title: Printed circuit board implementation of small inductors for domestic induction heating applications using a planar litz wire structure

abstract: In this paper, the design and implementation of small inductors in printed circuit board (PCB) for domestic induction heating applications is presented. With this purpose, we have developed both a manufacturing technique and an electromagnetic model of the system based on finite-element method (FEM) simulations. The inductor arrangement consists of a stack of printed circuit boards in which a planar litz wire structure is implemented. The developed PCB litz wire structure minimizes the losses in a similar way to the conventional multi-stranded litz wires; whereas the stack of PCBs allows increasing the power transferred to the pot. Different prototypes of the proposed PCB inductor have been measured at low signal levels. Finally, a PCB inductor has been integrated in an electronic stage to test at high signal levels, i.e. in the similar working conditions to the commercial application.


title: Cyberbullying and Cyber Law A Canadian Perspective

abstract: This paper examines two recent Canadian legislative initiatives intended in whole or in part to combat cyberbullying: the Cyber-Safety Act of Nova Scotia and the Protecting Canadians from Online Crime Act. Both pieces of legislation were responses to the suicide deaths of female teenagers Rehtaeh Parsons in Nova Scotia and Amanda Todd in British Columbia. It is questionable whether either suicide was directly caused by cyberbullying. Todd was primarily a victim of an adult “capper” living in the Netherlands, who persuaded her to expose her breasts via web cam, and then attempted to use the captured images to extort money and more images. Parsons was filmed engaging in what police concluded was consensual sex with several teenage boys at a house party. The cell phone video was posted on the Internet, making Parsons a victim of “sexting.” In 2014, the Supreme Court of Canada fired a shot across the bow of Bill C-30, the immediate predecessor to the Protecting Canadians from Online Crime Act, ruling that a police search (permissible under Bill C-30) in a child pornography case infringed upon constitutional protection against unreasonable search and seizure, and upon the right to privacy in cyberspace. In 2015, the Cyber-Safety Act of Nova Scotia was struck down by the Supreme Court of Nova Scotia because it violated constitutional rights to freedom of expression and to life, liberty and security of person. In a criminal harassment case involving Twitter, the Ontario Court of Justice ruled in 2016 that Twitter is like a public square, where freedom of expression is protected, and where there is no reasonable expectation of privacy. These three court decisions can be expected to limit the extent to which Canadian legislators and law enforcement agencies can go in their efforts to pursue and prosecute cyberbullies and/or cyber criminals.


title: Just-in-time compilation for SQL query processing

abstract: Just-in-time compilation of SQL queries into native code has recently emerged as a viable alternative to interpretation-based query processing. We present the salient results of research in this fresh area, addressing all aspects of the query processing stack. Throughout the discussion we draw analogies to the general code generation techniques used in contemporary compiler technology. At the same time we describe the open research problems of the area.


title: History and State of the Art in Commercial Electric Ship Propulsion, Integrated Power Systems, and Future Trends

abstract: Electric propulsion has emerged as one of the most efficient propulsion arrangements for several vessel types over the last decades. Even though examples can be found in the history at the end of 19th century, and further into the 20th century, the modern use of electric propulsion started in the 1980s along with the development of semiconductor switching devices to be used in high power drives (dc drives and later ac-to-ac drives). This development opened up for full rpm control of propellers and thrusters, and thereby enabling a simplification of the mechanical structure. However, the main reason for using electric propulsion in commercial ship applications is the potential for fuel savings compared to equivalent mechanical alternatives, except for icebreakers where the performance of an electric powered propeller is superior to a combustion engine powered propeller. The fuel saving potential lies within the fact that the applicable vessels have a highly varying operation profile and are seldom run at full power. This favors the power plant principle in which electric power can be produced at any time with optimum running of prime movers, e.g., diesel engines, by turning on and off units depending on the power demand for propulsion and other vessel loads. Icebreakers were among the first vessels to take advantage of this technology later followed by cruise vessel, and the offshore drilling vessels operating with dynamic positioning (DP). The converter technology was rapidly developing and soon the dc drives were replaced with ac drives. In the same period electric propulsion emerged as basic standard for large cruise liners, and DP operated drilling vessels, but also found its way into other segments as shuttle tankers, ferries, and other special vessels. At the same time podded propulsion were introduced, where the electric motor was mounted directly on the propeller shaft in a submerged 360 $^{\circ}$ steerable pod, adding better efficiency, improved maneuvering, and reduced installation space/cost to the benefits of electric propulsion. The future trends are now focusing on further optimization of efficiency by allowing multiple energy sources, independent operation of individual power producers, and energy storage for various applications, such as power back up, peak shaving, or emission free operation (short voyages).


title: Text mining and probabilistic language modeling for online review spam detection

abstract: In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.


title: An interactive augmented reality coloring book

abstract: Creating entertaining and educational books not only requires providing visually stimulating content but also means for students to interact, create, and express themselves.


title: Collaborative ranking

abstract: Typical recommender systems use the root mean squared error (RMSE) between the predicted and actual ratings as the evaluation metric. We argue that RMSE is not an optimal choice for this task, especially when we will only recommend a few (top) items to any user. Instead, we propose using a ranking metric, namely normalized discounted cumulative gain (NDCG), as a better evaluation metric for this task. Borrowing ideas from the learning to rank community for web search, we propose novel models which approximately optimize NDCG for the recommendation task. Our models are essentially variations on matrix factorization models where we also additionally learn the features associated with the users and the items for the ranking task. Experimental results on a number of standard collaborative filtering data sets validate our claims. The results also show the accuracy and efficiency of our models and the benefits of learning features for ranking.


title: A fast quantum mechanical algorithm for database search

abstract: were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ----------------------------------------------


Yet Another Visit to Paxos


title: Validation of a general measure of treatment satisfaction, the Treatment Satisfaction Questionnaire for Medication (TSQM), using a national panel study of chronic disease

abstract: BackgroundThe objective of this study was to develop and psychometrically evaluate a general measure of patients' satisfaction with medication, the Treatment Satisfaction Questionnaire for Medication (TSQM).MethodsThe content and format of 55 initial questions were based on a formal conceptual framework, an extensive literature review, and the input from three patient focus groups. Patient interviews were used to select the most relevant questions for further evaluation (n = 31). The psychometric performance of items and resulting TSQM scales were examined using eight diverse patient groups (arthritis, asthma, major depression, type I diabetes, high cholesterol, hypertension, migraine, and psoriasis) recruited from a national longitudinal panel study of chronic illness (n = 567). Participants were then randomized to complete the test items using one of two alternate scaling methods (Visual Analogue vs. Likert-type).ResultsA factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (Eigenvalues > 1.7) explaining 75.6% of the total variance; namely Side effects (4 items, 28.4%, Cronbach's Alpha = .87), Effectiveness (3 items, 24.1%, Cronbach's Alpha = .85), and Convenience (3 items, 23.1%, Cronbach's Alpha = .87). A second factor analysis of more generally worded items yielded a Global Satisfaction scale (3 items, Eigenvalue = 2.3, 79.1%, Cronbach's Alpha = .85). The final four scales possessed good psychometric properties, with the Likert-type scaling method performing better than the VAS approach. Significant differences were found on the TSQM by the route of medication administration (oral, injectable, topical, inhalable), level of illness severity, and length of time on medication. Regression analyses using the TSQM scales accounted for 40–60% of variation in patients' ratings of their likelihood to persist with their current medication.ConclusionThe TSQM is a psychometrically sound and valid measure of the major dimensions of patients' satisfaction with medication. Preliminary evidence suggests that the TSQM may also be a good predictor of patients' medication adherence across different types of medication and patient populations.


title: Fast Deep Matting for Portrait Animation on Mobile Phone

abstract: Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait image. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.


title: A Novel Ensemble Learning-Based Approach for Click Fraud Detection in Mobile Advertising

abstract: By diverting funds away from legitimate partners (a.k.a publishers), click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. As such, fraud detection algorithms which can identify fraudulent behavior based on user click patterns are extremely valuable. Based on the BuzzCity dataset, we propose a novel approach for click fraud detection which is based on a set of new features derived from existing attributes. The proposed model is evaluated in terms of the resulting precision, recall and the area under the ROC curve. A final ensemble model based on 6 different learning algorithms proved to be stable with respect to all 3 performance indicators. Our final model shows improved results on training, validation and test datasets, thus demonstrating its generalizability to different datasets.


title: Semantic classification of human behaviors in video surveillance systems

abstract: The semantic analysis of the human behavior in video streaming is still an open issue for the computer vision research community, especially when real-time analysis of complex scenes is concerned. The researchers' community has achieved many progresses in this field. A popular class of approaches has been devised to enhance the quality of the semantic analysis by exploiting some background knowledge about scene and/or the human behavior, thus narrowing the huge variety of possible behavioral patterns by focusing on a specific narrow domain. Aim of this paper is to present an innovative method for semantic analysis of human behavior in video surveillance systems. Typically, this kind of systems are composed of a set of fixed cameras each one monitoring a fixed area. In the proposed methodology, the actions performed by the human beings are described by means of symbol strings. For each camera a grammar is defined to classify the strings of symbols describing the various behaviors. This system proposes a generative approach to human behavior description so it does not require a learning stage. Another advantage of this approach consists in the simplicity of the scene and motion descriptions so that the behavior analysis will have limited computational complexity due to the intrinsic nature both of the representations and the related operations used to manipulate them. This methodology has been used to implement a system to classify human behaviors in a scene. The results are discussed in this paper and they seem to be encouraging.


title: Multimodal communication on tumblr: "i have so many feels!"

abstract: We manually analyzed a corpus of Tumblr posts for sentiment, looking at images, text, and their combination. A dataset was constructed of posts with both text and images, as well as a dataset of posts containing only text, along with a codebook for classifying and counting the content in each. This paper reports on the construction of the overall corpus and the codebook, and presents the results of a preliminary analysis that focuses on emotion. Posts containing images expressed more emotion, more intense emotion, and were more positive in valence than posts containing only text. The study contributes a micro-level analysis of multimodal communication in a social media platform, as well as a gold standard corpus that can be used to train learning algorithms to identify sentiment in multimodal Tumblr data.


title: Incorporating Global Visual Features into Attention-Based Neural Machine Translation

abstract: We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. We utilise global image features extracted using a pre-trained convolutional neural network and incorporate them (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate how these different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.


title: The “Metabolic Winter” Hypothesis: A Cause of the Current Epidemics of Obesity and Cardiometabolic Disease

abstract: Abstract The concept of the “Calorie” originated in the 1800s in an environment with limited food availability, primarily as a means to define economic equivalencies in the energy density of food substrates. Soon thereafter, the energy densities of the major macronutrients—fat, protein, and carbohydrates—were defined. However, within a few decades of its inception, the “Calorie” became a commercial tool for industries to promote specific food products, regardless of health benefit. Modern technology has altered our living conditions and has changed our relationship with food from one of survival to palatability. Advances in agriculture, food manufacturing, and processing have ensured that calorie scarcity is less prevalent than calorie excess in the modern world. Yet, many still approach dietary macronutrients in a reductionist manner and assume that isocalorie foodstuffs are isometabolic. Herein, we discuss a novel way to view the major food macronutrients and human diet in this era of excessive caloric ...


title: Backprop KF: Learning Discriminative Deterministic State Estimators

abstract: Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, generative models have limited capacity to handle rich sensory observations, such as camera images, since they must model the entire distribution over sensor readings. Discriminative models do not suffer from this limitation, but are typically more complex to train as latent variable models for state estimation. We present an alternative approach where the parameters of the latent state distribution are directly optimized as a deterministic computation graph, resulting in a simple and effective gradient descent algorithm for training discriminative state estimators. We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks. Our model can be viewed as a type of recurrent neural network, and the connection to probabilistic filtering allows us to design a network architecture that is particularly well suited for state estimation. We evaluate our approach on synthetic tracking task with raw image inputs and on the visual odometry task in the KITTI dataset. The results show significant improvement over both standard generative approaches and regular recurrent neural networks.


Four-quadrant/column classification of tibial plateau fractures


title: Markov Chains-Based Derivation of the Phase Detector Gain in Bang-Bang PLLs

abstract: Due to the presence of a binary phase detector (BPD) in the loop, bang-bang phase-locked loops (BBPLLs) are hard nonlinear systems. Since the BPD is usually also the only nonlinear element in the loop, in practical applications, BBPLLs are commonly analyzed by first linearizing the BPD and then using the traditional mathematical techniques for linear systems. To the author's knowledge, in the literature, the gain of the linearized BPD (Kbpd) is determined neglecting the effect of the BBPLL dynamics on the effective jitter seen by the BPD. In this brief, we develop an approach to the determination of Kbpd which takes into consideration also this effect. The approach is based on modeling the dynamics of a BBPLL as a Markov chain. This approach gives new insights into the behavior of the BBPLL and leads to an expression for the Kbpd, which is more general than the one currently known in literature


title: Multi-Party Secret Key Agreement Over State-Dependent Wireless Broadcast Channels

abstract: We consider a group of $m$ trusted and authenticated nodes that aim to create a shared secret key $K$ over a wireless channel in the presence of an eavesdropper Eve. We assume that there exists a state-dependent wireless broadcast channel from one of the honest nodes to the rest of them including Eve. All of the trusted nodes can also discuss over a cost-free, noiseless and unlimited rate public channel which is also overheard by Eve. For this setup, we develop an information-theoretically secure secret key agreement protocol. We show the optimality of this protocol for “linear deterministic” wireless broadcast channels. This model generalizes the packet erasure model studied in the literature for wireless broadcast channels. Here, the main idea is to convert a deterministic channel into multiple independent erasure channels by using superposition coding. For “state-dependent Gaussian” wireless broadcast channels, by using insights from the deterministic problem, we propose an achievability scheme based on a multi-layer wiretap code. By using the wiretap code, we can mimic the phenomenon of converting the wireless channel into multiple independent erasure channels. Then, finding the best achievable secret key generation rate leads to solving a non-convex power allocation problem over these channels (layers). We show that using a dynamic programming algorithm, one can obtain the best power allocation for this problem. Moreover, we prove the optimality of the proposed achievability scheme for the regime of high-SNR and large-dynamic range over the channel states in the (generalized) degrees of freedom sense.


title: A Survey of Modern Questions and Challenges in Feature Extraction

abstract: The problem of extracting features from given input data is of critical importance for the successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.


title: Opening Doors to Recovery: Recidivism and Recovery Among Persons With Serious Mental Illnesses and Repeated Hospitalizations.

abstract: OBJECTIVE ::: Repeated hospitalizations and arrests or incarcerations diminish the ability of individuals with serious mental illnesses to pursue recovery. Community mental health systems need new models to address recidivism as well as service fragmentation, lack of engagement by local stakeholders, and poor communication between mental health providers and the police. This study examined the initial effects on institutional recidivism and measures of recovery among persons enrolled in Opening Doors to Recovery, an intensive, team-based community support program for persons with mental illness and a history of inpatient psychiatric recidivism. A randomized controlled trial of the model is underway. ::: ::: ::: METHODS ::: The number of hospitalizations, days hospitalized, and arrests (all from state administrative sources) in the year before enrollment and during the first 12 months of enrollment in the program were compared. Longitudinal trajectories of recovery-using three self-report and five clinician-rated measures-were examined. Analyses accounted for baseline symptom severity and intensity of involvement in the program. ::: ::: ::: RESULTS ::: One hundred participants were enrolled, and 72 were included in the analyses. Hospitalizations decreased, from 1.9±1.6 to .6±.9 (p<.001), as did hospital days, from 27.6±36.4 to 14.9±41.3 (p<.001), although number of arrests (which are rare events) did not. Significant linear trends were observed for recovery measures, and trajectories of improvement were apparent across the entire follow-up period. ::: ::: ::: CONCLUSIONS ::: Opening Doors to Recovery holds promise as a new service approach for reducing hospital recidivism and promoting recovery in community mental health systems and is deserving of further controlled testing.


title: The Economic Value of Volatility Timing

abstract: Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional mean-variance analysis to assess the value of volatility timing to short-horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs. Copyright The American Finance Association 2001.


Beneficial Changes on Body Composition and Circulating Adiponectin and hsCRP Levels Following One Year of Exenatide Therapy, Compared with Insulin Glargine, in Metformin-Treated Patients with Type 2 Diabetes


title: A Supervised Machine Learning Approach to Conjunction Disambiguation in Named Entities

abstract: Although the literature contains reports of very high accuracy figures for the recognition of named entities in text, there are still some named entity phenomena that remain problematic for existing text processing systems. One of these is the ambiguity of conjunctions in candidate named entity strings, an all-too-prevalent problem in corporate and legal documents. In this paper, we distinguish four uses of the conjunction in these strings, and explore the use of a supervised machine learning approach to conjunction disambiguation trained on a very limited set of ‘name internal’ features that avoids the need for expensive lexical or semantic resources. We achieve 84% correctly classified examples using k-fold evaluation on a data set of 600 instances. We argue that further improvements are likely to require the use of wider domain knowledge and name external features.


title: Being watched or being special: how I learned to stop worrying and love being monitored, surveilled, and assessed

abstract: This paper explores the relationship between display of feedback (public vs. private) and the basis for evaluation (present vs. absent) of that feedback. Using a controlled, laboratory setting, we employ a fundamentally social, interpersonal context (speed-dating). Two participants (one male and one female) receive real-time performance feedback about either only themselves (private) or about both participants (public). We measure participant perceptions of monitoring, conformity, and self-consciousness about themselves and their dating partner. We also assess perceptions of system invasiveness, system competence, and system support. Results reveal a consistent pattern of significant interaction between feedback display and basis for evaluation conditions. In each of these interactions, public feedback with an added, trivial, basis for evaluation creates significantly lower perception of monitoring, conformity, self-consciousness, and system invasiveness, than the other three conditions. Additionally there is a main effect for basis for evaluation with respect to system competence and supportiveness. In each case, the presence of a basis produces more positive assessments than its absence. The experiment shows that reactions to being monitored and evaluated do not differ strictly along the dimension of public vs. private; basis for evaluation of feedback functions as a mediator and thus co-determines participant attitudinal responses. We discuss the implications of this at several levels, and present a broader cultural explanation in terms of the theory of rationalization. We also discuss the issues around and functionality of linking laboratory settings to larger cultural contexts in this and related fields of inquiry.


title: Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials

abstract: Quadratic differentials naturally define analytic orientation fields on planar surfaces. We propose to model orientation fields of fingerprints by specifying quadratic differentials. Models for all fingerprint classes such as arches, loops and whorls are laid out. These models are parametrised by few, geometrically interpretable parameters which are invariant under Euclidean motions. We demonstrate their ability in adapting to given, observed orientation fields, and we compare them to existing models using the fingerprint images of the NIST Special Database 4. We also illustrate that these model allow for extrapolation into unobserved regions. This goes beyond the scope of earlier models for the orientation field as those are restricted to the observed planar fingerprint region. Within the framework of quadratic differentials we are able to verify analytically Penrose's formula for the singularities on a palm [L. S. Penrose, "Dermatoglyphics"' Scientific American, vol. 221, no.~6, pp. 73--84, 1969]. Potential applications of these models are the use of their parameters as indices of large fingerprint databases, as well as the definition of intrinsic coordinates for single fingerprint images.


title: Lower-Limb Movement Assistance through Wearable Robots: State of the Art and Challenges

abstract: Recent technological advances made necessary the use of robots in various types of applications. Unlike traditional robot-like scenarios dedicated to industrial applications with repetitive tasks, the current focus of attention is on applications that require close human interactions. One of the main fields of such applications concerns assisting and rehabilitating of dependent/elderly persons. In this study, the state-of-the-art of the main research advances in lower-limbs human assistance is presented. This includes a review on research covering mainly lower-limb actuated exoskeletons. Some case studies related to full-limb exoskeletons are presented as well. Lower-limb movement restoration using functional electrical stimulation and treadmill-based rehabilitation devices is also investigated. In addition, the seamless integration of wearable robots in ambient intelligence spaces appears as one of the major challenges for ubiquitous environments and ambient assisted living. Some open issues related to t...


Playing along and Playing for on Twitch: Livestreaming from Tandem Play to Performance


title: Named Entity Recognition for South Asian Languages

abstract: Much work has already been done on building named entity recognition systems. However most of this work has been concentrated on English and other European languages. Hence, building a named entity recognition (NER) system for South Asian Languages (SAL) is still an open problem because they exhibit characteristics different from English. This paper builds a named entity recognizer which also identifies nested name entities for the Hindi language using machine learning algorithm, trained on an annotated corpus. However, the algorithm is designed in such a manner that it can easily be ported to other South Asian Languages provided the necessary NLP tools like POS tagger and chunker are available for that language. I compare results of Hindi data with English data of CONLL shared task of 2003.


title: Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+$$\epsilon $$ Study on ADNI

abstract: In interactive health care systems, Convolutional Neural Networks (CNN) are starting to have their applications, e.g. the classification of structural Magnetic Resonance Imaging (sMRI) scans for Alzheimer’s disease Computer-Aided Diagnosis (CAD). In this paper we focus on the hippocampus morphology which is known to be affected in relation with the progress of the illness. We use a subset of the ADNI (Alzheimer’s Disease Neuroimaging Initiative) database to classify images belonging to Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal control (NC) subjects. As the number of images in such studies is rather limited regarding the needs of CNN, we propose a data augmentation strategy adapted to the specificity of sMRI scans. We also propose a 2-D+\(\epsilon \) approach, where only a very limited amount of consecutive slices are used for training and classification. The tests conducted on only one - saggital - projection show that this approach provides good classification accuracies: AD/NC 82.8% MCI/NC 66% AD/MCI 62.5% that are promising for integration of this 2-D+\(\epsilon \) strategy in more complex multi-projection and multi-modal schemes.


title: The rea-DSL: a domain specific modeling language for business models

abstract: In the discipline of accounting, the resource-event-agent (REA) ontology is a well accepted conceptual accounting framework to analyze the economic phenomena within and across enterprises. Accordingly, it seems to be appropriate to use REA in the requirements elicitation to develop an information architecture of accounting and enterprise information systems. However, REA has received comparatively less attention in the field of business informatics and computer science. Some of the reasons may be that the REA ontology despite of its well grounded core concepts is (1) sometimes vague in the definition of the relationships between these core concepts, (2) misses a precise language to describe the models, and (3) does not come with an easy to understand graphical notation. Accordingly, we have started developing a domain specific modeling language specifically dedicated to REA models and corresponding tool support to overcome these limitations. In this paper we present our REA DSL which supports the basic set of REA concepts.


title: Age, Growth, Maturity, and Seasonal Food Habits of Round Whitefish, Prosopium cylindraceum, in Lake Michigan near Ludington, Michigan

abstract: Abstract Round whitefish were studied in 1972 and 1974 as a portion of a broad ecological study of a near-shore area of eastern Lake Michigan near Ludington, Michigan. Of 690 fish taken by gill nets, Age III fish were predominant in 1972 and Age IV in 1974. Male and female fish were taken in approximately equal numbers. About 50% of Age II and 90% of Age III fish were mature. Comparative growth and length-weight data indicated that the round whitefish were larger at any given age than those from previously reported populations. On a seasonal basis, benthic insect larvae and small mollusks were the organisms most frequently ingested.


title: Substituting Motion Effects with Vibrotactile Effects for 4D Experiences

abstract: In this paper, we present two methods to substitute motion effects using vibrotactile effects in order to improve the 4D experiences of viewers. This work was motivated by the needs of more affordable 4D systems for individual users. Our sensory substitution algorithms convert motion commands to vibrotactile commands to a grid display that uses multiple actuators. While one method is based on the fundamental principle of vestibular feedback, the other method makes use of intuitive visually-based mapping from motion to vibrotactile stimulation. We carried out a user study and could confirm the effectiveness of our substitution methods in improving 4D experiences. To our knowledge, this is the first study that investigated the feasibility of replacing motion effects using much simpler and less expensive vibrotactile effects.


title: Disruption of Cortical Association Networks in Schizophrenia and Psychotic Bipolar Disorder

abstract: Importance Psychotic disorders (including schizophrenia, schizoaffective disorder, and psychotic bipolar disorder) are devastating illnesses characterized by breakdown in the integration of information processing. Recent advances in neuroimaging allow for the estimation of brain networks on the basis of intrinsic functional connectivity, but the specific network abnormalities in psychotic disorders are poorly understood. Objective To compare intrinsic functional connectivity across the cerebral cortex in patients with schizophrenia spectrum disorders or psychotic bipolar disorder and healthy controls. Design, Setting, and Participants We studied 100 patients from an academic psychiatric hospital (28 patients with schizophrenia, 32 patients with schizoaffective disorder, and 40 patients with bipolar disorder with psychosis) and 100 healthy controls matched for age, sex, race, handedness, and scan quality from December 2009 to October 2011. Main Outcomes and Measures Functional connectivity profiles across 122 regions that covered the entire cerebral cortex. Results Relative to the healthy controls, individuals with a psychotic illness had disruption across several brain networks, with preferential reductions in functional connectivity within the frontoparietal control network ( P Conclusions and Relevance Our study results support the view that cortical information processing is disrupted in psychosis and provides new evidence that disruptions within the frontoparietal control network may be a shared feature across both schizophrenia and affective psychosis.


title: Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

abstract: We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relation-specific information into the word embeddings. The learned word embeddings are then used to construct feature vectors for a relation classification model. On a well-established semantic relation classification task, our method significantly outperforms a baseline based on a previously introduced word embedding method, and compares favorably to previous state-of-the-art models that use syntactic information or manually constructed external resources.


title: Octopus: An Edge-fog Mutual Authentication Scheme

abstract: Authentication is an important and challenging issue for the security of Fog computing since, services are offered to massive-scale end users (Fog users or Edge) by front Fog servers (or Fog nodes). In this paper, we propose a secure and efficient mutual authentication scheme for the Edge-Fog-Cloud network architecture, to mutually authenticate Fog users at the Edge of the network, with the Fog servers at the Fog layer. Our scheme requires a user － roaming randomly in the network { to hold only one long-lived master secret key (with long enough bit-length) allowing him to communicate with any of the Fog servers in the network, in a fully authenticated way. The Fog users are able to mutually authenticate with new Fog servers joining the network, without the need to re-register and without any extra overheads. Moreover, the servers in the Fog are required to store only one secret key for each Fog user. On the other hand, the Fog users are totally unrelated to any public-key infrastructure. The scheme requires the Fog user to perform very few hash invocations and symmetric encryptions/decryptions. Therefore, the scheme is suitable to be efficiently implemented on the Fog user's smart card/device.


title: A new rating scale for Alzheimer's disease

abstract: Abstract A new rating instrument, the Alzheimer's Disease Assessment Scale, was designed specifically to evaluate the severity of cognitive and noncognitive behavioral dysfunctions characteristic of persons with Alzheimer's disease. Item descriptions, administration procedures, and scoring are outlined. Twenty-seven subjects with Alzheimer's disease and 28 normal elderly subjects were rated on 40 items. Twenty-one items with significant intraclass correlation coefficients for interrater reliability (range, .650-.989) and significant Spearman rank-order correlation coefficients for test-retest reliability (range, .514-1) constitute the final scale. Subjects with Alzheimer's disease had significantly more cognitive and noncognitive dysfunction than the normal elderly subjects.


title: Melanoma skin cancer detection and classification based on supervised and unsupervised learning

abstract: Image processing is having very important role in medical domain. Melanoma skin cancer is critical and dangerous for human beings. Early detection of Melanoma skin cancer is very much necessary for the patient because this Melanoma skin cancer directly lead to the death of a person. If it is detected at early stage then Melanoma skin cancer is completely curable. In this paper early detection and classification of Melanoma skin cancer is done using different classifiers as Neural Network and Support Vector Machine.


title: Planning-based optimized path for automatic robot navigation

abstract: Automatic navigation is one of the primary features of a fully autonomous robotic system i.e, achieving the goal without human intervention. Laser-based navigation requires map of the environment in order to move around. The map not only imposes memory overhead but also asserts processing load for its access all the time. Moreover Laser sensor is an expensive device to be used. Instead we present a low-cost, simple, and faster approach for robot navigation based on PDDL-based planning. A wheeled mobile humanoid robot having one functioning arm, built for navigation and communication with other differential drive robots. Robot uses 2m/s as translation velocity and π/8 rad/s as rotational velocity. Ultrasonic sensor has been used for obstacle avoidance. In order to automatically navigate a PDDL-based path planner is used that requires a problem with initial and goal states and a set of actions as input. Planner generates a path plan e.g., {turn-left, turn-left, move-forward} which is also shared with other robots. The robot environment is represented as state space where robot's pose (position+orientation) is state, and connection between states are the actions Left, Right, and Forward. Our PDDL-based plan execution time is 30% time less than that of Logic-based planner time. Robot doesn't requre to calculate odometry data. We assume that robot can turn either left or right only 90degree. It can move only to adjacent state block not diagonal block. The planner takes average instantiation time 0.31secs and planning time 0.0177secs.


title: An Architecture for Automatic Scaling of Replicated Services

abstract: Replicated services that allow to scale dynamically can adapt to requests load. Choosing the right number of replicas is fundamental to avoid performance worsening when input spikes occur and to save resources when the load is low. Current mechanisms for automatic scaling are mostly based on fixed thresholds on CPU and memory usage, which are not sufficiently accurate and often entail late countermeasures. We propose Make Your Service Elastic (MYSE), an architecture for automatic scaling of generic replicated services based on queuing models for accurate response time estimation. Requests and service times patterns are analyzed to learn and predict over time their distribution so as to allow for early scaling. A novel heuristic is proposed to avoid the flipping phenomenon. We carried out simulations that show promising results for what concerns the effectiveness of our approach.


title: A Unified Approach for Mining Outliers

abstract: This paper deals with finding outliers (exceptions) in large datasets. The identification of outliers can often lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. One contribution of this paper is to show how our proposed, intuitive notion of outliers can unify or generalize many of the existing notions of outliers provided by discordancy tests for standard statistical distributions. Thus, when mining large datasets containing many attributes, a unified approach can replace many statistical discordancy tests, regardless of any knowledge about the underlying distribution of the attributes. A second contribution of this paper is the development of an algorithm to find all outliers in a dataset. An important advantage of this algorithm is that its time complexity is linear with respect to the number of objects in the dataset. We include preliminary performance results.


title: Discovering expressive process models by clustering log traces

abstract: Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability


title: Validation of the Synkinesis Assessment Questionnaire

abstract: Objective: To validate the Synkinesis Assessment Questionnaire (SAQ), a newly developed patientgraded instrument to evaluate facial synkinesis. Study Design: This was a prospective clinical questionnaire validation study. Methods: A total of 65 patients with facial nerve disorders were recruited from the Facial Nerve Center. In 28 patients (group I), the SAQ, a preliminary 10-item instrument, was administered at the initial visit and repeated within 3 weeks without any medical or surgical intervention. In 37 patients (group II), the SAQ was administered before botulinum toxin therapy for facial synkinesis and again 2 weeks after Botox therapy. In addition, the instrument was administered to 20 healthy subjects without facial nerve dysfunction (group III). The reliability, validity, and responsiveness of the SAQ were assessed. Results: Test-retest reliability for the SAQ was high (Spearman’s rank correlation r 0.876, P < .0001). Internal consistency of the instrument was assessed by calculating the Cronbach value. This analysis supported the deletion of one item from the final SAQ. The Cronbach for the final SAQ was 0.859. Construct validity analysis showed a statistically significant improvement in SAQ scores (P < .001) after successful administration of botulinum toxin for treatment of facial synkinesis. There was a highly statistically significant difference (P < .0001) in SAQ scores between normal subjects (group III) and patients with facial synkinesis (group II). Conclusions: The SAQ is a simple, patient-graded instrument designed to assess facial synkinesis. Instrument analysis suggests that it is a reliable and valid instrument for the assessment of facial synkinesis. The validated SAQ is the first dedicated instrument designed to assess facial synkinesis. Key Words: Facial paralysis, synkinesis, quality of life, instrument validation, botulinum toxin. Laryngoscope, 117:923–926, 2007


title: Factors Influencing the Adoption of E-government Services

abstract: E-government initiatives are in their infancy in many developing countries. The success of these initiatives is dependent on government support as well as citizens’ adoption of e-government services. This research identified the attitudes and perceptions of the citizens of Kuwait, a developing country, towards the adoption of e-government services. Based on previous research exploring the determinants of the adoption of e-government services using an amended version of the UTAUT model, the study reported here investigates the factors that influence the take-up of such services. These factors are related to usefulness, ease of use, reforming bureaucracy, cultural and social influences, technology issues and lack of awareness. Conclusions and implication for decision makers are also considered in this paper.


title: Improving SIMD Parallelism via Dynamic Binary Translation

abstract: Recent trends in SIMD architecture have tended toward longer vector lengths, and more enhanced SIMD features have been introduced in newer vector instruction sets. However, legacy or proprietary applications compiled with short-SIMD ISA cannot benefit from the long-SIMD architecture that supports improved parallelism and enhanced vector primitives, resulting in only a small fraction of potential peak performance. This article presents a dynamic binary translation technique that enables short-SIMD binaries to exploit benefits of new SIMD architectures by rewriting short-SIMD loop code. We propose a general approach that translates loops consisting of short-SIMD instructions to machine-independent IR, conducts SIMD loop transformation/optimization at this IR level, and finally translates to long-SIMD instructions. Two solutions are presented to enforce SIMD load/store alignment, one for the problem caused by the binary translator’s internal translation condition and one general approach using dynamic loop peeling optimization. Benchmark results show that average speedups of 1.51× and 2.48× are achieved for an ARM NEON to x86 AVX2 and x86 AVX-512 loop transformation, respectively.


title: Implicit Argument Prediction with Event Knowledge

abstract: Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.


title: Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps

abstract: An essential goal of quantitative color encoding is the accurate mapping of perceptual dimensions of color to the logical structure of data. Prior research identifies weaknesses of 'rainbow' colormaps and advocates for ramping in luminance, while recent work contributes multi-hue colormaps generated using perceptually-uniform color models. We contribute a comparative analysis of different colormap types, with a focus on comparing single- and multi-hue schemes. We present a suite of experiments in which subjects perform relative distance judgments among color triplets drawn systematically from each of four single-hue and five multi-hue colormaps. We characterize speed and accuracy across each colormap, and identify conditions that degrade performance. We also find that a combination of perceptual color space and color naming measures more accurately predict user performance than either alone, though the overall accuracy is poor. Based on these results, we distill recommendations on how to design more effective color encodings for scalar data.


title: A robust adaptive linear control for a ball handling mechanism

abstract: In this paper, a novel robust adaptive control design for a general class of Single-Input Single-Output (SISO) nonlinear time-varying systems is delineated. To this end, for a ball handling mechanism, a systematic approach is presented to modify solutions of the arm target angle tracking problem in this robot. Initially, a virtual model of simplified ball handling mechanism is sketched in MSC-ADAMS. Afterwards, assuming there is no priori knowledge about the dynamics of the system except its order, a brand new suggested online linearization technique based on Recursive Least Squares (RLS) method is applied inorder to identify the plant and to derive an Adaptive Linear Regression (ALR) model for the system. Concentrating on this pseudo linear form, an extended sliding mode controller, established upon Lyapanuv theory, is designed and developed step by step. Multifarious simulations were performed and results clearly demonstrated the privilege and effectiveness of the proposed approaches.


title: Philanthropy on the Blockchain

abstract: One of the significant innovations that came out of Bitcoin is the blockchain technology. This paper explores how the blockchain can be leveraged in the philanthropic sector, through charitable donation services in fiat currency or Bitcoin via a web-based donor platform. The philanthropic model is then used for a case study providing humanitarian aid for a community living in a challenging geographical environment with limited internet availability. An SMS based mobile payment system is proposed for provisioning the received donations using the existing GSM network, very basic mobile phones and One Time Password (OTP) security tokens. The proposed scheme is finally evaluated for security while discussing the impact it has on charities and donors.


title: Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

abstract: We provide an overview of the recent trends towards digitalization and large scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend towards continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally we discuss recent initiatives towards a personalized medicine, based on advances in molecular medicine, data management, and data analytics.


title: Mimicking human strategies in fighting games using a Data Driven Finite State Machine

abstract: Multiplayer fighting videogames have become an increasingly popular over the last few years, especially with the introduction of online play, making for a more competitive experience. Multiplayer fighting games give players the opportunity to utilize particular strategies and tactics to win, allowing them to use their own signature style. As a player can only play against a particular opponent who is actively participating in the game themselves, they cannot practice combating the opponent's style if the opponent is not participating in the game. This paper presents a novel approach for an avatar to learn and mimic the style of a player. It does this by recording and analyzing the data before splitting it up into two tiers; tactical data and strategic data. The approach uses a Naive Bayes classifier to classify the tactics to particular states, and a Data Driven Finite State Machine to dictate when certain tactics are used. Statistics recorded during an experiment involving the approach are discussed, which indicate that the architecture of the Artificial Intelligence is fit for purpose, but does require refinement. Limitations of the architecture are discussed, including that such an approach may not provide accurate results when more parameters are considered.


title: A new hierarchical clustering algorithm

abstract: The purpose of data clustering algorithm is to form clusters (groups) of data points such that there is high intra-cluster and low inter-cluster similarity. There are different types of clustering methods such as hierarchical, partitioning, grid and density based. Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. A hierarchical clustering method can be thought of as a set of ordinary (flat) clustering methods organized in a tree structure. These methods construct the clusters by recursively partitioning the objects in either a top-down or bottom-up fashion. In this paper we present a new hierarchical clustering algorithm using Euclidean distance. To validate this method we have performed some experiments with low dimensional artificial datasets and high dimensional fMRI dataset. Finally the result of our method is compared to some of existing clustering methods.


Communities in annotated, multilayer, and correlated networks


title: Combined scalable and mutiview extension of High Efficiency Video Coding (HEVC)

abstract: The scalable and multiview extension of High Efficiency Video Coding (HEVC) is being designed to support scalable layers and multiple views at the same time. Applications of the combined scalable and multiview HEVC coding include scalable stereoscopic video (e.g. 1080p stereo to the emerging 4K stereo), mixed resolution multiview coding, etc. However, there has been no prior work on the coding efficiency of the combined scalable and multiview HEVC coding. In this paper, the scalable multiview HEVC codec is developed based on the scalable HEVC reference software and the performances of inter-layer prediction and inter-view prediction are evaluated in the scalable stereo coding application. Two scalable HEVC coding frameworks, namely TextureRL and RefIdx, are compared as well in the same application.


title: High-speed inverter-fed AC drives

abstract: High speed drives offer advantages such as small size, low noise, integrated and thus compact design for many industrial applications due to the fact, that as direct drives they avoid the mechanical gear. Inverter supply provides variable speed, which allows high efficient operation for compressors and pumps. A wide speed and power range from 0.1 kW up to 80 MW is covered with circumference speeds of 100 ... 250 m/s. Recent progress is discussed and details of the design of high speed electric machinery are addressed, such as electromagnetic utilization, loss components, types of motors, bearing aspects and inverter-motor interaction.


title: Context dependent phone models for LSTM RNN acoustic modelling

abstract: Long Short Term Memory Recurrent Neural Networks (LSTM RNNs), combined with hidden Markov models (HMMs), have recently been show to outperform other acoustic models such as Gaussian mixture models (GMMs) and deep neural networks (DNNs) for large scale speech recognition. We argue that using multi-state HMMs with LSTM RNN acoustic models is an unnecessary vestige of GMM-HMM and DNN-HMM modelling since LSTM RNNs are able to predict output distributions through continuous, instead of piece-wise stationary, modelling of the acoustic trajectory. We demonstrate equivalent results for context independent whole-phone or 3-state models and show that minimum-duration modelling can lead to improved results. We go on to show that context dependent whole-phone models can perform as well as context dependent states, given a minimum duration model.


title: MACE: Model-inference-Assisted Concolic Exploration for Protocol and Vulnerability Discovery

abstract: Programstate-space exploration is central to software security, testing, and verification. In this paper, we propose a novel technique for state-space exploration of software that maintains an ongoing interaction with its environment. Our technique uses a combination of symbolic and concrete execution to build an abstract model of the analyzed application, in the form of a finite-state automaton, and uses the model to guide further state-space exploration. Through exploration, MACE further refines the abstract model. Using the abstract model as a scaffold, our technique wields more control over the search process. In particular: (1) shifting search to different parts of the search-space becomes easier, resulting in higher code coverage, and (2) the search is less likely to get stuck in small local state-subspaces (e.g., loops) irrelevant to the application's interaction with the environment. Preliminary experimental results show significant increases in the code coverage and exploration depth. Further, our approach found a number of new deep vulnerabilities.


title: The Rise of Bots: A Survey of Conversational Interfaces, Patterns, and Paradigms

abstract: This work documents the recent rise in popularity of messaging bots: chatterbot-like agents with simple, textual interfaces that allow users to access information, make use of services, or provide entertainment through online messaging platforms. Conversational interfaces have been often studied in their many facets, including natural language processing, artificial intelligence, human-computer interaction, and usability. In this work we analyze the recent trends in chatterbots and provide a survey of major messaging platforms, reviewing their support for bots and their distinguishing features. We then argue for what we call "Botplication", a bot interface paradigm that makes use of context, history, and structured conversation elements for input and output in order to provide a conversational user experience while overcoming the limitations of text-only interfaces.


title: Identification and compensation of torque ripple in high-precision permanent magnet motor drives

abstract: Permanent magnet synchronous machines generate parasitic torque pulsations owing to distortion of the stator flux linkage distribution, variable magnetic reluctance at the stator slots, and secondary phenomena. The consequences are speed oscillations which, although small in magnitude, deteriorate the performance of the drive in demanding applications. The parasitic effects are analyzed and modeled using the complex state-variable approach. A fast current control system is employed to produce high-frequency electromagnetic torque components for compensation. A self-commissioning scheme is described which identifies the machine parameters, particularly the torque ripple functions which depend on the angular position of the rotor. Variations of permanent magnet flux density with temperature are compensated by on-line adaptation. The algorithms for adaptation and control are implemented in a standard microcontroller system without additional hardware. The effectiveness of the adaptive torque ripple compensation is demonstrated by experiments.


title: Link mining: a new data mining challenge

abstract: A key challenge for data mining is tackling the problem of mining richly structured datasets, where the objects are linked in some way. Links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, security and law enforcement data, bibliographic citations and epidemiological records.


title: A CMOS VGA With DC Offset Cancellation for Direct-Conversion Receivers

abstract: A CMOS dB-linear variable gain amplifier (VGA) with a novel I/Q tuning loop for dc-offset cancellation is presented. The CMOS dB-linear VGA provides a variable gain of 60 dB while maintaining its 3-dB bandwidth greater than 2.5 MHz. A novel exponential circuit is proposed to obtain the dB-linear gain control characteristics. Nonideal effects on dB linearity are analyzed and the methods for improvement are suggested. A varying-bandwidth LPF is employed to achieve fast settling. The chip is fabricated in a 0.35-mum CMOS technology and the measurement results demonstrate the good dB linearity of the proposed VGA and show that the tuning loop can effectively remove dc offset and suppress I/Q mismatch effects simultaneously.


title: What are features? An ontology-based review of the literature

abstract: Abstract Feature-based product modeling is the leading approach for the integrated representation of engineering product data. On the one side, this approach has stimulated the development of formal models and vocabularies, data standards and computational ontologies. On the other side, the current ways to model features are considered problematic since it lacks a principled and uniform methodology for feature representation. This paper reviews the state of art of feature-based modeling approaches by concentrating on how features are conceptualized. It points out the drawbacks of current approaches and proposes a high-level ontology-based perspective to harmonize the definition of feature.


title: Feature Selection for Stock Market Analysis

abstract: The analysis of the financial market always draws a lot of attention from investors and researchers. The trend of stock market is very complex and is influenced by various factors. Therefore to find out the most significant factors to the stock market is very important. Feature Selection is such an algorithm that can remove the redundant and irrelevant factors, and figure out the most significant subset of factors to build the analysis model. This paper analyzes a series of technical indicators used in conventional studies of the stock market and uses various feature selection algorithms, such as principal component analysis, genetic algorithms, and sequential forward search, to find out the most important indicators.


title: Automating Network Application Dependency Discovery: Experiences, Limitations, and New Solutions

abstract: Large enterprise networks consist of thousands of services and applications. The performance and reliability of any particular application may depend on multiple services, spanning many hosts and network components. While the knowledge of such dependencies is invaluable for ensuring the stability and efficiency of these applications, thus far the only proven way to discover these complex dependencies is by exploiting human expert knowledge, which does not scale with the number of applications in large enterprises. ::: ::: Recently, researchers have proposed automated discovery of dependencies from network traffic [8, 18]. In this paper, we present a comprehensive study of the performance and limitations of this class of dependency discovery techniques (including our own prior work), by comparing with the ground truth of five dominant Microsoft applications. We introduce a new system, Orion, that discovers dependencies using packet headers and timing information in network traffic based on a novel insight of delay spike based analysis. Orion improves the state of the art significantly, but some shortcomings still remain. To take the next step forward, Orion incorporates external tests to reduce errors to a manageable level. Our results show Orion provides a solid foundation for combining automated discovery with simple testing to obtain accurate and validated dependencies.


title: Hemodynamic response of children with attention-deficit and hyperactive disorder (ADHD) to emotional facial expressions

abstract: Children with attention-deficit/hyperactivity disorder (ADHD) have difficulty recognizing facial expressions. They identify angry expressions less accurately than typically developing (TD) children, yet little is known about their atypical neural basis for the recognition of facial expressions. Here, we used near-infrared spectroscopy (NIRS) to examine the distinctive cerebral hemodynamics of ADHD and TD children while they viewed happy and angry expressions. We measured the hemodynamic responses of 13 ADHD boys and 13 TD boys to happy and angry expressions at their bilateral temporal areas, which are sensitive to face processing. The ADHD children showed an increased concentration of oxy-Hb for happy faces but not for angry faces, while TD children showed increased oxy-Hb for both faces. Moreover, the individual peak latency of hemodynamic response in the right temporal area showed significantly greater variance in the ADHD group than in the TD group. Such atypical brain activity observed in ADHD boys may relate to their preserved ability to recognize a happy expression and their difficulty recognizing an angry expression. We firstly demonstrated that NIRS can be used to detect atypical hemodynamic response to facial expressions in ADHD children.


title: Signature schemes based on the strong RSA assumption

abstract: We describe and analyze a new digital signature scheme. The new scheme is quite efficient, does not require the signer to maintain any state, and can be proven secure against adaptive chosen message attack under a reasonable intractability assumption, the so-called strong RSA assumption. Moreover, a hash function can be incorporated into the scheme in such a way that it is also secure in the random oracle model under the standard RSA assumption.


title: Detecting Moving Shadows: Algorithms and Evaluation

abstract: Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated "ground-truth" data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.


title: Geographic Dissection of the Twitter Network

abstract: Geography plays an important role in shaping societal interactions in the offline world. However, as more and more social interactions occur online via social networking sites like Twitter and Facebook, users can interact with others unconstrained by their geolocations, raising the question: does offline geography still matter in online social networks? In this paper, we attempt to address this question by dissecting the Twitter social network based on users’ geolocations and investigating how users’ geolocation impacts their participation in Twitter, including their connections to others and the information they exchange with them. Our in-depth analysis reveals that geography continues to have a significant impact on user interactions in the Twitter social network. The influence of geography could be potentially explained by the shared national, linguistic, and cultural backgrounds of users from the same geographic neighborhood.


title: Worth their watts? - an empirical study of datacenter servers

abstract: The management of power consumption in datacenters has become an important problem. This needs a systematic evaluation of the as-is scenario to identify potential areas for improvement and quantify the impact of any strategy. We present a measurement study of a production datacenter from a joint perspective of power and performance at the individual server level. Our observations help correlate power consumption of production servers with their activity, and identify easily implementable improvements. We find that production servers are underutilized from an activity perspective; are overrated from a power perspective; execute temporally similar workloads over a granularity of weeks; do not idle efficiently; and have power consumptions that are well tracked by their CPU utilizations. Our measurements suggest the following steps for improvement: staggering periodic activities on servers; enabling deeper sleep states; and provisioning based on measurement.


title: A Sequential Approach to 3D Human Pose Estimation: Separation of Localization and Identification of Body Joints

abstract: In this paper, we propose a new approach to 3D human pose estimation from a single depth image. Conventionally, 3D human pose estimation is formulated as a detection problem of the desired list of body joints. Most of the previous methods attempted to simultaneously localize and identify body joints, with the expectation that the accomplishment of one task would facilitate the accomplishment of the other. However, we believe that identification hampers localization; therefore, the two tasks should be solved separately for enhanced pose estimation performance. We propose a two-stage framework that initially estimates all the locations of joints and subsequently identifies the estimated joints for a specific pose. The locations of joints are estimated by regressing K closest joints from every pixel with the use of a random tree. The identification of joints are realized by transferring labels from a retrieved nearest exemplar model. Once the 3D configuration of all the joints is derived, identification becomes much easier than when it is done simultaneously with localization, exploiting the reduced solution space. Our proposed method achieves significant performance gain on pose estimation accuracy, thereby improving both localization and identification. Experimental results show that the proposed method exhibits an accuracy significantly higher than those of previous approaches that simultaneously localize and identify the body parts.


title: Real position of law in well regulated sport market.

abstract: Sport is today undoubtedly become the industry with a huge turnover of capital accumulation and other wellknown economic categories, but both brought with him, and inherited part of the socially existing relations, including the unacceptable decisions and unregulated activities. Since all this is happening in the market, it is obvious that it can easily happen to structure, like the Cuckoo egg, substructure with partial interest, ie. By pulling out part of improperly formatted using a smaller number of users. In this case obviously has systematically (in silence) steal a well established structure, and steal resources, but also a result. This work shows that the proper establishment of legal settings and procedures becomes possible to establish regulated sports market.


title: A broadband Orthomode Transducer for the new ALMA band 2+3 (67–116 GHz)

abstract: In this paper we present the design, construction and characterization of a broadband Orthomode Transducer (OMT) for the new Band 2+3 of the Atacama Large Millimeter/Submillimeter Array (ALMA). The term Band 2+3 (67–116 GHz) refers to the recent proposal of covering current Bands 2 and 3 with a single dual-polarization broadband receiver. One of the key components of the future receiver is the polarizer, also known as orthomode transducer (OMT). For this work we have selected the turnstile architecture since it presents low cross polarization leakage in a large wideband. We have obtained an OMT with reflection losses below −15 dB, an isolation between output ports below −26 dB, and a cross polar transmission better than −20 dB.


title: Identifying important places in people’s lives from cellular network data

abstract: People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these "important places" is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.


title: On the Temporal Dynamics of Opinion Spamming: Case Studies on Yelp

abstract: Recently, the problem of opinion spam has been widespread and has attracted a lot of research attention. While the problem has been approached on a variety of dimensions, the temporal dynamics in which opinion spamming operates is unclear. Are there specific spamming policies that spammers employ? What kind of changes happen with respect to the dynamics to the truthful ratings on entities. How do buffered spamming operate for entities that need spamming to retain threshold popularity and reduced spamming for entities making better success? We analyze these questions in the light of time-series analysis on Yelp. Our analyses discover various temporal patterns and their relationships with the rate at which fake reviews are posted. Building on our analyses, we employ vector autoregression to predict the rate of deception across different spamming policies. Next, we explore the effect of filtered reviews on (long-term and imminent) future rating and popularity prediction of entities. Our results discover novel temporal dynamics of spamming which are intuitive, arguable and also render confidence on Yelp's filtering. Lastly, we leverage our discovered temporal patterns in deception detection. Experimental results on large-scale reviews show the effectiveness of our approach that significantly improves the existing approaches.


title: Graph Based Clustering for Two-Tier Architecture in Internet of Things

abstract: The realization of IoT in real world without sensing devices is impossible. These sensing devices are battery operated and energy constrained in terms of computation and communication. The optimized communication leads to longer network life time. Minimum hop count, increased connectivity and scalability are challenging issues that can be effectively addressed by clustering mechanism. In the two tier architecture of IoT, namely IoT layer and sensing layer, to have minimum hop communication and connectivity among the devices, we utilize two well known graph theory concepts namely dominating set and bipartite graph. The device communication is modeled using bipartite graph theory for network coverage and connectivity. Dominating set is used for clustering the devices. Test cases have been considered using the SocNetV simulator and evaluated on IoT platform.


title: STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection

abstract: Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a tilebased spatio-temporally exclusive topic modeling approach called STExNMF, based on a novel nonnegative matrix factorization (NMF) technique. STExNMF mainly works based on the two following stages: (1) first running a standard NMF of each tile to obtain general topics of the tile and (2) running a spatiotemporally exclusive NMF on a weighted residual matrix. These topics likely reveal information on newly emerging events or topics of interest within a region. We demonstrate the advantages of our approach using the geo-tagged Twitter data of New York City. We also provide quantitative comparisons in terms of the topic quality, spatio-temporal exclusiveness, topic variation, and qualitative evaluations of our method using several usage scenarios. In addition, we present a fast topic modeling technique of our model by leveraging parallel computing.


title: MATIC: Adaptation and In-situ Canaries for Energy-Efficient Neural Network Acceleration

abstract: - The primary author has withdrawn this paper due to conflict of interest - We present MATIC (Memory-Adaptive Training and In-situ Canaries), a voltage scaling methodology that addresses the SRAM efficiency bottleneck in DNN accelerators. To overscale DNN weight SRAMs, MATIC combines specific characteristics of destructive SRAM reads with the error resilience of neural networks in a memory-adaptive training process. PVT-related voltage margins are eliminated using bit-cells from synaptic weights as in-situ canaries to track runtime environmental variation. Demonstrated on a low-power DNN accelerator fabricated in 65nm CMOS, MATIC enables up to 3.3x total energy reduction, or 18.6x application error reduction.


title: Mad Max: Affine Spline Insights into Deep Learning

abstract: We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. For instance, conditioned on the input signal, the output of a MASO DN can be written as a simple affine transformation of the input. This implies that a DN constructs a set of signal-dependent, class-specific templates against which the signal is compared via a simple inner product; we explore the links to the classical theory of optimal classification via matched filters and the effects of data memorization. Going further, we propose a simple penalty term that can be added to the cost function of any DN learning algorithm to force the templates to be orthogonal with each other; this leads to significantly improved classification performance and reduced overfitting with no change to the DN architecture. The spline partition of the input signal space that is implicitly induced by a MASO directly links DNs to the theory of vector quantization (VQ) and $K$-means clustering, which opens up new geometric avenue to study how DNs organize signals in a hierarchical fashion. To validate the utility of the VQ interpretation, we develop and validate a new distance metric for signals and images that quantifies the difference between their VQ encodings. (This paper is a significantly expanded version of A Spline Theory of Deep Learning from ICML 2018.)


title: Monocular-vision based SLAM using Line Segments

abstract: This paper presents a method to incorporate 3D line segments in vision based SLAM. A landmark initialization method that relies on the Plucker coordinates to represent a 3D line is introduced: a Gaussian sum approximates the feature initial state and is updated as new observations are gathered by the camera. Once initialized, the landmarks state is estimated along an EKF-based SLAM approach: constraints associated with the Plucker representation are considered during the update step of the Kalman filter. The whole SLAM algorithm is validated in simulation runs and results obtained with real data are presented.


title: Smart parking pricing: A machine learning approach

abstract: Crowded streets are a major problem in large cities. A large part of the problem stems from drivers seeking on-street parking. Cities such as San Francisco, Los Angeles and Seattle have tackled this problem with smart parking systems that aim to maintain the on-street parking occupancy rates around a target level, thus ensuring that empty spots are spread across the city rather than clustered in a single area. In this study, we use the San Francisco's SFpark system as a case study. Specifically, in each given parking area, the SFpark uses occupancy rate data from the previous month to adjust the price in the current month. Instead, we propose a machine learning approach that predicts the occupancy rate of a parking area based on past occupancy rates and prices from an entire neighborhood (which covers many parking areas). We further formulate an optimization problem for the prices in each parking area that minimize the root mean squared error (RMSE) between the predicted occupancy rates of all areas in the neighborhood and the target occupancy rates. This approach is novel in that 1) it responds to a predicted level of occupancy rate rather than past data and 2) it find prices that optimize the total occupancy rate of all neighborhoods, taking under account that prices in one area can impact the demand in adjacent areas. We conduct a numerical study, using data collected from the SFpark study, that shows that the prices obtained from our optimization lead to occupancy rates that are very close to the desired target level.


title: Feature Construction for Inverse Reinforcement Learning

abstract: The goal of inverse reinforcement learning is to find a reward function for a Markov decision process, given example traces from its optimal policy. Current IRL techniques generally rely on user-supplied features that form a concise basis for the reward. We present an algorithm that instead constructs reward features from a large collection of component features, by building logical conjunctions of those component features that are relevant to the example policy. Given example traces, the algorithm returns a reward function as well as the constructed features. The reward function can be used to recover a full, deterministic, stationary policy, and the features can be used to transplant the reward function into any novel environment on which the component features are well defined.


title: Don't be a phish: steps in user education

abstract: Phishing, e-mails sent out by hackers to lure unsuspecting victims into giving up confidential information, has been the cause of countless security breaches and has experienced in the last year an increase in frequency and diversity. While regular phishing attacks are easily thwarted, designing the attack to include user context information could potentially increase the user's vulnerability. To prevent this, phishing education needs to be considered. In this paper we provide an overview of phishing education, focusing on context aware attacks and introduce a new strategy for educating users by combining phishing IQ tests and class discussions. The technique encompasses displaying both legitimate and fraudulent e-mails to users and having them identify the phishing attempts from the authentic e-mails. Proper implementation of this system helps teach users what to look for in e-mails, and how to protect their confidential information from being caught in the nets of phishers. The strategy was applied in Introduction to Computing courses as part of the computer security component. Class assessment indicates an increased level of awareness and better recognition of attacks.


title: Ship-detection in SAR imagery using Low Pulse Repetition Frequency Radar

abstract: The paper introduces a novel approach for ship-detection and ambiguity removal in images acquired from Synthetic Aperture Radar (SAR) operating in Low Pulse Repetition Frequency (LPRF) mode. The procedure consists of four steps. In the detection step, the bright clusters of pixels representative of the ships are isolated using a Constant False Alarm Rate (CFAR) algorithm; in the maximum selection step a pixel maximum in amplitude is chosen for each cluster; in the third step the isolated pixels are interpolated and, finally, the stationary point of the curve, corresponding to the actual position of the target, is computed. The algorithm is tested on an airborne S-band SAR image of the Solent in the United Kingdom.


title: Static detection of cross-site scripting vulnerabilities

abstract: Web applications support many of our daily activities, but they often have security problems, and their accessibility makes them easy to exploit. In cross-site scripting (XSS), an attacker exploits the trust a web client (browser) has for a trusted server and executes injected script on the browser with the server's privileges. In 2006, XSS constituted the largest class of newly reported vulnerabilities making it the most prevalent class of attacks today. Web applications have XSS vulnerabilities because the validation they perform on untrusted input does not suffice to prevent that input from invoking a browser's JavaScript interpreter, and this validation is particularly difficult to get right if it must admit some HTML mark-up. Most existing approaches to finding XSS vulnerabilities are taint-based and assume input validation functions to be adequate, so they either miss real vulnerabilities or report many false positives. This paper presents a static analysis for finding XSS vulnerabilities that directly addresses weak or absent input validation. Our approach combines work on tainted information flow with string analysis. Proper input validation is difficult largely because of the many ways to invoke the JavaScript interpreter; we face the same obstacle checking for vulnerabilities statically, and we address it by formalizing a policy based on the W3C recommendation, the Firefox source code, and online tutorials about closed-source browsers. We provide effective checking algorithms based on our policy. We implement our approach and provide an extensive evaluation that finds both known and unknown vulnerabilities in real-world web applications.


title: Alternating decision trees for early diagnosis of heart disease

abstract: Recent survey shows that heart disease is a leading cause of death in India and in world wide. Significant life savings can be achieved, if a timely and cost effective clinical decision system is developed. Adverse reactions occur if a disease is not diagnosed properly. A clinical decision support system can assist health care professionals for early diagnosis of heart disease from patient's medical data. Machine learning and modern data mining methods are useful for predicting and classifying heart disease. In this paper we wish to develop effective alternating decision tree approach for early diagnosis of heart disease. Alternating decision tree is a new type of classification rule. It is a generalization of decision trees, voted decision stumps and voted decision trees. We have applied our approach on heart disease patient records collected from various hospitals in Hyderabad. Optimization of features improves efficiency of earning algorithm. We used PCA to determine essential features of heart disease data. Experimental results show that our decision support system achieves high accuracy and proving its usefulness in the diagnosis of heart disease.


title: Robust procedural model fitting with a new geometric similarity estimator

abstract: Abstract Procedural model fitting (PMF) is a generalization of classical model fitting and has numerous applications for computer vision and computer graphics. The task of PMF is to search a geometric model set for the model that is most similar to a set of data points. We propose a strict and robust similarity estimator for PMF to handle imperfect data. The proposed estimator is based on the error from model to data, while most other estimators are based on the error from data to model. We then use the proposed estimator to guide the cuckoo search algorithm to search for the most similar model. To accelerate the search process, we also propose a coarse-to-fine model dividing strategy to early reject dissimilar models. In this paper, the proposed PMF method is applied to fit building models on laser scanning data. It is also applied to fit character models on eighteen variants of imperfect MNIST data to achieve few-shot pattern recognition. In the 5-shot recognition, our method outperforms the state-of-the-art method on thirteen variants of the imperfect data. In particular, for one of the data corrupted by grid lines, our method obtains a high accuracy of 65%, whereas the state-of-the-art method only obtains an accuracy of 30%.


title: Study of the Navigation Method for a Snake Robot Based on the Kinematics Model with MEMS IMU

abstract: A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.


title: Beyond the Smart Home

abstract: Due to progress in wired and wireless home networking, sensor networks, networked appliances, mechanical and control engineering, and computers, we can build smart homes, and many smart home projects are currently proceeding throughout the world. However, we have to be careful not to repeat the same mistake that was made with home automation technologies that were booming in the 1970s. That is, [total?] automation should not be a goal of smart home technologies. I believe the following points are important in construction of smart homes from users? viewpoints: development of interface technologies between humans and systems for detection of human intensions, feelings, and situations; improvement of system knowledge; and extension of human activity support outside homes to the scopes of communities, towns, and cities.