title: Information technology management: the case of the Port of Singapore Authority

abstract: The Port of Singapore Authority (PSA) used information technology (IT) extensively to create a high-tech port that has become the busiest port in the world. Now corporatised as a commercial port operator, PSA Corporation Ltd employs four key management success factors in managing IT to meet the demands and challenges facing port operators. They are: (1) having a business-driven IT investment; (2) aligning business and IT plans; (3) maintaining a flexible and extensible IT infrastructure; and (4) encouraging IT innovation and creativity. These management success factors are discussed and examples are given to illustrate how they help PSA more effectively leverage IT to streamline operations and sustain its competitive advantage.


title: Functional Federated Learning in Erlang (ffl-erl)

abstract: The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.


title: Sharp curve lane boundaries projective model and detection

abstract: An effective lane boundaries projective model (LBPM) and improved detection method in the images captured with a vehicle-mounted monocular camera in complex environments, especially for sharp circular curve lane, is proposed in this paper. Firstly, a lane boundaries projective model is deduced. This lane model can not only express straight-line lane boundaries, but also describe the actual sharp circular curve lane boundaries very well. Secondly, the lane posterior probability function is derived by employing the lane model, the gradient direction feature, the lane likelihood function, and the lane prior information. And then the lane maximum posteriori probability is found out by using the improved particle swarm optimization algorithm. Further the lane boundaries is positioned, and the lane geometric structure, such as the lane left and right boundaries curve radiuses, can be calculated accurately through the lane model. The experimental results show that the proposed lane boundaries projective model and the improved detection method are more effective and accurate for sharp curve lane detection.


title: Effective persistent homology of digital images

abstract: In this paper, three Computational Topology methods (namely effective homology, persistent homology and discrete vector fields) are mixed together to produce algorithms for homological digital image processing. The algorithms have been implemented as extensions of the Kenzo system and have shown a good performance when applied on some actual images extracted from a public dataset.


SocialMatching++: A Novel Approach for Interlinking User Profiles on Social Networks


title: nDPI: Open-source high-speed deep packet inspection

abstract: Network traffic analysis was traditionally limited to packet header, because the transport protocol and application ports were usually sufficient to identify the application protocol. With the advent of port-independent, peer-to-peer, and encrypted protocols, the task of identifying application protocols became increasingly challenging, thus creating a motivation for creating tools and libraries for network protocol classification. This paper covers the design and implementation of nDPI, an open-source library for protocol classification using both packet header and payload. nDPI was extensively validated in various monitoring projects ranging from Linux kernel protocol classification, to analysis of 10 Gbit traffic, reporting both high protocol detection accuracy and efficiency. Keywords—Passive traffic classification, Deep Packet Inspection, network traffic monitoring


title: VolumeDeform: Real-time Volumetric Non-rigid Reconstruction

abstract: We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method builds up the scene model from scratch during the scanning process, thus it does not require a pre-defined shape template to start with. Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation. Motion tracking is based on a set of extracted sparse color features in combination with a dense depth constraint. This enables accurate tracking and drastically reduces drift inherent to standard model-to-depth alignment. We cast finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints. The problem is tackled in real-time at the camera’s capture rate using a data-parallel flip-flop optimization strategy. Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.


title: SmartAds: bringing contextual ads to mobile apps

abstract: A recent study showed that while US consumers spent 30% more time on mobile apps than on traditional web, advertisers spent 1600% less money on mobile ads. One key reason is that unlike most web ad providers, today's mobile ads are not contextual---they do not take into account the content of the page they are displayed on. Thus, most mobile ads are irrelevant to what the user is interested in. For example, it is not uncommon to see gambling ads being displayed in a Bible app. This irrelevance results in low clickthrough rates, and hence advertisers shy away from the mobile platform. Using data from top 1200 apps in Windows Phone marketplace, and a one-week trace of ad keywords from Microsoft's ad network, we show that content displayed by mobile apps is a potential goldmine of keywords that advertisers are interested in. However, unlike web pages, which can be crawled and indexed offline for contextual advertising, content shown on mobile apps is often either generated dynamically, or is embedded in the apps themselves; and hence cannot be crawled. The only solution is to scrape the content at runtime, extract keywords and fetch contextually relevant ads. The challenge is to do this without excessive overhead and without violating user privacy. In this paper, we describe a system called SmartAds to address this challenge. We have built a prototype of SmartAds for Windows Phone apps. In a large user study with over 5000 ad impressions, we found that SmartAds nearly doubles the relevance score, while consuming minimal additional resources and preserving user privacy.


title: A Study of Information Retrieval Weighting Schemes for Sentiment Analysis

abstract: Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic tf.idf scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge.


title: Seven-switch five-level active neutral-point clamped converter and optimal modulation strategy

abstract: The five-level active-neutral-point-clamped (ANPC) converters are newly hybrid topologies which have the advantages of traditional neutral-point-clamped (NPC) and flying-capacitor (FC) multilevel converters. A novel seven-switch five-level ANPC (7S-5L-ANPC) inverter topology is proposed, which reduces the number of active semiconductor switches over traditional 5L-ANPC inverter topologies. It is capable of operating under any power factor conditions. The special modulation strategy for 7S-5L-ANPC is proposed and simulation shows only reactive current is flowing through the seventh switch. Thus, low current rating switch can be selected for the seventh switch. The proposed 7S-5L-ANPC inverter is very suitable for unity and high power factor applications such as photovoltaic (PV) application. A 1KW single-phase inverter prototype is built to verify the validity and flexibility of the proposed topology and modulation.


title: On the relation between SPARQL1.1 and Answer Set Programming

abstract: Abstract In the context of the emerging Semantic Web and the quest for a common logical framework underpinning its architecture, the relation of rule-based languages such as Answer Set Programming (ASP) and ontology languages such as the Web Ontology Language (OWL) has attracted a lot of attention in the literature over the past years. With its roots in Deductive Databases and Datalog though, ASP shares much more commonality with another Semantic Web standard, namely the Simple Protocol and RDF Query Language (SPARQL). In this paper, we take the recent approval of the SPARQL1.1 standard by the World Wide Web consortium (W3C) as an opportunity to introduce this standard to the Logic Programming community by providing a translation of SPARQL1.1 into ASP. In this translation, we explain and highlight peculiarities of the new W3C standard. Along the way, we survey existing literature on foundations of SPARQL and SPARQL1.1, and also combinations of SPARQL with ontology and rules languages. Thereby, apart from ...


title: A trace-based framework for analyzing and synthesizing educational progressions

abstract: A key challenge in teaching a procedural skill is finding an effective progression of example problems that the learner can solve in order to internalize the procedure. In many learning domains, generation of such problems is typically done by hand and there are few tools to help automate this process. We reduce this effort by borrowing ideas from test input generation in software engineering. We show how we can use execution traces as a framework for abstracting the characteristics of a given procedure and defining a partial ordering that reflects the relative difficulty of two traces. We also show how we can use this framework to analyze the completeness of expert-designed progressions and fill in holes. Furthermore, we demonstrate how our framework can automatically synthesize new problems by generating large sets of problems for elementary and middle school mathematics and synthesizing hundreds of levels for a popular algebra-learning game. We present the results of a user study with this game confirming that our partial ordering can predict user evaluation of procedural difficulty better than baseline methods.


title: ANTIGENIC ANALYSIS OF THE COAT PROTEIN OF ALFALFA MOSAIC VIRUS AND ITS INVOLVEMENT IN APHID TRANSMISSION

abstract: Two strains -425 and Y87/47- of Alfalfa mosaic virus (AMV) were propagated in and purified from Nicotiana tabacum cv. Samsun NN. Thirty-three AMV specific monoclonal antibodies (MAbs) from two fusions were raised against strain 425. These antibodies were of isotypes IgG1 and IgM. MAbs recognised three types of epitope. Group I did not react with the virus particle surface or viral coat protein of two strains in PTA-ELISA, but they reacted with a 30-kDa structural coat protein of AMV by immunoblot analysis only and were able to recognise cryptotopes. Group II reacted with metatopes of both strains in PTA-ELISA. Group III reacted with a 30-kDa structural coat protein of AMV by immunoblot analysis and in PTA-ELISA for the Y87/47 strain only. Immunoblocking experiments in which suspensions of purified AMV and MAb were offered between parafilm membranes for acquisition by Myzus persicae revealed that MAb-2 was effective in blocking (inhibiting) transmission. This result suggests that the epitope which was localised by MAb-2 plays a role in the aphid transmission of AMV.


title: Turkopticon: interrupting worker invisibility in amazon mechanical turk

abstract: As HCI researchers have explored the possibilities of human computation, they have paid less attention to ethics and values of crowdwork. This paper offers an analysis of Amazon Mechanical Turk, a popular human computation system, as a site of technically mediated worker-employer relations. We argue that human computation currently relies on worker invisibility. We then present Turkopticon, an activist system that allows workers to publicize and evaluate their relationships with employers. As a common infrastructure, Turkopticon also enables workers to engage one another in mutual aid. We conclude by discussing the potentials and challenges of sustaining activist technologies that intervene in large, existing socio-technical systems.


title: Extremely fast text feature extraction for classification and indexing

abstract: Most research in speeding up text mining involves algorithmic improvements to induction algorithms, and yet for many large scale applications, such as classifying or indexing large document repositories, the time spent extracting word features from texts can itself greatly exceed the initial training time. This paper describes a fast method for text feature extraction that folds together Unicode conversion, forced lowercasing, word boundary detection, and string hash computation. We show empirically that our integer hash features result in classifiers with equivalent statistical performance to those built using string word features, but require far less computation and less memory.


title: Occurrence of sialic acids in healthy humans and different disorders

abstract: Sialic acid (SA), N-acetylated derivatives of neuraminic acid, play a central role in the biomedical functioning of humans. The normal range of total sialic acid (TSA) level in serum/plasma is 1.58-2.22 mmol L-1, the free form of SA only constituting 0.5-3 mumol L-1 and the lipid-associated (LSA) forms 10-50 mumol L-1. Notably, considerably higher amounts of free SA are found in urine than in serum/plasma (approximately 50% of the total SA). In inherited SA storage diseases such as Salla's disease, SA levels are elevated many times over, and their determination during clinical investigation is well established. Furthermore, a number of reports describe elevated SA levels in various other diseases, tentatively suggesting broader clinical utility for SA markers. Increased SA concentrations have been reported during inflammatory processes, probably resulting from increased levels of richly sialylated acute-phase glycoproteins. A connection between increased SA levels and elevated stroke and cardiovascular mortality risk has also been reported. In addition, SA levels are slightly increased in cancer, positively correlating with the degree of metastasis, as well as in alcohol abuse, diabetes, chronic renal failure and chronic glomerulonephritis. Several different mechanisms are assumed to underlie the elevated SA concentrations in these disorders. The apparent non-specificity of SA to a given disease limits the potential clinical usefulness of SA determination. In addition, some non-pathological factors, such as aging, pregnancy and smoking, may cause changes in SA concentrations. The absolute increases in SA levels are also rather small (save those in inherited SA storage disorders); this further limits the clinical potential of SA as a marker. Tentatively, SA markers might serve as adjuncts, when combined with other markers, in disease screening, disease progression follow-up, and in the monitoring of treatment response. To become clinically useful, however, the existing SA determination assays need to be considerably refined to reduce interferences, to be specific for certain SA forms, and to be more easy to use.


title: Space-Time Aware Behavioral Topic Modeling for Microblog Posts

abstract: Off-the-Shelf (OTS), relational databases can be challenging when deployed in the cloud. Given their architectures, limitations arise regarding performance (because of virtualization), scalability (because of multi-tenancy), and elasticity (because existing engines cannot easily take advantage of the application migration possibilities of virtualized environments). As a result, many database engines tailored to cloud computing differ from conventional engines in functionality, consistency levels, support for queries or transactions, and even interfaces. Efficiently supporting Off-the-Shelf databases in the cloud would allow to port entire application stacks without rewriting them. In this paper we present a system that combines snapshot isolation (SI) replication with virtualization to provide a flexible solution for running unmodified databases in the cloud while taking advantage of the opportunities cloud architectures provide. Unlike replication-only solutions, our system works well both within larger servers and across clusters. Unlike virtualization only solutions, our system provides better performance and more flexibility in the deployment.


title: Estimation of R\'enyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

abstract: We present simple and computationally efficient nonparametric estimators of R\'enyi entropy and mutual information based on an i.i.d. sample drawn from an unknown, absolutely continuous distribution over $\R^d$. The estimators are calculated as the sum of $p$-th powers of the Euclidean lengths of the edges of the `generalized nearest-neighbor' graph of the sample and the empirical copula of the sample respectively. For the first time, we prove the almost sure consistency of these estimators and upper bounds on their rates of convergence, the latter of which under the assumption that the density underlying the sample is Lipschitz continuous. Experiments demonstrate their usefulness in independent subspace analysis.


title: A Response Threshold Sigmoid Function Model for Swarm Robot Collaboration

abstract: We present a multi agent collaboration algorithm to recruit an approximate number of individually simple robots with controllable variance. We propose a sigmoid response threshold function motivated by task allocation in social insects, and describe macro-level models backed by micro-level simulations to predict the resulting team sizes and their variance. These results are further validated through physical experiments using the “Droplet” swarm robotics platform. We show that the slope of the response threshold function can be used to control the variance of group size, allowing agents to trade off deterministic team size with coordination speed, and making the proposed mechanism applicable to a variety of applications.


title: A randomized, wait-list controlled clinical trial: the effect of a mindfulness meditation-based stress reduction program on mood and symptoms of stress in cancer outpatients.

abstract: Objective: The objective of this study was to assess the effects of participation in a mindfulness meditation‐ based stress reduction program on mood disturbance and symptoms of stress in cancer outpatients. Methods: A randomized, wait-list controlled design was used. A convenience sample of eligible cancer patients enrolled after giving informed consent and were randomly assigned to either an immediate treatment condition or a wait-list control condition. Patients completed the Profile of Mood States and the Symptoms of Stress Inventory both before and after the intervention. The intervention consisted of a weekly meditation group lasting 1.5 hours for 7 weeks plus home meditation practice. Results: Ninety patients (mean age, 51 years) completed the study. The group was heterogeneous in type and stage of cancer. Patients’ mean preintervention scores on dependent measures were equivalent between groups. After the intervention, patients in the treatment group had significantly lower scores on Total Mood Disturbance and subscales of Depression, Anxiety, Anger, and Confusion and more Vigor than control subjects. The treatment group also had fewer overall Symptoms of Stress; fewer Cardiopulmonary and Gastrointestinal symptoms; less Emotional Irritability, Depression, and Cognitive Disorganization; and fewer Habitual Patterns of stress. Overall reduction in Total Mood Disturbance was 65%, with a 31% reduction in Symptoms of Stress. Conclusions: This program was effective in decreasing mood disturbance and stress symptoms in both male and female patients with a wide variety of cancer diagnoses, stages of illness, and ages. Key words: meditation, cancer, stress, mood, intervention, mindfulness. ANOVA 5 analysis of variance; MANOVA 5 multiple analysis of variance; POMS 5 Profile of Mood States; SOSI 5 Symptoms of Stress Inventory.


ASPEC: Asian Scientific Paper Excerpt Corpus.


title: Smart-watch life saver: smart-watch interactive-feedback system for improving bystander CPR

abstract: In this work a Smart-Watch application, that is able to monitor the frequency and depth of Cardiopulmonary Resuscitation (CPR) and provide interactive corrective feedback is described. We have evaluated the system with a total of 41 subjects who had undertaken a single episode of CPR training several years previously. This training was part of a First Aid course for lay people, commonly accessed in this population. The evaluation was conducted by measuring participant CPR competence using the "gold standard" of CPR training [10], namely frequency and compression depth. The evaluation demonstrated that the Smart Watch feedback system provided a significant improvement in the participant performance. For example, it doubled the number of people who could maintain bot the parameters in the recommended range for at least 50% of the time.


1 FEEDFORWARD NEURAL NETWORKS : AN INTRODUCTION


title: Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions

abstract: Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/aimagelab/show-control-and-tell.


title: Exploring the chemical safety of fly larvae as a source of protein for animal feed

abstract: There is an urgent need to increase the supply of sustainable protein for use in animal feed and the use of insect protein provides a potential alternative to protein crops and fishmeal. For example, fly larvae are highly compatible with use in animal feed containing much digestible protein with levels of key amino acids that are comparable with those found in high value alternatives such as soybean. However, the safety of protein from insects and subsequently the meat and fish from animals fed on such a diet requires further assessment. Here we present safety data from the larvae of the four fly species that have perhaps the greatest economic relevance in relation to their use as animal feed being: house fly (Musca domestica), blue bottle (Calliphora vomitoria), blow fly (Chrysomya spp.) and black soldier fly (Hermetia illucens). Diverse rearing methods were used to produce larvae fed on a range of waste substrates and in four geographically dispersed locations being; UK, China, Mali and Ghana. Chemical ...


title: Glaucoma Detection Using Dwt Based Energy Features and Ann Classifier

abstract: Glaucoma is a disease in which fluid pressure in the eye increases continously, and damage the optic nerve and leads to vision loss.It is the second leading cause of blindness. For identification of disease in human eyes we are using clinical decision support system which is based on retinal image analysis technique,that used to extract structure,contextual or texture features.Texture features within images which gives accurate and efficicent glaucoma classification.For finding this texture features we use energy distribution over wavelet subband.In this paper we focus on fourteen features which is obtained from daubecies(db3),symlets(sym3),and biorthogonal(bio3.3,bio3.5,and bio3.7).We propose a novel technique to extract this energy signatures using 2- D wavelet transform and passed these signatures to different feature ranking and feature selection strategies.The energy obtained from detailed coefficent are used to classify normal and glaucomatous image with high accuracy. This will be classified using support vector machines, sequential minimal optimization, random forest, naive Bayes and artificial neural network.We observed an accuracy of 94% using the ANN classifier.Performance graph is shown for all classifiers.Finally the defected region founded by segmentation and this will be post processed by morphological processing technique for smoothing operation. Index Terms: Artificial neural network,data mining,feature extraction,glaucoma,imagetexture,wavelet transforms. I. Introduction GLAUCOMA is an eye disease and it is the second leading cause of peripheral blindness nowadays.This leads to neurodegeneration of the optic nerve.It is estiamted that above six million peoples have glaucoma(1),and half of them are unaware that they have this.Approximately 140000 are blind from glaucoma,about 5% of population between 40-50 yers old and 10% over 70 years old ,whcih increases the risk of significant vision loss. Glaucoma is normally associated with increased fluid pressure in the eye.It is divided into two types "open-angle and "closed-angle" glaucoma.Closed -angle glaucoma appears suddenly which is painful and also visual loss occurs quickly.Open-angle is a chronic glaucoma, It progresses at a slower rate and patients may not notice they have lost vision until the disease has increased significantly. In closed angle glaucoma, fluid pressure in the eye increases because of inadequate fluid flow between iris and the cornea. For the detection and pigmentation of glaucoma,effective quantitative imaging alternatives are offered in biomedical imaging.Eye images manually analysing is fairly time consuming,and the accuracy of parameter measurements varies between experts.Some of the prominent existing modalities and their enhancements,includes optical coherence tomography(2) and multifocal electroretinograph (mfERG) (3),are teh most prominent techniques employed for the quantitatively analyze stuctural and functional abnormalities in the eye,which will observe variability and to quantify the progresion of the disease. Automated clinical decision support system(CDSSs) in opthalmology(4),which are designed mainly for the identification of the disease pathology in human eyes.CDSSs are based on the retinal image analysis techniques that are used to extract structural,textural or contextual features from the images and it effectively distinguish between normal and diseased samples.Retinal image analysis technique which is mainly based on computational techniques to make qualitative checking of eye more objectively.Main goal behind this method is to , reduce the variability that may arising between different clinicla trackingsand for the progresion of the structural characteristics of the human eye. In CDSSs features extracted are categorized into two types structural features and texture features.In structural features commonly includes disk area ,disk diameter ,rim area,cup diameter,cup-to-disk ratio, and topological features extracted from the image(5).Proper orthogonal decomposistion technique (POD) which uses structural features to identify glaucomatous progression(6).Pixel level information is given by POD which is used to gauge significant changes across samples which is locatrion or region specific.The measurement of texture features, is roughly defined as spatial variation of pixel intensity.Texture featrures are not bound to any specific location on image.Several feature extraction techniques are there to mine texture features.From the experiments, we can say that texture based techniques have been proven successful.


title: Classification of ADHD patients on the basis of independent ERP components using a machine learning system

abstract: Background ::: In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects.


title: Android Mobile Malware Surveillance Exploitation via Call Logs: Proof of Concept

abstract: The invention of smartphone have made life easier as it is capable of providing important functions used in user's daily life. While different operating system (OS) platform was built for smartphone, Android has become one of the most popular choice. Nonetheless, it is also the most targeted platform for mobile malware attack causing financial loss to the victims. Therefore, in this research, the exploitation on system calls in Android OS platform caused by mobile malware that could lead to financial loss were examined. The experiment was conducted in a controlled lab environment using open source tools by implementing dynamic analysis on 1260 datasets from the Android Malware Genome Project. Based on the experiment conducted, a new system call classification to exploit call logs for mobile attacks has been developed using Covering Algorithm. This new system call classification can be used as a reference for other researcher in the same field to secure against mobile malware attacks by exploiting call logs. In the future, this new system call classification could be used as a basis to develop a new model to detect mobile attacks exploitation via call logs.


title: Citeology: visualizing paper genealogy

abstract: Citeology is an interactive visualization that looks at the relationships between research publications through their use of citations. The sample corpus uses all 3,502 papers published at ACM CHI and UIST between 1982 and 2010, and the 11,699 citations between them. A connection is drawn between each paper and all papers which it referenced from the collection. For an individual paper, the resulting visualization represents a "family tree" of sorts, showing multiple generations of referenced papers which the target paper built upon, and all descendant generations of future papers.


1 The maximum number of colorings of graphs of given order and size


title: Mirrored Coupling Structures for Microwave Signal Splitting and Combining

abstract: This paper presents a method of creating multi-port couplers by mirroring conventional coupler structures, such as 90° hybrids, Saleh power dividers, and Wilkinson couplers. These created structures maintain some very useful features of the original structures, for example, stable phase shifting between ports, equal power dividing, and port isolation. Theoretical analysis method of these structures is discussed for S-parameter derivation. Example structures: a four-way coupler by mirroring 90° hybrid couplers, and an eight-way coupler by mirroring Saleh power-dividing couplers were built and tested according to the method discussed. A good match was found between theoretical analysis and measurements.


title: Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity

abstract: We study the convergence properties of the VR-PCA algorithm introduced by \cite{shamir2015stochastic} for fast computation of leading singular vectors. We prove several new results, including a formal analysis of a block version of the algorithm, and convergence from random initialization. We also make a few observations of independent interest, such as how pre-initializing with just a single exact power iteration can significantly improve the runtime of stochastic methods, and what are the convexity and non-convexity properties of the underlying optimization problem.


title: Hate is in the air! But where? Introducing an algorithm to detect hate speech in digital microenvironments

abstract: With the objective of facilitating and reducing analysis tasks undergone by law enforcement agencies and service providers, and using a sample of digital messages (i.e., tweets) sent via Twitter following the June 2017 London Bridge terror attack (N = 200,880), the present study introduces a new algorithm designed to detect hate speech messages in cyberspace. Unlike traditional designs based on semantic and syntactic approaches, the algorithm hereby implemented feeds solely on metadata, achieving high level of precision. Through the application of the machine learning classification technique Random Forests, our analysis indicates that metadata associated with the interaction and structure of tweets are especially relevant to identify the content they contain. However, metadata of Twitter accounts are less useful in the classification process. Collectively, findings from the current study allow us to demonstrate how digital microenvironment patterns defined by metadata can be used to create a computer algorithm capable of detecting online hate speech. The application of the algorithm and the direction of future research in this area are discussed.


title: Secure code update for embedded devices via proofs of secure erasure

abstract: Remote attestation is the process of verifying internal state of a remote embedded device. It is an important component of many security protocols and applications. Although previously proposed remote attestation techniques assisted by specialized secure hardware are effective, they not yet viable for low-cost embedded devices. One notable alternative is software-based attestation, that is both less costly and more efficient. However, recent results identified weaknesses in some proposed software-based methods, thus showing that security of remote software attestation remains a challenge. ::: ::: Inspired by these developments, this paper explores an approach that relies neither on secure hardware nor on tight timing constraints typical of software-based technqiques. By taking advantage of the bounded memory/storage model of low-cost embedded devices and assuming a small amount of read-only memory (ROM), our approach involves a new primitive - Proofs of Secure Erasure (PoSE-s). We also show that, even though it is effective and provably secure, PoSE-based attestation is not cheap. However, it is particularly well-suited and practical for two other related tasks: secure code update and secure memory/storage erasure. We consider several flavors of PoSE-based protocols and demonstrate their feasibility in the context of existing commodity embedded devices.


title: Face detection and recognition based on skin segmentation and CNN

abstract: Face Detection and Recognition is an active research area in surveillance, biometric security and computer vision community. Several methods have been developed for face segmentation and recognition. Many of these are computationally time taking and expensive. This paper proposes another methodology for the same. For face detection, we use a conjunction of RGB and YCbCr color space bounds to segment skin pixels in the image. After noise removal, facial regions are identified by applying the knowledge of general geometric properties of human faces and a Haar cascade for eyes. The method is tested on CMU database, and is found to give accurate results with high detection rates. In this paper, we try to recognize faces by feeding region proposals for faces into a Convolutional Neural Network for feature extraction and finally into a Softmax Classifier for classification.


title: Transition to drug addiction: a negative reinforcement model based on an allostatic decrease in reward function

abstract: Rationale ::: The transition from initial drug use to drug addiction has been proposed to result from an allostatic decrease in reward function driven by an overactivation of brain antireward processes.


title: Maximizing the Spread of Positive Influence in Online Social Networks

abstract: Online social networks (OSNs) provide a new platform for product promotion and advertisement. Influence maximization problem arisen in viral marketing has received a lot of attentions recently. Most of the existing diffusion models rely on one fundamental assumption that an influenced user necessarily adopts the product and encourages his/her friends to further adopt it. However, an influenced user may be just aware of the product. Due to personal preference, neutral or negative opinion can be generated so that product adoption is uncertain. Maximizing the total number of influenced users is not the uppermost concern, instead, letting more activated users hold positive opinions is of first importance. Motivated by above phenomenon, we proposed a model, called Opinion-based Cascading (OC) model. We formulate an opinion maximization problem on the new model to take individual opinion into consideration as well as capture the change of opinions at the same time. We show that under the OC model, opinion maximization is NP-hard and the objective function is no longer submodular. We further prove that there does not exist any approximation algorithm with finite ratio unless P=NP. We have designed an efficient algorithm to compute the total positive influence based on this new model. Comprehensive experiments on real social networks are conducted, and results show that previous methods overestimate the overall positive influence, while our model is able to distinguish between negative opinions and positive opinions, and estimate the overall influence more accurately.


title: Girls and Women, Sport, and Self-Confidence

abstract: This article explores research examining girls' and women's self-confidence in sport and physical activity. The article begins by addressing how self-confidence has been conceptualized and how researchers have measured it. A theoretical groundwork is presented by comparing three models that link confidence to achievement: Bandura's theory of self-efficacy, Harter's competence motivation theory, and Eccles (Parsons) et al.'s expectancy-value model. Next, research examining variables that have been hypothesized to influence female self-confidence, as well as a discussion of gender differences, is presented. Finally, enhancement strategies and future research directions are offered. It is suggested that researchers examine the impact of specific socializing influences when studying girls' and women's self-confidence and also that they undertake more model testing instead of examining variables in isolation.


title: AppPrint: Automatic Fingerprinting of Mobile Applications in Network Traffic

abstract: Increased adoption of mobile devices introduces a new spin to Internet: mobile apps are becoming a key source of user traffic. Surprisingly, service providers and enterprises are largely unprepared for this change as they increasingly lose understanding of their traffic and fail to persistently identify individual apps. App traffic simply appears no different than any other HTTP data exchange. This raises a number of concerns for security and network management. In this paper, we propose AppPrint, a system that learns fingerprints of mobile apps via comprehensive traffic observations. We show that these fingerprints identify apps even in small traffic samples where app identity cannot be explicitly revealed in any individual traffic flows. This unique AppPrint feature is crucial because explicit app identifiers are extremely scarce, leading to a very limited characterization coverage of the existing approaches. In fact, our experiments on a nation-wide dataset from a major cellular provider show that AppPrint significantly outperforms any existing app identification. Moreover, the proposed system is robust to the lack of key app-identification sources, i.e., the traffic related to ads and analytic services commonly leveraged by the state-of-the-art identification methods.


Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading


title: RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network

abstract: Quantization index modulation (QIM) steganography makes it possible to hide secret information in voice-over IP (VoIP) streams, which could be utilized by unauthorized entities to set up covert channels for malicious purposes. Detecting short QIM steganography samples, as is required by real circumstances, remains an unsolved challenge. In this paper, we propose an effective online steganalysis method to detect QIM steganography. We find four strong codeword correlation patterns in VoIP streams, which will be distorted after embedding with hidden data. To extract those correlation features, we propose the codeword correlation model, which is based on recurrent neural network (RNN). Furthermore, we propose the feature classification model to classify those correlation features into cover speech and stego speech categories. The whole RNN-based steganalysis model (RNN-SM) is trained in a supervised learning framework. Experiments show that on full embedding rate samples, RNN-SM is of high detection accuracy, which remains over 90% even when the sample is as short as 0.1 s, and is significantly higher than other state-of-the-art methods. For the challenging task of conducting steganalysis towards low embedding rate samples, RNN-SM also achieves a high accuracy. The average testing time for each sample is below 0.15% of sample length. These clues show that RNN-SM meets the short sample detection demand and is a state-of-the-art algorithm for online VoIP steganalysis.


title: A split ring resonator (SRR) based metamaterial structure for bandstop filter applications

abstract: In this paper, a bandstop filter has been realized by using split ring resonator (SRR) based metamaterial structure. The geometrical dimensions of the SRRs have been parametrically optimized in such a way that zero transmission (S21 < − 10 dB) occurs over the frequency range 3.24 to 4.78 GHz with fractional bandwidth of 38.4%. The SRRs are printed on both sides of a 1.6 mm thin FR-4 dielectric, making the structure ultra-thin in nature (∼ λ/46 referred to the center frequency of the transmission zero band). The effective permeability of the structure has been retrieved to conclude the mu-resonance at the corresponding transmission zero frequencies. The surface current distributions at different frequencies of transmission minima implicate the different SRRs responsible for bandstop phenomena.


title: Data Storage Management in Cloud Environments: Taxonomy, Survey, and Future Directions

abstract: Storage as a Service (StaaS) is a vital component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Application providers deploy these storage classes across different cloud-based data stores not only to tackle the challenges arising from reliance on a single cloud-based data store but also to obtain higher availability, lower response time, and more cost efficiency. Hence, in this article, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data management cost. Finally, we map various cloud-based data stores projects to our proposed taxonomy to validate the taxonomy and identify areas for future research.


title: Agent-based interaction analysis of consumer behavior

abstract: Our goal is to create a virtual consumer population that can be used for simulating the effects of marketing strategies in a competing market context. That requires having a consumers' behavioral model allowing the representation of observed individual behaviors and the simulation of a large population of consumers. That also requires finding the parameters' values characterizing the virtual population that reproduces real market evolutions. This paper proposes a consumer behavioral model based on a set of behavioral primitives such as imitation, conditioning and innovativeness, which are founded on the new concept of behavioral attitude. It shows that this model provides an interpretation of the main concepts and cognitive features, issued from marketing research and psycho-sociology works on consumption. The paper presents also the CUstomer BEhavior Simulator (CUBES), which has been realized for implementing the customer model and leading multi-agents simulations. It shows how genetic algorithms (GA), in addition to multi-agent systems, are used to fit the characteristics of the virtual consumers' population into a global realistic market behavior.


Generative Knowledge Distillation for General Purpose Function Compression


title: Optimizing Players' Expected Enjoyment in Interactive Stories.

abstract: In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player’s expected enjoyment without removing player agency. Our personalized drama manager models a player’s preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player’s expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players’ enjoyment ratings in an interactive storytelling testbed, compared to drama managers in pre-


title: Digit recognition in noisy environments via a sequential GMM/SVM system

abstract: This paper exploits the fact that when GMM and SVM classifiers with roughly the same level of performance exhibit uncorrelated errors they can be combined to produce a better classifier. The gain accrues from combining the descriptive strength of GMM models with the discriminative power of SVM classifiers. This idea, first exploited in the context of speaker recognition [1, 2], is applied to speech recognition - specifically to a digit recognition task in a noisy environment - with significant gains in performance.


title: A comparative study on quality assessment of high resolution fingerprint images

abstract: High resolution fingerprint images have been increasingly used in fingerprint recognition. They can provide more fine features (e.g. pores) than standard fingerprint images to improve the recognition accuracy. It is however still an open issue whether or not existing quality assessment methods are suitable for high resolution fingerprint images. This paper compares some typical quality indexes by analyzing the correlation between them and their prediction ability on minutia-based and pore-based high resolution fingerprint recognition accuracy. Experimental results show that the indexes based on ridge orientation are more effective for high resolution fingerprint recognition systems.


title: Introduction to the special issue on practice-oriented approaches to sustainable HCI

abstract: Since Blevis's seminal paper [Blevis 2007] in 2007, environmental sustainability has established itself as a mainstream concern for HCI. After an initial flurry of excitement about the potential for existing HCI techniques, such as persuasive computing and interaction design, to contributemeaningfully to increasing sustainability, we have begun to recognize that the complexity and apparent intractability of working towards sustainability is providing serious challenges to current HCI ways of approaching problems. For example, some are working to deepen the engagement between HCI and established approaches of environmental psychology [Froehlich et al. 2010] and behavioral theory [Hekler et al. 2013] in order to develop more rigorous methods and theoretical connections for pursuing sustainability. Others are deploying and evaluating eco-feedback technologies at a scale not yet common in HCI [Erickson et al. 2013] to get a better sense of their effects and potentialities. Thus, sustainability is becoming not simply a problem that HCI can contribute to solving, but also an opportunity to understand the limits of HCI as it is currently constituted and to develop new possibilities for the discipline.


title: Next-Generation Sequence Assembly: Four Stages of Data Processing and Computational Challenges

abstract: Decoding DNA symbols using next-generation sequencers was a major breakthrough in genomic research. Despite the many advantages of next-generation sequencers, e.g., the high-throughput sequencing rate and relatively low cost of sequencing, the assembly of the reads produced by these sequencers still remains a major challenge. In this review, we address the basic framework of next-generation genome sequence assemblers, which comprises four basic stages: preprocessing filtering, a graph construction process, a graph simplification process, and postprocessing filtering. Here we discuss them as a framework of four stages for data analysis and processing and survey variety of techniques, algorithms, and software tools used during each stage. We also discuss the challenges that face current assemblers in the next-generation environment to determine the current state-of-the-art. We recommend a layered architecture approach for constructing a general assembler that can handle the sequences generated by different sequencing platforms.


title: Predicting and Mitigating Jobs Failures in Big Data Clusters

abstract: In large-scale data enters, software and hardware failures are frequent, resulting in failures of job executions that may cause significant resource waste and performance deterioration. To proactively minimize the resource inefficiency due to job failures, it is important to identify them in advance using key job attributes. However, so far, prevailing research on datacenter workload characterization has overlooked job failures, including their patterns, root causes, and impact. In this paper, we aim to develop prediction models and mitigation policies for unsuccessful jobs, so as to reduce the resource waste in big data enters. In particular, we base our analysis on Google cluster traces, consisting of a large number of big-data jobs with a high task fan-out. We first identify the time-varying patterns of failed jobs and the contributing system features. Based on our characterization study, we develop an on-line predictive model for job failures by applying various statistical learning techniques, namely Linear Discriminate Analysis (LDA), Quadratic Discriminate Analysis (QDA), and Logistic Regression (LR). Furthermore, we propose a delay-based mitigation policy which, after a certain grace period, proactively terminates the execution of jobs that are predicted to fail. The particular objective of postponing job terminations is to strike a good tradeoffs between resource waste and false prediction of successful jobs. Our evaluation results show that the proposed method is able to significantly reduce the resource waste by 41.9% on average, and keep false terminations of jobs low, i.e., only 1%.


title: Weakly Connected Oscillators

abstract: In this chapter we study weakly connected networks ::: ::: $$ \dot X_i = F_i \left( {X_i ,\lambda } \right) + \varepsilon G_i \left( {X,\lambda ,\rho ,\varepsilon } \right),{\text{ i = 1,}} \ldots {\text{,n,}} $$ ::: ::: (9.1) ::: ::: of oscillatory neurons. Our basic assumption is that there is a value of λ ∈ Λ such that every equation in the uncoupled system (e = 0) ::: ::: $$ \dot X_i = F_i \left( {X_i ,\lambda } \right),{\text{ X}}_i \in \mathbb{R}^m , $$ ::: ::: (9.2) ::: ::: has a hyperbolic stable limit cycle attractor γ ⊂ ℝ m The activity on the limit cycle can be described in terms of its phase \( \theta \in \mathbb{S}^1 \) of oscillation ::: ::: $$ \dot \theta _i = \Omega _i \left( \lambda \right), $$ ::: ::: where Ωi(λ) is the natural frequency of oscillations. The dynamics of the oscillatory weakly connected system (9.1) can also be described in terms of phase variables: ::: ::: $$ \dot \theta _i = \Omega _i \left( \lambda \right), + \varepsilon g_i \left( {\theta ,\lambda ,\rho ,\varepsilon } \right),{\text{ i = 1,}} \ldots {\text{,n}}{\text{.}} $$


title: On the design of LDA models for aspect-based opinion mining

abstract: Aspect-based opinion mining, which aims to extract aspects and their corresponding ratings from customers reviews, provides very useful information for customers to make purchase decisions. In the past few years several probabilistic graphical models have been proposed to address this problem, most of them based on Latent Dirichlet Allocation (LDA). While these models have a lot in common, there are some characteristics that distinguish them from each other. These fundamental differences correspond to major decisions that have been made in the design of the LDA models. While research papers typically claim that a new model outperforms the existing ones, there is normally no "one-size-fits-all" model. In this paper, we present a set of design guidelines for aspect-based opinion mining by discussing a series of increasingly sophisticated LDA models. We argue that these models represent the essence of the major published methods and allow us to distinguish the impact of various design decisions. We conduct extensive experiments on a very large real life dataset from Epinions.com (500K reviews) and compare the performance of different models in terms of the likelihood of the held-out test set and in terms of the accuracy of aspect identification and rating prediction.


Forecasting European Industrial Production with Multivariate Singular Spectrum Analysis


title: Macro-operators: a weak method for learning

abstract: Abstract This article explores the idea of learning efficient strategies for solving problems by searching for macro-operators . A macro-operator, or macro for short, is simply a sequence of operators chosen from the primitive operators of a problem. The technique is particularly useful for problems with non-serializable subgoals, such as Rubik's Cube, for which other weak methods fail. Both a problem-solving program and a learning program are described in detail. The performance of these programs is analyzed in terms of the number of macros required to solve all problem instances, the length of the resulting solutions (expressed as the number of primitive moves), and the amount of time necessary to learn the macros. In addition, a theory of why the method works, and a characterization of the range of problems for which it is useful are presented. The theory introduces a new type of problem structure called operator decomposability . Finally, it is concluded that the macro technique is a new kind of weak method, a method for learning as opposed to problem solving.


Evaluation of the informatician perspective: determining types of research papers preferred by clinicians


title: Catechol-O-Methyltransferase (COMT): A Gene Contributing to Sex Differences in Brain Function, and to Sexual Dimorphism in the Predisposition to Psychiatric Disorders

abstract: Catechol- O -Methyltransferase (COMT): A Gene Contributing to Sex Differences in Brain Function, and to Sexual Dimorphism in the Predisposition to Psychiatric Disorders


title: Meaningful communities detection in medias network

abstract: Most of the existing algorithms proposed for communities determination are based on the topological features of social networks. They do not take into account information shared between users. In this paper, We introduce a graph mining algorithm to detect social network communities a MeanCD (meaningful community detection). The proposed algorithm encloses a function measuring the strength of links describing the information (topics) shared and friends and more generally between users. This information is called Link-importance. It is based on two main steps. The first one consists in defining the community after cleaning the networks, while the second step is to determine the good partitioning which maximizes the new concept of the quality function called Modularity.


title: Large signal stability analysis of DC microgrid under droop control with constant power load

abstract: The stability of bus voltage is the main target of DC microgrid operation. DC microgrid including constant power load, distributed generations employed droop control, and resistive load is investigated. The constant power load can reduce system stability margin, and even cause bus voltage oscillating. The droop control can share the current among different distributed generations, but it can result in the bus voltage deviating from the nominal stable operating point and even destroys the stable operation. Take into account above factors, the model of DC microgrid is established and its stability analysis is researched by using the method of Lyapunov and the corresponding constraints of stable operation are obtained. The stable region of the system with different parameters is explored. Finally, the MATLAB/Simulink simulation model of the DC microgrid is established and the simulation results verify the effectiveness and correctness of the analysis method.


title: A survey on hardware trojan detection techniques

abstract: Hardware Trojans recently emerged as a serious issue for computer systems, especially for those used in critical applications such as medical or military. Trojan proposed so far can affect the reliability of a device in various ways. Proposed effects range from the leakage of secret information to the complete malfunctioning of the device. A crucial point for securing the overall operation of a device is to guarantee the absence of hardware Trojans. In this paper, we survey several techniques for detecting malicious modification of circuit introduced at different phases of the design flow. We also highlight their capabilities limitations in thwarting hardware Trojans.


title: Toward a Computational Model of Narrative

abstract: Narratives structure our understanding of the world and of ourselves. They exploit the shared cognitive structures of human motivations, goals, actions, events, and outcomes. We report on a computational model that is motivated by results in neural computation and captures fine-grained, context sensitive information about human goals, processes, actions, policies, and outcomes. We describe the use of the model in the context of a pilot system that is able to interpret simple stories and narrative fragments in the domain of international politics and economics. We identify problems with the pilot system and outline extensions required to incorporate several crucial dimensions of narrative structure.


The Effect of Electronic Word of Mouth , Trust and Perceived Value on Behavioral Intention from the Perspective of Consumers


Reputation systems


title: Piezoelectric MEMS energy harvesting systems driven by harmonic and random vibrations

abstract: Switching power conditioning techniques are known to greatly enhance the performance of linear piezoelectric energy harvesters subject to harmonic vibrations. With such circuits, little is known about the effect of mechanical stoppers that limit the motion or about waveforms other than harmonic vibrations. This work presents SPICE simulations of piezoelectric micro energy harvester systems that differ in choice of power conditioning circuits and stopper models. We consider in detail both harmonic and random vibrations. The nonlinear switching conversion circuitry performs better than simple passive circuitry, especially when mechanical stoppers are in effect. Stopper loss is important under broadband vibrations. Stoppers limit the output power for sinusoidal excitations, but result in the same output power whether the stoppers are lossy or not. When the mechanical stoppers are hit by the proof mass during high-amplitude vibrations, nonlinear effects such as saturation and jumps are present.


title: Learning Topical Translation Model for Microblog Hashtag Suggestion

abstract: Hashtags can be viewed as an indication to the context of the tweet or as the core idea expressed in the tweet. They provide valuable information for many applications, such as information retrieval, opinion mining, text classification, and so on. However, only a small number of microblogs are manually tagged. To address this problem, in this work, we propose a topical translation model for microblog hashtag suggestion. We assume that the content and hashtags of the tweet are talking about the same themes but written in different languages. Under the assumption, hashtag suggestion is modeled as a translation process from content to hashtags. Moreover, in order to cover the topic of tweets, the proposed model regards the translation probability to be topic-specific. It uses topic-specific word trigger to bridge the vocabulary gap between the words in tweets and hashtags, and discovers the topics of tweets by a topic model designed for microblogs. Experimental results on the dataset crawled from real world microblogging service demonstrate that the proposed method outperforms state-of-the-art methods.


title: Learning temporal evolution of probability distribution with Recurrent Neural Network

abstract: We propose to tackle a time series regression problem by computing temporal evolution of a probability density function to provide a probabilistic forecast. A Recurrent Neural Network (RNN) based model is employed to learn a nonlinear operator for temporal evolution of a probability density function. We use a softmax layer for a numerical discretization of a smooth probability density functions, which transforms a function approximation problem to a classification task. Explicit and implicit regularization strategies are introduced to impose a smoothness condition on the estimated probability distribution. A Monte Carlo procedure to compute the temporal evolution of the distribution for a multiple-step forecast is presented. The evaluation of the proposed algorithm on three synthetic and two real data sets shows advantage over the compared baselines.


title: Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer?

abstract: This article introduces the IEEE 1451 standard for networked smart transducers. It discusses the concepts of smart transducers, IEEE 1451 smart transducers, the architecture of the IEEE 1451 family of standards, application of IEEE 1451, and example implementations of the IEEE 1451 standards. In conclusion, the IEEE 1451 suite of standards provides a set of standard interfaces for networked smart transducers, helping to achieve sensor plug and play and interoperability for industry and government.


title: Anti-Inflammatory Properties and Chemical Characterization of the Essential Oils of Four Citrus Species

abstract: Citrus fruits have potential health-promoting properties and their essential oils have long been used in several applications. Due to biological effects described to some citrus species in this study our objectives were to analyze and compare the phytochemical composition and evaluate the anti-inflammatory effect of essential oils (EO) obtained from four different Citrus species. Mice were treated with EO obtained from C. limon, C. latifolia, C. aurantifolia or C. limonia (10 to 100 mg/kg, p.o.) and their anti-inflammatory effects were evaluated in chemical induced inflammation (formalin-induced licking response) and carrageenan-induced inflammation in the subcutaneous air pouch model. A possible antinociceptive effect was evaluated in the hot plate model. Phytochemical analyses indicated the presence of geranial, limonene, γ-terpinene and others. EOs from C. limon, C. aurantifolia and C. limonia exhibited anti-inflammatory effects by reducing cell migration, cytokine production and protein extravasation induced by carrageenan. These effects were also obtained with similar amounts of pure limonene. It was also observed that C. aurantifolia induced myelotoxicity in mice. Anti-inflammatory effect of C. limon and C. limonia is probably due to their large quantities of limonene, while the myelotoxicity observed with C. aurantifolia is most likely due to the high concentration of citral. Our results indicate that these EOs from C. limon, C. aurantifolia and C. limonia have a significant anti-inflammatory effect; however, care should be taken with C. aurantifolia.


title: Selection Criteria for Software as a Service: An Explorative Analysis of Provider Requirements

abstract: Currently, customers can choose among many Cloud providers for enterprise systems. The provider selection involves several challenges to match individual customer requirements and provided service characteristics. Unfortunately, this process is not transparent and characterized by the lack of appropriate selection criteria. Research is mainly concentrated on capabilities and success factors on the customer side. A set of Cloud provider requirements from a customer perspective, especially within the context of an adoption of on-demand enterprise systems, have barely been discussed so far. In this paper we present a set of selection criteria for Software as a Service (SaaS). These criteria are developed to enable a Cloud provider comparison and match the customer requirements with the provider characteristics. We followed a design science approach and conducted a systematic literature review, an extensive market analysis of 651 providers and an evaluation based on expert interviews to develop the presented selection criteria.


Optimal Proposal Distributions and Adaptive MCMC


title: Assessment of multi-hop interpersonal trust in social networks by Three-Valued Subjective Logic

abstract: Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSN topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncer- tainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs. Index Terms—Interpersonal trust, trust establishment, online social networks, three-valued subjective logic, Dirichlet distribu- tion


title: Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents

abstract: Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and to demonstrate the performance when using Forex chart images as input to geometrical regularity aware indirectly encoded neural network systems, enabling them to use the patterns & trends within, when trading. This paper presents the benchmark results of NN based automated currency trading systems evolved using TWEANNs, and compares the performance and generalization capabilities of these direct encoded NNs which use the standard sliding-window based price vector inputs, and the indirect (substrate) encoded NNs which use charts as input. The TWEANN algorithm I will use in this paper to evolve these currency trading agents is the memetic algorithm based TWEANN system called Deus Ex Neural Network (DXNN) platform.


title: Word sense and semantic relations in noun compounds

abstract: In this article, we investigate word sense distributions in noun compounds (NCs). Our primary goal is to disambiguate the word sense of component words in NCs, based on investigation of “semantic collocation” between them. We use sense collocation and lexical substitution to build supervised and unsupervised word sense disambiguation (WSD) classifiers, and show our unsupervised learner to be superior to a benchmark WSD system. Further, we develop a word sense-based approach to interpreting the semantic relations in NCs.


title: A game theory based energy efficient clustering routing protocol for WSNs

abstract: The energy constraint is one of the inherent defects of the Wireless Sensor Networks (WSNs). How to prolong the lifespan of the network has attracted more and more attention. Numerous achievements have emerged successively recently. Among these mechanisms designing routing protocols is one of the most promising ones owing to the large amount of energy consumed for data transmission. The background and related works are described firstly in detail in this paper. Then a game model for selecting the Cluster Head is presented. Subsequently, a novel routing protocol named Game theory based Energy Efficient Clustering routing protocol (GEEC) is proposed. GEEC, which belongs to a kind of clustering routing protocols, adopts evolutionary game theory mechanism to achieve energy exhaust equilibrium as well as lifetime extension at the same time. Finally, extensive simulation experiments are conducted. The experimental results indicate that a significant improvement in energy balance as well as in energy conservation compared with other two kinds of well-known clustering routing protocols is achieved.


title: Machine Learning for Digital Document Processing: From Layout Analysis To Metadata Extraction

abstract: In the last years, the spread of computers and the Internet caused a significant amount of documents to be available in digital format. Collecting them in digital repositories raised problems that go beyond simple acquisition issues, and cause the need to organize and classify them in order to improve the effectiveness and efficiency of the retrieval procedure. The success of such a process is tightly related to the ability of understanding the semantics of the document components and content. Since the obvious solution of manually creating and maintaining an updated index is clearly infeasible, due to the huge amount of data under consideration, there is a strong interest in methods that can provide solutions for automatically acquiring such a knowledge. This work presents a framework that intensively exploits intelligent techniques to support different tasks of automatic document processing from acquisition to indexing, from categorization to storing and retrieval. The prototypical version of the system DOMINUS is presented, whose main characteristic is the use of a Machine Learning Server, a suite of different inductive learning methods and systems, among which the more suitable for each specific document processing phase is chosen and applied. The core system is the incremental first-order logic learner INTHELEX. Thanks to incrementality, it can continuously update and refine the learned theories, dynamically extending its knowledge to handle even completely new classes of documents. Since DOMINUS is general and flexible, it can be embedded as a document management engine into many different Digital Library systems. Experiments in a real-world domain scenario, scientific conference management, confirmed the good performance of the proposed prototype.


title: Early Detection of Topical Expertise in Community Question Answering

abstract: We focus on detecting potential topical experts in community question answering platforms early on in their lifecycle. We use a semi-supervised machine learning approach. We extract three types of feature: (i) textual, (ii) behavioral, and (iii) time-aware, which we use to predict whether a user will become an expert in the longterm. We compare our method to a machine learning method based on a state-of-the-art method in expertise retrieval. Results on data from Stack Overflow demonstrate the utility of adding behavioral and time-aware features to the baseline method with a net improvement in accuracy of 26% for very early detection of expertise.


title: Please Share! Online Word of Mouth and Charitable Crowdfunding

abstract: During the last few years, charitable crowdfunding has become an increasingly popular method of online fundraising for personal and charitable causes. Many such crowdfunding platforms encourage and facilitate the use of online word of mouth (WOM) through social networks and social media, to spread the word about the crowdfunding campaigns. While online WOM is commonly used to share information about crowdfunding campaigns, there is hitherto limited understanding as to whether or how this information sharing affect individuals’ contribution behavior or the outcome of crowdfunding campaigns. In this study, using a unique dataset from 590 crowdfunding campaigns observed over 12 days, we examine to what extent, and how quickly online WOM affect the rate of contributions. In addition, we explore the effect of different phases of fundraising (over time or as they approach their target goals) and of the coverage of the campaigns in major online news websites.


title: Design and Implementation of PLC based Elevator

abstract: This paper explains about an elevators operation which uses a AC motor to drive the elevator cabin. The elevator mentioned here is fully automated using PLC. Its control is based on the input that we get from the operator as well as from the sensors. According to that signal PLC will make the drive motor and door motor to work correspondingly.


title: The nanomanipulator: a virtual-reality interface for a scanning tunneling microscope

abstract: We have developed a virtual-reality interface to a scanning tunneling microscope (STM); the resulting system is called the Nanomanipulator. The user interface comprises a stereoscopic color head-mounted display, a force-feedback remote manipulator master station, and a high-performance graphics computer. It provides the illusion of a surface floating in space in front of the user. The user's hand gestures are translated into commands that are sent to the STM in real time; the returned video and haptic signals allow the user to see and feel the surface topography and to control the timing and location of voltage pulses applied between the tip of the STM probe and the sample under study. ::: My thesis is that a virtual-reality interface is a powerful and effective user interface to an STM--allowing qualitatively different types of experiments to be performed. The success of our investigations using this system demonstrates the validity of the thesis. ::: We have used the Nanomanipulator to examine various surfaces and to perform surface modification experiments. This investigation has led to new insight into the meaning of certain surface features and into the mechanisms by which voltage pulses change the tip and sample. These insights were the direct results of the real-time visualization and the more interactive nature of our system compared to standard methods. ::: The key to the success of the Nanomanipulator system is that it provides an intuitive two-way interface to the instrument. Raw data from an STM is not in a format easily understood by a scientist, and the Etch-a-Sketch type of controls required for positioning an STM tip are neither natural nor familiar to a user. The Nanomanipulator system acts as a translator between the instrument and the scientist, allowing the scientist to concentrate on interacting with the surface under study rather than on the computer interface or the STM itself. This system seeks to put the scientists on the surface, in control, while the experiment is happening--thus turning the STM from a remote, batch surface modifier into a real-time, user-guided surface modifier.


title: Evaluation of Background Subtraction Algorithms with Post-Processing

abstract: Processing a video stream to segment foreground objects from the background is a critical first step in many computer vision applications. Background subtraction (BGS) is a commonly used technique for achieving this segmentation. The popularity of BGS largely comes from its computational efficiency, which allows applications such as human-computer interaction, video surveillance, and traffic monitoring to meet their real-time goals. Numerous BGS algorithms and a number of post-processing techniques that aim to improve the results of these algorithms have been proposed. In this paper, we evaluate several popular, state-of-the-art BGS algorithms and examine how post-processing techniques affect their performance. Our experimental results demonstrate that post-processing techniques can significantly improve the foreground segmentation masks produced by a BGS algorithm. We provide recommendations for achieving robust foreground segmentation based on the lessons learned performing this comparative study.


title: Vehicle classification framework: a comparative study

abstract: Video surveillance has significant application prospects such as security, law enforcement, and traffic monitoring. Visual traffic surveillance using computer vision techniques can be non-invasive, cost effective, and automated. Detecting and recognizing the objects in a video is an important part of many video surveillance systems which can help in tracking of the detected objects and gathering important information. In case of traffic video surveillance, vehicle detection and classification is important as it can help in traffic control and gathering of traffic statistics that can be used in intelligent transportation systems. Vehicle classification poses a difficult problem as vehicles have high intra-class variation and relatively low inter-class variation. In this work, we investigate five different object recognition techniques: PCA + DFVS, PCA + DIVS, PCA + SVM, LDA, and constellation-based modeling applied to the problem of vehicle classification. We also compare them with the state-of-the-art techniques in vehicle classification. In case of the PCA-based approaches, we extend face detection using a PCA approach for the problem of vehicle classification to carry out multi-class classification. We also implement constellation model-based approach that uses the dense representation of scale-invariant feature transform (SIFT) features as presented in the work of Ma and Grimson (Edge-based rich representation for vehicle classification. Paper presented at the international conference on computer vision, 2006, pp. 1185–1192) with slight modification. We consider three classes: sedans, vans, and taxis, and record classification accuracy as high as 99.25% in case of cars vs vans and 97.57% in case of sedans vs taxis. We also present a fusion approach that uses both PCA + DFVS and PCA + DIVS and achieves a classification accuracy of 96.42% in case of sedans vs vans vs taxis. 68T10; 68T45; 68U10


title: Dopaminergic mechanisms in actions and habits.

abstract: Recent studies suggest new ways to interpret dopaminergic actions in goal-directed performance and habitual responding. In the early stages of learning dopamine plays an essential role, but with extended training dopamine appears to play a decreasing role in response expression. Experimental manipulation of dopamine levels alters the correlation of cortical and striatal neural activity in behaving animals, and these dopamine-dependent changes in corticostriatal correlations may be reflected in changes in action selection in the basal ganglia. Consistent with this hypothesis, changes in dopamine signaling brought about by sensitization with amphetamine mimic the transition from goal-directed to habit-based instrumental performance. At the cellular level, dopamine-dependent synaptic plasticity may be important initially, and subsequently lead to more persistent changes that no longer require dopamine. The locus of these actions within the cortical and corticostriatal circuitry is a focus on ongoing research.


title: Association between Comorbidities and Selected Sociodemographic Factors with Complications of Diabetes: Results from the National Diabetic Registry Malaysia

abstract: Background: This study aims to determine the hazard ratio of having any complication from diabetes mellitus, and the associations between comorbidities and risk of having any complications from diabetes mellitus among diabetic patients who have attended government primary care clinics. Methods: Secondary data were retrieved from the Malaysian National Diabetic Registry which included all patients who received care. The data from the study on the socio-demographic, diabetes complications, clinical and treatment characteristics were analyzed using descriptive statistics. Cox regression was performed to estimate the hazard ratio for comorbidities, tobacco use, duration of diabetes and socio-demography characteristics upon time to diabetic complications. Results: Adjusted for other covariates, increase number of comorbidities contributed the highest hazard ratio risk: 1 comorbid (aHR: 2.47, 95% CI: 2.39, 2.55), 2 comorbidities (aHR: 4.34, 95% CI: 4.22, 4.47), 3 comorbidities (aHR: 6.56, 95% CI: 6.31, 6.81) and 4 comorbidities (aHR: 9.13, 95% CI: 8.20, 10.17). Other factors: age > 40 years (8%) Malays (27%) and smokers (10%) have hazard risks to develop diabetic complications. Conclusions: Increase in number of comorbidities will increase the risk of getting diabetes complications. Other factors such as age, gender, race, smoking status and duration of diabetes are also noted to contribute to increase risk for diabetes complications.


title: Sonoliards: Rendering Audible Sound Spots by Reflecting the Ultrasound Beams

abstract: This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.


title: Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

abstract: Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules require a multiply and accumulate operation for each synaptic weight, which is challenging to implement compactly using CMOS. In this paper, a method for performing these update operations simultaneously (incremental outer products) using memristor-based arrays is proposed. The method is based on the fact that, approximately, given a voltage pulse, the conductivity of a memristor will increment proportionally to the pulse duration multiplied by the pulse magnitude if the increment is sufficiently small. The proposed method uses a synaptic circuit composed of a small number of components per synapse: one memristor and two CMOS transistors. This circuit is expected to consume between 2% and 8% of the area and static power of previous CMOS-only hardware alternatives. Such a circuit can compactly implement hardware MNNs trainable by scalable algorithms based on online gradient descent (e.g., backpropagation). The utility and robustness of the proposed memristor-based circuit are demonstrated on standard supervised learning tasks.


title: "How Bound the Arab Is to His Land": The House of Rajani and the Limits of Zionist Discourse

abstract: This article examines the ways in which Alon Hilu undermines conventional literary representations of the Arab in The House of Rajani , offering alternative possibilities for contact and communication between Jews and Arabs. The division of the text between two narrators, each of whom reflects a distinct viewpoint, represents the contrast between the Zionist world of the First Aliyah and the Arab world of people who had dwelled in Palestine for generations. Hilu returns to Palestinian history in order to situate the experience of Arab exile center stage. Not only does Hilu read national history and the place of the Arab within it anew, but he signals the instability of national categories, their tendency to merge and thereby to create an intermediate space. The Arab in Hilu’s novel is immeasurably more complex and interesting than the familiar Arab as imagined by Hebrew and Israeli fiction in the past. He is understood through a lens of post-colonialism inconsistent with the dominant Israeli historiography and his human and collective experiences are presented with unprecedented power. A RE T URN TO PALESTINIAN HISTORY? The many novels about Arabs and Jews published in Hebrew since the Oslo Accords (1993) demonstrate an ever increasing awareness of the need to acknowledge the core of the Israeli-Palestinian conflict, as well as the major political ques tions arising in the wake of the stalled peace process. The Palestinian refugees’ right of return and Israel’s responsibility for the aftermath of the War of Independence have become the stuff of Israeli fiction in recent years, more than five decades


title: For sale : your data: by : you

abstract: Monetizing personal information is a key economic driver of online industry. End-users are becoming more concerned about their privacy, as evidenced by increased media attention. This paper proposes a mechanism called 'transactional' privacy that can be applied to personal information of users. Users decide what personal information about themselves is released and put on sale while receiving compensation for it. Aggregators purchase access to exploit this information when serving ads to a user. Truthfulness and efficiency, attained through an unlimited supply auction, ensure that the interests of all parties in this transaction are aligned. We demonstrate the effectiveness of transactional privacy for web-browsing using a large mobile trace from a major European capital. We integrate transactional privacy in a privacy-preserving system that curbs leakage of information. These mechanisms combine to form a market of personal information that can be managed by a trusted third party.


title: Zyzzyva: Speculative Byzantine fault tolerance

abstract: We present Zyzzyva, a protocol that uses speculation to reduce the cost and simplify the design of Byzantine fault tolerant state machine replication. In Zyzzyva, replicas respond to a client's request without first running an expensive three-phase commit protocol to reach agreement on the order in which the request must be processed. Instead, they optimistically adopt the order proposed by the primary and respond immediately to the client. Replicas can thus become temporarily inconsistent with one another, but clients detect inconsistencies, help correct replicas converge on a single total ordering of requests, and only rely on responses that are consistent with this total order. This approach allows Zyzzyva to reduce replication overheads to near their theoretical minimal.


title: An analysis of the Steam community network evolution

abstract: The Steam community network is a large social network of players on the Steam gaming platform, with over 30 million users to date. In this paper we introduce an analysis of the Steam community network in 2011, looking at the characteristics of the users network and the connectivity graph. We next present the evolution of the network over time and show how the network has changed over the years. Last, we analyze the role of games and groups in the Steam community. This work is the first to analyze the Steam network, and to provide a large scale analysis of the characteristics of gaming platforms communities.


title: Joint word segmentation, POS-tagging and syntactic chunking

abstract: Chinese chunking has traditionally been solved by assuming gold standard word segmentation. We find that the accuracies drop drastically when automatic segmentation is used. Inspired by the fact that chunking knowledge can potentially improve segmentation, we explore a joint model that performs segmentation, POS-tagging and chunking simultaneously. In addition, to address the sparsity of full chunk features, we employ a semi-supervised method to derive chunk cluster features from large-scale automatically-chunked data. Results show the effectiveness of the joint model with semi-supervised features.


title: Semantic shape editing using deformation handles

abstract: We propose a shape editing method where the user creates geometric deformations using a set of semantic attributes, thus avoiding the need for detailed geometric manipulations. In contrast to prior work, we focus on continuous deformations instead of discrete part substitutions. Our method provides a platform for quick design explorations and allows non-experts to produce semantically guided shape variations that are otherwise difficult to attain. We crowdsource a large set of pairwise comparisons between the semantic attributes and geometry and use this data to learn a continuous mapping from the semantic attributes to geometry. The resulting map enables simple and intuitive shape manipulations based solely on the learned attributes. We demonstrate our method on large datasets using two different user interaction modes and evaluate its usability with a set of user studies.


title: Network Utility Maximization-Based Congestion Control Over Wireless Networks: A Survey and Potential Directives

abstract: Since the initial publication of Kelly et al.'s seminal paper on resource allocation in wired networks, a large number of studies based on the network utility maximization (NUM) framework have been conducted, for example, on resource allocations in wired networks as well as wireless networks. Congestion control, used to adapt the traffic rates injected into a network, can be controlled separately at the transport layer or mutually with other layers, using a cross-layer design. The focus of this paper is a review of the research following the NUM framework and addressing the problem of congestion control from several dimensions, specifically, elastic traffic or inelastic traffic, same-timescale or multi-timescale, and single-path or multipath transmissions. First, cross-layer designs for wireless networks, basics of optimization theory, and an example of cross-layer congestion control are summarized. Then, studies on NUM-based congestion control are sequentially presented from several points of view. In addition, we survey the recent literature on cross-layer resource allocation in next generation networks, software-defined networking, and cloud radio access networks, which can enhance the performances and address issues found in current networks. Finally, we outline what we consider to be the current challenges and open issues in the area of NUM-based congestion control for wireless networks.


title: Designing Cyberbullying Mitigation and Prevention Solutions through Participatory Design With Teenagers

abstract: While social media platforms enable individuals to easily communicate and share experiences, they have also emerged as a tool for cyberbullying. Teenagers represent an especially vulnerable population for negative emotional responses to cyberbullying. At the same time, attempts to mitigate or prevent cyberbullying from occurring in these networked spaces have largely failed because of the complexity and nuance with which young people bully others online. To address challenges related to designing for cyberbullying intervention and mitigation, we detail findings from participatory design work with two groups of high school students in spring 2015. Over the course of five design sessions spanning five weeks, participants shared their experiences with cyberbullying and iteratively designed potential solutions. We provide an in-depth discussion of the range of cyberbullying mitigation solutions participants designed. We focus on challenges participants' identified in designing for cyberbullying support and prevention and present a set of five potential cyberbullying mitigation solutions based on the results of the design sessions.


title: Signaling Foreign Policy Interests

abstract: The author distinguishes between two types of costly signals that state leaders might employ in trying to credibly communicate their foreign policy interests to other states, whether in the realm of grand strategy or crisis diplomacy. Leaders might either (a) tie hands by creating audience costs that they will suffer ex post if they do not follow through on their threat or commitment (i.e., costs arising from the actions of domestic political audiences) or (b) sink costs by taking actions such as mobilizing troops that are financially costly ex ante. Analysis of a game model depicting the essentials of each case yields two principal results. First, in the games' equilibria, leaders never bluff with either type of signal; they do not incur or create costs and then fail to respond if challenged. Second, leaders do better on average by tying hands, despite the fact that the ability to do so creates a greater ex ante risk of war than does the use of sunk-cost signals. These results and the logic behind them may help explain some empirical features of international signaling, such as many crises' appearance as competitions in creating domestic political audience costs. They also generate empirical puzzles, such as why the seemingly plausible logic of inference that undermines bluffing in the model does not operate in all empirical cases.


Artificial Neuron Network Implementation of Boolean Logic Gates by Perceptron and Threshold Element as Neuron Output Function


title: Performance evaluation of pattern classifiers for handwritten character recognition

abstract: This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance.


title: Wideband dumbbell-shaped dipole quasi-fractal antenna

abstract: This paper presents the design of a wideband dumbbell-shaped dipole quasi-fractal antenna with a partial ground plane under the feed point. Several sizes of the partial ground plane were simulated and compared. The antenna has good impedance and radiation characteristics for frequencies from 450 MHz to 1725 MHz. The antenna is suitable for HDTV reception and other applications within its operating bandwidth.