalanced and distorted conditions, i.e., the dual second order generalised integrator - frequency-locked loop (DSOGI-FLL). This grid synchronization system results from the application of the instantaneous symmetrical components method on the stationary and orthogonal alphabeta reference frame. The second order generalized integrator concept (SOGI) is exploited to generate in-quadrature signals used on the alphabeta reference frame. The frequency-adaptive characteristic is achieved by a simple control loop, without using either phase-angles or trigonometric functions. In this paper, the development of the DSOGI-FLL is plainly exposed and hypothesis and conclusions are verified by simulation and experimental results


Machine Learning, Game Theory, and Mechanism Design for a Networked World


title: Integrating e-commerce and data mining: architecture and challenges

abstract: We show that the e-commerce domain can provide all the right ingredients for successful data mining. We describe an integrated architecture for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the Web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We conclude with a set of challenges.


title: A new PWM/PFM control technique for improving efficiency over wide load range

abstract: This paper proposed a novel controlling technique of pulse width modulation (PWM) mode and pulse frequency modulation (PFM) mode to keep the high efficiency within width range of loading. The novel control method is using PWM and PFM detector to achieve two modes switching appropriately. The controlling technique can make the efficiency of current mode DC-DC buck converter up to 88% at light loading and this paper is implemented by TSMC 0.35 mum CMOS process.