title: Background subtraction using encoder-decoder structured convolutional neural network

abstract: A background subtraction algorithm using an encoderdecoder structured convolutional neural network is proposed in this work, in order to segment out moving objects from the background. A target frame, its previous frame, and a background model are concatenated and fed into the network as the input. Then, the encoder generates a highlevel feature vector, and the decoder converts the feature vector into a segmentation map, which roughly identifies moving object regions. Moreover, we develop background modeling and foreground extraction techniques, which exploit contour information. Experimental results on the CD-net2014 dataset demonstrate that the proposed algorithm outperforms state-of-the-art techniques significantly.


title: Implementation of machine learning for gender detection using CNN on raspberry Pi platform

abstract: Gender Detection has numerous application in the field of authentication, security and surveillance systems, social platforms and social media. The proposed system describes gender detection based on Computer Vision and Machine Learning Approach using Convolutional Neural Network (CNN) which is used to extract various facial feature. First, the facial-extraction is investigated and best features are introduced which would be useful for training and testing the dataset. This learning representation is taken through the use of convolution neural network. Which reveals that the proposed system is tested across various challenging levels of face datasets and gives excellent performance efficiency of the system with gender detection rate for each of the database. This whole system is introduced by the simple and easy hardware implementation on Raspberry Pi programmed using Python.


title: Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

abstract: In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function $\func$. Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to $\func$ may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of $\func$ in a small but promising region and speedily identify the optimum. We formalise this task as a \emph{multi-fidelity} bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop \mfgpucb, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour, and achieves better regret than strategies which ignore multi-fidelity information. \mfgpucbs outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments.


title: A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures

abstract: The authors present a compile-time scheduling heuristic called dynamic level scheduling, which accounts for interprocessor communication overhead when mapping precedence-constrained, communicating tasks onto heterogeneous processor architectures with limited or possibly irregular interconnection structures. This technique uses dynamically-changing priorities to match tasks with processors at each step, and schedules over both spatial and temporal dimensions to eliminate shared resource contention. This method is fast, flexible, widely targetable, and displays promising performance. >


title: Implementation of BCH Code (n, k) Encoder and Decoder for Multiple Error Correction Control

abstract: 2 Abstract — In this paper we have designed and implemented (15, k) a BCH Encoder and decoder using VHDL for reliable data transfer in AWGN channel with multiple error correction control. The digital logic implementation of binary encoding of multiple error correcting BCH code (15, k) of length n=15 over GF (2 4 ) with irreducible primitive polynomial x 4 +x+1 is organized into shift register circuits. Using the cyclic codes, the reminder b(x) can be obtained in a linear (15-k) stage shift register with feedback connections corresponding to the coefficients of the generated polynomial. Three encoders are designed using VHDL to encode the single, double and triple error correcting BCH code (15, k) corresponding to the coefficient of generated polynomial. Information bit is transmitted in unchanged form up to K clock cycles and during this period parity bits are calculated in the LFSR then the parity bits are transmitted from k+1 to 15 clock cycles. Total 15-k numbers of parity bits with k information bits are transmitted in 15 code word. In multiple error correction method, we have implemented (15, 5 ,3 ) ,(15,7, 2) and (15, 11, 1) BCH encoder and decoder using VHDL and the simulation is done using Xilinx ISE 14.2.


title: Associative Memory using Dictionary Learning and Expander Decoding

abstract: An associative memory is a framework of content-addressable memory that stores a collection of message vectors (or a dataset) over a neural network while enabling a neurally feasible mechanism to recover any message in the dataset from its noisy version. Designing an associative memory requires addressing two main tasks: 1) learning phase: given a dataset, learn a concise representation of the dataset in the form of a graphical model (or a neural network), 2) recall phase: given a noisy version of a message vector from the dataset, output the correct message vector via a neurally feasible algorithm over the network learnt during the learning phase. This paper studies the problem of designing a class of neural associative memories which learns a network representation for a large dataset that ensures correction against a large number of adversarial errors during the recall phase. Specifically, the associative memories designed in this paper can store dataset containing $\exp(n)$ $n$-length message vectors over a network with $O(n)$ nodes and can tolerate $\Omega(\frac{n}{{\rm polylog} n})$ adversarial errors. This paper carries out this memory design by mapping the learning phase and recall phase to the tasks of dictionary learning with a square dictionary and iterative error correction in an expander code, respectively.


title: KP-Miner: Participation in SemEval-2

abstract: This paper briefly describes the KP-Miner system which is a system developed for the extraction of keyphrases from English and Arabic documents, irrespective of their nature. The paper also outlines the performance of the system in the "Automatic Keyphrase Extraction from Scientific Articles" task which is part of SemEval-2.


title: Design and Operation of Interior Permanent-Magnet Motors With Two Axial Segments and High Rotor Saliency

abstract: Skewing the magnets in a brushless interior permanent-magnet motor can be difficult. One method to overcome this problem is to use axial segments that are rotated (“twisted”) with respect to each other. Compared to other methods of rotor skewing, this method may reduce manufacturing cost and the complexity of the rotor. This paper addresses the use of two axial segments and the associated effects on the back-electromotive force (EMF) waveform and motor performance. The back-EMF waveforms of an interior permanent-magnet motor are deeply influenced by the tooth-slot and winding harmonics. They should be sinusoidal to reduce torque ripple for ac motor servo drives and other applications where smooth operation is required. In the paper, we present the two-segment rotor structure together with a simple technique for reducing high-order back-EMF harmonics, and we derive the optimal twisted angle of the proposed two-segment rotor. This minimizes the total harmonic distortion of the back-EMF waveform due to tooth-slot effects. We examine cogging torque and the reduction in cogging torque. We apply the twisted angle rotor to two different compressor motors. In addition to the back-EMF, we address the torque ripple under load and the effect of twist on back-EMF constant. We examine the results using finite-element analysis and validate them by experimental measurement.


title: Semantic Role Labeling for Open Information Extraction

abstract: Open Information Extraction is a recent paradigm for machine reading from arbitrary text. In contrast to existing techniques, which have used only shallow syntactic features, we investigate the use of semantic features (semantic roles) for the task of Open IE. We compare TextRunner (Banko et al., 2007), a state of the art open extractor, with our novel extractor SRL-IE, which is based on UIUC's SRL system (Punyakanok et al., 2008). We find that SRL-IE is robust to noisy heterogeneous Web data and outperforms TextRunner on extraction quality. On the other hand, TextRunner performs over 2 orders of magnitude faster and achieves good precision in high locality and high redundancy extractions. These observations enable the construction of hybrid extractors that output higher quality results than TextRunner and similar quality as SRL-IE in much less time.


title: ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams

abstract: Publisher Summary This chapter implements ATLAS, a powerful database language and system that enables users to develop complete data-intensive applications in structured query language (SQL)—by writing new aggregates and table functions in SQL, rather than in procedural languages as in current Object- Relational systems. As a result, ATLAS' SQL is Turing-complete, and is very suitable for advanced data intensive applications, such as data mining and stream queries. The ATLAS system is now available for download along with a suite of applications including various data mining functions that have been coded in ATLAS' SQL, and execute with a modest performance overhead with respect to the same applications written in C/C++. The demonstration reveals ATLAS' internal architecture by focusing on several of its key components, such as the query rewrite module, the query-plan generation module, and the stream-data management engine.


title: Towards rich query interpretation: walking back and forth for mining query templates

abstract: We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.


title: Low power VLSI transposed structure FIR filter using shift/add architecture

abstract: This paper presents the implementation of the novel FIR filter with the filter architecture using shift and add multiplier. The designed is compared to the previous novel FIR designs such as Direct form and previous shift add architecture in Direct form and Transposed form. The design is done on the Transposed structure form of FIR as it is desirable in low power applications. The filters are optimized for low power but other trade-offs can still be found. The design was found to be improved in low power. The novel FIR filter is described using the in-built multipliers and adders of Vivado Design Suite 2015.2 and implemented on the device xc7a100tcsg324-1. Static power of the device was found higher in the frequency of 100 MHz.


title: Aggregating multiple types of complex data in stock market prediction: A model-independent framework

abstract: The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those models might be constrained to a certain type of data. Aiming at aggregating differently sourced information and offering type-free capability to existing models, a framework for predicting stock market of scenarios with mixed data, including scalar data, compositional data (pie-like) and functional data (curve-like), is established. The presented framework is model-independent, as it serves like an interface to multiple types of data and can be combined with various prediction models. And it is proved to be effective through numerical simulations. Regarding to price prediction, we incorporate the trading volume (scalar data), intraday return series (functional data), and investors' emotions from social media (compositional data) through the framework to competently forecast whether the market goes up or down at opening in the next day. The strong explanatory power of the framework is further demonstrated. Specifically, it is found that the intraday returns impact the following opening prices differently between bearish market and bullish market. And it is not at the beginning of the bearish market but the subsequent period in which the investors' "fear" comes to be indicative. The framework would help extend existing prediction models easily to scenarios with multiple types of data and shed light on a more systemic understanding of the stock market.


title: A Conversational Dialogue Manager for the Humanoid Robot ERICA

abstract: We present a dialogue system for a conversational robot, Erica. Our goal is for Erica to engage in more human-like conversation, rather than being a simple question-answering robot. Our dialogue manager integrates question-answering with a statement response component which generates dialogue by asking about focused words detected in the user’s utterance, and a proactive initiator which generates dialogue based on events detected by Erica. We evaluate the statement response component and find that it produces coherent responses to a majority of user utterances taken from a human-machine dialogue corpus. An initial study with real users also shows that it reduces the number of fallback utterances by half. Our system is beneficial for producing mixed-initiative conversation.


title: Group formation in large social networks: membership, growth, and evolution

abstract: The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences - political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time.Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are connected to one another. We use decision-tree techniques to identify the most significant structural determinants of these properties. We also develop a novel methodology for measuring movement of individuals between communities, and show how such movements are closely aligned with changes in the topics of interest within the communities.


title: Problems with the measurement of banking services in a national accounting framework

abstract: The paper considers some of the problems associated with the indirectly measured components of financial service outputs in the System of National Accounts (SNA), termed FISIM (Financial Intermediation Services Indirectly Measured). The paper utilizes a user cost and supplier benefit approach to the determination of the value of various financial services in the banking sector. The present paper also attempts to integrate the balance sheet accounts in the SNA with the usual flow accounts. An empirical example of various nominal output concepts that could be applied to the U.S. commercial banking sector is presented.


title: Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information

abstract: Although the usefulness of belief networks for reasoning under uncertainty is widely accepted, obtaining numerical probabilities that they require is still perceived a major obstacle. Often not enough statistical data is available to allow for reliable probability estimation. Available information may not be directly amenable for encoding in the network. Finally, domain experts may be reluctant to provide numerical probabilities. In this paper, we propose a method for elicitation of probabilities from a domain expert that is non-invasive and accommodates whatever probabilistic information the expert is willing to state. We express all available information, whether qualitative or quantitative in nature, in a canonical form consisting of (in) equalities expressing constraints on the hyperspace of possible joint probability distributions. We then use this canonical form to derive second-order probability distributions over the desired probabilities.


Architecture for Intelligent Big Data Analysis based on Automatic Service Composition


title: Prenatal Diagnosis and Genetic Counseling for Mosaic Trisomy 13

abstract: Abstract Counseling parents of a fetus with trisomy 13 mosaicism remains difficult because of the phenotypic variability associated with the condition; some patients exhibit the typical phenotype of complete trisomy 13 with neonatal death, while others have few dysmorphic features and prolonged survival. This article provides a comprehensive review of the prenatal diagnosis and genetic counseling for mosaic trisomy 13, including confined placental mosaicism 13, mosaic trisomy 13 diagnosed at amniocentesis, and phylloid hypomelanosis in association with mosaic trisomy 13.


title: Style Transfer as Unsupervised Machine Translation

abstract: Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.


title: Basal ganglia and cerebellar loops: motor and cognitive circuits

abstract: The traditional view that the basal ganglia and cerebellum are simply involved in the control of movement has been challenged in recent years. One of the pivotal reasons for this reappraisal has been new information about basal ganglia and cerebellar connections with the cerebral cortex. In essence, recent anatomical studies have revealed that these connections are organized into discrete circuits or 'loops'. Rather than serving as a means for widespread cortical areas to gain access to the motor system, these loops reciprocally interconnect a large and diverse set of cerebral cortical areas with the basal ganglia and cerebellum. The properties of neurons within the basal ganglia or cerebellar components of these circuits resembles the properties of neurons within the cortical areas subserved by these loops. For example, neuronal activity within basal ganglia and cerebellar loops with motor areas of the cerebral cortex is highly correlated with parameters of movement, while neuronal activity within basal ganglia and cerebellar loops with areas of the prefrontal cortex is more related to aspects of cognitive function. Thus, individual loops appear to be involved in distinct behavioral functions. Studies of basal ganglia and cerebellar pathology support this conclusion. Damage to the basal ganglia or cerebellar components of circuits with motor areas of cortex leads to motor symptoms, whereas damage of the subcortical components of circuits with non-motor areas of cortex causes higher-order deficits. In this report, we review some of the new anatomical, physiological and behavioral findings that have contributed to a reappraisal of function concerning the basal ganglia and cerebellar loops with the cerebral cortex.


title: Overview of Spatial Processing Approaches for GNSS Structural Interference Detection and Mitigation

abstract: GNSS-dependent positioning, navigation, and timing synchronization procedures have a significant impact on everyday life. Thus, such an extensively used system progressively become an attractive target for illegal exploitation and attacks. Position and timing solutions provided by GNSS receivers can be threatened by structural interference such as spoofing threats. This paper provides an overview of recent research work on GNSS signal authentication utilizing spatial processing methods. Different spatial processing approaches for spoofing detection, classification and mitigation are characterized and compared. Three different processing methods, namely antenna array processing, moving receiver and cloud based spoofing countermeasure are analyzed in details. The benefits and disadvantages of each are discussed.


title: Investigating the usability of iPad mobile tablet in formative assessment of a mathematics course

abstract: Formative assessment allows students to judge their learning progress against a given standard or assessment criteria during the course of study. There are challenges related to the generation and delivery of feedback for formative purposes, for instance the students' engagement in their learning, the pedagogy adopted and the usability of the technology tools used in this regard. This paper presents a study of iPad mobile tablet technology-supported peer-to-peer assessment (P2PASS), which is a modern form of formative assessment. The experimental work was undertaken at Kigali Institute of Science and Technology (KIST) in Rwanda. The implementation of P2PASS in this study shows the students' engagement in learning mathematics, and allowed the investigation on the possibilities of improving their mastery of the subject. This paper further discusses the students' attitudes towards the P2PASS process as well as the technological tools used. The usability problems of the iPad applications are discussed, together with the implications of using mobile tablet technology in providing the feedback on the mathematical work.


title: The Science and Theory behind Facial Aging

abstract: Summary: ::: The etiology of age-related facial changes has many layers. Multiple theories have been presented over the past 50–100 years with an evolution of understanding regarding facial changes related to skin, soft tissue, muscle, and bone. This special topic will provide an overview of the current literature and evidence and theories of facial changes of the skeleton, soft tissues, and skin over time.


Some Epistemic Benefits of Action: Tetris, a Case Study


title: Pornography, Sexual Socialization, and Satisfaction Among Young Men

abstract: In spite of a growing presence of pornography in contemporary life, little is known about its potential effects on young people’s sexual socialization and sexual satisfaction. In this article, we present a theoretical model of the effects of sexually explicit materials (SEM) mediated by sexual scripting and moderated by the type of SEM used. An on-line survey dataset that included 650 young Croatian men aged 18–25 years was used to explore empirically the model. Descriptive findings pointed to significant differences between mainstream and paraphilic SEM users in frequency of SEM use at the age of 14, current SEM use, frequency of masturbation, sexual boredom, acceptance of sex myths, and sexual compulsiveness. In testing the model, a novel instrument was used, the Sexual Scripts Overlap Scale, designed to measure the influence of SEM on sexual socialization. Structural equation analyses suggested that negative effects of early exposure to SEM on young men’s sexual satisfaction, albeit small, could be stronger than positive effects. Both positive and negative effects—the latter being expressed through suppression of intimacy—were observed only among users of paraphilic SEM. No effect of early exposure to SEM was found among the mainstream SEM users. To counterbalance moral panic but also glamorization of pornography, sex education programs should incorporate contents that would increase media literacy and assist young people in critical interpretation of pornographic imagery.


title: Low-energy treatment of colourant wastes using sponge biofilters for the personal care product industry

abstract: Abstract Four trickling biofilter designs were assessed as low-energy alternatives to aerobic activated sludge (AS) for the treatment of personal care product industry wastes. The designs included partially submerged packed-media and sponge reactors with and without active aeration. Partial submergence was used to reduce active aeration needs. Simulated colourant wastes (up to COD = 12,480 mg/L, TN = 128 mg/L) were treated for 201 days, including wastes with elevated oxidant levels. COD and TN removal efficiencies were always >79% and >30% (even without aeration). However, aerated sponge reactors consistently had the highest removal efficiencies, especially for TN (∼60%), and were most tolerant of elevated oxidants. This study shows sponge biofilters have great potential for treating colourant wastes because they achieve high treatment efficiencies and reduce energy use by >40% relative to AS systems.


title: EvilSeed: A Guided Approach to Finding Malicious Web Pages

abstract: Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user's computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honey clients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the "toxicity" of the stream of URLs being analyzed is low. In this paper, we present EVILSEED, an approach to search the web more efficiently for pages that are likely malicious. EVILSEED starts from an initial seed of known, malicious web pages. Using this seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the initial seed. By doing so, EVILSEED leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSEED increases the "toxicity" of the input URL stream. Also, we envision that the features that EVILSEED presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSEED is able to identify malicious web pages more efficiently when compared to crawler-based approaches.


title: The earth is round (p < .05

abstract: After 4 decades of severe criticism, the ritual of null hypothesis significance testing (mechanical dichotomous decisions around a sacred .05 criterion) still persists. This article reviews the problems with this practice, including near universal misinterpretation of p as the probability that H₀ is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects H₀ one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods are suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences, on replication. (PsycINFO Database Record (c) 2012 APA, all rights reserved)


title: Neuroevolution with Analog Genetic Encoding

abstract: The evolution of artificial neural networks (ANNs) is often used to tackle difficult control problems. There are different approaches to the encoding of neural networks in artificial genomes. Analog Genetic Encoding (AGE) is a new implicit method derived from the observation of biological genetic regulatory networks. This paper shows how AGE can be used to simultaneously evolve the topology and the weights of ANNs for complex control systems. AGE is applied to a standard benchmark problem and we show that its performance is equivalent or superior to some of the most powerful algorithms for neuroevolution in the literature.


title: A Real-World Analysis of Kerberos Password Security

abstract: Kerberos is a distributed authentication system that many organizations use to handle domain-wide password security. Although it has been known for quite some time that Kerberos is vulnerable to brute-force password searches, there has so far been little analysis of the scope and extent of this vulnerability. This paper discusses the nature of this weakness in detail and attempts to quantify the severity of the danger it poses to existing Kerberized installations. The results of a controlled experiment, in which a large number of passwords from a Kerberos realm were broken o -line and subjected to analysis, will be presented. The author explores possible strategies for repairing this security hole, the most viable of which is the use of Kerberos V5 preauthentication coupled with a secure password authentication protocol such as SRP,


title: Easily Identifiable Discourse Relations

abstract: We present a corpus study of local discourse relations based on the Penn Discourse Tree Bank, a large manually annotated corpus of explicitly or implicitly realized relations. We show that while there is a large degree of ambiguity in temporal explicit discourse connectives, overall connectives are mostly unambiguous and allow high-accuracy prediction of discourse relation type. We achieve 93.09% accuracy in classifying the explicit relations and 74.74% accuracy overall. In addition, we show that some pairs of relations occur together in text more often than expected by chance. This finding suggests that global sequence classification of the relations in text can lead to better results, especially for implicit relations.


title: The Knowledge-creating Theory Revisited: Knowledge Creation as a Synthesizing Process

abstract: This paper is a part of our attempt to build a new knowledge-based theory of the firm and organization to explain the dynamic process of knowledge creation and utilization. For this, we revisit the theory of knowledge creation through the SECI process and ba, and try to advance them further by incorporating the dialectic thinking. In this paper, knowledge creation is conceptualized as a dialectical process, in which various contradictions are synthesized through dynamic interactions among individuals, the organization, and the environment. With the view of a firm as a dialectic being, and strategy and organization should be re-examined as the synthesizing and selftranscending process instead of a logical analysis of structure or action. An organization is not an information-processing machine that is composed of small tasks to carry out a given task, but an organic configuration of ba. Ba, which is conceptualized as a shared context in motion, can transcend time, space, and organization boundaries to create knowledge.


title: Cryopreserved femoral vein grafts for difficult hemodialysis access.

abstract: Improved survival of patients with renal failure has led to the need for complex angioaccess procedures. The use of cryopreserved femoral vein for angioaccess when prosthetic arteriovenous grafts (AVG) could not be placed, owing to infection or the loss of conventional angioaccess sites from multiple AVG failures, was prospectively evaluated. Forty-eight cryopreserved femoral vein AVGs were placed in 44 patients. Thirty-eight (82%) of the cryopreserved femoral vein AVGs were placed for infection, whereas the other 10 (18%) were placed for multiple graft failures with compromised venous outflow. Even with implantation of the cryoveins into infected patients, there were no cryopreserved femoral vein AVG infections. The 1-year primary graft patency rate was 49% and the secondary graft patency rate was 75%. During the same time interval, 68 prosthetic brachial artery-to-axillary vein AVGs were placed. The 1-year primary and secondary patency rates for the prosthetic AVGs were 65 and 78%, respectively. In this study the overall patency rate of the cryopreserved femoral vein AVGs was similar to that for the PTFE AVGs (p = 0.519). In conclusion, the cryopreserved femoral vein proved useful in difficult angioaccess cases. The lack of infection after cryovein implantation around an infected area shows promise for salvaging an angioaccess site that would otherwise have been abandoned.


Convenient or Useful? Consumer Adoption of Smartphones for Mobile Commerce


title: Towards trustworthy end-to-end communication in industry 4.0

abstract: Industry 4.0 considers integration of IT and control systems with physical objects, software, sensors and connectivity in order to optimize manufacturing processes. It provides advanced functionalities in control and communication for an infrastructure that handles multiple tasks in various locations automatically. Automatic actions require information from trustworthy sources. Thus, this work is focused on how to ensure trustworthy communication from the edge devices to the backend infrastructure. We derive a meta-model based on RAMI 4.0, which is used to describe an end-to-end communication use case for an Industry 4.0 application scenario and to identify dependabilities in case of security challenges. Furthermore, we evaluate secure messaging protocols and the integration of Trusted Platform Module (TPM) as a root of trust for dataexchange. We define a set of representative measurable indicator points based on existing standards and use them for automated dependability detection within the whole system.


title: Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media

abstract: In this paper, we describe the Lithium Natural Language Processing (NLP) system - a resource-constrained, high- throughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currently incorporated in Lithium products. We also compare our system with existing commercial and academic NLP systems in terms of performance, information extracted and languages supported. We show that Lithium NLP is at par with and in some cases, outperforms state- of-the-art commercial NLP systems.


title: Low-latency encryption: is Lightweight = light + wait?

abstract: The processing time required by a cryptographic primitive implemented in hardware is an important metric for its performance but it has not received much attention in recent publications on lightweight cryptography. Nevertheless, there are important applications for cost effective low-latency encryption. As the first step in the field, this paper explores the low-latency behavior of hardware implementations of a set of block ciphers. The latency of the implementations is investigated as well as the trade-offs with other metrics such as circuit area, time-area product, power, and energy consumption. The obtained results are related back to the properties of the underlying cipher algorithm and, as it turns out, the number of rounds, their complexity, and the similarity of encryption and decryption procedures have a strong impact on the results. We provide a qualitative description and conclude with a set of recommendations for aspiring low-latency block cipher designers.


title: A unified framework for context assisted face clustering

abstract: Automatic face clustering, which aims to group faces referring to the same people together, is a key component for face tagging and image management. Standard face clustering approaches that are based on analyzing facial features can already achieve high-precision results. However, they often suffer from low recall due to the large variation of faces in pose, expression, illumination, occlusion, etc. To improve the clustering recall without reducing the high precision, we leverage the heterogeneous context information to iteratively merge the clusters referring to same entities. We first investigate the appropriate methods to utilize the context information at the cluster level, including using of "common scene", people co-occurrence, human attributes, and clothing. We then propose a unified framework that employs bootstrapping to automatically learn adaptive rules to integrate this heterogeneous contextual information, along with facial features, together. Experimental results on two personal photo collections and one real-world surveillance dataset demonstrate the effectiveness of the proposed approach in improving recall while maintaining very high precision of face clustering.


title: Studying Applicability Feasibility of OFDM in Upcoming 5G Network

abstract: Orthogonal frequency-division multiplexing (OFDM) is one of unbeatable multiplexing technique till date. However with increasing version of next generation mobile standards like 5G, the applicability of OFDM is quite questionable. The prime reason behind this is in order to offer higher data rates and extensive networking services in 5G, OFDM will be required to overcome some inherent problems of spectral leakage, power consumption, less supportability of increased channel capacity. The reason community still believes that there is positive scope of OFDM to be applicable in 5G network provided it undergoes certain changes. This paper reviews some of the complimentary waveforms that have been theoretically proven to be adding edge to OFDM system. The paper reviews different waveforms as well as multiple access techniques to be used in designing 5G technology and assess their effectiveness in viewpoint of research applicability and effectiveness towards leveraging 5G.


title: AUTHSCOPE: Towards Automatic Discovery of Vulnerable Authorizations in Online Services

abstract: When accessing online private resources (e.g., user profiles, photos, shopping carts) from a client (e.g., a desktop web-browser or a mobile app), the service providers must implement proper access control, which typically involves both authentication and authorization. However, not all of the service providers follow the best practice, resulting in various access control vulnerabilities. To understand such a threat in a large scale, and identify the vulnerable access control implementations in online services, this paper introduces AuthScope, a tool that is able to automatically execute a mobile app and pinpoint the vulnerable access control implementations, particularly the vulnerable authorizations, in the corresponding online service. The key idea is to use differential traffic analysis to recognize the protocol fields and then automatically substitute the fields and observe the server response. One of the key challenges for a large scale study lies in how to obtain the post-authentication request-and-response messages for a given app. We have thus developed a targeted dynamic activity explorer to perform an in-context analysis and drive the app execution to automatically log in the service. We have tested AuthScope with 4,838 popular mobile apps from Google Play, and identified 597 0-day vulnerable authorizations that map to 306 apps.


title: Defects4J: a database of existing faults to enable controlled testing studies for Java programs

abstract: Empirical studies in software testing research may not be comparable, reproducible, or characteristic of practice. One reason is that real bugs are too infrequently used in software testing research. Extracting and reproducing real bugs is challenging and as a result hand-seeded faults or mutants are commonly used as a substitute. This paper presents Defects4J, a database and extensible framework providing real bugs to enable reproducible studies in software testing research. The initial version of Defects4J contains 357 real bugs from 5 real-world open source pro- grams. Each real bug is accompanied by a comprehensive test suite that can expose (demonstrate) that bug. Defects4J is extensible and builds on top of each program’s version con- trol system. Once a program is configured in Defects4J, new bugs can be added to the database with little or no effort. Defects4J features a framework to easily access faulty and fixed program versions and corresponding test suites. This framework also provides a high-level interface to common tasks in software testing research, making it easy to con- duct and reproduce empirical studies. Defects4J is publicly available at http://defects4j.org.


title: Real-Time Volume Rendering in Dynamic Lighting Environments Using Precomputed Photon Mapping

abstract: We present a framework for precomputed volume radiance transfer that achieves real-time rendering of global illumination effects for volume data sets such as multiple scattering, volumetric shadows, and so on. Our approach incorporates the volumetric photon mapping method into the classical precomputed radiance transfer pipeline. We contribute several techniques for light approximation, radiance transfer precomputation, and real-time radiance estimation, which are essential to make the approach practical and to achieve high frame rates. For light approximation, we propose a new discrete spherical function that has better performance for construction and evaluation when compared with existing rotational invariant spherical functions such as spherical harmonics and spherical radial basis functions. In addition, we present a fast splatting-based radiance transfer precomputation method and an early evaluation technique for real-time radiance estimation in the clustered principal component analysis space. Our techniques are validated through comprehensive evaluations and rendering tests. We also apply our rendering approach to volume visualization.


A simple introduction to maximum entropy models for natural language processing


title: Key Properties for Successful Ultra Thin Die Pickup

abstract: The four major key properties for successful, damage-free ultra thin die (<; 50 um) pickup from a wafer foil are described in detail: die bending stress, die strength, edge peel force, and bulk peel force. First, bending stress for different pickup methods (multi stage, multi disc, multi needle) are calculated and compared using a dynamic FEA model. Second, we summarize how the die strength is influenced by die structures and wafer processing steps, especially by thinning and dicing methods. In addition, we present die strength measurements for TSV test dies. Third, the property “wafer foil edge peel force” is introduced, and the dependency on the dicing method is experimentally verified. It clearly shows, that dicing-before-grinding is to be preferred over single cut dicing. Fourth, we give an overview of bulk peel forces of various commercial wafer foils. Values are taken from datasheet specifications, and compared using Kendall's equation.


title: Using linear graph theory and the principle of orthogonality to model multibody, multi-domain systems

abstract: This paper presents a unified formulation capable of systematically generating the governing symbolic equations for multibody, multi-domain systems. The formulation is based on the principle of orthogonality, a powerful concept that serves as a generalization of the principle of virtual work and virtual power. Since it is a graph-theoretic approach, the formulation also provides significant flexibility with respect to the system's modeling variables. This allows the user to model the mechanical portion of the system using joint, absolute, absolute angular, or some hybrid set of coordinates. To demonstrate the robustness of the approach, the paper compares the algorithm's results for a forward dynamic analysis of a flexible parking lot barrier to those in the literature. The parking lot barrier model includes a three-phase induction motor, a six bar mechanism and a flexible beam.


title: Systemic staging for urate crystal deposits with dual-energy CT and ultrasound in patients with suspected gout

abstract: Objective of the study is to compare the diagnostic accuracy for detecting monosodium urate crystal deposits between dual-energy CT (DECT) and ultrasound (US). Sixty consecutive patients (49 men, mean age 62 years) with clinically suspected gout were included in this case-control study. DECT and US of feet, knees, hands and elbows were performed in all patients. Polarisation microscopy of synovial fluid or a score incorporating serum uric acid level, first MTP joint involvement, gender, previous patient-reported arthritis attack, cardiovascular diseases, joint redness and onset within 1 day was used as standard of reference. Standard of reference classified 39 patients as gout positive. Sixteen patients had gout and a concomitant rheumatic disease. Sensitivities for diagnosis of gout disease were 84.6 % (33/39) for DECT and 100 % (39/39) for US. Specificities were 85.7 % (18/21) for DECT and 76.2 % (16/21) for US. Positive and negative predictive values were 91.7 % (33/36) and 75.0 % (18/24) for DECT, 88.6 % (39/44) and 100 % (16/16) for US, respectively. Urate crystals were detected most frequently in MTP1 joints (DECT 20/78, US 58/78), any other toe joints (DECT 25/78, US 62/78) and knees (DECT 41/78, US 31/78). The volumetry of DECT computed a mean urate crystal deposit load of 2.1 cm(3) (SD 9.6 cm(3)). A mean effective dose of ≤0.5 mSv was estimated. DECT is more specific for the diagnosis of gout than US. However, it fails to detect small urate crystal deposits. It might be particularly useful for patients with ambivalent findings, concomitant rheumatic diseases and with non-conclusive joint aspiration.


title: Nonlinear control of variable speed wind turbines for power regulation

abstract: A nonlinear controller has been designed for variable speed wind turbines electric power regulation. The efficiency and reliability of wind power is shown to be depending on the applied control strategy of the wind turbine. Up to now, classical regulation is implemented only. However, under sudden wind profile variations, the wind turbine performance decrease which may cause troubles in the electrical network. For the limitation of mechanical loads and rotational speed variations, and to avoid the wind turbine stall above rated wind speeds while controlling the output power, a cascade structure asymptotic output tracking based approach has been applied. Simulations and validation have been performed using the wind turbine modelling and using wind turbine simulator as well. The wind turbine is torque generator controlled. The required performance is met for both


Fast and memory efficient segmentation of lung tumors using graph cuts


title: The neuropsychological impact of sports-related concussion: A meta-analysis

abstract: There is increasing interest in the potential neuropsychological impact of sports-related concussion. A meta-analysis of the relevant literature was conducted to determine the impact of sports-related concussion across six cognitive domains. The analysis was based on 21 studies involving 790 cases of concussion and 2014 control cases. The overall effect of concussion ( d = 0.49) was comparable to the effect found in the non-sports-related mild traumatic brain injury population ( d = 0.54; Belanger et al., 2005 ). Using sports-concussed participants with a history of prior head injury appears to inflate the effect sizes associated with the current sports-related concussion. Acute effects (within 24 hr of injury) of concussion were greatest for delayed memory, memory acquisition, and global cognitive functioning ( d = 1.00, 1.03, and 1.42, respectively). However, no residual neuropsychological impairments were found when testing was completed beyond 7 days postinjury. These findings were moderated by cognitive domain and comparison group (control group versus preconcussion self-control). Specifically, delayed memory in studies utilizing a control group remained problematic at 7 days. The implications and limitations of these findings are discussed. ( JINS , 2005, 11 , 345–357.)


The user-centred iterative design of collaborative writing software


title: Double Refinement Network for Efficient Indoor Monocular Depth Estimation

abstract: Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image. It is an important problem in computer vision and is usually solved using neural networks. Though recent works in this area have shown significant improvement in accuracy, the state-of-the-art methods tend to require massive amounts of memory and time to process an image. The main purpose of this work is to improve the performance of the latest solutions with no decrease in accuracy. To this end, we introduce the Double Refinement Network architecture. The proposed method achieves state-of-the-art results on the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second rate is significantly higher (up to 18 times speedup per image at batch size 1) and the RAM usage per image is lower.


title: Theoretical Foundations of Equitability and the Maximal Information Coefficient

abstract: The maximal information coecient (MIC) is a tool for nding the strongest pairwise relationships in a data set with many variables [1]. MIC is useful because it gives similar scores to equally noisy relationships of dierent types. This property, called equitability, is important for analyzing high-dimensional data sets. Here we formalize the theory behind both equitability and MIC in the language of estimation theory. This formalization has a number of advantages. First, it allows us to show that equitability is a generalization of power against statistical independence. Second, it allows us to compute and discuss the population value of MIC, which we call MIC . In doing so we generalize and strengthen the mathematical results proven in [1] and clarify the relationship between MIC and mutual information. Introducing MIC also enables us to reason about the properties of MIC more abstractly: for instance, we show that MIC is continuous and that there is a sense in which it is a canonical \smoothing" of mutual information. We also prove an alternate, equivalent characterization of MIC that we use to state new estimators of it as well as an algorithm for explicitly computing it when the joint probability density function of a pair of random variables is known. Our hope is that this paper provides a richer theoretical foundation for MIC and equitability going forward. This paper will be accompanied by a forthcoming companion paper that performs extensive empirical analysis and comparison to other methods and discusses the practical aspects of both equitability and the use of MIC and its related statistics.


title: Robust Scale-Adaptive Mean-Shift for Tracking

abstract: Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker.


title: High-dimensional dynamics of generalization error in neural networks

abstract: We perform an average case analysis of the generalization dynamics of large neural networks trained using gradient descent. We study the practically-relevant "high-dimensional" regime where the number of free parameters in the network is on the order of or even larger than the number of examples in the dataset. Using random matrix theory and exact solutions in linear models, we derive the generalization error and training error dynamics of learning and analyze how they depend on the dimensionality of data and signal to noise ratio of the learning problem. We find that the dynamics of gradient descent learning naturally protect against overtraining and overfitting in large networks. Overtraining is worst at intermediate network sizes, when the effective number of free parameters equals the number of samples, and thus can be reduced by making a network smaller or larger. Additionally, in the high-dimensional regime, low generalization error requires starting with small initial weights. We then turn to non-linear neural networks, and show that making networks very large does not harm their generalization performance. On the contrary, it can in fact reduce overtraining, even without early stopping or regularization of any sort. We identify two novel phenomena underlying this behavior in overcomplete models: first, there is a frozen subspace of the weights in which no learning occurs under gradient descent; and second, the statistical properties of the high-dimensional regime yield better-conditioned input correlations which protect against overtraining. We demonstrate that naive application of worst-case theories such as Rademacher complexity are inaccurate in predicting the generalization performance of deep neural networks, and derive an alternative bound which incorporates the frozen subspace and conditioning effects and qualitatively matches the behavior observed in simulation.


title: Continuous Angle-based Outlier Detection on High-dimensional Data Streams

abstract: Outlier detection over data streams is an increasingly important task in data mining. Traditional distance-based data stream outlier detection is unsuitable for high-dimensional data sets, since the discrimination of distances between different data points becomes rather poor in high dimensional space. ABOD (Angle-based Outlier Detection) is an effective approach to detecting outliers in high-dimensional space. In this paper, the problem of continuous ABOD over data streams is studied. Generally, only a few data objects may change their states during two consecutive timestamps. Therefore, we propose several incremental angle-based outlier detection approaches over data streams based on ABOD and its variants that provide visible speed-up without loss of accuracy. Firstly, the basic ideas of these incremental algorithms are introduced. Then, we explain the time complexity of them. Finally, we use synthetic data streams to prove their efficiency.


title: Six-Degree-of-Freedom Digital Simulations for Missile Guidance, Navigation, and Control

abstract: This article presents a brief history of missile simulations and a discussion of the programming languages and paradigms used for developing them. Evolving language and programming paradigms elicit requirements for new simulation architectures. Within this execution framework, engineering-level guidance, navigation, and control simulations must include certain functional modules to capture the performance characteristics of the missile system. The level of model sophistication required depends on the particular engineering question to be answered. Six-degree-of-freedom simulations are effective tools for cost and risk reduction during the development and deployment of missile systems.


title: Can encrypted traffic be identified without port numbers, IP addresses and payload inspection?

abstract: Abstract Identifying encrypted application traffic represents an important issue for many network tasks including quality of service, firewall enforcement and security. Solutions should ideally be both simple – therefore efficient to deploy – and accurate. This paper presents a machine learning based approach employing simple packet header feature sets and statistical flow feature sets without using the IP addresses, source/destination ports and payload information to unveil encrypted application tunnels in network traffic. We demonstrate the effectiveness of our approach as a forensic analysis tool on two encrypted applications, Secure SHell (SSH) and Skype, using traces captured from entirely different networks. Results indicate that it is possible to identify encrypted traffic tunnels with high accuracy without inspecting payload, IP addresses and port numbers. Moreover, it is also possible to identify which services run in encrypted tunnels.


title: IRIS performer: a high performance multiprocessing toolkit for real-time 3D graphics

abstract: This paper describes the design and implementation of IRIS Performer, a toolkit for visual simulation, virtual reality, and other real-time 3D graphics applications. The principal design goal is to allow application developers to more easily obtain maximal performance from 3D graphics workstations which feature multiple CPUs and support an immediate-mode rendering library. To this end, the toolkit combines a low-level library for high-performance rendering with a high-level library that implements pipelined, parallel traversals of a hierarchical scene graph. While discussing the toolkit architecture, the paper illuminates and addresses performance issues fundamental to immediate-mode graphics and coarse-grained, pipelined multiprocessing. Graphics optimizations focus on efficient data transfer to the graphics subsystem, reduction of mode settings, and restricting state inheritance. The toolkit's multiprocessing features solve the problems of how to partition work among multiple processes, how to synchronize these processes, and how to manage data in a pipelined, multiprocessing environment. The paper also discusses support for intersection detection, fixed-frame rates, run-time profiling and special effects such as geometric morphing.


title: Low-Rank Modeling and Its Applications in Image Analysis

abstract: Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic. In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis. We first give an overview to the concept of low-rank modeling and challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this paper with some discussions.


title: Smart farming using IOT

abstract: Even today, different developing countries are also using traditional methods and backward techniques in agriculture sector. Little or very less technological advancement is found here that has increased the production efficiency significantly. To increase the productivity, a novel design approach is presented in this paper. Smart farming with the help of Internet of Things (IOT) has been designed. A remote controlled vehicle operates on both automatic and manual modes, for various agriculture operations like spraying, cutting, weeding etc. The controller keeps monitoring the temperature, humidity, soil condition and accordingly supplies water to the field.


title: Control of a multilevel converter using resultant theory

abstract: In this work, a method is given to compute the switching angles in a multilevel converter to produce the required fundamental voltage while at the same time cancel out specified higher order harmonics. Specifically, a complete analysis is given for a seven-level converter (three dc sources), where it is shown that for a range of the modulation index m/sub I/, the switching angles can be chosen to produce the desired fundamental V/sub 1/=m/sub I/(s4V/sub dc///spl pi/) while making the fifth and seventh harmonics identically zero.


title: Design procedure for 2D slotted waveguide antenna with controllable sidelobe level

abstract: This paper presents an inventive and simple procedure for the design of a 2D slotted waveguide antenna (SWA) having a desired sidelobe level (SLL) and a pencil shape pattern. The 2D array is formed by a defined number of 1D broadwall SWAs, which are fed using an extra broadwall SWA. For specified number of identical longitudinal slots in both dimensions, the desired SLL and the required operating frequency, this procedure finds the slots length, width, locations along the length of the waveguide, and offsets from its centerline. This is done for the radiating SWAs as well as the feed SWA. An example SWA with 8×8 elliptical slots is designed using this procedure for an SLL lower than −20 dB, where the design results are also reported in this paper.


title: An efficient access control scheme with outsourcing capability and attribute update for fog computing

abstract: Fog computing as an extension of cloud computing provides computation, storage and application services to end users. Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic technology for guaranteeing data confidentiality and fine-grained data access control. It enables data owners to define flexible access policy for data sharing. However, in CP-ABE systems, the problems of the time required to encrypt, decrypt and attribute update are long-standing unsolved in the literature. In this paper, we propose the first access control (CP-ABE) scheme supporting outsourcing capability and attribute update for fog computing. Specifically, the heavy computation operations of encryption and decryption are outsourced to fog nodes, thus the computation operations for data owners to encrypt and users to decrypt are irrelevant to the number of attributes in the access structure and secret keys, respectively. The cost brought by attribute update is efficient in the sense that we only concentrate on the update of the ciphertext associated with the corresponding updated attribute. The security analysis shows that the proposed scheme is secure under the decisional bilinear Diffie–Hellman assumption. The proposed scheme is efficient, and the time of encryption for data owners and decryption for users are small and constant. The computational ability of fog nodes are fully utilizing during the access control, so the tiny computing cost is left to end users with resource-constrained devices.


title: An investigation on the chip formation and forces in the grinding of Inconel718 alloy using the single-grain method

abstract: This paper investigates the chip formation and forces in the grinding of nickel-based superalloy Inconel718 using a single CBN grain. A two-dimensional finite element analysis was carried out to understand the thermo-mechanical process. The variations of chip formation stages, plastic strain, strain rate, equivalent stress, shear stress and grinding force, which are difficult to be measured precisely in-situ, were studied under the grinding speeds of 20 m/s, 80 m/s and 120 m/s, respectively. It was found that the wheel speed influences the chip formation process. When the maximum undeformed chip thickness is a constant, however, the average force at different speed during a single-grain grinding could be controlled in a small variation range. This provides a theoretical support and good evidence that high and super-high speed grinding can significantly improve the machining efficiency without introducing higher grinding forces provided that the maximum undeformed chip thickness could be controlled accurately.


Viewpoint: The role of the educational setting in preventing burnout


title: Clinical and molecular characterization of Rubinstein-Taybi syndrome patients carrying distinct novel mutations of theEP300gene

abstract: Rubinstein-Taybi syndrome (RSTS) is a rare congenital neurodevelopmental disorder characterized by postnatal growth deficiency, skeletal abnormalities, dysmorphic features and cognitive deficit. Mutations in two genes, CREBBP and EP300, encoding two homologous transcriptional co-activators, have been identified in ˜55% and ˜3–5% of affected individuals, respectively. To date, only eight EP300-mutated RSTS patients have been described and 12 additional mutations are reported in the database LOVD. In this study, EP300 analysis was performed on 33 CREBBP-negative RSTS patients leading to the identification of six unreported germline EP300 alterations comprising one deletion and five point mutations. All six patients showed a convincing, albeit mild, RSTS phenotype with minor skeletal anomalies, slight cognitive impairment and few major malformations. Beyond the expansion of the RSTS-EP300-mutated cohort, this study indicates that EP300-related RSTS cases occur more frequently than previously thought (˜8% vs 3–5%); furthermore, the characterization of novel EP300 mutations in RSTS patients will enhance the clinical practice and genotype–phenotype correlations.


title: An Ontology-based Context-aware System for Smart Homes: E-care@home

abstract: Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typica ...


title: Exploration of Deep Belief Networks for Vowel-like regions detection

abstract: This work explores Deep Belief Networks (DBN) for the task of detecting Vowel-like regions (VLRs). Vowels and semivowels are considered as VLRs. By using vocal tract features at the input layer of DBN, we extract an evidence for VLRs by transforming the vocal tract features through multiple non-linear hidden layers. The linear classifier is used to predict the class of evidence, i.e.,whether it is VLR or not. The DBN method is then combined with excitation source (ES) based method for VLRs detection. Even though DBN method provides comparable performance with the existing methods, the combination provides improved performance confirming the different way of modeling VLR information in the DBN.


title: Rectification and 3D reconstruction of curved document images

abstract: Distortions in images of documents, such as the pages of books, adversely affect the performance of optical character recognition (OCR) systems. Removing such distortions requires the 3D deformation of the document that is often measured using special and precisely calibrated hardware (stereo, laser range scanning or structured light). In this paper, we introduce a new approach that automatically reconstructs the 3D shape and rectifies a deformed text document from a single image. We first estimate the 2D distortion grid in an image by exploiting the line structure and stroke statistics in text documents. This approach does not rely on more noise-sensitive operations such as image binarization and character segmentation. The regularity in the text pattern is used to constrain the 2D distortion grid to be a perspective projection of a 3D parallelogram mesh. Based on this constraint, we present a new shape-from-texture method that computes the 3D deformation up to a scale factor using SVD. Unlike previous work, this formulation imposes no restrictions on the shape (e.g., a developable surface). The estimated shape is then used to remove both geometric distortions and photometric (shading) effects in the image. We demonstrate our techniques on documents containing a variety of languages, fonts and sizes.


Service Composition Formal Techniques in Service Oriented Architecture : A Survey


title: Accounting Conservatism , the Quality of Earnings , and Stock Returns

abstract: A safety-shielded trocar having an obturator with a linear cutting edge surface defining a base width substantially less than the diameter of the safety shield is disclosed. When the trocar is inserted through tissue, the tissue dilates from the width of the linear incision to accommodate the size of the safety shield of the trocar, thus making it possible to provide an access opening greater than the incisional width made by the linear cutting edge surface of the obturator. In a preferred embodiment, the safety shield has a shield tip region which is asymmetric to facilitate the dilation of the tissue as the trocar is inserted.


title: Variation is function: Are single cell differences functionally important?

abstract: There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single-cell molecular variation per se might be crucial for population-level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of identical cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population-level function. We consider several possible methods to further pursue the hypothesis that variation is function through the use of comparative analysis and novel experimental techniques.


title: Building event-centric knowledge graphs from news

abstract: Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations-e.g.?a celebrity's birth date, occupation and family members-obtained from large repositories such as Freebase or Wikipedia.In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs.We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user's understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts.


title: A New Car Selection in the Market using TOPSIS Technique

abstract: Now days purchasing of automobiles i.e, especially cars in the market is very tough task to the customers due to day to day changes in various technical and operational parameter specifications like style, life span, fuel economy, suspension and cost etc. Therefore to overcome from this confusion state some selection procedure techniques are required. TOPSIS is one the selection procedure technique is adopted for this problem. This technique provides a base for decision-making processes where there are limited numbers of choices but each has large number of attributes. In this paper some cars are considered with different attributes and select the best car using TOPSIS technique.


title: Deer Forests, Sporting Estates and the Aristocracy

abstract: PCT No. PCT/AU95/00192 Sec. 371 Date Oct. 1, 1996 Sec. 102(e) Date Oct. 1, 1996 PCT Filed Apr. 5, 1995 PCT Pub. No. WO95/27231 PCT Pub. Date Oct. 12, 1995A seven color separation process is provided in which, as well as the conventional Cyan, Magenta, Yellow and Black (CMYK) separations traditionally used in the four color printing process, additional Red, Green and Blue (RGB) separations are produced on a conventional scanner. The additional separations are produced using the Cyan, Magenta and Yellow channels of the scanner to measure Red, Green and Blue absorption characteristics of the source and to compensate the measured densities such that the separations represent a range of print densities from 0.0 D to 2.0 D corresponding to densities in the source in the range 1.0 D to 3.0 D. The transfer duration is also compensated for the compression which takes place in the preparation of the CMYK separations.


title: HOW DO SUPPLIERS BENEFIT FROM INFORMATION TECHNOLOGY USE IN SUPPLY CHAIN RELATIONSHIPS ? 1

abstract: Supply chain management systems (SCMS) championed by network leaders in their supplier networks are now ubiquitous. While prior studies have examined the benefits to network leaders from these systems, little attention has been paid to the benefits to supplier firms. This study draws from organizational theories of learning and action and transaction cost theory to propose a model relating suppliers' use of SCMS to benefits. It proposes that two patterns of SCMS use by suppliers-exploitation and exploration-create contexts for suppliers to make relationship-specific investments in business processes and domain knowledge. These, in turn, enable suppliers to both create value and retain a portion of the value created by the use of these systems in interfirm relationships. ::: ::: Data from 131 suppliers using an SCMS implemented by one large retailer support hypotheses that relationship-specific intangible investments play a mediating role linking SCMS use to benefits. Evidence that patterns of information technology use are significant determinants of relationship-specific investments in business processes and domain expertise provides a finer-grained explanation of the logic of IT-enabled electronic integration. The results support the vendors-to-partners thesis that IT deployments in supply chains lead to closer buyer-supplier relationships (Bakos and Brynjyolfsson 1993). The results also suggest the complementarity of the transaction-cost and resource-based views, elaborating the logic by which specialized assets can also be strategic assets.


title: Dynamic modeling of the universal motor used in washer

abstract: In this paper, firstly, the structural features and operating conditions of a universal motor are presented. Alternating current (AC) and direct current (DC) operating conditions of the motor and the difference between them are explained. Universal motors give similar speed and torque characteristics on both AC and DC operations when the effective current is kept equal. When AC and DC operations are compared, the differences arise because of, transformer voltage, armature reaction, reactance voltage, saturation effects and commutation. The motor is modeled dynamically by using MATLAB/Simulink and these differences are analyzed and compared with the experimental.


title: Pyramid-based texture analysis/synthesis

abstract: This paper describes a method for synthesizing images that match the texture appearance of a given digitized sample. This synthesis is completely automatic and requires only the "target" texture as input. It allows generation of as much texture as desired so that any object can be covered. The approach is based on a model of human texture perception, and has potential to be a practically useful tool for image processing and graphics applications.


title: Averageness or symmetry: Which is more important for facial attractiveness?

abstract: Abstract Effects of averageness and symmetry on the judgment of facial attractiveness were investigated using a generalized Procrustes method and multiple regression analyses. Participants ( n = 114) rated attractiveness of 96 photographs of faces with neutral expressions. Through a generalized Procrustes method, the faces and their mirror-reversed versions were represented as points on a hyperplane. Both averageness and symmetry of each individual were defined as distances on the plane. A multiple regression analysis was performed to examine the effect of symmetry and averageness for each gender. For male faces, both symmetry and averageness affected attractiveness ratings positively , and there was no difference between the effects of averageness and symmetry. On the other hand, for female faces only averageness affected attractiveness, whereas symmetry did not. However, these effects were not large.


title: Continuous data cleaning

abstract: In declarative data cleaning, data semantics are encoded as constraints and errors arise when the data violates the constraints. Various forms of statistical and logical inference can be used to reason about and repair inconsistencies (errors) in data. Recently, unified approaches that repair both errors in data and errors in semantics (the constraints) have been proposed. However, both data-only approaches and unified approaches are by and large static in that they apply cleaning to a single snapshot of the data and constraints. We introduce a continuous data cleaning framework that can be applied to dynamic data and constraint environments. Our approach permits both the data and its semantics to evolve and suggests repairs based on the accumulated evidence to date. Importantly, our approach uses not only the data and constraints as evidence, but also considers the past repairs chosen and applied by a user (user repair preferences). We introduce a repair classifier that predicts the type of repair needed to resolve an inconsistency, and that learns from past user repair preferences to recommend more accurate repairs in the future. Our evaluation shows that our techniques achieve high prediction accuracy and generate high quality repairs. Of independent interest, our work makes use of a set of data statistics that are shown to be sensitive to predicting particular repair types.


Action Space Defined by Natural Language


title: P2P Lending Survey: Platforms, Recent Advances and Prospects

abstract: P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.


title: Genetic Process Mining: Alignment-Based Process Model Mutation

abstract: The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that enables the user to guide the discovery process based on preferences with respect to four process model quality dimensions: replay fitness, precision, generalization and simplicity.


title: Combustion Reaction of Pulverized Coal on the Deposit Formation in the Kiln for Iron Ore Pellet Production

abstract: The serious deposits on the refractory bricks are found in the grate kiln in iron ore pellet plants, which significantly influence the pellet production. The effect of the combustion reaction of pulverized coal on the deposit formation in kiln during iron ore pellet production was investigated in this work. Hematite iron ore was used as raw material to be pelletized, and the pulverized coal, in general, was used as the fuel. The chemical compositions and microstructures of the deposit samples were detected through the chemical analysis methods, scanning electron microscopy, and energy-dispersive spectroscopy. Effects of different residual carbon contents in coal ash and roasting temperature on the deposit formation were investigated by simulated experiments. Then, the results indicated that the combustion reaction of coal had a significant influence on the deposit formation, under which condition the hematite grains in the preheated pellet were reduced by surrounding residual carbon particles in unburned ...


title: Linear Algorithms for Online Multitask Classification

abstract: Stainless steel articles such as support pins for color television tube shadow mask assemblies are rendered resistant to the formation of surface nodules promoting tube breakage by processing through a vacuum-firing heat treatment prior to use.


title: Breaking the speed and scalability Barriers for Graph exploration on distributed-memory machines

abstract: In this paper, we describe the challenges involved in designing a family of highly-efficient Breadth-First Search (BFS) algorithms and in optimizing these algorithms on the latest two generations of Blue Gene machines, Blue Gene/P and Blue Gene/Q. With our recent winning Graph 500 submissions in November 2010, June 2011, and November 2011, we have achieved unprecedented scalability results in both space and size. On Blue Gene/P, we have been able to parallelize a scale 38 problem with 238 vertices and 242 edges on 131,072 processing cores. Using only four racks of an experimental configuration of Blue Gene/Q, we have achieved a processing rate of 254 billion edges per second on 65,536 processing cores. This paper describes the algorithmic design and the main classes of optimizations that we have used to achieve these results.


title: A mixed-e ects multinomial logistic regression model

abstract: A mixed-effects multinomial logistic regression model is described for analysis of clustered or longitudinal nominal or ordinal response data. The model is parameterized to allow flexibility in the choice of contrasts used to represent comparisons across the response categories. Estimation is achieved using a maximum marginal likelihood (MML) solution that uses quadrature to numerically integrate over the distribution of random effects. An analysis of a psychiatric data set, in which homeless adults with serious mental illness are repeatedly classified in terms of their living arrangement, is used to illustrate features of the model.


title: Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

abstract: The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub-industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories. We use financial news articles from multiple categories to predict price movements.The multiple kernel learning approach is proposed for integrating information.Articles are assigned to news categories based on the relevance to the target stock.Simultaneous usage of several news categories improves the forecasting performance.Increasing the number of relevant news categories improves the performance.


title: Multi-agent reinforcement learning as a rehearsal for decentralized planning

abstract: Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features. We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsal-based learners, and demonstrate fast, (near) optimal performance on many existing benchmark Dec-POMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR's policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied.


title: The Presentation of Self in Everyday Life

abstract: hen an individual enters the presence of oth ers, they commonly seek to acquire information about him or to bring into play information about him already possessed. They will be interested in his general socio-economic status, his concep tion of self, his attitude toward them, his compe tence, his trustworthiness, etc. Although some of this information seems to be sought almost as an end in itself, there are usually quite practical reasons for acquiring it. Information about the individual helps to define the situation, enabling others to know in advance what he will expect of them and what they may expect of him. Informed in these ways, the others will know how best to act in order to call forth a desired response from him. For those present, many sources of information become accessible and many carriers (or “signvehicles”) become available for conveying this information. If unacquainted with the individual, observers can glean clues from his conduct and appearance which allow them to apply their previ ous experience with individuals roughly similar to the one before them or, more important, to apply untested stereotypes to him. They can also assume from past experience that only individuals of a par ticular kind are likely to be found in a given social setting. They can rely on what the individual says about himself or on documentary evidence he provides as to who and what he is. If they know, or know of, the individual by virtue of experience prior to the interaction, they can rely on assumptions as to the persistence and generality of psychological traits as a means of predicting his present and future behavior. However, during the period in which the indi vidual is in the immediate presence of the others, few events may occur which directly provide the others with the conclusive information they will need if they are to direct wisely their own activity . Many crucial facts lie beyond the time and place of interaction or lie concealed within it. For example, the “true” or “real” attitudes, beliefs, and emotions of the individual can be ascertained only indirectly , through his avowals or through what appears to be involuntary expressive behavior. Similarly , if the individual offers the others a product or service, they will often find that during the interaction there will be no time and place immediately available for eating the pudding that the proof can be found in. They will be forced to accept some events as con ventional or natural signs of something not directly available to the senses. In Ichheiser ’s terms, 1 the individual will have to act so that he intentionally or unintentionally expresses himself, and the others will in turn have to be impressed in some way by him.…


title: Continuous Global Optimization in Multiview 3D Reconstruction

abstract: In this article, we introduce a new global optimization method to the field of multiview 3D reconstruction. While global minimization has been proposed in a discrete formulation in form of the maxflow-mincut framework, we suggest the use of a continuous convex relaxation scheme. Specifically, we propose to cast the problem of 3D shape reconstruction as one of minimizing a spatially continuous convex functional. In qualitative and quantitative evaluation we demonstrate several advantages of the proposed continuous formulation over the discrete graph cut solution. Firstly, geometric properties such as weighted boundary length and surface area are represented in a numerically consistent manner: The continuous convex relaxation assures that the algorithm does not suffer from metrication errors in the sense that the reconstruction converges to the continuous solution as the spatial resolution is increased. Moreover, memory requirements are reduced, allowing for globally optimal reconstructions at higher resolutions. ::: ::: We study three different energy models for multiview reconstruction, which are based on a common variational template unifying regional volumetric terms and on-surface photoconsistency. The three models use data measurements at increasing levels of sophistication. While the first two approaches are based on a classical silhouette-based volume subdivision, the third one relies on stereo information to define regional costs. Furthermore, this scheme is exploited to compute a precise photoconsistency measure as opposed to the classical estimation. All three models are compared on standard data sets demonstrating their advantages and shortcomings. For the third one, which gives the most accurate results, a more exhaustive qualitative and quantitative evaluation is presented.


title: Ziegler-Nichols Based Proportional-Integral-Derivative Controller for a Line Tracking Robot

abstract: Line tracking robots have been widely implemented in various applications. Among various control strategies, a proportional-integral-derivative (PID) algorithm has been widely proposed to optimize the performance of a line tracking robot. However, the motivation of using a PID controller, instead of a proportional (P) or a proportional-integral (PI) controller, in a line tracking task has seldom been discussed. Particularly, the use of a systematic tuning approach e.g. closed loop Ziegler Nichols rule to optimize the parameters of a PID controller has rarely been investigated. Thus, this paper investigates the performance of P, PI, and PID controllers in a line tracking task, and the ability of Ziegler Nichols rule to optimize the parameters of the P, PI, and PID controllers. First, the ultimate gain value, K u and ultimate period of oscillation, P u were estimated using a proposed approach. Second, the values of K P , K I and K D were estimated using the Ziegler Nichols formulae. The performance of a differential wheeled robot in the line tracking task was evaluated using three different speeds. Results indicate that the Ziegler Nichols rule coupled with the proposed method is able to identify the parameters of the P, PI, and PID controllers systematically in the line tracking task. Findings indicate that the mobile robot coupled with a proportional controller achieved the best performance compared to PI and PID controllers in the line tracking process when the estimated initial parameters were used.


title: Topology preserving hashing for similarity search

abstract: Binary hashing has been widely used for efficient similarity search. Learning efficient codes has become a research focus and it is still a challenge. In many cases, the real-world data often lies on a low-dimensional manifold, which should be taken into account to capture meaningful neighbors with hashing. The importance of a manifold is its topology, which represents the neighborhood relationships between its subregions and the relative proximities between the neighbors of each subregion, e.g. the relative ranking of neighbors of each subregion. Most existing hashing methods try to preserve the neighborhood relationships by mapping similar points to close codes, while ignoring the neighborhood rankings. Moreover, most hashing methods lack in providing a good ranking for query results since they use Hamming distance as the similarity metric, and in practice, there are often a lot of results sharing the same distance to a query. In this paper, we propose a novel hashing method to solve these two issues jointly. The proposed method is referred to as Topology Preserving Hashing (TPH). TPH is distinct from prior works by preserving the neighborhood rankings of data points in Hamming space. The learning stage of TPH is formulated as a generalized eigendecomposition problem with closed form solutions. Experimental comparisons with other state-of-the-art methods on three noted image benchmarks demonstrate the efficacy of the proposed method.


title: Empath: Understanding Topic Signals in Large-Scale Text

abstract: Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.