title: Reliable and Privacy-Preserving Selective Data Aggregation for Fog-Based IoT

abstract: Internet of Things (IoT) is reshaping our daily lives by bridging the gaps between physical and digital world. To enable ubiquitous sensing, seamless connection and real-time processing for IoT applications, fog computing is considered as a key component in a heterogeneous IoT architecture, which deploys storage and computing resources to network edges. However, the fog-based IoT architecture can lead to various security and privacy risks, such as compromised fog nodes that may impede developments of IoT by attacking the data collection and gathering period. In this paper, we propose a novel privacy-preserving and reliable scheme for the fog-based IoT to address the data privacy and reliability challenges of the selective data aggregation service. Specifically, homomorphic proxy re-encryption and proxy re-authenticator techniques are respectively utilized to deal with the data privacy and reliability issues of the service, which supports data aggregation over selective data types for any type-driven applications. We define a new threat model to formalize the non-collusive and collusive attacks of compromised fog nodes, and it is demonstrated that the proposed scheme can prevent both non-collusive and collusive attacks in our model. In addition, performance evaluations show the efficiency of the scheme in terms of computational costs and communication overheads.


Policy Optimization with Demonstrations


title: Smart Grid automation: Distributed protection application with IEC61850/IEC61499

abstract: This paper proposes solutions for distributed protection applications, to improve current and design of future protection schemes. This solution utilises advantages of fast and reliable peer-to-peer communication of IEC 61850 and interoperability and configurability of IEC 61499. The integration of the two standards improves protection schemes by decreasing fault clearing times and minimizes the effect of short circuit faults on sensitive loads. Additional advantages include the reductions of the number of hard-wired connections, especially in a large substation where all protection IEDs has a significant number of binary inputs and relay outputs. IEC 61499 is an open standard for designing distributed control systems to promote portability, interoperability and configurability, i.e. vendor independent, flexible and robust solutions. IEC 61499 can be used to represent programmable logic of power system devices, in a standard form and in a vendor independent way. The proposed distributed protection system is implemented and simulated using the developed co-simulation environment.


Automatic Query Expansion for Patent Passage Retrieval using Paradigmatic and Syntagmatic Information


title: Label-Embedding for Image Classification

abstract: Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as, e.g., class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples.


title: A Ka-band Rectangular Waveguide to microstrip transition using SIR coupling slot with LTCC technology

abstract: In this paper, a Rectangular Waveguide (RW) to microstrip transition with Low-Temperature Co-fired Ceramic (LTCC) technology in Ka-band is designed, fabricated and measured. Compared to the traditional transition using a rectangular slot, the proposed Stepped-Impedance Resonator (SIR) slot enlarges the bandwidth of the transition. By introducing an additional design parameter, it generates multi-modes within the transition. To further improve the bandwidth and to adjust the performance of the transition, a resonant strip is embedded between the open microstrip line and its ground plane. Measured results agree well with that of the simulation, showing an effective bandwidth about 22% (from 28.5 GHz to 36.5GHz), an insertion loss approximately 3 dB and return loss better than 15 dB in the pass-band.


Insight lab: an immersive team environment linking paper, displays, and data


title: Ka-Band Omnidirectional High Gain Stacked Dual Bicone Antenna

abstract: This communication presents a new omnidirectional antenna based on biconical antenna element to realize high gain and high omnidirectivity at Ka-band. Two biconical antenna elements are stacked and fed differentially by a specially designed differential feeding network embedded inside the antenna. Measurement shows that the $- {10}$ -dB impedance bandwidth of the antenna is 14.2% (26.2–30.2 GHz), within which the antenna can achieve a high gain of around 12.5 dBi corresponding to a sharp 3-dB beamwidth of about 5° in the elevation plane. The ripple of the omnidirectional radiation pattern in the azimuth plane is smaller than ${\pm }{0}.{5}\;{\text{dBi}}$ .


title: Understanding the determinants of cloud computing adoption

abstract: Purpose – The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high‐tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure.Design/methodology/approach – A questionnaire‐based survey was used to collect data from 111 firms belonging to the high‐tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis.Findings – The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing.Research limitations/implications – The research was conducted in the high‐tech industry, which may limit the generalisability of the findings.Practical implications – The findings offer cloud computing service providers ...


title: Stochastic Alternating Direction Method of Multipliers

abstract: The Alternating Direction Method of Multipliers (ADMM) has received lots of attention recently due to the tremendous demand from large-scale and data-distributed machine learning applications. In this paper, we present a stochastic setting for optimization problems with non-smooth composite objective functions. To solve this problem, we propose a stochastic ADMM algorithm. Our algorithm applies to a more general class of convex and nonsmooth objective functions, beyond the smooth and separable least squares loss used in lasso. We also demonstrate the rates of convergence for our algorithm under various structural assumptions of the stochastic function: O(1/√t)) for convex functions and O(log t/t) for strongly convex functions. Compared to previous literature, we establish the convergence rate of ADMM for convex problems in terms of both the objective value and the feasibility violation. A novel application named Graph-Guided SVM is proposed to demonstrate the usefulness of our algorithm.


title: A Novel Block-based Watermarking Scheme Using the SVD Transform

abstract: In this paper, a block-based watermarking scheme based on the Singular Value Decomposition (SVD) is proposed. Our watermark, a pseudo-random Gaussian sequence, is embedded by modifying the angles formed by the right singular vectors of each block of the original image. The orthogonality property of the right singular vector matrix is preserved during the embedding process. Several experiments have been carried out to test the performance of the proposed scheme against different attack scenarios. We conclude that the proposed scheme is resistant against common signal processing operations and attacks, while it preserves the quality of the original image.


title: Clustering of time-series subsequences is meaningless: implications for previous and future research

abstract: Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it's own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention. We make an amazing claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work.


title: The Fast Fourier Transform and Its Applications

abstract: The advent of the fast Fourier transform method has greatly extended our ability to implement Fourier methods on digital computers. A description of the alogorithm and its programming is given here and followed by a theorem relating its operands, the finite sample sequences, to the continuous functions they often are intended to approximate. An analysis of the error due to discrete sampling over finite ranges is given in terms of aliasing. Procedures for computing Fourier integrals, convolutions and lagged products are outlined.


title: Beyond big and little: The four c model of creativity.

abstract: Most investigations of creativity tend to take one of two directions: everyday creativity (also called “little-c”), which can be found in nearly all people, and eminent creativity (also called “Big-C”), which is reserved for the great. In this paper, the authors propose a Four C model of creativity that expands this dichotomy. Specifically, the authors add the idea of “mini-c,” creativity inherent in the learning process, and Pro-c, the developmental and effortful progression beyond little-c that represents professional-level expertise in any creative area. The authors include different transitions and gradations of these four dimensions of creativity, and then discuss advantages and examples of the Four C Model.


title: Regression model for appraisal of real estate using recurrent neural network and boosting tree

abstract: Automated valuation model (AVM) is a mathematical program to estimate the market value of real estates based on the analysis of locations, neighborhood characteristics, and relevant property characteristics. The most common AVMs em-ployed by the appraisal industry are based on multiple regression analysis. Other analytic tools such as statistical learning and fuzzy algorithms have become more popular because of the increasing capability of collecting a high volume of data and the advancement of machine learning. The new analytic model thus becomes possible to build a more sophisticated model to exploit the information embedded in the collected data. In this work, we proposed a boosting tree model facilitated with a Recurrent Neural Network (RNN) to forecast the average price of an area. The experimental results indicate that our model outperforms the existing models adopted in the appraisal industry.


title: A Deep Neural Network Sentence Level Classification Method with Context Information

abstract: In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.


title: Motion State Classification for Automotive LIDAR Based on Evidential Grid Maps and Transferable Belief Model

abstract: Point clouds generated by Lidar sensors provide detailed information about the geometry of the environment. Yet they lack semantic information, which is paramount for the choice of choosing the appropriate modelling, e.g. within a tracking system. This contribution tries to provide semantic information in the form of stationary and dynamic classification by applying a transferable belief model. In particular, we build an occupancy grid representation of the environment and correct it with a tailored transferable belief model that accounts for inconsistencies as well as non-local features. Based on these results we define a classifier that distinguishes between stationary and dynamic cells. The presented algorithm is evaluated qualitatively on real-world Valeo Scala LIDAR data and quantitatively based on an IPG Carmaker simulation.


title: Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications

abstract: In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc.) and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A2 SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.


title: A crowdsourcing model for new idea development in the fashion industry

abstract: Crowdsourcing is a powerful tool for new product development; companies create online platforms where customers can contribute their own ideas to design tasks, often motivated by either a monetary prize or gaining social status. The phenomenon can help companies gain significant competitive advantage due to e.g. reduced R&D costs and increased customer satisfaction, but to be successful, crowdsourcing needs to be implemented efficiently. The crowdsourcing platform and the practices related to it need to be built to enable effective data management and evaluation. Fashion is a very promising area for crowdsourcing due to its rapidly evolving nature. Styles can change overnight, which means that brands need to rely more and more on the knowledge of their customers to be able to provide them with the newest trends. This article first provides an overview on current crowdsourcing practices and crowdsourcing platforms used by companies in general, and then discusses special requirements that the fashion industry sets for crowdsourcing practices and crowdsourcing platforms. At the end, the article presents a new model of crowdsourcing processes that are especially suitable for the fashion industry in the design phase of new fashion garments.


title: Medical Applications for 3D Printing: Current and Projected Uses.

abstract: 3D printing is expected to revolutionize health care through uses in tissue and organ fabrication; creation of customized prosthetics, implants, and anatomical models; and pharmaceutical research regarding drug dosage forms, delivery, and discovery.


title: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

abstract: Physiological computing represents a mode of human-computer interaction where the computer monitors, analyzes and responds to the user's psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subject's psychological state.


title: Facial image processing for sleepiness estimation

abstract: Sleepiness could cause different accidents at work and in our daily lives. Activities such as driving a car, handling heavy machinery, being in charge of controlling a satellite or monitoring a nuclear plant, are activities that require short visual and motor reaction times. These reactions could be affected by sleepiness. The highest number of these accidents is related to vehicular accidents. WHO statistics show that 1.2 million people worldwide die every year due to road crashes, 50 million people are injured, and more than 3,000 people die daily from road traffic injuries. The primary objective of the present work is to develop a tool based on techniques of artificial intelligence and image processing that can detect states of sleepiness in people whose work needs the greatest attention — to avoid accidents. The idea is to develop an algorithm that can be executed on a computer and through a webcam can record the face of the person or worker to analyze the video and through facial recognition techniques can identify the eyes of the person, and thus identify eyelashes that would be an indicator of sleepiness. It is expected to have an algorithm developed in MATLAB to detect drowsiness and provide some notification.


title: A semi-automated approach for generating natural language requirements documents based on business process models

abstract: Context: The analysis of requirements for business-related software systems is often supported by using business process models. However, the final requirements are typically still specified in natural language. This means that the knowledge captured in process models must be consistently transferred to the specified requirements. Possible inconsistencies between process models and requirements represent a serious threat for the successful development of the software system and may require the repetition of process analysis activities. Objective: The objective of this paper is to address the problem of inconsistency between process models and natural language requirements in the context of software development. Method: We define a semi-automated approach that consists of a process model-based procedure for capturing execution-related data in requirements models and an algorithm that takes these models as input for generating natural language requirements. We evaluated our approach in the context of a multiple case study with three organizations and a total of 13 software development projects. Results: We found that our approach can successfully generate well-readable requirements, which do not only positively contribute to consistency, but also to the completeness and maintainability of requirements. The practical use of our approach to identify a suitable subcontractor on the market in 11 of the 13 projects further highlights the practical value of our approach. Conclusion: Our approach provides a structured way to obtain high-quality requirements documents from process models and to maintain textual and visual representations of requirements in a consistent way.


title: AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

abstract: A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost.


title: Does Television Viewing Cultivate Unrealistic Expectations About Marriage

abstract: One explanation for the high divorce rate in our society focuses on the idealistic expectations with which many people enter into marriage. The media have been cited as the source of or major contributor to these expectations; however, no empirical evidence exists to support that claim. Based on a survey of 285 never-married university students, this study sought to examine the relationship between television viewing, holding idealistic expectations about marriage, and intentions to marry. Results from regression and path analyses indicate that, although overall television viewing has a negative association with idealistic marriage expectations, viewing of romantic genre programming (e.g., romantic comedies, soap operas) was positively associated with idealistic expectations about marriage. Further, a strong and positive association between these expectations and marital intentions was evidenced. These findings are discussed in terms of both cultivation theory and the uses and gratifications perspective of media influence.


title: The 3D electromagnetic response of the Earth to ring current and auroral oval excitation

abstract: SUMMARY We report on a new compilation of functions representing the geoelectromagnetic response of the Earth’s mantle. The c response functions and newly defined d response functions, the latter of which constitutes a sensitive indicator of lateral heterogeneities in conductivity, were estimated for periods from 5 to 106.7 days. The spatial coverage and statistical quality of these response-function estimates were improved over previous compilations, in part by making use of a newly assembled global catalogue of magnetic observatory hourly mean values, and by applying a statistically robust form of empirical orthogonal function (EOF) analysis. The EOF results suggest that for periods longer than 5 days, the geomagnetic field variation can be represented predominantly by a single eigenmode. We have identified that the primarysource fields of the dominant mode consist of two current systems in the same direction: an equatorial ring current in the magnetosphere, and two conjugate auroral oval ring currents in the ionosphere. The influence of the auroral current system is seen at surprisingly low latitudes, 40 ◦ and 50 ◦ for the vertical and northern components, respectively, which results in non-negligible bias on the conventional c response at high latitudes. A preliminary correction of the auroral current effect has been made on the c and d responses obtained for data from 55 and 52 observatories, respectively, at latitudes from −60 ◦ to 60 ◦ . We also present a set of approximate equivalent internal (mantle) electrical current distributions that prove a useful indicator of the presence of deep Earth heterogeneities. These new sets of electromagnetic information will increase the resolving power of 3D distribution of electrical conductivity in the Earth’s mantle.


title: Harvesting High Value Foreign Currency Transactions from EMV Contactless Credit Cards Without the PIN

abstract: In this paper we present an attack, which allows fraudulent transactions to be collected from EMV contactless credit and debit cards without the knowledge of the cardholder. The attack exploits a previously unreported vulnerability in EMV protocol, which allows EMV contactless cards to approve unlimited value transactions without the cardholder's PIN when the transaction is carried out in a foreign currency. For example, we have found that Visa credit cards will approve foreign currency transactions for any amount up to ∈999,999.99 without the cardholder's PIN, this side-steps the £20 contactless transaction limit in the UK. This paper outlines our analysis methodology that identified the flaw in the EMV protocol, and presents a scenario in which fraudulent transaction details are transmitted over the Internet to a "rogue merchant" who then uses the transaction data to take money from the victim's account. In reality, the criminals would choose a value between ∈100 and ∈200, which is low enough to be within the victim's balance and not to raise suspicion, but high enough to make each attack worthwhile. The attack is novel in that it could be operated on a large scale with multiple attackers collecting fraudulent transactions for a central rogue merchant which can be located anywhere in the world where EMV payments are accepted.


title: Perception of premenstrual syndrome and attitude of evaluations of work performance among incoming university female students

abstract: Background: Premenstrual syndrome (PMS) is a common condition, and for 5% of women, the influence is so severe as to interfere with their mental health, interpersonal relationships, or studies. Severe PMS may result in decreased occupational productivity. The aim of this study was to investigate the influence of perception of PMS on evaluation of work performance. Methods: A total of 1971 incoming female university students were recruited in September 2009. A simulated clinical scenario was used, with a test battery including measurement of psychological symptoms and the Chinese Premenstrual Symptom Questionnaire. Results: When evaluating employee performance in the simulated scenario, 1565 (79.4%) students neglected the impact of PMS, while 136 (6.9%) students considered it. Multivariate logistic regression showed that perception of daily function impairment due to PMS and frequency of measuring body weight were significantly associated with consideration of the influence of PMS on evaluation of work performance. Conclusion: It is important to increase the awareness of functional impairments related to severe PMS.


title: Political inclusion and educational investment: Estimates from a national policy experiment in India

abstract: I examine whether the “role model effect” of female politicians on adolescent girls' school enrollment comes only from female representation in the lowest level of local government. To do this, I exploit variation in the implementation of seat quotas for women applied to a three-tiered local governance structure in India. Enrollment effects were substantially larger than those previously estimated for exposure to female leaders in the lowest tier of local government. The policy response is larger among girls in poorer households and those with less-educated women in the household, and was commensurate with reductions in idle time and employment in household enterprises. There is no evidence of additional school infrastructure, nor a reallocation of schooling resources. Effect magnitudes imply that more than two thirds of the net effect of the policy comes from female politicians serving in reserved positions other than the village council chairperson seat.


title: Teacher Turnover and Teacher Shortages: An Organizational Analysis

abstract: Contemporary educational theory holds that one of the pivotal causes of inadequate school performance is the inability of schools to adequately staff classrooms with qualified teachers. This theory also holds that these school staffing problems are primarily due to shortages of teachers, which, in turn, are primarily due to recent increases in teacher retirements and student enrollments. This analysis investigates the possibility that there are other factors—those tied to the organizational characteristics and conditions of schools—that are driving teacher turnover and, in turn, school staffing problems. The data utilized in this investigation are from the Schools and Staffing Survey and its supplement, the Teacher Followup Survey conducted by the National Center for Education Statistics. The results of the analysis indicate that school staffing problems are not primarily due to teacher shortages, in the technical sense of an insufficient supply of qualified teachers. Rather, the data indicate that school...


title: DKPro Similarity: An Open Source Framework for Text Similarity

abstract: We present DKPro Similarity, an open source framework for text similarity. Our ::: goal is to provide a comprehensive repository of text similarity measures which ::: are implemented using standardized interfaces. DKPro Similarity comprises a ::: wide variety of measures ranging from ones based on simple n-grams and common subsequences to high-dimensional vector comparisons and structural, stylistic, and phonetic measures. In order to promote the reproducibility of experimental results and to provide reliable, permanent experimental conditions for future studies, DKPro Similarity additionally comes with a set of full-featured ::: experimental setups which can be run out-of-the-box and be used for future ::: systems to built upon.


title: Green production — Strategies and dynamics: A simulation based study

abstract: There are a number of issues for enterprises to implement green production. From operations perspective, selecting green improvement strategy is critical but difficult due to the fact that it affects not only green performance, but also production economy. Important trade-off exists between different objectives and decisions are subjected to dynamic and uncertain conditions. From system dynamics perspective, there exist multiple factors interacting with one another to drive system's behavior and the trade-offs. Decision makers need to evaluate different scenarios to find appropriate balance between strategies. We report studies addressing both issues through an integrated approach emphasizing the use of simulation. First it focused on the optimization of green improvement strategies. A simulation model was developed to capture operations flow and decision logic. A multi-objective genetic algorithm, combined with improving heuristics, was developed to search for best solutions. Secondly, system dynamic models were developed to characterize the dynamic behavior of production systems under Cap and Trade conditions. Simulation experiments were run to analyze the relationship between system states and among the factors that cause the state transitions that influence the overall system behavior.


title: The Human Hippocampus and Spatial and Episodic Memory

abstract: Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.


title: Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

abstract: Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.


YeAH-TCP: Yet Another Highspeed TCP


title: Platform IO and system memory test using L3 cache based test (CBT) and parallel execution of CPGC Intel BIST engine

abstract: As the memory industry pushes to increase memory density, device variation is creating more defects. Furthermore, new form factors (phone, tablet, mobile and client PC) and low cost board and platform limits physical access to JTAG or TAP. Taking advantage of the x86 architecture's high functional bandwidth to memory, the quickest way to access and test memory is through CPU core, by storing and running parallel CPGC/BIST test content via CPU L3 cache, one CPGC BIST engine per memory channel. We propose a cache based testing framework that speeds up test time 60× to 170× compared to JTAG or TAP based testing using the same test content. We will present the cache based test (CBT) architecture and infrastructure (MRC/NEM setup, CPGC/IBIST), test content, results, and a side by side comparison of test time to JTAG or TAP. Finally we will discuss and compare this approach to generalized cache based tests.


title: Parametric Exponential Linear Unit for Deep Convolutional Neural Networks

abstract: Object recognition is an important task for improving the ability of visual systems to perform complex scene understanding. Recently, the Exponential Linear Unit (ELU) has been proposed as a key component for managing bias shift in Convolutional Neural Networks (CNNs), but defines a parameter that must be set by hand. In this paper, we propose learning a parameterization of ELU in order to learn the proper activation shape at each layer in the CNNs. Our results on the MNIST, CIFAR-10/100 and ImageNet datasets using the NiN, Overfeat, All-CNN and ResNet networks indicate that our proposed Parametric ELU (PELU) has better performances than the non-parametric ELU. We have observed as much as a 7.28% relative error improvement on ImageNet with the NiN network, with only 0.0003% parameter increase. Our visual examination of the non-linear behaviors adopted by Vgg using PELU shows that the network took advantage of the added flexibility by learning different activations at different layers.


title: Encryption and hash based security in Internet of Things

abstract: The Internet of Things (IoT) promises to be the next big revolution of the World Wide Web. It has a very wide range of applications, ranging from smart cities, smart homes, monitoring radiation levels in nuclear plants, animal tracking, health surveillance and a lot more. When nodes in wireless sensor networks are monitored through internet it becomes a part of Internet of Things. This brings in a lot of concerns related to security, privacy, standardization, power management. This paper aims at enhancing security in smart home systems. Devices like thermostat, air conditioners, doors and lighting systems are connected with each other and the internet through the internet of things technologies. Encryption and hash algorithms are proposed in this paper through which devices in the IoT can securely send messages between them. Encryption algorithm is used to ensure confidentiality as the attackers cannot interpret the cipher text that is sent. In order to ensure integrity (cipher text is not changed) hash algorithm is used.


title: From Aardvark to Zorro: A Benchmark for Mammal Image Classification

abstract: Current object recognition systems aim at recognizing numerous object classes under limited supervision conditions. This paper provides a benchmark for evaluating progress on this fundamental task. Several methods have recently proposed to utilize the commonalities between object classes in order to improve generalization accuracy. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. In order to facilitate the development, as well as the assessment of methods for dealing with multiple related classes, a new dataset including images of several hundred mammal classes, is provided, together with preliminary results of its use. The images in this dataset are organized into five levels of variability, and their labels include information on the objects' identity, location and pose. From this dataset, a classification benchmark has been derived, requiring fine distinctions between 72 mammal classes. It is then demonstrated that a recognition method which is highly successful on the Caltech101, attains limited accuracy on the current benchmark (36.5%). Since this method does not utilize the shared structure between classes, the question remains as to whether interclass transfer methods can increase the accuracy to the level of human performance (90%). We suggest that a labeled benchmark of the type provided, containing a large number of related classes is crucial for the development and evaluation of classification methods which make efficient use of interclass transfer.


title: The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations

abstract: We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency words, which carry greater semantic content. We compare a range of state-of-the-art models, each with a different way of encoding what has been previously read. We show that models which store explicit representations of long-term contexts outperform state-of-the-art neural language models at predicting semantic content words, although this advantage is not observed for syntactic function words. Interestingly, we find that the amount of text encoded in a single memory representation is highly influential to the performance: there is a sweet-spot, not too big and not too small, between single words and full sentences that allows the most meaningful information in a text to be effectively retained and recalled. Further, the attention over such window-based memories can be trained effectively through self-supervision. We then assess the generality of this principle by applying it to the CNN QA benchmark, which involves identifying named entities in paraphrased summaries of news articles, and achieve state-of-the-art performance.


title: Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons

abstract: We study the recognition of surfaces made from different materials such as concrete, rug, marble, or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. In this paper, we provide a unified model to address both these aspects of natural texture. The main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. We call these 3D textons. Examples might be ridges, grooves, spots or stripes or combinations thereof. Associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions. ::: ::: Given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3D texton vocabulary. Given a few (1 to 4) images of any material, it can be characterized using these textons. We demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions. We also illustrate how the 3D texton model can be used to predict the appearance of materials under novel conditions.


title: Accidental Aspiration of Dental Crowns and Retrieval

abstract: Aim: The aim of this article is to create awareness among the dental practitioners regarding the various accidents which can happen during a dental procedure and stress upon the preventive measures that can be taken to reduce the same. Background: This article presents a case of aspiration of dental crowns by the patient and its retrieval by coughing and preventive measures which can be taken to avoid such circumstances. Case description: A patient reporting to private dental practice accidentally aspirated the two-unit prosthesis (crowns) in the right lung during cementation. He was immediately taken to the hospital, where chest radiographs and computed tomographic (CT) scan were done in order to locate the actual position of the prosthesis. Bronchoscopy was planned to retrieve the crowns but the patient had a bout of cough and the crowns came out. A chest radiograph was taken to confirm the absence of crowns. Clinical significance: Taking adequate precautions while performing any dental procedure in supine position can decrease the occurrence of such incidents. Rigid bronchoscopy is the advised method of retrieval in case the crowns are not coughed out.


title: Performance and functional verification of microprocessors

abstract: We address the problem of verifying the correctness of pre-silicon models of a microprocessor. We touch on the latest advances in this area by considering two different aspects of the validation problem: (a) verifying the functional integrity of the model and (b) testing the model for timing accuracy at the architectural level. The latter area, that of performance verification, is of increasing importance in the design of server-class processor chips, with one or more high performance cores on a single die. We show how simulation-based test cases can be generated under a unified defect and coverage model to detect both performance and functional bugs. We present and discuss examples of such integrated validation methodologies used in real processor development projects.


title: High Lipid Induction in Microalgae for Biodiesel Production

abstract: Oil-accumulating microalgae have the potential to enable large-scale biodiesel production without competing for arable land or biodiverse natural landscapes. High lipid productivity of dominant, fast-growing algae is a major prerequisite for commercial production of microalgal oil-derived biodiesel. However, under optimal growth conditions, large amounts of algal biomass are produced, but with relatively low lipid contents, while species with high lipid contents are typically slow growing. Major advances in this area can be made through the induction of lipid biosynthesis, e.g., by environmental stresses. Lipids, in the form of triacylglycerides typically provide a storage function in the cell that enables microalgae to endure adverse environmental conditions. Essentially algal biomass and triacylglycerides compete for photosynthetic assimilate and a reprogramming of physiological pathways is required to stimulate lipid biosynthesis. There has been a wide range of studies carried out to identify and develop efficient lipid induction techniques in microalgae such as nutrients stress (e.g., nitrogen and/or phosphorus starvation), osmotic stress, radiation, pH, temperature, heavy metals and other chemicals. In addition, several genetic strategies for increased triacylglycerides production and inducibility are currently being developed. In this review, we discuss the potential of lipid induction techniques in microalgae and also their application at commercial scale for the production of biodiesel.


title: Bidirectional Dynamics of Materialism and Loneliness : Not Just a Vicious Cycle

abstract: This research is the first to test the hypothesis that consumers face a “material trap” in which materialism fosters social isolation which in turn reinforces materialism. It provides evidence that materialism and loneliness are engaged in bidirectional relationships over time. Importantly, it finds that loneliness contributes more to materialism than the other way around. Moreover, it finds that materialism's contribution to loneliness is not uniformly vicious but critically differs between specific subtypes of materialism. That is, valuing possessions as a happiness medicine or as a success measure increased loneliness, and these subtypes also increased most due to loneliness. Yet seeking possessions for material mirth decreased loneliness and was unaffected by it. These findings are based on longitudinal data from over 2,500 consumers across 6 years and a new latent growth model. They reveal how materialism and loneliness form a self-perpetuating vicious and virtuous cycle depending on the materialism subtype.


title: Docker Containers across Multiple Clouds and Data Centers

abstract: Emerging lightweight cloud technologies, such as Docker containers, are gaining wide traction in IT due to the fact that they allow users to deploy applications in any environment faster and more efficiently than using virtual machines. However, current Docker-based container deployment solutions are aimed at managing containers in a single-site, which limits their capabilities. As more users look to adopt Docker containers in dynamic, heterogenous environments, the ability to deploy and effectively manage containers across multiple clouds and data centers becomes of utmost importance. In this paper, we propose a prototype framework, called C-Ports, that enables the deployment and management of Docker containers across multiple hybrid clouds and traditional clusters while taking into consideration user and resource provider objectives and constraints. The framework leverages a constraint-programming model for resource selection and uses CometCloud to allocate/deallocate resources as well as to deploy containers on top of these resources. Our prototype has been effectively used to deploy and manage containers in a dynamic federation composed of five clouds and two clusters.


title: Joint Link-Attribute User Identity Resolution in Online Social Networks

abstract: In the modern Web, it is common for an active person to have several proles in dierent online social networks. As new general-purpose and niche social network services arise every year, the problem of social data integration will likely remain actual in the nearest future. Discovering multiple proles of a single person across dierent social networks allows to merge all user’s contacts from dierent social services or compose more complete social graph that is helpful in many social-powered applications. In this paper we propose a new approach for user prole matching based on Conditional Random Fields that extensively combines usage of prole attributes and social linkage. It is extremely suitable for cases when prole data is poor, incomplete or hidden due to privacy settings. Evaluation on Twitter and Facebook sample datasets showed that our solution signicantly outperforms common attribute-based approach and is able to nd matches that are not discoverable by using only prole information. We also demonstrate the importance of social links for identity resolution task and show that certain proles can be matched based only on social relationships between online social networks users.


title: Multi-cloud oblivious storage

abstract: We present a 2-cloud oblivious storage (ORAM) system that achieves 2.6X bandwidth cost between the client and the cloud. Splitting an ORAM across 2 or more non-colluding clouds allows us to reduce the client-cloud bandwidth cost by at least one order of magnitude, shifting the higher-bandwidth communication to in-between the clouds where bandwidth provisioning is abundant. Our approach makes ORAM practical for bandwidth-constrained clients such as home or mobile Internet connections. We provide a full-fledged implementation of our 2-cloud ORAM system, and report results from a real-world deployment over Amazon EC2 and Microsoft Azure.


title: Self-Processing and the Default Mode Network: Interactions with the Mirror Neuron System

abstract: Recent evidence for the fractionation of the default mode network (DMN) into functionally distinguishable subdivisions with unique patterns of connectivity calls for a reconceptualization of the relationship between this network and self-referential processing. Advances in resting-state functional connectivity analyses are beginning to reveal increasingly complex patterns of organization within the key nodes of the DMN – medial prefrontal cortex and posterior cingulate cortex – as well as between these nodes and other brain systems. Here we review recent examinations of the relationships between the DMN and various aspects of self-relevant and social-cognitive processing in light of emerging evidence for heterogeneity within this network. Drawing from a rapidly evolving social-cognitive neuroscience literature, we propose that embodied simulation and mentalizing are processes which allow us to gain insight into another’s physical and mental state by providing privileged access to our own physical and mental states. Embodiment implies that the same neural systems are engaged for self- and other-understanding through a simulation mechanism, while mentalizing refers to the use of high-level conceptual information to make inferences about the mental states of self and others. These mechanisms work together to provide a coherent representation of the self and by extension, of others. Nodes of the DMN selectively interact with brain systems for embodiment and mentalizing, including the mirror neuron system, to produce appropriate mappings in the service of social-cognitive demands.


title: The Role of Community in Disaster Response: Conceptual Models

abstract: We focus on the role that community plays in the continuum of disaster preparedness, response and recovery, and we explore where community fits in conceptual frameworks concerning disaster decision-making. We offer an overview of models developed in the literature as well as insights drawn from research related to Hurricane Katrina. Each model illustrates some aspect of the spectrum of disaster preparedness and recovery, beginning with risk perception and vulnerability assessments, and proceeding to notions of resiliency and capacity building. Concepts like social resilience are related to theories of “social capital,” which stress the importance of social networks, reciprocity, and interpersonal trust. These allow individuals and groups to accomplish greater things than they could by their isolated efforts. We trace two contrasting notions of community to Tocqueville. On the one hand, community is simply an aggregation of individual persons, that is, a population. As individuals, they have only limited capacity to act effectively or make decisions for themselves, and they are strongly subject to administrative decisions that authorities impose on them. On the other hand, community is an autonomous actor, with its own interests, preferences, resources, and capabilities. This definition of community has also been embraced by community-based participatory researchers and has been thought to offer an approach that is more active and advocacy oriented. We conclude with a discussion of the strengths and weaknesses of community in disaster response and in disaster research.


title: Verification validation and accreditation of simulation models

abstract: This paper presents guidelines for conducting verification, validation and accreditation (VV&A) of simulation models. Fifteen guiding principles are introduced to help the researchers, practitioners and managers better comprehend what VV&A is all about. The VV&A activities are described in the modeling and simulation life cycle. A taxonomy of more than 77 V&V techniques is provided to assist simulationists in selecting proper approaches for conventional simulation model V&V. Another taxonomy of 38 V&V techniques is presented for object-oriented simulation models. 1. INTRODUCTION Assuring total quality in a modeling and simulation (M&S) effort involves the measurement and assessment of a variety of quality characteristics such as accuracy, execution efficiency, maintainability, portability, reusability, and usability (human-computer interface). This paper is concerned only with the accuracy quality characteristic. Verification, validation, testing, accreditation, certification and credibility assessment activities primarily deal with the measurement and assessment of accuracy of models and simulations (M&S). Model VeriJication is substantiating that the model is transformed from one form into another, as intended, with sufficient accuracy. Model verification deals with building the model right. The accuracy of transforming a problem formulation into a model specification or the accuracy of converting a model representation from a micro flowchart form into an executable computer program is evaluated in model verification. Model Validation is substantiating that the model, within its domain of applicability, behaves with satisfactory accuracy consistent with the M&S objectives. Model validation deals with building the right model. An activity of accuracy assessment can be labeled as verification or validation based on an answer to the following question: In assessing the accuracy, is the model behavior compared with respect to the corresponding system behavior through mental or computer execution? If


Object Recognition in 3 D Lidar Data with Recurrent Neural Network


title: Zero-Shot Learning and Clustering for Semantic Utterance Classification

abstract: We propose two novel zero-shot learning methods for semantic utterance classification (SUC) using deep learning. Both approaches rely on learning deep semantic embeddings from a large amount of Query Click Log data obtained from a search engine. Traditional semantic utterance classification systems require large amounts of labelled data, whereas our proposed methods make use of the structure of the task to allow classification without labeled data. We also develop a zero-shot semantic clustering algorithm for extracting discriminative features for supervised semantic utterance classification systems. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by [1]. Furthermore, we show that extracting features using zero-shot semantic clustering for a linear SVM reaches state-of-the-art result on that dataset.


title: Electronically Temperature Compensated Silicon Bulk Acoustic Resonator Reference Oscillators

abstract: The paper describes the design and implementation of an electronically temperature compensated reference oscillator based on capacitive silicon micromechanical resonators. The design of a 5.5-MHz silicon bulk acoustic resonator has been optimized to offer high quality factor (> 100 000) while maintaining tunability in excess of 3000 ppm for fine-tuning and temperature compensation. Oscillations are sustained with a CMOS amplifier. When interfaced with the temperature compensating bias circuit, the oscillator exhibits a frequency drift of 39 ppm over 100degC as compared to an uncompensated frequency drift of 2830 ppm over the same range. The sustaining amplifier and compensation circuitry were fabricated in a 2P3M 0.6-mum CMOS process.


title: Coarse-To-Fine N-Best Parsing And MaxEnt Discriminative Reranking

abstract: Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less.


title: Understanding Nonmalicious Security Violations in the Workplace: A Composite Behavior Model

abstract: End users are said to be "the weakest link" in information systems (IS) security management in the workplace. They often knowingly engage in certain insecure uses of IS and violate security policies without malicious intentions. Few studies, however, have examined end user motivation to engage in such behavior. To fill this research gap, in the present study we propose and test empirically a nonmalicious security violation (NMSV) model with data from a survey of end users at work. The results suggest that utilitarian outcomes (relative advantage for job performance, perceived security risk), normative outcomes (workgroup norms), and self-identity outcomes (perceived identity match) are key determinants of end user intentions to engage in NMSVs. In contrast, the influences of attitudes toward security policy and perceived sanctions are not significant. This study makes several significant contributions to research on security-related behavior by (1) highlighting the importance of job performance goals and security risk perceptions on shaping user attitudes, (2) demonstrating the effect of workgroup norms on both user attitudes and behavioral intentions, (3) introducing and testing the effect of perceived identity match on user attitudes and behavioral intentions, and (4) identifying nonlinear relationships between constructs. This study also informs security management practices on the importance of linking security and business objectives, obtaining user buy-in of security measures, and cultivating a culture of secure behavior at local workgroup levels in organizations.


title: Residual Squeeze VGG16

abstract: Deep learning has given way to a new era of machine learning, apart from computer vision. Convolutional neural networks have been implemented in image classification, segmentation and object detection. Despite recent advancements, we are still in the very early stages and have yet to settle on best practices for network architecture in terms of deep design, small in size and a short training time. In this work, we propose a very deep neural network comprised of 16 Convolutional layers compressed with the Fire Module adapted from the SQUEEZENET model. We also call for the addition of residual connections to help suppress degradation. This model can be implemented on almost every neural network model with fully incorporated residual learning. This proposed model Residual-Squeeze-VGG16 (ResSquVGG16) trained on the large-scale MIT Places365-Standard scene dataset. In our tests, the model performed with accuracy similar to the pre-trained VGG16 model in Top-1 and Top-5 validation accuracy while also enjoying a 23.86% reduction in training time and an 88.4% reduction in size. In our tests, this model was trained from scratch.


title: Effect of Extended Idle on Oil Degradation Rates of Heavy-Duty Vehicles

abstract: Given the high level of extended idle endured by a heavy-duty vehicle, there is a need to understand the effects of extended idle operation on its engine oil life. This study uses routinely-collected oil samples from multiple heavy duty vehicles to determine the effects extended idle has on engine oil degradation rate. The results of the oil analysis show very little degradation of oil in spite of high levels of extended idle operation. A negative but normal correlation is seen in viscosity and total base number (TBN). Significant degradation is not seen even after the vehicle exceeded the manufacturer's recommended oil change interval. The results from the study seem to show that extended idling in heavy-duty vehicles can be treated similarly to long trip intervals, in terms of oil degradation. Additionally, it seems extended idling does not result in abnormal engine wear or excessive oil contamination.


title: Precision agriculture robot for seeding function

abstract: In recent years, robotics in agriculture sector with its implementation based on precision agriculture concept is the newly emerging technology. The main reason behind automation of farming processes are saving the time and energy required for performing repetitive farming tasks and increasing the productivity of yield by treating every crop individually using precision farming concept. Designing of such robots is modeled based on particular approach and certain considerations of agriculture environment in which it is going to work. These considerations and different approaches are discussed in this paper. Also, prototype of an autonomous Agriculture Robot is presented which is specifically designed for seed sowing task only. It is a four wheeled vehicle which is controlled by LPC2148 microcontroller. Its working is based on the precision agriculture which enables efficient seed sowing at optimal depth and at optimal distances between crops and their rows, specific for each crop type.


title: Automatic 3D facial expression recognition using geometric scattering representation

abstract: Facial Expression Recognition (FER) is one of the most active topics in the domain of computer vision and pattern recognition, and it has received increasing attention for its wide application potentials as well as attractive scientific challenges. In this paper, we present a novel method to automatic 3D FER based on geometric scattering representation. A set of maps of shape features in terms of multiple order differential quantities, i.e. the Normal Maps (NOM) and the Shape Index Maps (SIM), are first jointly adopted to comprehensively describe geometry attributes of the facial surface. The scattering operator is then introduced to further highlight expression related cues on these maps, thereby constructing geometric scattering representations of 3D faces for classification. The scattering descriptor not only encodes distinct local shape changes of various expressions as by several milestone descriptors, such as SIFT, HOG, etc., but also captures subtle information hidden in high frequencies, which is quite crucial to better distinguish expressions that are easily confused. We evaluate the proposed approach on the BU-3DFE database, and the performance is up to 84.8% and 82.7% with two commonly used protocols respectively which is superior to the state of the art ones.


title: Linear subspace design for real-time shape deformation

abstract: We propose a method to design linear deformation subspaces, unifying linear blend skinning and generalized barycentric coordinates. Deformation subspaces cut down the time complexity of variational shape deformation methods and physics-based animation (reduced-order physics). Our subspaces feature many desirable properties: interpolation, smoothness, shape-awareness, locality, and both constant and linear precision. We achieve these by minimizing a quadratic deformation energy, built via a discrete Laplacian inducing linear precision on the domain boundary. Our main advantage is speed: subspace bases are solutions to a sparse linear system, computed interactively even for generously tessellated domains. Users may seamlessly switch between applying transformations at handles and editing the subspace by adding, removing or relocating control handles. The combination of fast computation and good properties means that designing the right subspace is now just as creative as manipulating handles. This paradigm shift in handle-based deformation opens new opportunities to explore the space of shape deformations.


title: Equalization-Based Digital Background Calibration Technique for Pipelined ADCs

abstract: In this paper, we present a digital background calibration technique for pipelined analog-to-digital converters (ADCs). In this scheme, the capacitor mismatch, residue gain error, and amplifier nonlinearity are measured and then corrected in digital domain. It is based on the error estimation with nonprecision calibration signals in foreground mode, and an adaptive linear prediction structure is used to convert the foreground scheme to the background one. The proposed foreground technique utilizes the LMS algorithm to estimate the error coefficients without needing high-accuracy calibration signals. Several simulation results in the context of a 12-b 100-MS/s pipelined ADC are provided to verify the usefulness of the proposed calibration technique. Circuit-level simulation results show that the ADC achieves 28-dB signal-to-noise and distortion ratio and 41-dB spurious-free dynamic range improvement, respectively, compared with the noncalibrated ADC.


Electrical conduction mechanism in Fe 70 Pd 30 catalyzed multi-walled carbon nano


title: Bengali Named Entity Recognition Using Support Vector Machine

abstract: Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is nowadays considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali using Support Vector Machine (SVM). Though this state of the art machine learning method has been widely applied to NER in several well-studied languages, this is our first attempt to use this method to Indian languages (ILs) and particularly for Bengali. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes. A portion of a partially NE tagged Bengali news corpus, developed from the archive of a leading Bengali newspaper available in the web, has been used to develop the SVM-based NER system. The training set consists of approximately 150K words and has been manually annotated with the sixteen NE tags. Experimental results of the 10-fold cross validation test show the effectiveness of the proposed SVM based NER system with the overall average Recall, Precision and F-Score of 94.3%, 89.4% and 91.8%, respectively. It has been shown that this system outperforms other existing Bengali NER systems.


title: Design procedure for two-stage CMOS operational amplifiers employing current buffer

abstract: The design procedure of the two-stage CMOS operational amplifiers employing Miller capacitor in conjunction with the common-gate current buffer is presented. Unlike the previously reported design strategy of the opamp of this type, which results in the opamp with a pair of nondominant complex conjugate poles and a finite zero, the proposed procedure is based upon the design strategy that results in the opamp with only one dominant pole. Design example of the proposed procedure is given.


title: Answering graph pattern queries using views

abstract: We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.


title: Privacy-Preserving Deep Learning: Revisited and Enhanced

abstract: We build a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without actually revealing the participants’ local data to a curious server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and point out that local data information may be actually leaked to an honest-but-curious server. We then move on to fix that problem via building an enhanced system with following properties: (1) no information is leaked to the server; and (2) accuracy is kept intact, compared to that of the ordinary deep learning system also over the combined dataset. Our system makes use of additively homomorphic encryption, and we show that our usage of encryption adds little overhead to the ordinary deep learning system.


title: Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey

abstract: New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective.


title: Shifter: Containers for HPC

abstract: Bringing HEP computing to HPC can be difficult. Software stacks are often very complicated with numerous dependencies that are difficult to get installed on an HPC system. To address this issue, NERSC has created Shifter, a framework that delivers Docker-like functionality to HPC. It works by extracting images from native formats and converting them to a common format that is optimally tuned for the HPC environment. We have used Shifter to deliver the CVMFS software stack for ALICE, ATLAS, and STAR on the supercomputers at NERSC. As well as enabling the distribution multi-TB sized CVMFS stacks to HPC, this approach also offers performance advantages. Software startup times are significantly reduced and load times scale with minimal variation to 1000s of nodes. We profile several successful examples of scientists using Shifter to make scientific analysis easily customizable and scalable. We will describe the Shifter framework and several efforts in HEP and NP to use Shifter to deliver their software on the Cori HPC system.


title: A Broadband Dual-Polarized Base Station Antenna With Anti-Interference Capability

abstract: A broadband dual-polarized antenna with antiinterference capability is presented in this letter for base station applications. The proposed antenna contains three parts: main radiator, feed structures, and reflector. Specifically, the main radiator has two crossed dipoles to realize ±45° polarizations; two vertical substrates, which have Γ-shaped feeding lines on the front side and rectangle patches on the back side, are placed between the main radiator and reflector for feeding; the reflector placed below is used to get high gain and unidirectional radiation. In addition, C-shaped stubs are arranged next to the feeding lines in order to filter the unwanted frequency band. A prototype of the antenna was fabricated and tested. The measured results show that it has a bandwidth of 52.6% (VSWR <; 1.5) along with a sharp notch band from 2.27 to 2.53 GHz. Measured isolation of higher than 25.4 dB, half-power beamwidth of around 60° and average gain of 7.57 dBi across the operating band are also obtained. Having the advantages of anti-interference capability, dual polarization, wide bandwidth, high isolation, and good radiation performance, the proposed antenna can find its application in the next-generation wireless communication systems.


title: A Compact Frequency-Reconfigurable Narrowband Microstrip Slot Antenna

abstract: A frequency-reconfigurable microstrip slot antenna is proposed. The antenna is capable of frequency switching at six different frequency bands between 2.2 and 4.75 GHz. Five RF p-i-n diode switches are positioned in the slot to achieve frequency reconfigurability. The feed line and the slot are bended to reduce 33% of the original size of the antenna. The biasing circuit is integrated into the ground plane to minimize the parasitic effects toward the performance of the antenna. Simulated and measured results are used to demonstrate the performance of the antenna. The simulated and measured return losses, together with the radiation patterns, are presented and compared.


abstract: Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various similarity measures are categorized in both syntactic and semantic relationships. In this paper we present a novel similarity, Unilateral Jaccard Similarity Coefficient (uJaccard), which doesn’t only take into consideration the space among two points but also the semantics among them.


title: A GUI Crawling-Based Technique for Android Mobile Application Testing

abstract: As mobile applications become more complex, specific development tools and frameworks as well as cost effective testing techniques and tools will be essential to assure the development of secure, high-quality mobile applications. This paper addresses the problem of automatic testing of mobile applications developed for the Google Android platform, and presents a technique for rapid crash testing and regression testing of Android applications. The technique is based on a crawler that automatically builds a model of the application GUI and obtains test cases that can be automatically executed. The technique is supported by a tool for both crawling the application and generating the test cases. In the paper we present an example of using the technique and the tool for testing a real small size Android application that preliminary shows the effectiveness and usability of the proposed testing approach.


The DaMiRseq package-Data Mining for RNA-Seq data : normalization , feature selection and classification


title: Assessing the veracity of identity assertions via OSNs

abstract: Anonymity is one of the main virtues of the Internet, as it protects privacy and enables users to express opinions more freely. However, anonymity hinders the assessment of the veracity of assertions that online users make about their identity attributes, such as age or profession. We propose FaceTrust, a system that uses online social networks to provide lightweight identity credentials while preserving a user's anonymity. Face-Trust employs a “game with a purpose” design to elicit the opinions of the friends of a user about the user's self-claimed identity attributes, and uses attack-resistant trust inference to assign veracity scores to identity attribute assertions. FaceTrust provides credentials, which a user can use to corroborate his assertions. We evaluate our proposal using a live Facebook deployment and simulations on a crawled social graph. The results show that our veracity scores strongly correlate with the ground truth, even when a large fraction of the social network users is dishonest and employs the Sybil attack.


title: A Statistical Deformable Model for the Segmentation of Liver CT Volumes

abstract: We present a fully automated method based on an evolu- tionary algorithm, a statistical shape model (SSM), and a deformable mesh to tackle the liver segmentation task of the MICCAI Grand Chal- lenge workshop. To model the expected shape and appearance, the SSM is trained on the 20 provided training datasets. Segmentation is started by a global search with the evolutionary algorithm, which provides the initial parameters for the SSM. Subsequently, a local search similar to the Active Shape method is used to refine the detected parameters. The resulting model is used to initialize the main component of our approach: a deformable mesh that strives for an equilibrium between internal and external forces. The internal forces describe the deviation of the mesh from the underlying SSM, while the external forces model the fit to the image data. To constrain the allowed deformation, we employ a graph- based optimal surface detection during calculation of the external forces. Applied to the ten test datasets of the workshop, our method delivers comparable results to the human second rater in six cases and scores an average of 59 points.


title: Benchmarks for Grid-Based Pathfinding

abstract: The study of algorithms on grids has been widespread in a number of research areas. Grids are easy to implement and offer fast memory access. Because of their simplicity, they are used even in commercial video games. But, the evaluation of work on grids has been inconsistent between different papers. Many research papers use different problem sets, making it difficult to compare results between papers. Furthermore, the performance characteristics of each test set are not necessarily obvious. This has motivated the creation of a standard test set of maps and problems on the maps that are open for all researchers to use. In addition to creating these sets, we use a variety of metrics to analyze the properties of the test sets. The goal is that these test sets will be useful to many researchers, making experimental results more comparable across papers, and improving the quality of research on grid-based domains.


title: Drawing on Air: Input Techniques for Controlled 3D Line Illustration

abstract: We present Drawing on Air, a haptic-aided input technique for drawing controlled 3D curves through space. Drawing on Air addresses a control problem with current 3D modeling approaches based on sweeping movement of the hands through the air. While artists praise the immediacy and intuitiveness of these systems, a lack of control makes it nearly impossible to create 3D form beyond quick design sketches or gesture drawings. Drawing on Air introduces two new strategies for more controlled 3D drawing: one-handed drag drawing and two-handed tape drawing. Both approaches have advantages for drawing certain types of curves. We describe a tangent preserving method for transitioning between the two techniques while drawing. Haptic-aided redrawing and line weight adjustment while drawing are also supported in both approaches. In a quantitative user study evaluation by illustrators, the one and two-handed techniques performed at roughly the same level, and both significantly outperformed freehand drawing and freehand drawing augmented with a haptic friction effect. We present the design and results of this experiment as well as user feedback from artists and 3D models created in a style of line illustration for challenging artistic and scientific subjects.


title: Learning to Importance Sample in Primary Sample Space

abstract: Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.


title: TransSQL: A Translation and Validation-Based Solution for SQL-injection Attacks

abstract: SQL injection attacks, a class of injection flaw in which specially crafted input strings leads to illegal queries to databases, are one of the topmost threats to web applications. A number of research prototypes and commercial products that maintain the queries structure in web applications have been developed. But these techniques either fail to address the full scope of the problem or have limitations. Based on our observation that the injected string in a SQL injection attack is interpreted differently on different databases, in this paper, we propose a novel and effective solution TransSQL to solve this problem. TransSQL automatically translates a SQL request to a LDAP-equivalent request. After queries are executed on a SQL database and a LDAP one, TransSQL checks the difference in responses between a SQL database and a LDAP one to detect and block SQL injection attacks. Experimental results show that TransSQL is an effective and efficient solution against SQL injection attacks.


title: Loop closure detection for visual SLAM using PCANet features

abstract: Loop closure detection benefits simultaneous localization and mapping (SLAM) in building a consistent map of the environment by reducing the accumulate error. Handcrafted features have been successfully used in traditional approaches, whereas in this paper, we show that unsupervised features extracted by deep learning models, can improves the accuracy of loop closure detection. In particular, we employ a cascaded deep network, namely the PCANet, to extract features as image descriptors. We tested the performance of our proposed method on open datasets to compare with traditional approaches. We found that the PCANet features outperform state-of-the-art handcrafted competitors, and are computational efficient to be implemented in practical robotics.


title: Scaling SGD Batch Size to 32K for ImageNet Training.

abstract: The most natural way to speed-up the training of large networks is to use data-parallelism on multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one need to increase the batch size to make full use of the computational power of each GPU. However, keeping the accuracy of network with increase of batch size is not trivial. Currently, the state-of-the art method is to increase Learning Rate (LR) proportional to the batch size, and use special learning rate with "warm-up" policy to overcome initial optimization difficulty. ::: By controlling the LR during the training process, one can efficiently use large-batch in ImageNet training. For example, Batch-1024 for AlexNet and Batch-8192 for ResNet-50 are successful applications. However, for ImageNet-1k training, state-of-the-art AlexNet only scales the batch size to 1024 and ResNet50 only scales it to 8192. The reason is that we can not scale the learning rate to a large value. To enable large-batch training to general networks or datasets, we propose Layer-wise Adaptive Rate Scaling (LARS). LARS LR uses different LRs for different layers based on the norm of the weights and the norm of the gradients. By using LARS algoirithm, we can scale the batch size to 32768 for ResNet50 and 8192 for AlexNet. Large batch can make full use of the system's computational power. For example, batch-4096 can achieve 3x speedup over batch-512 for ImageNet training by AlexNet model on a DGX-1 station (8 P100 GPUs).


title: Splicing image forgery identification based on artificial neural network approach and texture features

abstract: Splicing Image Forgery Identification controls and picture tampering with no proof being left behind has turned out to be exceptionally moderate and exceedingly utilized, because of presence of to a great degree intense altering apparatuses, for example, Adobe Photoshop. Along these lines, there has been a quick expansion of the digitally adjusted pictures on the Internet and prevailing press. The genuineness of a digital image experiences extreme dangers because of the ascent of capable digital image altering devices that effectively adjust the image substance without leaving any obvious hints of such changes. The splicing forgery should be possible by replicated a one/more area from source image and pasted into an objective picture to create a composite image called spliced image. In this manner, this sort of forgery is viewed as challenge issue difficult from tamper identification perspective. To influence the issue most exceedingly awful some to post preparing impacts, for example, blurring, JPEG compression, rotation and scaling perhaps presented in the spliced image. This study aims to perform quantification and data analysis following feature extraction using computational techniques to detect interesting textural and anatomical changes, these extracted features then can be used as a key to distinguish between different classes (Splicing Image and non-Splicing image). This paper presents a new framework to identify the spliced image by exploiting the image texture features, and to automatically identification of spliced images. To accurately identify the spliced image, the proposed solution uses different texture features to capture deferent texture related to the edge of the object and the colour features. We have combine these features to produce a good vector to describe the splice object. Two models have been proposed in this paper first combine the vectors of the three features and feed them to the ANN classifier. Second, use the majority voting of the result of three features to take the decision. This is followed by a ANN classifier; in this model we have trained the system with 30 training 20% validation 50% testing. We evaluated the effectiveness of the classification framework for identifying spliced images by compare the result with the manual label which is done by the people who have created the data sets. In this approach we have combine different features to capture different information and feed them to the neural network to identify the splicing image. The findings outcome from this study have shown an improved approach that automatically splicing image forgery identification. We have evaluated the splicing image forgery identification using the texture features. The identification accuracy in the technique used is about 98.06%., with 99.03% sensitivity and 96.07% specificity.


title: Note-taking with computers: Exploring alternative strategies for improved recall.

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


title: In-air handwritten English word recognition using attention recurrent translator

abstract: As a new human–computer interaction way, in-air handwriting allows users to write in the air in a natural, unconstrained way. Compared with conventional online handwriting based on touch devices, in-air handwriting is much more challenging due to its unique characteristics. The in-air handwriting is always finished in a single stroke and thus lacks pen-down and pen-up information. Moreover, the in-air handwriting suffers less friction and space restriction so that the users write more casually. In this paper, we present an in-air handwriting system for effectively recognizing handwritten English words. An attention-based model, called attention recurrent translator, is proposed for the in-air handwritten English word recognition, which is considerably different from connectionist temporal classification (CTC). We evaluate the proposed approach on a newly collected dataset containing a total of 150,480 recordings that cover 2280 English words. The proposed approach achieves a word recognition accuracy of 97.74%. The experimental results show that the proposed recognizer is comparable with CTC and is extremely effective for in-air handwritten English word recognition.


title: Practice tests, spaced practice, and successive relearning: Tips for classroom use and for guiding students’ learning.

abstract: Several techniques have been identified that can help students of all ages and abilities learn, understand, and retain materials across a wide variety of classes. Although these techniques are not the panacea for all the learning hurdles that students must overcome, they do offer an easy and low-cost solution to boosting student achievement in many classes. Thus, in this teacher-ready research review, we provide some pointers about how to use two particularly potent techniques—practice testing and spaced practice—to improve student learning in the classroom and on how another technique—successive relearning—can be used by your students to guide their learning outside of the classroom.


title: Local parameter for armor weight estimation

abstract: A new surf parameter called the ‘wave action slope’ is introduced to represent local wave conditions in shallow waters by employing local values of wave length or wave celerity and wave height. The use of wave theory on a flat bed of depth at the front of the breakwater might be considered far superior to the simple adoption of the deep water wave length for characterizing surfing waves at a shoaling depth. The wave action slope is formed by the product of the breakwater slope and the ratio of the celerity to the wave height. The optimum or minimum weight of the armor unit is related to the wave action slopes, which are employed for developing new empirical equations. Analyzing the laboratory data of van der Meer revealed that the armor weights are closely related to the wave action slope. By using the new surf parameter given with local parameter values, we suggest a simple but accurate equation to estimate the breakwater armor weight. Several empirical equations are applied to cases of failure experienced along the eastern coast of Korea. The values given by the Hudson equation are satisfactory for 26 of the 57 total failure cases. The number of satisfactory cases decreases by using recent empirical equations; of the 57 failure cases, the number of satisfactory cases yielded is 17 for the van der Meer/de Jong equation and 13 for the equation of the present study considering local parameters. The reduced number of satisfactory cases may indicate improvement of the new empirical equations over the Hudson equation.


title: OpenPAWS: An open source PAWS and UHF TV White Space database implementation for India

abstract: TV white space (TVWS) geolocation database is being used for the protection of the terrestrial TV broadcast receivers, and the coexistence of secondary devices. To the best of our knowledge, though TV White Space calculations are available, an active online database does not exist for India. In this paper, the development of the first TVWS database for India is detailed and is released for public access. A standardized protocol to access the TVWS database on a readily available hardware platform is implemented. A hardware prototype, which is capable of querying the TVWS database and operating in the TV band without causing harmful interference to the TV receivers in UHF TV bands, is developed. The source code of our implementation has been released under the GNU general public license version 2.0.


title: Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition

abstract: Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7%, in experiments performed over three different benchmark databases. An implementation of our robot data augmentation layer has been made publicly available.