title: Toward a neurology of loneliness.

abstract: Social isolation has been recognized as a major risk factor for morbidity and mortality in humans for more than a quarter century. The brain is the key organ of social connections and processes, however, and the same objective social relationship can be experienced as caring and protective or as exploitive and isolating. We review evidence that the perception of social isolation (i.e., loneliness) impacts brain and behavior and is a risk factor for broad-based morbidity and mortality. However, the causal role of loneliness on neural mechanisms and mortality is difficult to test conclusively in humans. Mechanistic animal studies provide a lens through which to evaluate the neurological effects of a member of a social species living chronically on the social perimeter. Experimental studies show that social isolation produces significant changes in brain structures and processes in adult social animals. These effects are not uniform across the brain or across species but instead are most evident in brain regions that reflect differences in the functional demands of solitary versus social living for a particular species. The human and animal literatures have developed independently, however, and significant gaps also exist. The current review underscores the importance of integrating human and animal research to delineate the mechanisms through which social relationships impact the brain, health, and well-being.


title: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features

abstract: We developed and evaluated a multimodal affect detector that combines conversational cues, gross body language, and facial features. The multimodal affect detector uses feature-level fusion to combine the sensory channels and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion, frustration, delight, and neutral. Training and validation data for the affect detector were collected in a study where 28 learners completed a 32- min. tutorial session with AutoTutor, an intelligent tutoring system with conversational dialogue. Classification results supported a channel × judgment type interaction, where the face was the most diagnostic channel for spontaneous affect judgments (i.e., at any time in the tutorial session), while conversational cues were superior for fixed judgments (i.e., every 20 s in the session). The analyses also indicated that the accuracy of the multichannel model (face, dialogue, and posture) was statistically higher than the best single-channel model for the fixed but not spontaneous affect expressions. However, multichannel models reduced the discrepancy (i.e., variance in the precision of the different emotions) of the discriminant models for both judgment types. The results also indicated that the combination of channels yielded superadditive effects for some affective states, but additive, redundant, and inhibitory effects for others. We explore the structure of the multimodal linear discriminant models and discuss the implications of some of our major findings.


title: Suffocation Using Plastic Bags: A Retrospective Study of Suicides in Ontario, Canada

abstract: One hundred and ten cases of suicidal suffocation using a plastic bag were identified in the files of the Office of the Chief Coroner of Ontario, Canada, between 1993 and 1997. The records were reviewed to determine the demographic characteristics of this group compared with all cases of suicide in Ontario, the scene information, autopsy findings and toxicology results. Most suicides occurred in people over 60 years of age, with older women making up a considerable proportion of cases as compared with other methods of suicide. In 40% of cases the deceased was suffering from a serious illness. Autopsy findings were usually minimal, with facial, conjunctival and visceral petechiae present in a minority of cases. One or more drugs were detected in the blood in 92.6% of cases where toxicologic testing was performed. Benzodiazepines, diphenhydramine and antidepressants were the most common drugs found, with diphenhydramine the most common drug present at an elevated concentration. Information at the scene from "right to die" societies was uncommon. One quarter of decedents took additional measures, besides the use of drugs or alcohol, to ensure the rapidity, certainty or comfort of their death. This study further elucidates the characteristics of this uncommon method of suicide. It emphasizes additional scene findings, such as the presence of dust masks, physical restraints and modification of the plastic bag that may be of use to death investigators in determining the correct manner of death.


title: An Empirical Study of Click Fraud in Mobile Advertising Networks

abstract: Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of a type of mobile advertisement where advertisers are charged for their advertisements only when a user clicks (or touches) on the advertisements in their applications. In the Android platform, we design an automated click generation attack and empirically evaluate eight popular advertising networks by performing real attacks on them. Our experimental results show that six advertising networks (75%) out of eight (Millennial Media, App Lovin, Ad Fit, Mdot M, Rev Mob and Cauly Ads) are vulnerable to our attacks. We also discuss how to develop effective defense mechanisms to mitigate such automated click fraud attacks.


title: Discovering Object Functionality

abstract: Object functionality refers to the quality of an object that allows humans to perform some specific actions. It has been shown in psychology that functionality (affordance) is at least as essential as appearance in object recognition by humans. In computer vision, most previous work on functionality either assumes exactly one functionality for each object, or requires detailed annotation of human poses and objects. In this paper, we propose a weakly supervised approach to discover all possible object functionalities. Each object functionality is represented by a specific type of human-object interaction. Our method takes any possible human-object interaction into consideration, and evaluates image similarity in 3D rather than 2D in order to cluster human-object interactions more coherently. Experimental results on a dataset of people interacting with musical instruments show the effectiveness of our approach.


title: Middle School Transition from the Strengths Perspective: Young Adolescents’ Character Strengths, Subjective Well-Being, and School Adjustment

abstract: The study examined the longitudinal associations of adolescents’ character strengths with subjective well-being and school adjustment during the pivotal period of the transition to middle school. The study followed 417 students and 13 teachers from four public middle schools in the center of Israel during seventh and eighth grade. Students completed questionnaires about their character strengths, life satisfaction, and positive and negative affects. Students’ school adjustment was measured through grade point average and questionnaires given to the students’ teachers. Findings provided extensive support for the associations among character strengths, subjective well-being (SWB), and middle school adjustment. Intellectual and temperance strengths were central in the prediction of students’ school performance and achievements. Interpersonal strengths were significantly related to social functioning at school. Temperance and transcendence strengths were robust positive predictors of students’ SWB. The findings extend existing knowledge about character and its relation to optimal functioning and well-being among young adolescents, and have important practical implications for applying strengths-building practices at middle schools. Copyright Springer Science+Business Media B.V. 2013


title: A novel stair-climbing wheelchair with transformable wheeled four-bar linkages

abstract: This paper describes the development of a novel multi-wheel stair-climbing wheelchair. The necessity for mobility aid technology for elderly and handicapped people that has “minimal invasiveness for use in an historical environment” is described. With this goal in mind, a prototype of a novel wheelchair having a stair-climbing function resulting from transformable wheeled four-bar linkages is proposed. The mechanical design, principle of operation and statics of the proposed mechanism are thoroughly illustrated. The basic performance of the proposed mechanism has been confirmed through experimental results.


title: Comparative study of modern convolutional neural networks for smoke detection on image data

abstract: This work evaluates modern convolutional neural networks (CNN) for the task of smoke detection on image data. The networks that were tested are AlexNet, Inception-V3, Inception-V4, ResNet, VGG, and Xception. They all have shown high performance on huge ImageNet dataset, but the possibility of using such CNNs needed to be checked for a very specific task of smoke detection with a high diversity of possible scenarios and a small available dataset. Experimental results have shown that inception-based networks reach high performance when samples in the training dataset cover enough scenarios while accuracy dramatically drops when older networks are utilized.


title: Rethinking Flow Classification in SDN

abstract: Software-Defined Networking (SDN) increasingly attracts more researchers as well as industry attentions. Most of current SDN packet processing approaches classify packets based on matching a set of fields on the packet against a flow table and then applying an action on the packet. We argue we can simplify this mechanism using single-field classification and reduce the overhead. We propose a tag-based packet classification architecture to reduce filtering and flow management overhead. Then, we show how to use this extra capacity to perform application layer classification for different purposes. In this work-in-progress paper we demonstrate our preliminary evaluation results to indicate the effectiveness of the proposal.


title: Copenhagen vs Everett, Teleportation, and ER=EPR

abstract: Quantum gravity may have as much to tell us about the foundations and interpretation of quantum mechanics as it does about gravity. The Copenhagen interpretation of quantum mechanics and Everett's Relative State Formulation are complementary descriptions which in a sense are dual to one another. My purpose here is to discuss this duality in the light of the of ER=EPR conjecture.


Efficient Hair Rendering under Dynamic, Low-Frequency Environmental Light Using Spherical Harmonics


title: A Survey on Collaborative Deep Learning and Privacy-Preserving

abstract: In the era of big data, the amount of data that individuals and enterprises hold is increasing, and the efficiency and effectiveness of data analysis are increasingly demanding. Collaborative deep learning, as a machine learning framework that can share users' data and improve learning efficiency, has drawn more and more attention and started to be applied in practical problems. In collaborative deep learning, data sharing and interaction among multi users may lead data leakage especially when data are very sensitive to the user. Therefore, how to protect the data privacy when processing collaborative deep learning becomes an important problem. In this paper, we review the current state of art researches in this field and summarize the application of privacy-preserving technologies in two phases of collaborative deep learning. Finally we discuss the future direction and trend on this problem.


title: Multilevel selective harmonic elimination PWM technique in series-connected voltage inverters

abstract: Selective harmonic elimination pulsewidth modulation (SHEPWM) method is systematically applied for the first time to multilevel series-connected voltage-source PWM inverters. The method is implemented based on optimization techniques. The optimization starting point is obtained using a phase-shift harmonic suppression approach. Another less computationally demanding harmonic suppression technique, called a mirror surplus harmonic method, is proposed for double-cell (five-level) inverters. Theoretical results of both methods are verified by experiments and simulations for a double-cell inverter. Simulation results for a five-cell (11-level) inverter are also presented for the multilevel SHEPWM method.


title: SurroundSense: mobile phone localization via ambience fingerprinting

abstract: A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.


title: The Psychedelic State Induced by Ayahuasca Modulates the Activity and Connectivity of the Default Mode Network

abstract: The experiences induced by psychedelics share a wide variety of subjective features, related to the complex changes in perception and cognition induced by this class of drugs. A remarkable increase in introspection is at the core of these altered states of consciousness. Self-oriented mental activity has been consistently linked to the Default Mode Network (DMN), a set of brain regions more active during rest than during the execution of a goal-directed task. Here we used fMRI technique to inspect the DMN during the psychedelic state induced by Ayahuasca in ten experienced subjects. Ayahuasca is a potion traditionally used by Amazonian Amerindians composed by a mixture of compounds that increase monoaminergic transmission. In particular, we examined whether Ayahuasca changes the activity and connectivity of the DMN and the connection between the DMN and the task-positive network (TPN). Ayahuasca caused a significant decrease in activity through most parts of the DMN, including its most consistent hubs: the Posterior Cingulate Cortex (PCC)/Precuneus and the medial Prefrontal Cortex (mPFC). Functional connectivity within the PCC/Precuneus decreased after Ayahuasca intake. No significant change was observed in the DMN-TPN orthogonality. Altogether, our results support the notion that the altered state of consciousness induced by Ayahuasca, like those induced by psilocybin (another serotonergic psychedelic), meditation and sleep, is linked to the modulation of the activity and the connectivity of the DMN.


title: Caching doesn't improve mobile web performance (much)

abstract: A recent NSDI paper [1] reported that increasing the cache hit ratio for an HTTP proxy from 22% to 32% improved median page load time (PLT) for mobile clients by less than 2%. We argue that there are two main causes for this weak improvement: objects on the critical path are often not cached, and the limited computational power of mobile devices causes computational delays to comprise a large portion of the critical path. ::: ::: Both of these factors were, in fact, outlined by a previous analysis of desktop web performance [2]. However, we (as the authors of the HTTP proxy [1]) did not properly understand the analysis and could have saved ourselves substantial engineering costs if we had. We therefore argue for the need to highlight this prior analysis, and extend the analysis to include mobile devices with slow CPUs, precise cache hit ratios, and a controlled reproduction of the HTTP proxy caching results [1]. In the extreme case of a perfect cache hit ratio, desktop page load times are improved notably by 34% compared to no caching, but mobile page load times only improve by 13% in the median case. We extract a back-of-envelope performance model from these results to help understand their underlying causes.


title: Agile Team Members Perceptions on Non-functional Testing: Influencing Factors from an Empirical Study

abstract: Non-functional requirements define the overall qualities or attributes of a system. Although important, they are often neglected for many reasons, such as pressure of time and budget. In agile software development, there is a focus on the feature implementation and delivery of value to the customer and, as such, non-functional aspects of a system should also be of attention. Non-functional requirements testing is challenging due its cross-functional aspects and lack of clarity of their needs by business in the most part of projects. The goal of this paper is to empirically investigate how do agile team members handle non-functional testing in their projects, aiming to identify preliminary factors influencing the testing of non-functional requirements, specifically performance and security in agile development. We conducted interviews with twenty IT professionals in large multinational company. As result we could identify seven main factors influencing non-functional testing and four main practices adopted by them to overcome the challenges faced. We aim to replicate our investigation in a larger scale. Meanwhile, our work provides initial contributions to practitioners and inspires our future research.


title: Revisiting Snodgrass and Vanderwart's object pictorial set : the role of surface detail in basic-level object recognition

abstract: Theories of object recognition differ to the extent that they consider object representations as being mediated only by the shape of the object, or shape and surface details, if surface details are part of the representation. In particular, it has been suggested that color information may be helpful at recognizing objects only in very special cases, but not during basic-level object recognition in good viewing conditions. In this study, we collected normative data (naming agreement, familiarity, complexity, and imagery judgments) for Snodgrass and Vanderwart's object database of 260 black-and-white line drawings, and then compared the data to exactly the same shapes but with added gray-level texture and surface details (set 2), and color (set 3). Naming latencies were also recorded. Whereas the addition of texture and shading without color only slightly improved naming agreement scores for the objects, the addition of color information unambiguously improved naming accuracy and speeded correct response times. As shown in previous studies, the advantage provided by color was larger for objects with a diagnostic color, and structurally similar shapes, such as fruits and vegetables, but was also observed for man-made objects with and without a single diagnostic color. These observations show that basic-level 'everyday' object recognition in normal conditions is facilitated by the presence of color information, and support a 'shape + surface' model of object recognition, for which color is an integral part of the object representation. In addition, the new stimuli (sets 2 and 3) and the corresponding normative data provide valuable materials for a wide range of experimental and clinical studies of object recognition.


title: Lead poisoning associated with ayurvedic medications--five states, 2000-2003.

abstract: Although approximately 95% of lead poisoning among U.S. adults results from occupational exposure, lead poisoning also can occur from use of traditional or folk remedies. Ayurveda is a traditional form of medicine practiced in India and other South Asian countries. Ayurvedic medications can contain herbs, minerals, metals, or animal products and are made in standardized and nonstandardized formulations. During 2000-2003, a total of 12 cases of lead poisoning among adults in five states associated with ayurvedic medications or remedies were reported to CDC. This report summarizes these 12 cases. Culturally appropriate educational efforts are needed to inform persons in populations using traditional or folk medications of the potential health risks posed by these remedies.


title: The evidence for 'flipping out': A systematic review of the flipped classroom in nursing education.

abstract: BACKGROUND ::: The flipped classroom has generated interest in higher education providing a student-centred approach to learning. This has the potential to engage nursing students in ways that address the needs of today's students and the complexity of contemporary healthcare. Calls for educational reform, particularly in healthcare programs such as nursing, highlight the need for students to problem-solve, reason and apply theory into practice. The drivers towards student-based learning have manifested in team, problem and case-based learning models. Though there has been a shift towards the flipped classroom, comparatively little is known about how it is used in nursing curricula. ::: ::: ::: OBJECTIVES ::: The aims of this systematic review were to examine how the flipped classroom has been applied in nursing education and outcomes associated with this style of teaching. ::: ::: ::: DATA SOURCES ::: Five databases were searched and resulted in the retrieval of 21 papers: PubMed, CINAHL, EMBASE, Scopus and ERIC. ::: ::: ::: REVIEW METHODS ::: After screening for inclusion/exclusion criteria, each paper was evaluated using a critical appraisal tool. Data extraction and analysis were completed on all included studies. ::: ::: ::: RESULTS ::: This systematic review screened 21 titles and abstracts resulting in nine included studies. All authors critically appraised the quality of the included studies. Five studies were identified and themes identified were: academic performance outcomes, and student satisfaction implementing the flipped classroom. ::: ::: ::: CONCLUSIONS ::: Use of the flipped classroom in higher education nursing programmes yielded neutral or positive academic outcomes and mixed results for satisfaction. Engagement of students in the flipped classroom model was achieved when academics informed and rationalised the purpose of the flipped classroom model to students. However, no studies in this review identified the evaluation of the process of implementing the flipped classroom. Studies examining the process and ongoing evaluation and refinement of the flipped classroom in higher education nursing programmes are warranted.


title: Real-Time Monocular Object-Model Aware Sparse SLAM

abstract: Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While sparse point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art object detection methods provide rich information about entities present in the scene from a single image. This work incorporates a real-time deep-learned object detector to the monocular SLAM framework for representing generic objects as quadrics that permit detections to be seamlessly integrated while allowing the real-time performance. Finer reconstruction of an object, learned by a CNN network, is also incorporated and provides a shape prior for the quadric leading further refinement. To capture the structure of the scene, additional planar landmarks are detected by a CNN-based plane detector and modelled as independent landmarks in the map. Extensive experiments support our proposed inclusion of semantic objects and planar structures directly in the bundle-adjustment of SLAM - Semantic SLAM- that enriches the reconstructed map semantically, while significantly improving the camera localization.


An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model


title: SLASH: Self-learning and adaptive smart home framework by integrating IoT with big data analytics

abstract: Over the last decades, smart home systems have failed to spread widely in every home and be a coherent part of our life like what smartphones did in half this period of time. However, the latest evolution of Internet of Things (IoT) and Big Data analysis gave new insights of smart platforms that can potentially lead to the new dream of ‘smart cities’. The paper presented a survey such latest technologies that can lead to a new paradigm of a smart home, based on classifying the basic components of such systems and present the previous work in each component. Moreover, SLASH framework is proposed for designing and implementing smart home systems that are both adaptive and self-learning. Our framework suggests integrating IoT across every home with a large network connected to Big Data analyser. Such an engine that supports analysing inhabitants' behaviours on a large-scale can enable a new type of home automation that depends on machine learning and develops on-going automation decision over time. Essentially, this approach holds some challenges that are considered throughout the framework or state for future enhancements.


Twitter, Google, iPhone/iPad, and Facebook (TGIF) and Smart Technology Environments: How Well Do Educators Communicate with Students via TGIF?


title: Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs

abstract: Abstract With the increasing availability of locating and navigation technologies on portable wireless devices, huge amounts of location data are being captured at ever growing rates. Spatial and temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale ubiquitous urban sensing data play an important role in understanding urban dynamics and facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems on uniprocessors based on serial algorithms, which makes them incapable of handling large-scale data on parallel hardware architectures that have already been equipped with commodity computers. In this study, we report our designs, implementations and experiments on developing a data management platform and a set of parallel techniques to support high-performance online spatial and temporal aggregations on multi-core CPUs and many-core Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially associate nearly 170 million taxi pickup location points with their nearest street segments among 147,011 candidates in about 5–25 s on both an Nvidia Quadro 6000 GPU device and dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized for computing intensive tasks. After spatially associating points with road segments, spatial, temporal and spatiotemporal aggregations are reduced to relational aggregations and can be processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition to demonstrating the feasibility of building a high-performance OLAP system for processing large-scale taxi trip data for real-time, interactive data explorations, our work also opens the paths to achieving even higher OLAP query efficiency for large-scale applications through integrating domain-specific data management platforms, novel parallel data structures and algorithm designs, and hardware architecture friendly implementations.


title: Challenges, issues and trends in fall detection systems

abstract: Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.


title: Naturalizing a Programming Language via Interactive Learning

abstract: Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to "naturalize" the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9\% of the last 10K utterances.


title: Fast High Dimensional Vector Multiplication Face Recognition

abstract: This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework. First, the Over-Complete Local Binary Patterns (OCLBP) face representation scheme is introduced as a multi-scale modified version of the Local Binary Patterns (LBP) scheme. Second, we propose an efficient matrix-vector multiplication-based recognition system. The system is based on Linear Discriminant Analysis (LDA) coupled with Within Class Covariance Normalization (WCCN). This is further extended to the unsupervised case by proposing an unsupervised variant of WCCN. Lastly, we introduce Diffusion Maps (DM) for non-linear dimensionality reduction as an alternative to the Whitened Principal Component Analysis (WPCA) method which is often used in face recognition. We evaluate the proposed framework on the LFW face recognition dataset under the restricted, unrestricted and unsupervised protocols. In all three cases we achieve very competitive results.


Design of a Comfortable Pure Moment Knee Exoskeleton


title: New Frontiers in Formal Methods: Learning, Cyber-Physical Systems, Education, and Beyond

abstract: We survey promising directions for research in the area of formal methods and its applications, including fundamental questions about the combination of formal methods and machine learning, and applications to cyber- physical systems and education. Index Terms—Formal Methods, Machine Learning, Cyber-Physical Systems, Education. F


title: A 17.5-to-20.94GHz and 35-to-41.88GHz PLL in 65nm CMOS for wireless HD applications

abstract: This work shows a complete PLL that is integrated in standard industrial 65nm CMOS technology. This frequency synthesizer is fully compliant with IEEE 802.15.3c normalization [1–4]. This PLL delivers a quadrature LO signal around 20GHz and a differential LO signal around 40GHz and has 17.9% tuning range. The wide tuning range of 17.9% permits to cover the full IEEE 802.15.3c band with industrial margin. The phase noise is −100dBc/Hz at 1MHz offset and the total power dissipation is only 80mW including the output buffers and amplifiers. Short-range wireless multi-Gb/sec communication systems use the mm-wave band of 57GHz to 66GHz, according to the IEEE 802.15.3c normalization. The frequency synthesis is one of the key elements for these transceivers. Indeed, one must take into account the antagonist tradeoff between large band tuning range of the frequency synthesizer and phase noise performance. In transceivers using super-heterodyne architecture with double conversion, the frequency synthesizer signal fLO can be equal to 2fRF /3 and fRF /3. In this case, to cover the four channels of the IEEE 802.15.3c normalization, the frequency synthesizer has to deliver a first local oscillator (LO) signal between 19.44GHz and 21.6GHz and a second LO signal between 38.88GHz and 43.2GHz, respectively. This architecture offers a good trade off between the required large frequency tuning range (≫15%) and low phase noise (≪−95dBc/Hz).


title: How to Build an Embodiment Lab: Achieving Body Representation Illusions in Virtual Reality

abstract: Advances in computer graphics algorithms and virtual reality (VR) systems, together with the reduction in cost of associated equipment, have led scientists to consider VR as a useful tool for conducting experimental studies in fields such as neuroscience and experimental psychology. In particular virtual body ownership, where the feeling of ownership over a virtual body is elicited in the participant, has become a useful tool in the study of body representation, in cognitive neuroscience and psychology, concerned with how the brain represents the body. Although VR has been shown to be a useful tool for exploring body ownership illusions, integrating the various technologies necessary for such a system can be daunting. In this paper we discuss the technical infrastructure necessary to achieve virtual embodiment. We describe a basic VR system and how it may be used for this purpose, and then extend this system with the introduction of real-time motion capture, a simple haptics system and the integration of physiological and brain electrical activity recordings.


title: IoT-based fall detection system with energy efficient sensor nodes

abstract: Fall needs to be attentively considered due to its highly frequent occurrence especially with old people — up to one third of 65 and above year-old people around the world are risk of being injured due to falling. Furthermore, fall is a direct or indirect factor causing severe traumas such as brain injuries or bone fractures. However, timely medical attention might help to avoid serious consequences from a fall. A viable solution to solve this is an IoT-based system which takes advantage of wireless sensor networks, wearable devices, Fog and Cloud computing. To deliver sufficient degree of reliability, wearable devices working at the core of a fall detection system, are required to work for prolonged period of time. In this paper we investigate energy consumption of sensor nodes in an IoT-based fall detection system and present a design of a customized sensor node. In addition, we compare the customized sensor node with other sensor nodes, built on general purpose development boards. The results show that sensor nodes based on delicate customized devices are more energy efficient than the others based on general purpose devices while considering identical specification of micro-controller and memory capacity. Furthermore, our customized sensor node with energy efficiency selections can operate continuously up to 35 hours.


title: PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

abstract: Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object. This prevents the network from concentrating too much on parts of objects instead of whole objects. We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and MS-COCO benchmarks for WSOD. Results show that our method outperforms the previous state of the art significantly.


title: The Miner's Dilemma

abstract: An open distributed system can be secured by requiring participants to present proof of work and rewarding them for participation. The Bit coin digital currency introduced this mechanism, which is adopted by almost all contemporary digital currencies and related services. A natural process leads participants of such systems to form pools, where members aggregate their power and share the rewards. Experience with Bit coin shows that the largest pools are often open, allowing anyone to join. It has long been known that a member can sabotage an open pool by seemingly joining it but never sharing its proofs of work. The pool shares its revenue with the attacker, and so each of its participants earns less. We define and analyze a game where pools use some of their participants to infiltrate other pools and perform such an attack. With any number of pools, no-pool-attacks is not a Nash equilibrium. We study the special cases where either two pools or any number of identical pools play the game and the rest of the participants are uninvolved. In both of these cases there exists an equilibrium that constitutes a tragedy of the commons where the participating pools attack one another and earn less than they would have if none had attacked. For two pools, the decision whether or not to attack is the miner's dilemma, an instance of the iterative prisoner's dilemma. The game is played daily by the active Bit coin pools, which apparently choose not to attack. If this balance breaks, the revenue of open pools might diminish, making them unattractive to participants.


title: Evolutionary timeline summarization: a balanced optimization framework via iterative substitution

abstract: Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.


title: Proposal and Synthesis Design of Wideband Phase Shifters on Multimode Resonator

abstract: In this paper, a new class of wideband phase shifters on multimode resonator is proposed and developed. Compared with its conventional counterparts, these new phase shifters have a few advantageous features such as simple structure, intrinsic wideband characteristic, large phase-shift value, and easy adjustment in phase shift. The phase properties of multimode resonator are at first studied to derive the quantitative relationship between the phase slope and their respective resonant frequencies. On the one hand, a prescribed wideband phase shift can be achieved by adjusting the impedance ratio $R_{z}$ of the multimode resonator and the electrical length of the reference line. On the other hand, the wide operating band can be intrinsically obtained over a frequency range covered by multiple resonant frequencies in the multimode resonator. In this context, the transmission-line models of the proposed phase shifters are deduced and synthesized to design these phase shifters with a prescribed phase shift value and return loss within the operating band. Moreover, the synthesized model can be directly mapped into all the physical dimensions, thus allowing for a quick design process. Finally, two 180° wideband phase shifters are designed, fabricated, and tested to verify the proposed design method and predicted frequency responses.


title: Learning for control from multiple demonstrations

abstract: We consider the problem of learning to follow a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts the---initially unknown---desired trajectory from the sub-optimal expert's demonstrations and (ii) learns a local model suitable for control along the learned trajectory. We apply our algorithm to the problem of autonomous helicopter flight. In all cases, the autonomous helicopter's performance exceeds that of our expert helicopter pilot's demonstrations. Even stronger, our results significantly extend the state-of-the-art in autonomous helicopter aerobatics. In particular, our results include the first autonomous tic-tocs, loops and hurricane, vastly superior performance on previously performed aerobatic maneuvers (such as in-place flips and rolls), and a complete airshow, which requires autonomous transitions between these and various other maneuvers.


title: NeuroPipe-Chip: A Digital Neuro-Processor for Spiking Neural Networks

abstract: Computing complex spiking artificial neural networks (SANNs) on conventional hardware platforms is far from reaching real-time requirements. Therefore we propose a neuro-processor, called NeuroPipe-Chip, as part of an accelerator board. In this paper, we introduce two new concepts on chip-level to speed up the computation of SANNs. These concepts are implemented in a prototype of the NeuroPipe-Chip. We present the hardware structure of the prototype and evaluate its performance in a system simulation based on a hardware description language (HDL). For the computation of a simple SANN for image segmentation, the NeuroPipe-Chip operating at 100 MHz shows an improvement of more than two orders of magnitude compared to an Alpha 500 MHz workstation and approaches real-time requirements for the computation of SANNs in the order of 10/sup 6/ neurons. Hence, such an accelerator would allow for applications of complex SANNs to solve real-world tasks like real-time image processing. The NeuroPipe-Chip has been fabricated in an Alcatel 0.35-/spl mu/m digital CMOS technology.


title: Distributed Circuit Modeling of Galvanic and Capacitive Coupling for Intrabody Communication

abstract: Modeling of intrabody communication (IBC) entails the understanding of the interaction between electromagnetic fields and living tissues. At the same time, an accurate model can provide practical hints toward the deployment of an efficient and secure communication channel for body sensor networks. In the literature, two main IBC coupling techniques have been proposed: galvanic and capacitive coupling. Nevertheless, models that are able to emulate both coupling approaches have not been reported so far. In this paper, a simple model based on a distributed parameter structure with the flexibility to adapt to both galvanic and capacitive coupling has been proposed. In addition, experimental results for both coupling methods were acquired by means of two harmonized measurement setups. The model simulations have been subsequently compared with the experimental data, not only to show their validity but also to revise the practical frequency operation range for both techniques. Finally, the model, along with the experimental results, has also allowed us to provide some practical rules to optimally tackle IBC design.


title: Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

abstract: Bearing faults account for a large portion of all faults in wind turbine generators (WTGs). Current-based bearing fault diagnosis techniques have great economic benefits and are potential to be adopted by the wind energy industry. This paper models the modulation effects of bearing faults on the stator currents of a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) operating with a variable shaft rotating frequency. Based on the analysis, a method consisting of appropriate current frequency and amplitude demodulation algorithms and a 1P-invariant power spectrum density algorithm is proposed for bearing fault diagnosis of variable-speed direct-drive wind turbines using only one-phase stator current measurements, where 1P frequency stands for the shaft rotating frequency of a wind turbine. Experimental results on a direct-drive wind turbine equipped with a PMSG operating in a wind tunnel are provided to verify the proposed fault diagnosis method. The proposed method is demonstrated to have advantages over the method of directly using stator current measurements for WTG bearing fault diagnosis.


title: Face Recognition Using Gabor Filter And Convolutional Neural Network

abstract: Face recognition involves the extraction of different features of human face and its classification for discriminating it from other persons. Face recognition is quite challenging task because faces are complex, multidimensional visual stimuli and also face recognition rate depends on variations in pose, expression, occlusion, resolution, and illumination. Most of the existing face recognition algorithms give poor performance in the presence of high degree variations in human face images. Hence we propose an approach based on deep learning which uses Gabor filter of feature extraction and Convolution Neural Network for classification in order to improve the performance of face recognition with mentioned variations. The experiments conducted on AT & T database and we attained the efficiency of 89.50%.It is seen that 2.5% improvement in the efficiency as reported in literature. Future work is dedicated to evaluate the performance of proposed algorithm on different dataset with varying illumination and its classification.


title: A 2-nW 1.1-V self-biased current reference in CMOS technology

abstract: This work presents the design of an ultra-low-power self-biased 400-pA current source. We propose the use of a very simple topology along with a design methodology based on the concept of inversion level. An efficient design methodology has resulted in a cell area around 0.045 mm/sup 2/ in the AMI 1.5-/spl mu/m CMOS technology and power consumption around 2 nW for 1.2-V supply. Simulated and experimental results validate the design and show that the current source can operate at supply voltages down to 1.1 V with a good regulation (<6% /V variation of the supply voltage) in a 1.5-/spl mu/m technology.


title: A 1.8 $\mu$ W 60 nV$/\surd$ Hz Capacitively-Coupled Chopper Instrumentation Amplifier in 65 nm CMOS for Wireless Sensor Nodes

abstract: This paper presents a low-power precision instrumentation amplifier intended for use in wireless sensor nodes. It employs a capacitively-coupled chopper topology to achieve a rail-to-rail input common-mode range as well as high power efficiency. A positive feedback loop is employed to boost its input impedance, while a ripple reduction loop suppresses the chopping ripple. To facilitate bio-potential sensing, an optional DC servo loop may be employed to suppress electrode offset. The IA achieves 1 μV offset, 0.16% gain inaccuracy, 134 dB CMRR, 120 dB PSRR and a noise efficiency factor of 3.3. The instrumentation amplifier was implemented in a 65 nm CMOS technology. It occupies only 0.1 mm2 chip area (0.2 mm2 with the DC servo loop) and consumes 1.8 μA current (2.1 μA with the DC servo loop) from a 1 V supply.


title: A broadband high efficiency class AB GaN HEMT balanced power amplifier

abstract: In this paper, a broadband high efficiency Class-AB balanced power amplifier (PA) is presented. The proposed PA offers a high efficiency of > 43% for a band of 400 MHz to 1.4 GHz. The broadband matching circuits were realized with microstrip-radial-stubs (MRS) on low loss Rogers 5880 substrate with 0.78 mm thickness and 2.2 dielectric constant. The input and output matching is better than −13 dB throughout the band. The PA delivers maximum output power of 41.5 dBm with a flat gain of 11.4–13.5 dB. Due to high gain, stability, efficiency, and broadband, the proposed PA is thus suitable for recent and upcoming wireless communication systems.


title: Retrospective study on effectiveness of activ L total disc replacement: clinical and radiographical results of 1- to 3-year follow-up.

abstract: Abstract Retrospective case series study. To assess the effectiveness of activ L total disc replacement (TDR) on degenerative disc diseases with the clinical and radiographical results. There are few reports on activ L TDR. This is the first one from China. From March 2009 to March 2012, 32 patients with degenerative disc diseases underwent mono- or bisegmental lumbar TDR. Mean age was 45.1 years (32-58 yr). Clinical outcomes were measured by Oswestry Disability Index and Visual analogue scale pre- and postoperatively (1, 2, and 3 yr). Radiographical parameters as range of motion and intervertebral disc height of the index- and adjacent segments were also measured. Prosthesis subsidence and heterotopic ossification were observed during the follow-up period. Work status was also tracked. Thirty patients were available for a mean follow-up of 28.8 months and had complete radiographical data. At the final follow-up, the success rate was 86.7%. Visual analogue scale score for low back pain and leg pain, and Oswestry Disability Index scores significantly improved after surgery. Average intervertebral disc heights of patients with more than 3 years' follow-up at the index segment and upper and lower adjacent segments were 12.87 mm, 12.61 mm, and 11.62 mm, respectively, showing no significant difference compared with preoperative scores. The range of motion of the index and upper adjacent segments showed a significant increase for patients with more than 3 years' follow-up. Changes of range of motion at lower adjacent segment were not significant. We observed tears of the iliac vein in 2 patients, prosthesis subsidence in 3 patients, and heterotopic ossification in 1 patient. At the final follow-up, 18 patients went back to their original work, 8 patients changed jobs, and 4 patients stopped working. The 1- to 3-year follow-up of this cohort of patients showed satisfactory clinical outcomes. The long-term results of activ L TDR need more investigation. 2.


Multidisciplinary Model for Smart Agriculture using Internet-of-Things ( IoT ) , Sensors , Cloud-Computing , Mobile-Computing & Big-Data Analysis


title: A spatio‐temporal reference model of the aging brain

abstract: Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9–91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.


title: New CAPWAP architectures for IEEE 802.11ad based Wi-Fi/WiGig WLANs

abstract: Recently, the unlicensed 60 GHz millimeter wave (mmWave) band has drawn attention as a promising solution for enabling future multi-Gbps wireless local area networks (WLANs). IEEE 802.11 ad (WiGig) standard was developed for 60 GHz WLANs, where directional transmission is utilized to compensate severe propagation losses in mmWave bands. Due to mmWave signals propagation limitations and its high vulnerability to blocking, dense WiGig wireless terminal points (WTPs) deployment strategy is used to ensure continuous mmWave service in a typical target area. To effectively manage mmWave access while overcoming its shortcomings and limitations, interoperability with legacy bands (i.e. Wi-Fi band) via centralized control architectures are needed. Control and provisioning of wireless access points (CAPWAP) protocol, particularity IEEE 802.11 binding, is used to configure, monitor, control and manage a large-scale deployment of centralized IEEE 802.11 WLANs. However, this binding does not support the special and unique features of IEEE 802.11ad specification in addition to the new specifications come from Wi-Fi/WiGig interplay. In this paper, we propose new updates of CAPWAP 802.11 binding for enabling the use of CAPWAP capabilities in conjunction with IEEE 802.11ad-based Wi-Fi/ WiGig WLANs.


title: Twitter Sentiment Analysis: A Bootstrap Ensemble Framework

abstract: Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.


Grounded Theory


title: Successful Islet Transplantation From the Pancreata of Non-Heart-Beating Donors

abstract: Abstract We performed 6 islet transplantations in 4 type 1 diabetes mellitus patients. From September 2003 to April 2007, 23 islet isolations were performed from pancreata of non–heart-beating donors. The pancreata preserved using a 2-layer method or simple cold storage in University of Wisconsin solution were transferred to our cell processing center. The islet isolation was performed according to the Edmonton protocol with some modifications. The immunosuppressive protocol was achieved using sirolimus, tacrolimus, and anti-CD25 antibody (basiliximab). Islet yield was 400 to 491,040 IEQ and purity was 1% to 70%. Stimulation indices upon static incubation were 1.38 to 11.69. All patients who underwent islet transplantation showed positive serum C-peptide levels immediately after transplantation. Although insulin independence was not achieved, they displayed stabilized blood glucose levels, reduced insulin doses, and disappearance of hypoglycemic unawareness. Although stomatitis and diarrhea due to the side effects of sirolimus were observed in 2 patients, there were no severe complications. In patient 1, serum C-peptide levels decreased gradually from 1 year after transplantation. In conclusion, successful islet transplantation was possible using islets isolated from the pancreata of non–heart-beating donors. Further improvements are needed to achieve prolonged graft survival.


title: Minimally invasive kidney transplantation.

abstract: Minimally invasive kidney transplantation (MIKT) procedures, starting with lymphocele fenestration and continuing with laparoscopic donor nephrectomy, have been performed in recipients since 2006. From November 2011 to May 2012, we performed 86 consecutive renal transplantation with 43 conventional kidney transplantations (COKT) and 43 MIKTs using an apendectomy like, 4 to 5-cm incision. There were no significant differences between the groups according to age, sex, body mass index, donor type, surgical side, donor kidney or artery number. Mean operative time in the MIKT group was 164.2 minutes versus 153.5 minutes in the COKT group. The cold ischemia times in MIKT and COKT groups were 60.8 and 63.3 minutes, respectively. The lengths of hospital stay, blood creatinine levels at postoperative days 7, 30, and 90, and the 90th day creatinine clearances were similar. In conclusion, considering that the complication rate was equal and the graft functions equal, MIKT seemed to be a safe method for renal transplantation.


title: Fine-Grained Access Control in the Chirp Distributed File System

abstract: Although the distributed file system is a widely used technology in local area networks, it has seen less use on the wide area networks that connect clusters, clouds, and grids. One reason for this is access control: existing file system technologies require either the client machine to be fully trusted, or the client process to hold a high value user credential, neither of which is practical in large scale systems. To address this problem, we have designed a system for fine-grained access control which dramatically reduces the amount of trust required of a batch job accessing a distributed file system. We have implemented this system in the context of the Chirp user-level distributed file system used in clusters, clouds, and grids, but the concepts can be applied to almost any other storage system. The system is evaluated to show that performance and scalability are similar to other authentication methods. The paper concludes with a discussion of integrating the authentication system into workflow systems.


title: Systematic Review of Clustering High-Dimensional and Large Datasets

abstract: Technological advancement has enabled us to store and process huge amount of data in relatively short spans of time. The nature of data is rapidly changing, particularly its dimensionality is more commonly multi- and high-dimensional. There is an immediate need to expand our focus to include analysis of high-dimensional and large datasets. Data analysis is becoming a mammoth task, due to incremental increase in data volume and complexity in terms of heterogony of data. It is due to this dynamic computing environment that the existing techniques either need to be modified or discarded to handle new data in multiple high-dimensions. Data clustering is a tool that is used in many disciplines, including data mining, so that meaningful knowledge can be extracted from seemingly unstructured data. The aim of this article is to understand the problem of clustering and various approaches addressing this problem. This article discusses the process of clustering from both microviews (data treating) and macroviews (overall clustering process). Different distance and similarity measures, which form the cornerstone of effective data clustering, are also identified. Further, an in-depth analysis of different clustering approaches focused on data mining, dealing with large-scale datasets is given. These approaches are comprehensively compared to bring out a clear differentiation among them. This article also surveys the problem of high-dimensional data and the existing approaches, that makes it more relevant. It also explores the latest trends in cluster analysis, and the real-life applications of this concept. This survey is exhaustive as it tries to cover all the aspects of clustering in the field of data mining.


title: A scalable location service for geographic ad hoc routing

abstract: GLS is a new distributed location service which tracks mobile node locations. GLS combined with geographic forwarding allows the construction of ad hoc mobile networks that scale to a larger number of nodes than possible with previous work. GLS is decentralized and runs on the mobile nodes themselves, requiring no fixed infrastructure. Each mobile node periodically updates a small set of other nodes (its location servers) with its current location. A node sends its position updates to its location servers without knowing their actual identities, assisted by a predefined ordering of node identifiers and a predefined geographic hierarchy. Queries for a mobile node's location also use the predefined identifier ordering and spatial hierarchy to find a location server for that node. Experiments using the ns simulator for up to 600 mobile nodes show that the storage and bandwidth requirements of GLS grow slowly with the size of the network. Furthermore, GLS tolerates node failures well: each failure has only a limited effect and query performance degrades gracefully as nodes fail and restart. The query performance of GLS is also relatively insensitive to node speeds. Simple geographic forwarding combined with GLS compares favorably with Dynamic Source Routing (DSR): in larger networks (over 200 nodes) our approach delivers more packets, but consumes fewer network resources.


title: The End of a Myth: Distributed Transactions Can Scale

abstract: The common wisdom is that distributed transactions do not scale. But what if distributed transactions could be made scalable using the next generation of networks and a redesign of distributed databases? There would be no need for developers anymore to worry about co-partitioning schemes to achieve decent performance. Application development would become easier as data placement would no longer determine how scalable an application is. Hardware provisioning would be simplified as the system administrator can expect a linear scale-out when adding more machines rather than some complex sub-linear function, which is highly application specific. ::: In this paper, we present the design of our novel scalable distributed database system NAM-DB and show that distributed transactions with the very common Snapshot Isolation guarantee can indeed scale using the next generation of RDMA-enabled network technology without any inherent bottlenecks. Our experiments with the TPC-C benchmark show that our system scales linearly to over 6.5 million distributed transactions per second on 56 machines.


title: Probabilistic and grouping methods for morphological root identification for Afaan Oromo

abstract: Morphological models are used in many natural language processing tasks including machine translation and speech recognition. We investigated probabilistic and grouping methods to develop a morphological root identification model for Afaan Oromo. In this paper, we have experimentally shown that the proposed methods can improve the morphological root identification performance of some state-of-the-art methods.


title: Performance of Feature Selection Methods

abstract: High-throughput biological technologies offer the promise of finding feature sets to serve as biomarkers for medical applications; however, the sheer number of potential features (genes, proteins, etc.) means that there needs to be massive feature selection, far greater than that envisioned in the classical literature. This paper considers performance analysis for feature-selection algorithms from two fundamental perspectives: How does the classification accuracy achieved with a selected feature set compare to the accuracy when the best feature set is used and what is the optimal number of features that should be used? The criteria manifest themselves in several issues that need to be considered when examining the efficacy of a feature-selection algorithm: (1) the correlation between the classifier errors for the selected feature set and the theoretically best feature set; (2) the regressions of the aforementioned errors upon one another; (3) the peaking phenomenon, that is, the effect of sample size on feature selection; and (4) the analysis of feature selection in the framework of high-dimensional models corresponding to high-throughput data.


title: Survey on security threats of smartphones in Internet of Things

abstract: Internet of Things (IoT) is an emerging concept which will interconnect billions of devices (such as smartphones, sensors and other networking devices), to communicate with each other. This interconnection of devices can be beneficial in many ways such as improving location based services and timely coordination. However, this also impose severe security concerns specially on smartphones which contains sensitive data. In this paper, we provide a comprehensive analysis of different security issues in smartphones which are integral part of IoT. We provide an easy and concise overview of different security threats. Our categorization helps in identifying specific security breach and applying relevant countermeasures.


Curbing Android Permission Creep


title: Adult age differences in decision making across domains: Increased discounting of social and health-related rewards.

abstract: Although research on aging and decision making continues to grow, the majority of studies examine decisions made to maximize monetary earnings or points. It is not clear whether these results generalize to other types of rewards. To investigate this, we examined adult age differences in 92 healthy participants aged 22 to 83. Participants completed 9 hypothetical discounting tasks, which included 3 types of discounting factors (time, probability, effort) across 3 reward domains (monetary, social, health). Participants made choices between a smaller magnitude reward with a shorter time delay/higher probability/lower level of physical effort required and a larger magnitude reward with a longer time delay/lower probability/higher level of physical effort required. Older compared with younger individuals were more likely to choose options that involved shorter time delays or higher probabilities of experiencing an interaction with a close social partner or receiving health benefits from a hypothetical drug. These findings suggest that older adults may be more motivated than young adults to obtain social and health rewards immediately and with certainty. (PsycINFO Database Record


title: Generating 3D Building Models from Architectural Drawings: A Survey

abstract: Automatically generating 3D building models from 2D architectural drawings has many useful applications in the architecture engineering and construction community. This survey of model generation from paper and CAD-based architectural drawings covers the common pipeline and compares various algorithms for each step of the process.


title: Analysis of the Performance Enhancement of MIMO Systems Employing Circular Polarization

abstract: The advantages of adopting circular polarization in multiple-input-multiple-output (MIMO) systems are illustrated for both line-of-sight (LOS) and multipath propagation. More in detail, an analysis of the MIMO performance attainable by employing orthogonal circularly polarized (CP) radiators with respect to orthogonal linearly polarized (LP) ones, has been addressed. At first, an accurate analysis is presented aimed at the evaluation of the channel matrix by comprehensively including also the effects of the antenna in LOS condition. In particular, the channel matrix has been calculated as a function of the antenna parameters and orientation, demonstrating that CP radiators are capable of obtaining better average values of the matrix eigenvalues with respect to LP ones. The analysis is therefore completed by evaluating the characteristics of a CP MIMO system operating in indoor environment representing this latter a more challenging condition where multipath propagation occurs. In this latter case, some meaningful numerical experiments have been performed by using a reliable ray-tracing solver, followed by a measurements campaign conducted in a real environment for validation purposes. Measurements, which are in good agreement with simulations, confirm the benefits of adopting circular polarization in MIMO systems with respect to LP.


title: Control of Memory, Active Perception, and Action in Minecraft

abstract: In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existing deep reinforcement learning (DRL) architectures with our new memory-based DRL architectures. These tasks are designed to emphasize, in a controllable manner, issues that pose challenges for RL methods including partial observability (due to first-person visual observations), delayed rewards, high-dimensional visual observations, and the need to use active perception in a correct manner so as to perform well in the tasks. While these tasks are conceptually simple to describe, by virtue of having all of these challenges simultaneously they are difficult for current DRL architectures. Additionally, we evaluate the generalization performance of the architectures on environments not used during training. The experimental results show that our new architectures generalize to unseen environments better than existing DRL architectures.


title: Optimizing a virtual impedance droop controller for parallel inverters

abstract: This paper explores a previously developed frequency and voltage droop control scheme via virtual impedance emulation. Previously, the control scheme utilized a Kalman observer to extract the fundamental voltage and frequency which increased the complexity of the control loop. This paper investigates the use of a second-order generalized integrator-frequency locked loop (SOGI-FLL) to accomplish the same task in a straightforward fashion. Moreover, a linear quadratic Gaussian (LQG) servo controller was used to implement the control scheme instead of the LQG optimal control approach used formerly. The performance of the modified control algorithm was studied using Matlab/Simulink simulations under various linear and non-linear load conditions. Furthermore, two inverters were operated in parallel to assess the load-sharing capability of the modified control method. Simulations showed that the optimized control scheme kept output voltage and frequency within acceptable levels. Additionally, simulations exhibited the load sharing capability of the control scheme.


Using LWD Tools To Enhance Drilling Operations - A Case Study


title: Monocular Object Instance Segmentation and Depth Ordering with CNNs

abstract: In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural nets and train them to directly predict instance-level segmentations where the instance ID encodes the depth ordering within image patches. To provide a coherent single explanation of an image we develop a Markov random field which takes as input the predictions of convolutional neural nets applied at overlapping patches of different resolutions, as well as the output of a connected component algorithm. It aims to predict accurate instance-level segmentation and depth ordering. We demonstrate the effectiveness of our approach on the challenging KITTI benchmark and show good performance on both tasks.


title: An Open Trial of a Transdiagnostic Cognitive-Behavioral Group Therapy for Anxiety Disorder ☆

abstract: Transdiagnostic models of anxiety, and cognitive-behavioral treatments based on these models, have been gaining increased attention in recent years. Preliminary efficacy studies generally suggest strong treatment effects, although few of these studies have examined to what extent treatment effects are similar across clients with different anxiety disorders. The purpose of the current study was to examine the efficacy of a 12-week transdiagnostic group cognitive-behavioral therapy for anxiety disorders and compare outcome across diagnoses. Mixed-effect regression modeling of data from 52 participants with anxiety disorders (predominantly panic disorder and social phobia) participating in an open outcome trial indicated that participants tended to improve over treatment, with no differential outcome for any primary or comorbid disorders. The results of this study add to the growing evidence base for transdiagnostic anxiety treatment models and provide preliminary support for the assumption that individuals with different anxiety diagnoses can be treated equally within the same treatment protocol.


title: Learning a 3D descriptor for cross-source point cloud registration from synthetic data

abstract: As the development of 3D sensors, registration of 3D data (e.g. point cloud) coming from different kind of sensor is dispensable and shows great demanding. However, point cloud registration between different sensors is challenging because of the variant of density, missing data, different viewpoint, noise and outliers, and geometric transformation. In this paper, we propose a method to learn a 3D descriptor for finding the correspondent relations between these challenging point clouds. To train the deep learning framework, we use synthetic 3D point cloud as input. Starting from synthetic dataset, we use region-based sampling method to select reasonable, large and diverse training samples from synthetic samples. Then, we use data augmentation to extend our network be robust to rotation transformation. We focus our work on more general cases that point clouds coming from different sensors, named cross-source point cloud. The experiments show that our descriptor is not only able to generalize to new scenes, but also generalize to different sensors. The results demonstrate that the proposed method successfully aligns two 3D cross-source point clouds which outperforms state-of-the-art method.


title: TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations

abstract: We present TranslatorX, a web server designed to align protein-coding nucleotide sequences based on their corresponding amino acid translations. Many comparisons between biological sequences (nucleic acids and proteins) involve the construction of multiple alignments. Alignments represent a statement regarding the homology between individual nucleotides or amino acids within homologous genes. As protein-coding DNA sequences evolve as triplets of nucleotides (codons) and it is known that sequence similarity degrades more rapidly at the DNA than at the amino acid level, alignments are generally more accurate when based on amino acids than on their corresponding nucleotides. TranslatorX novelties include: (i) use of all documented genetic codes and the possibility of assigning different genetic codes for each sequence; (ii) a battery of different multiple alignment programs; (iii) translation of ambiguous codons when possible; (iv) an innovative criterion to clean nucleotide alignments with GBlocks based on protein information; and (v) a rich output, including Jalview-powered graphical visualization of the alignments, codon-based alignments coloured according to the corresponding amino acids, measures of compositional bias and first, second and third codon position specific alignments. The TranslatorX server is freely available at http://translatorx.co.uk.


Advanced control and on-line process optimization in multilayer structures


Learning from Noisy Labels with Deep Neural Networks.


title: Spice: Socially-driven learning-based mobile media prefetching

abstract: Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide Quality of Experience (QoE) support for mobile OSN services, in this paper we propose a socially-driven learning-based framework, namely Spice, for media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17,000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 67.2% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve speed-up of a factor of 1000 over the local data training execution on smartphones.


title: Does multitasking with mobile phones affect learning? A review

abstract: Mobile phone multitasking is widely considered to be a major source of distraction in academic performance. This paper attempts to review the emerging literature by focusing on three questions concerning the influence of mobile phone multitasking on academic performance: (a) How does mobile phone multitasking impair learning? (b) Why does mobile phone use impair learning? (c) How to prevent from mobile phone distraction? We use multiple strategies to locate the existing research literature and identified 132 studies published during 1999-2014. The mobile phone multitasking and distractibility are reviewed in three major aspects: distraction sources (ring of mobile phone, texting, and social application), distraction targets (reading and attending), and distraction subjects (personality, gender, and culture). We also compare the results of these studies with the findings on mobile phone multitasking and driving, the earliest area of mobile phone multitasking research. Both limitations of existing research and future research directions are discussed. Mobile phone multitasking is prevalent among learners.Multitasking with mobile phone distracts learning via different ways and mechanisms.The effect of multitasking varies on different mobile phone uses, learning tasks, and learners.


title: Mitigating Docker Security Issues

abstract: It is very easy to run applications in Docker. Docker offers an ecosystem that offers a platform for application packaging, distributing and managing within containers. However, Docker platform is yet not matured. Presently, Docker is less secured as compare to virtual machines (VM) and most of the other cloud technologies. The key of reason of Docker inadequate security protocols is containers sharing of Linux kernel, which can lead to risk of privileged escalations. This research is going to outline some major security vulnerabilities at Docker and counter solutions to neutralize such attacks. There are variety of security attacks like insider and outsider. This research will outline both types of attacks and their mitigations strategies. Taking some precautionary measures can save from huge disasters. This research will also present Docker secure deployment guidelines. These guidelines will suggest different configurations to deploy Docker containers in a more secure way.


title: Data-driven models for monthly streamflow time series prediction

abstract: Data-driven techniques such as Auto-Regressive Moving Average (ARMA), K-Nearest-Neighbors (KNN), and Artificial Neural Networks (ANN), are widely applied to hydrologic time series prediction. This paper investigates different data-driven models to determine the optimal approach of predicting monthly streamflow time series. Four sets of data from different locations of People's Republic of China (Xiangjiaba, Cuntan, Manwan, and Danjiangkou) are applied for the investigation process. Correlation integral and False Nearest Neighbors (FNN) are first employed for Phase Space Reconstruction (PSR). Four models, ARMA, ANN, KNN, and Phase Space Reconstruction-based Artificial Neural Networks (ANN-PSR) are then compared by one-month-ahead forecast using Cuntan and Danjiangkou data. The KNN model performs the best among the four models, but only exhibits weak superiority to ARMA. Further analysis demonstrates that a low correlation between model inputs and outputs could be the main reason to restrict the power of ANN. A Moving Average Artificial Neural Networks (MA-ANN), using the moving average of streamflow series as inputs, is also proposed in this study. The results show that the MA-ANN has a significant improvement on the forecast accuracy compared with the original four models. This is mainly due to the improvement of correlation between inputs and outputs depending on the moving average operation. The optimal memory lengths of the moving average were three and six for Cuntan and Danjiangkou, respectively, when the optimal model inputs are recognized as the previous twelve months.


title: as a Period of Extended Optional Infinitive

abstract: English-speaking children with specific language impairment (SLI) are known to have particular difficulty with the acquisition of grammatical morphemes that carry tense and agreement features, such...


title: Borosilicate glass matrix composites containing multi-wall carbon nanotubes

abstract: Abstract Carbon nanotubes (CNTs) are in the range of the most promising reinforcing materials for structural composites, since they possess extraordinary Young’s modulus and mechanical strength. In this study, multi-wall CNTs are considered for the first time as reinforcing elements in a borosilicate glass matrix. The densification behaviour and the microstructure characterisation of composites containing 10 wt.% multi-wall CNTs are presented. The coating of CNTs with silica by a sol–gel process is effective in promoting the homogenisation and the densification of the CNTs/glass powder mixtures. Cold pressing and pressureless sintering or hot pressing at moderate temperatures were employed to fabricate the composites. Pressureless sintering did not lead to well densified composites. The compaction degree achieved by hot pressing (85% theoretical density), on the contrary, is found to be promising for the development of structural composites, although optimisation of the hot-pressing parameters is required to achieve higher densities.


title: Generalized Risk-Aversion in Stochastic Multi-Armed Bandits

abstract: We consider the problem of minimizing the regret in stochastic multi-armed bandit, when the measure of goodness of an arm is not the mean return, but some general function of the mean and the variance.We characterize the conditions under which learning is possible and present examples for which no natural algorithm can achieve sublinear regret.


title: Multitarget miss distance via optimal assignment

abstract: The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.


title: Psycho-education in bipolar disorder: effect on expressed emotion

abstract: Abstract In a waiting-list controlled study on a multi-family psycho-educational intervention in bipolar disorder, key relatives in the treatment group showed a significant change from high to low levels of expressed emotion (EE) compared with the control group. In addition, patients with low-EE key relatives had a significantly lower number of hospital admissions compared with those living with high-EE key relatives. The multi-family groups were well received by the participants, and there were only a few drop-outs. © 1997 Elsevier Science Ireland Ltd.


title: Automated classification of non-functional requirements

abstract: This paper describes a technique for automating the detection and classification of non-functional requirements related to properties such as security, performance, and usability. Early detection of non-functional requirements enables them to be incorporated into the initial architectural design instead of being refactored in at a later date. The approach is used to detect and classify stakeholders’ quality concerns across requirements specifications containing scattered and non-categorized requirements, and also across freeform documents such as meeting minutes, interview notes, and memos. This paper first describes the classification algorithm and then evaluates its effectiveness through reporting a series of experiments based on 30 requirements specifications developed as term projects by MS students at DePaul University. A new and iterative approach is then introduced for training or retraining a classifier to detect and classify non-functional requirements (NFR) in datasets dissimilar to the initial training sets. This approach is evaluated against a large free-form requirements document obtained from Siemens Logistics and Automotive Organization. Although to the NFR classifier is unable to detect all of the NFRs, it is useful for supporting an analyst in the error-prone task of manually discovering NFRs, and furthermore can be used to quickly analyse large and complex documents in order to search for NFRs.


A Guide to Cost Allocation Procedures: Implementing Strategies for Universal Pre-Kindergarten.


title: 3D texture analysis on MRI images of Alzheimer’s disease

abstract: This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimer’s disease (AD). T1-weighted magnetic resonance (MR) images were obtained from 17 AD patients and 17 age and gender-matched healthy controls. 3D texture features were extracted from the circular 3D ROIs placed using a semi-automated technique in the hippocampus and entorhinal cortex. We found that classification accuracies based on texture analysis of the ROIs varied from 64.3% to 96.4% due to different ROI selection, feature extraction and selection options, and that most 3D texture features selected were correlated with the mini-mental state examination (MMSE) scores. The results indicated that 3D texture could detect the subtle texture differences between tissues in AD patients and normal controls, and texture features of MR images in the hippocampus and entorhinal cortex might be related to the severity of AD cognitive impairment. These results suggest that 3D texture might be a useful aid in AD diagnosis.


title: Chaos-based support vector regressions for exchange rate forecasting

abstract: This study implements a chaos-based model to predict the foreign exchange rates. In the first stage, the delay coordinate embedding is used to reconstruct the unobserved phase space (or state space) of the exchange rate dynamics. The phase space exhibits the inherent essential characteristic of the exchange rate and is suitable for financial modeling and forecasting. In the second stage, kernel predictors such as support vector machines (SVMs) are constructed for forecasting. Compared with traditional neural networks, pure SVMs or chaos-based neural network models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced.


title: A Reliability-Augmented Particle Filter for Magnetic Fingerprinting Based Indoor Localization on Smartphone

abstract: Using magnetic field data as fingerprints for smartphone indoor positioning has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which result in unreliable systems, or impose strong restrictions on smartphone such as fixed phone orientation, which are not practical for real-life use. In this paper, we present a novel indoor positioning system for smartphones, which is built on our proposed reliability-augmented particle filter. We create several innovations on the motion model, the measurement model, and the resampling model to enhance the basic particle filter. To minimize errors in motion estimation and improve the robustness of the basic particle filter, we propose a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model, combining a new magnetic fingerprinting model and the existing magnitude fingerprinting model, to improve system performance, and importantly avoid calibrating magnetometers for different smartphones. In addition, we propose an adaptive sampling algorithm to reduce computation overhead, which in turn improves overall usability tremendously. Finally, we also analyze the “Kidnapped Robot Problem” and present a practical solution. We conduct comprehensive experimental studies, and the results show that our system achieves an accuracy of 1 $\sim$ 2 m on average in a large building.


title: A Comprehensive Analysis on the Security Threats and their Countermeasures of IoT

abstract: Internet of Things referred as a pervasive network ::: architecture which provides services to the physical world by ::: processing and analyzing data. In this modern era Internet of ::: Things has been shown much significance and rapidly developing ::: by connecting heterogeneous devices with various technologies. By ::: this way interconnectivity of large number of electronic devices ::: connected with the IoT network leads the risk of security and ::: confidentiality of data. This paper analyzes different security ::: issues, their counter measures and discusses the future directions ::: of security in IoT. Furthermore, this paper also discusses essential ::: technologies of security like encryption in the scenario of IoT for ::: the prevention of harmful threats in the light of latest research.


Bringing Deployable Key Transparency to End Users


title: 3D Object Recognition in Cluttered Scenes With Robust Shape Description and Correspondence Selection

abstract: Recognizing 3-D objects in cluttered scenes is a challenging task. Common approaches find potential feature correspondences between a scene and candidate models by matching sampled local shape descriptors and select a few correspondences with the highest descriptor similarity to identify models that appear in the scene. However, real scans contain various nuisances, such as noise, occlusion, and featureless object regions. This makes selected correspondences have a certain portion of false positives, requiring adopting the time-consuming model verification many times to ensure accurate recognition. This paper proposes a 3-D object recognition approach with three key components. First, we construct a Signature of Geometric Centroids descriptor that is descriptive and robust, and apply it to find high-quality potential feature correspondences. Second, we measure geometric compatibility between a pair of potential correspondences based on isometry and three angle-preserving components. Third, we perform effective correspondence selection by using both descriptor similarity and compatibility with an auxiliary set of “less” potential correspondences. Experiments on publicly available data sets demonstrate the robustness and/or efficiency of the descriptor, selection approach, and recognition framework. Comparisons with the state-of-the-arts validate the superiority of our recognition approach, especially under challenging scenarios.


title: Semi-Supervised Facial Animation Retargeting

abstract: This paper presents a system for facial animation retargeting that al- lows learning a high-quality mapping between motion capture data and arbitrary target characters. We address one of the main chal- lenges of existing example-based retargeting methods, the need for a large number of accurate training examples to define the corre- spondence between source and target expression spaces. We show that this number can be significantly reduced by leveraging the in- formation contained in unlabeled data, i.e. facial expressions in the source or target space without corresponding poses. In contrast to labeled samples that require time-consuming and error-prone manual character posing, unlabeled samples are easily obtained as frames of motion capture recordings or existing animations of the target character. Our system exploits this information by learning a shared latent space between motion capture and character param- eters in a semi-supervised manner. We show that this approach is resilient to noisy input and missing data and significantly improves retargeting accuracy. To demonstrate its applicability, we integrate our algorithm in a performance-driven facial animation system.


Indian Ocean in the Globalizing World


title: Find, Remind, and Bind: The Functions of Gratitude in Everyday Relationships

abstract: Though interest in the emotion of gratitude has historically focused on its role in social exchange, new evidence suggests a different and more important role for gratitude in social life. The find-remind-and-bind theory of gratitude posits that the positive emotion of gratitude serves the evolutionary function of strengthening a relationship with a responsive interaction partner (Algoe, Haidt, & Gable, 2008). The current article identifies prior, economic models of gratitude, elaborates on unique features of the find-remind-and-bind theory, reviews the accumulating evidence for gratitude in social life in light of this novel perspective, and discusses how the find-remind-and-bind theory is relevant to methodology and hypothesis testing. In sum, within the context of reciprocally-altruistic relationships, gratitude signals communal relationship norms and may be an evolved mechanism to fuel upward spirals of mutually responsive behaviors between recipient and benefactor. In this way, gratitude is important for forming and maintaining the most important relationships of our lives, those with the people we interact with every day.


title: A Worm Propagation Model based on Scale Free Network Structures and People's Email Acquaintance Profiles

abstract: In light of the rise of malicious attacks on the internet and the various networks and applications attached to it, new approaches towards modeling worm activity in networks is called for. One frequently utilized method for W32/Novarg [7], Sober X, Netsky P and Mytob ED [18] propagation exploited by worms is through the victim's contact book. The contact book, which reflects the acquaintance profiles of people, is used as a ``hit-list'', to which the worm can try and send itself in order to spread fast. In this paper we propose a discrete worm propagation model that relies upon a combined email and Instant Messaging (IM) communication behavior of users in a scale free environment. We also model the effect in propagation based on user reaction when a threat is recognized, the installation and update of antivirus software as well as the network connectivity, arising conclusions about the behavior of the network infrastructure in presence of a worm. Our analysis is based on Wormald's differential equations method for approximating ``well-behaving'' random processes with deterministic functions.