title: Zero-day malware detection based on supervised learning algorithms of API call signatures

abstract: Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. ::: ::: The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naive Bayes (NB) Algorithm, k--Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO -- PolyKernel, SMO -- Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. ::: ::: Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.


title: Elucidating the Role of TREM2 in Alzheimer’s Disease

abstract: Alzheimer's disease (AD) is the sixth leading cause of death in the United States and the most common cause of dementia in the elderly. Genetic factors, such as rare variants in the microglial-expressed gene TREM2 , strongly impact the lifetime risk of developing AD. Several recent studies have described dramatic TREM2-dependent phenotypes in mouse models of amyloidosis that point to an important role for TREM2 in regulating the response of the innate immune system to Aβ pathology. Furthermore, elevations in the CSF levels of soluble TREM2 fragments implicate changes in inflammatory pathways as occurring coincident with markers of neuronal damage and the onset of clinical dementia in AD. Here, we review the rapidly developing literature surrounding TREM2 in AD that may provide novel insight into the broader role of the innate immune system in neurodegenerative disease.


title: Image Super-Resolution Based on Dual Path Network

abstract: Recently, Convolutional Neural Network (CNN) ba-sed methods have demonstrated high-quality reconstruction for Single Image Super-Resolution (SISR). Particularly, resi-dual learning shows improved performance. For the SR recon-struction performance of the CNN-based models, the design of model architecture is very important. In this paper, we present SRDPN, a super-resolution network based on Dual Path Net-work (DPN). The DPN combines the advantages of the state-of-the-art Residual Network (ResNet) and Dense Convolu-tional Network (DenseNet), which enjoys lower computational cost, lower memory consumption and higher parameter effici-ency. Extensive qualitative and quantitative evaluations on benchmark datasets show that our proposed method performs better than the state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and image quality.


DSC-17A-2 SMOOTH AND TIME-OPTIMAL TRAJECTORY PLANNING FOR INDUSTRIAL MANIPULATORS ALONG SPECIFIED PATHS


title: Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

abstract: BackgroundWe present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population.MethodsWe used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories.ResultsFor Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold.ConclusionsSupport vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.


title: Pajek - Program for Large Network Analysis

abstract: Large networks, having thousands of vertices and lines, can be found in many different areas, e. g: genealogies, flo w graphs of programs, molecule, computer networks, transportation networks, social networks, intra/inter organisational networks ... Many standard network algorithms are very time and space consuming and therefore unsuitable for analysis of such networks. In the article we present some approaches to analysis and visualisation of large networks implemented in programPajek. Some typical examples are also given.


title: Real-Time View Recognition and Event Detection for Sports Video.” JVCIR

abstract: In this paper, we present a general framework and new effective algorithms to detect the syntactic structures that are at a level higher than shots. In sports video, such high-level structures are often characterized by the specific views (e.g., pitching or serve) and the subsequent temporal transition patterns within each temporal structural segment. We have developed robust statistical models for detecting the domain-specific views with real-time performance and high accuracy. The models combine domain-independent global color filtering method and domain-specific constraints on the spatio-temporal properties of the segmented regions (e.g., locations, shapes, and motion of the objects). The real-time performance was accomplished by using efficient compressed-domain processing at the front end and computational expensive object-level processing on filtered candidates only. High-level events (e.g., strokes, net plays, and baseline plays) are also detected after the view recognition. Results of such structure and event detection allow for efficient browsing and summarization of long sports video programs.


title: Strategic IT Alignment: Twenty-Five Years On

abstract: All research streams can trace their lineage to an initial paper or series of papers. The information systems (IS) field – despite its relative youthfulness – can point to examples of seminal research by Davis (1989) on technology acceptance or Brynjolfsson and Hitt (1996) on information technology (IT) payoffs as the foundation of whole new areas of research. After almost a quarter century and 3200 citations (as of October 2014), many researchers would also include work by Henderson and Venkatraman (1993) – first published in the IBM Systems Journal under the heading, ‘Strategic Alignment: Leveraging Information Technology for Transforming Organizations’ – on the list of seminal and transformative IS publications.


title: Cryptography without (Hardly Any) Secrets

abstract: The absolute privacy of the secret-keys associated with cryptographic algorithms has been the corner-stone of modern cryptography. Still, in practice, keys do get compromised at times for a variety or reasons. A particularly disturbing loss of secrecy is as a result of side channel attacks. These attacks exploit the fact that every cryptographic algorithm is ultimately implemented on a physical device and such implementations enable `observations' which can be made and measured on secret data and secret keys. Indeed, side channel observations can lead to information leakage about secret keys, which in turn can and have lead to complete breaks of systems which have been proved mathematically secure, without violating any of the underlying mathematical principles or assumptions. Traditionally, such attacks have been followed by ad-hoc `fixes' which make particular implementation invulnerable to particular attacks, only to potentially be broken anew by new examples of side-channel attacks. ::: ::: In recent years, starting with the work on physically observable cryptography by [MR04] Micali and Reyzin, a new goal has been set to build a general theory of physical security against a large class of families of side channel attacks which one may call computational side-channel attacks. These include any side channel attack in which leakage of information on secrets occurs as a result of performing a computation on secrets. Some well-known examples of such attacks include Kocher's timing attacks [Koc96] and power attacks [kJJ99]. A basic defining feature of a computational side-channel attack, as put forth by [MR04] is that computation and only computation leaks information . Namely, portions of memory which are not involved in computation do not leak information. A growing number of works [MR04, ISW03, PSP + 08, GKR08, DP08] have proposed cryptographic algorithms provably robust against computational side-channel attacks, by limiting in various ways the portions of the secret key which are involved in each step of the computation. ::: ::: In the work on one time programs this is taken to an extreme [GKR08] . Goldwasser, Tauman-Kalai, and Rothblum show how by using a new proposed type of secure-memory which never touches any secrets or data which is not ultimately fully revealed, it is possible to perform any secure computations which is provably secure against all computational side channel attacks. ::: ::: Memory-attacks proposed by Akavia, Goldwasser, and Vaikuntanathan [AGV09] are an entirely very different family of side-channel attacks that are not included in the computational side-channel attack family, as they violate the basic premise of [MR04] that only computation leaks information. This class of attacks was inspired by (although not restricted to) the memory-freezing attack introduced recently by Halderman et al. [HSH + 08] , where its is shown how to measure a significant fraction of the bits of secret keys if the keys were ever stored in a part of memory (e.g. DRAM), which could be accessed by an adversary even after the power of the machine has been turned off. Thus, information leaks about portions of the secret key which may have never been involved in any computation. A memory-attack leaks a bounded number of bits computed as a result of applying an arbitrary function of bounded length (smaller than than the size of the secret key) to the content of the secret key of a cryptographic algorithm. Naturally, this family of attacks is inherently parameterized and quantitative in nature, as if the attack would uncover the entire secret key at the outset, there would be no hope for any cryptography. The work of [AGV09] exhibits a public-key encryption algorithm which is especially robust against memory-attacks. Its security is based on the computationally intractability of the learning with errors (LWE) problem which is related to the intractability of approximating the length of the shortest vector in an integer lattice. Finally, a new interesting variant on the idea of memory attacks, had been proposed by Tauman-Kalai etal [DTKL09] in their work on security with auximlary-inputs. They propose to replace the restriction of revealing a length shrinking function of the secret, to revealing functions of the secret which are exponentially hard to invert. ::: ::: In this talk we will survery this development, with special emphasis on the works of [GKR08, AGV09, DTKL09].


title: Communication Efficient Distributed Optimization using an Approximate Newton-type Method

abstract: We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably \emph{improves} with the data size, requiring an essentially constant number of iterations under reasonable assumptions. We provide theoretical and empirical evidence of the advantages of our method compared to other approaches, such as one-shot parameter averaging and ADMM.


The Rocket Chip Generator Krste


title: Embodiment and cognitive learning --- can a humanoid robot help children with autism to learn about tactile social behaviour?

abstract: The work presented in this paper is part of our investigation in the ROBOSKIN project. The project aims to develop new robot capabilities based on the tactile feedback provided by novel robotic skin. The main objective of the project is to develop cognitive mechanisms to improve human-robot interaction capabilities. One application domain that is investigated in the project is robot-assisted play in the context of autism therapy. The article provides a case study evaluation of segments of trials where tactile interactions were observed between children with autism and the humanoid robot KASPAR which was equipped with the newly developed tactile sensing capabilities. A preliminary observational analysis was undertaken which applied, in abbreviated form, certain principles from ethnography and conversation analysis. The analysis first reports initial observations concerning range of tactile behaviours that children displayed towards KASPAR and the change in these across the trials. Subsequently the analysis examines in detail one sequence of interaction in which a child's tactile actions towards KASPAR are considered in terms of their responsiveness to the sequence of interaction in which they occur --- and specifically to the intricate details of KASPAR's responses to the child's tactile behaviour. In this way the paper suggests that children appear to interact in a tactile manner quite spontaneously with KASPAR, that the child's tactile actions become modified through exposure to KASPAR and that children with an Autistic Spectrum Disorder (ASD) can demonstrate a marked responsiveness to the behaviours that KASPAR displays in sequences of tactile interaction.


title: Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding

abstract: There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations. An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.


title: Trajectory Based Autonomous Vehicle Following using a Robotic Driver

abstract: An autonomous vehicle following system including control approaches is presented in this paper. An existing robotic driver is used to control a standard passenger vehicle such that no modications to the car are necessary. Only information about the relative position of the lead vehicle and the motion of the following vehicle is required, and methods are presented to construct a reference trajectory to enable accurate following. A laser scanner is used to detect the lead vehicle and the following vehicle’s ego-motion is sensed using an IMU and wheel encoder. An algorithm was developed and tested to locate the lead vehicle with RMS position and orientation errors of 65mm and 5:8 respectively. Several trajectory-based lateral controllers were tested in simulation and then experimentally, with the best controller having an RMS lateral deviation of 37cm from the path of the lead vehicle. A new trajectorybased spacing controller was tested in simulation which allows the following vehicle to reproduce the velocity of the lead vehicle.


title: Bots and political influence: a sociotechnical investigation of social network capital

abstract: This study explains how bots interact with human users and influence conversational networks on Twitter. We analyze a high-stakes political environment, the UK general election of May 2015, asking human volunteers to tweet from purpose-made Twitter accounts—half of which had bots attached—during three events: the last Prime Minister’s Question Time before Parliament was dissolved (#PMQs), the first leadership interviews of the campaign (#BattleForNumber10), and the BBC Question Time broadcast of the same evening (#BBCQT). Based on previous work, our expectation was that our intervention would make a significant difference to the evolving network, but we found that the bots we used had very little effect on the conversation network at all. There are economic, social, and temporal factors that impact how a user of bots can influence political conversations. Future research needs to account for these forms of capital when assessing the impact of bots on political discussions.


title: Bandwidth management using software defined network and comparison of the throughput performance with traditional network

abstract: Software Defined Networking (SDN) is a new network design which brings certain advantageous to the network management compared to the Traditional Network. This research study implements best effort based traditional network. The ability of SDN to support better allocation of bandwidth makes it possible to apply the SDN concept in this research study in order to manage the usage of limited bandwidth in the network. Available network bandwidth must be managed efficiently to ensure a stable network throughput to be delivered to the end user. In SDN, the bandwidth can be managed by utilizing per-port output queues and bandwidth reservation according to the needs of the traffic. This research study assumes the traffics in the network are classified into Sensitive Traffics and Non-Sensitive Traffics. The purpose of this research study is to propose a new model. The model implemented the SDN concept to replace the Traditional Network concept in order to simplify the network bandwidth management while giving priority to the Sensitive Traffics to access the network throughput. The performance of the network throughput of the proposed model with the Traditional Network also has been compared. Preliminary results show the sensitive traffics successfully achieved a higher throughput compared with the non-sensitive traffic.


title: Parameter Space Noise for Exploration

abstract: Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.


title: Signal strength indoor localization using a single DASH7 message

abstract: In the Internet of Things, location information is crucial for many applications. We want to obtain location information from a device by using its existing communication modality. DASH7 is designed for low power sensor and actuator communication on a medium range, using the license exempt radio frequency channels below one gigahertz. In this paper, we present a method to localize a DASH7 mobile node based on a single message and a deterministic propagation model. The propagation model is used to indicate the distance between sender and receiver so that single measurement localization approach is possible without maintaining a fingerprint database. We obtain a median location error of 3.9 m, where we still see room for improvement.


title: Research on the relationship between some important split measure functions for decision tree with purity law

abstract: This paper analysis some split measure functions of classical decision tree algorithm. After the research on the structure of these functions, we found out all of them are separable probability measure function, and their core functions are semi-purity functions. They achieve their minimums at row-column independent point, maximums at full-distinguish point, and accordance with purity law. Because chi-square does not support the symmetry, the purity law proposed has wider adaptability than the impurity theory. These can help us to analysis the theory of measure function and the relationship between measure functions and data, and it is important to find some more simple and effective split-measure functions in some special area.


title: Block diagonalization for multi-user MIMO with other-cell interference

abstract: Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.


title: Introduction to machine learning for brain imaging

abstract: Abstract Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences.


title: FUAT - A fuzzy clustering analysis tool

abstract: As it is known, fuzzy clustering is a kind of soft clustering method and primarily based on idea of segmenting data by using membership degrees of cases which are computed for each cluster. However, most of the current fuzzy clustering modules packaged in both open source and commercial products have lack of enabling users to explore fuzzy clusters deeply and visually in terms of investigation of different relations among clusters. Furthermore, without a decision maker or an expert, it is hard to decide the number of clusters in fuzzy clustering studies. Therefore, in this study, a desktop software, namely FUAT, is developed to analyze, explore and visualize different aspects of obtained fuzzy clusters which are segmented by fuzzy c-means algorithm. Moreover, to obtain and inform possible natural cluster number, FUAT is equipped with Expectation Maximization algorithm.


title: 3-D Deep Feature Construction for Mobile Laser Scanning Point Cloud Registration

abstract: Due to errors in sensors and positioning, there exist mismatches between different phases of mobile laser scanning point clouds, which impede the application of point cloud, such as changing detection and deformation monitoring. To rectify such mismatches, we designed a 3-D deep feature construction method for point cloud registration. The proposed method combines two 3-D convolutional neural networks into a uniform deep learning model to extract 3-D deep features. First, the corresponding points and noncorresponding points are set to train the deep learning model to minimize the distance between corresponding points’ features and maximize the distance between features of noncorresponding points. Second, in the test phase, the 3-D deep feature for each keypoint was extracted by the trained deep learning model. This could be used to determine the corresponding points by the $k$ -dimensional tree and random sample consensus (RANSAC) algorithm. Finally, a transformation matrix was calculated based on the corresponding points and was then applied to point cloud registration. The experimental results illustrated that the proposed method of using 3-D deep features is more efficient at a corresponding point search than representatives of three existing methods. It also improved registration accuracy.


title: 3D sketch-based 3D model retrieval with convolutional neural network

abstract: 3D sketch-based 3D model retrieval is to retrieve similar 3D models using users' hand-drawn 3D sketches as input. Compared with traditional 2D sketch-based retrieval, 3D sketch-based 3D model retrieval is a brand new and challenging research topic. In this paper, we employ advanced deep learning method and propose a novel 3D sketch based 3D model retrieval system. Our system has been comprehensively tested on two benchmark datasets and compared with other existing 3D model retrieval algorithms. The experimental results reveal our approach outperforms other competing state-of-the-arts and demonstrate promising potential of our approach on 3D sketch based applications.


title: A SERS-Based Sandwich Assay for Ultrasensitive and Selective Detection of Alzheimer’s Tau Protein

abstract: In this study, a simple and highly selective homogeneous sandwich assay was developed for fast and ultrasensitive detection of the tau protein using a combination of monoclonal antitau functionalized hybrid magnetic nanoparticles and polyclonal antitau immobilized gold nanoparticles as the recognition and surface-enhanced Raman scattering (SERS) component, respectively. The magnetic silica particles were first coated with poly(2-hydroxyethyl methacrylate) via surface-mediated reversible addition–fragmentation chain transfer (RAFT) polymerization and then biofunctionalized with monoclonal antitau, which are both specific for tau and can be collected via a simple magnet. After separating tau from the sample matrix, they were sandwiched with the SERS substrate composed of polyclonal antitau and 5,5-dithiobis(2-dinitrobenzoic acid) on gold nanoparticles. The correlation between the tau concentration and SERS signal was found to be linear within the range of 25 fM to 500 nM. The limit of detection for the sand...


title: The conduct of drug metabolism studies considered good practice (II): in vitro experiments.

abstract: In vitro drug metabolism studies, which are inexpensive and readily carried out, serve as an adequate screening mechanism to characterize drug metabolites, elucidate their pathways, and make suggestions for further in vivo testing. This publication is a sequel to part I in a series and aims at providing a general framework to guide designs and protocols of the in vitro drug metabolism studies considered good practice in an efficient manner such that it would help researchers avoid common pitfalls and misleading results. The in vitro models include hepatic and non-hepatic microsomes, cDNA-expressed recombinant human CYPs expressed in insect cells or human B lymphoblastoid, chemical P450 inhibitors, S9 fraction, hepatocytes and liver slices. Important conditions for conducting the in vitro drug metabolism studies using these models are stated, including relevant concentrations of enzymes, co-factors, inhibitors and test drugs; time of incubation and sampling in order to establish kinetics of reactions; appropriate control settings, buffer selection and method validation. Separate in vitro data should be logically integrated to explain results from animal and human studies and to provide insights into the nature and consequences of in vivo drug metabolism. This article offers technical information and data and addresses scientific rationales and practical skills related to in vitro evaluation of drug metabolism to meet regulatory requirements for drug development.


title: Spatial Partitioning Techniques in Spatial Hadoop

abstract: SpatialHadoop is an extended MapReduce framework that supports global indexing that spatial partitions the data across machines providing orders of magnitude speedup, compared to traditional Hadoop. In this paper, we describe seven alternative partitioning techniques and experimentally study their effect on the quality of the generated index and the performance of range and spatial join queries. We found that using a 1% sample is enough to produce high quality partitions. Also, we found that the total area of partitions is a reasonable measure of the quality of indexes when running spatial join. This study will assist researchers in choosing a good spatial partitioning technique in distributed environments.


title: Scheduling in Densified Networks: Algorithms and Performance

abstract: With increasing data demand, wireless networks are evolving to a hierarchical architecture where coverage is provided by both wide-area base stations (BS) and dense deployments of short-range access nodes (ANs) (e.g., small cells). The dense scale and mobility of users provide new challenges for scheduling: 1) high flux in mobile-to-AN associations, where mobile nodes quickly change associations with ANs (time scale of seconds) due to their small footprint and 2) multi-point connectivity, where mobile nodes are simultaneously connected to several ANs at any time. We study such a densified scenario with multi-channel wireless links (e.g., multi-channel OFDM) between nodes (BS/AN/mobile). We first show that traditional algorithms that forward each packet at most once, either to a single AN or a mobile user, do not have good delay performance. We argue that the fast association dynamics between ANs and mobile users necessitate a multi-point relaying strategy, where multiple ANs have duplicate copies of the data, and coordinate to deliver data to the mobile user. Surprisingly, despite data replication and no coordination between ANs, we show that our algorithm (a distributed scheduler—DIST) can approximately stabilize the system in large-scale instantiations of this setting, and further, performs well from a queue-length/delay perspective (shown via large deviation bounds).


title: Cloud-Based Design and Manufacturing: Status and Promise

abstract: The information technology industry has benefited considerably from cloud computing, which allows organizations to shed some of their expensive information technology infrastructure and shifts computing costs to more manageable operational expenses. In light of these benefits, we propose a new paradigm for product design and manufacturing, referred to as cloud-based design and manufacturing (CBDM). This chapter introduces a definition and vision for CBDM, articulates the differences and similarities between CBDM and traditional paradigms such as web- and agent-based technologies, highlights the fundamentals of CBDM, and presents a prototype system, developed at Georgia Tech, called the Design and Manufacturing Cloud (DMCloud). Finally, we conclude this chapter with an outline of future research directions.


title: VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals

abstract: Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.


title: Factors and Effects of Information Credibility

abstract: Website success hinges on how credible the consumers consider the information on the website. Unless consumers believe the website's information is credible, they are not likely to be willing to act on the advice and will not develop loyalty to the website. This paper reports on how individual differences and initial website impressions affect perceptions of information credibility of an unfamiliar advice website. Results confirm that several individual difference variables and initial impression variables (perceived reputation, perceived website quality, and willingness to explore the website) play an important role in developing information credibility of an unfamiliar website, with first impressions and individual differences playing equivalent roles. The study also confirms the import of information credibility by demonstrating it positively influences perceived usefulness, perceived site risk, willingness to act on website advice, and perceived consumer loyalty toward the website.


title: An Empirical Study of Credit Scoring Model for Credit Card

abstract: The purpose of this paper is to propose an optimal credit scoring model to reassess the default risk of credit card holders for credit card issuing banks in Taiwan. This paper adopted four credit scoring models which are the linear discriminant analysis, decision tree, back-propagation neural network, and a hybrid method to evaluate the default risk. By comparing the evaluation results of these models, it shows that the decision tree method has the best classification performance in terms of accuracy and sensitivity. These results of this empirical study will be provided to credit card issuing banks for achieving efficient automatically credit reassessment of default risk.


title: Adaptive grids for clustering massive data sets

abstract: Clustering is a key data mining problem. Density and grid based technique is a popular way to mine clusters in a large multi-dimensional space wherein clusters are regarded as dense regions than their surroundings. The attribute values and ranges of these attributes characterize the clusters. Fine grid sizes lead to a huge amount of computation while coarse grid sizes result in loss in quality of clusters found. Also, varied grid sizes result in discovering clusters with different cluster descriptions. The technique of Adaptive grids enables to use grids based on the data distribution and does not require the user to specify any parameters like the grid size or the density thresholds. Further, clusters could be embedded in a subspace of a high dimensional space. We propose a modified bottom-up subspace clustering algorithm to discover clusters in all possible subspaces. Our method scales linearly with the data dimensionality and the size of the data set. Experimental results on a wide variety of synthetic and real data sets demonstrate the effectiveness of Adaptive grids and the effect of the modified subspace clustering algorithm. Our algorithm explores at-least an order of magnitude more number of subspaces than the original algorithm and the use of adaptive grids yields on an average of two orders of magnitude speedup as compared to the method with user specified grid size and threshold.


title: A Novel Mobile Edge Computing-Based Architecture for Future Cellular Vehicular Networks

abstract: The evolving cellular network with centric-deployed Cloud Centers has greatly improved the mobile user experience. However, the advent of vehicular networks with a wide variety of new services and devices changes the existing cellular network landscape, and the cellular-based vehicular networks are confronted with serious challenge in terms of latency reduction and flexible service delivery. In this paper, we propose to deploy the specific server, called Mobile-Edge Computing (MEC) server, within the Radio Access Network, and allow them to connect with a set of base stations alongside roads, so as to provide flexible vehicle-related service and efficiently control the radio network. Then, the vision of MEC- assisted slicing network and a traffic scheduling policy are presented to promote network customization. As an instance of the MEC-based tailored network service, a time-predicted handover mechanism for vehicles is further developed by leveraging available road information and the enhanced capacity of the MEC server to satisfy the demand for high mobility and reliability.


title: Ego-Lane Analysis System (ELAS): Dataset and Algorithms

abstract: Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research detection, estimation, and tracking in the past two decades. The interest in this topic has increased even more with the demand for advanced driver assistance systems (ADAS) and self-driving cars. Although extensively studied independently, there is still need for studies that propose a combined solution for the multiple problems related to the ego-lane, such as lane departure warning (LDW), lane change detection, lane marking type (LMT) classification, road markings detection and classification, and detection of adjacent lanes (i.e., immediate left and right lanes) presence. In this paper, we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating ego-lane position, classifying LMTs and road markings, performing LDW and detecting lane change events. The proposed vision-based system works on a temporal sequence of images. Lane marking features are extracted in perspective and Inverse Perspective Mapping (IPM) images that are combined to increase robustness. The final estimated lane is modeled as a spline using a combination of methods (Hough lines with Kalman filter and spline with particle filter). Based on the estimated lane, all other events are detected. To validate ELAS and cover the lack of lane datasets in the literature, a new dataset with more than 20 different scenes (in more than 15,000 frames) and considering a variety of scenarios (urban road, highways, traffic, shadows, etc.) was created. The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes). ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.


title: A 6.3 GHz BFSK Transmitter with On-Chip Antenna for Self-Powered Medical Sensor Applications

abstract: This paper presents a completely integrated, low-power 6.3 GHz oscillator transmitter which includes an on-chip antenna suitable for short-range medical sensor applications. The transmitter, implemented in a 1.2 V 0.13 mum CMOS process, utilizes open-loop direct VCO modulation for BFSK data at a rate of 300 kbps. For communicating a 1 kbit packet once per second, an average power consumption of 14 muW is achieved. During a packet transmission, the power consumption of the transmitter is 4.25 mW, enabling a self-powered design using integrated ultracapacitors for an SoC solution. With a radiated power of 0 dBm, the transmitter has a communication range of 2 m.


title: Risk aware portfolio construction using deep deterministic policy gradients

abstract: Allocation of liquid capital to the financial instruments in a portfolio is typically done using a two-step process. In the first step, predictive techniques are used to determine the future risk and rewards for the instrument. In the subsequent step, a quadratic optimization problem is solved to obtain the allocation that maximizes a relevant measure of the portfolio performance computed using a combination of the risks and the rewards. Deep Reinforcement Learning (DRL) eliminates the need for a two step process to find the allocation across the instruments that will optimize a measure of portfolio performance obtained from the market. DRL based portfolio construction autonomously adjusts to a change in the environment unlike traditional machine learning algorithms used in prediction. The existing DRL methods suffer from the challenges of stability, and do not lend themselves well to the portfolio construction problem that has a continuous action space. Proposed in 2015, Deep Deterministic Policy Gradients (DDPG) is a type of actorcritic DRL algorithm that provides support for continuous action space which is encountered in portfolio construction. This paper evaluates the use of DDPG to solve the problem of risk aware portfolio construction. Simulations are done on a portfolio of twenty stocks and the use of both Rate of Return and Sortino ratio as a measure of portfolio performance are evaluated. Results are presented that demonstrate the effectiveness of DDPG for risk aware portfolio construction. The simulation results presented in this paper show that having a risk-aware measure of portfolio performance such as Sortino ratio give a portfolio with superior return and lower variance.


title: An automotive radar network based on 77 GHz FMCW sensors

abstract: Automotive radar sensors will be used in many future applications to increase comfort and safety. Adaptive cruise control (ACC), parking aid, collision warning or avoidance and pre crash are some automotive applications, which are based on high performance radar sensors. This paper describes results gained by an European Commission funded research project and a new radar network based on four individual monostatic 77 GHz FMCW radar sensors. Each sensor measures target range and velocity simultaneously and extremely accurate even in multiple target situations with a maximum range of 40 m. The azimuth angle is estimated by multilateration techniques in a radar network architecture. The system design and the signal processing of this network are described in detail in this paper. Due to the high range resolution performance of each radar sensor ordinary targets cannot be considered any longer as point but as extended targets. System capability, performance and reliability can be improved essentially if the single sensor FMCW signal processing, the target position processing in the radar network and the tracking scheme are jointly optimized.


title: A 200-ps-Response-Time Output-Capacitorless Low-Dropout Regulator With Unity-Gain Bandwidth >100 MHz in 130-nm CMOS

abstract: An output-capacitorless low-dropout regulator (OCL-LDO) using enhanced multipath nested Miller compensation with embedded feedforward path for system-on-chip (SoC) applications is presented in this paper. While state-of-the-art LDOs are restricted to low-frequency operation with a maximum unity-gain bandwidth (UGB) typically ranging from several hundred kilohertz to no higher than 10 MHz even in advanced technologies (65 nm or beyond), the prototype fabricated in a standard 130-nm CMOS process features over 100-MHz UGB for all loading conditions up to 25-mA output current while consuming a quiescent current of 112 μ A. With this ultra-wide bandwidth, the proposed LDO achieves 200-ps response time for load transient with 300-ps edge time. The extension of UGB also magnificently contributes to improvement of power-supply rejection (PSR), making it comparable to OCL-LDOs that use ripple-feedforward techniques to specifically boost PSR or LDOs with large off-chip decoupling capacitors, while enjoying much faster speed. The chip area is 0.008 mm2.


title: Vind: a robot self-localization framework

abstract: In this paper we present a framework for robot localization codenamed Vind. The framework allows to configure a multi-sensor setup by describing the configuration and entering the sensor's parameters in a series of text-based and human-readable configuration files. The framework provides, among others, distributed communication capabilities and a state estimation implementation based on the Extended Kalman Filter (EKF). Vind can also be extended to include other state estimation implementations based on clearly defined interfaces and message structures. The aim of the framework is to foster reusability, and provide developers with tools to minimize the effort required to deploy a solution for the self-localization problem. In case of researchers working on the implementation of new state estimate algorithms, it also supports them by providing high level tools for the system integration aspects.


title: Advanced Techniques For Personalized, Interactive Question Answering

abstract: Using a computer to answer questions has been a human dream since the beginning of ::: the digital era. A first step towards the achievement of such an ambitious goal is to deal ::: with naturallangilage to enable the computer to understand what its user asks. ::: The discipline that studies the conD:ection between natural language and the represen~ ::: tation of its meaning via computational models is computational linguistics. According ::: to such discipline, Question Answering can be defined as the task that, given a question ::: formulated in natural language, aims at finding one or more concise answers in the form ::: of sentences or phrases. ::: Question Answering can be interpreted as a sub-discipline of information retrieval ::: with the added challenge of applying sophisticated techniques to identify the complex ::: syntactic and semantic relationships present in text. Although it is widely accepted that ::: Question Answering represents a step beyond standard infomiation retrieval, allowing a ::: more sophisticated and satisfactory response to the user's information needs, it still shares ::: a series of unsolved issues with the latter. ::: First, in most state-of-the-art Question Answering systems, the results are created ::: independently of the questioner's characteristics, goals and needs. This is a serious limitation ::: in several cases: for instance, a primary school child and a History student may ::: need different answers to the questlon: When did, the Middle Ages begin? ::: Moreover, users often issue queries not as standalone but in the context of a wider ::: information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces ::: would encourage and accommodate the submission of multiple related questions ::: and handle the user's requests for clarification, interactive Question Answering is still at ::: its early stages: ::: Furthermore, an i~sue which still remains open in current Question Answering is ::: that of efficiently answering complex questions, such as those invoking definitions and ::: descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess ::: the correctness of answers to such complex questions. ::: .. These are the central research problems addressed by this thesis, and are solved as ::: follows. ::: An in-depth study on complex Question Answering led to the development of classifiers ::: for complex answers. These exploit a variety of lexical, syntactic and shallow ::: semantic features to perform textual classification using tree-~ernel functions for Support ::: Vector Machines. ::: The issue of personalization is solved by the integration of a User Modelling corn': ::: ponent within the the Question Answering model. The User Model is able to filter and ::: fe-rank results based on the user's reading level and interests. ::: The issue ofinteractivity is approached by the development of a dialogue model and a ::: dialogue manager suitable for open-domain interactive Question Answering. The utility ::: of such model is corroborated by the integration of an interactive interface to allow reference ::: resolution and follow-up conversation into the core Question Answerin,g system and ::: by its evaluation. ::: Finally, the models of personalized and interactive Question Answering are integrated ::: in a comprehensive framework forming a unified model for future Question Answering ::: research.


title: Parsing Strategies With 'Lexicalized' Grammars: Application To Tree Adjoining Grammars

abstract: In this paper we present a general parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from recent linguistic work in TAGs (Abeille 1988).In our approach elementary structures are associated with their lexical heads. These structures specify extended domains of locality (as compared to a context-free grammar) over which constraints can be stated. These constraints either hold within the elementary structure itself or specify what other structures can be composed with a given elementary structure.We state the conditions under which context-free based grammars can be 'lexicalized' without changing the linguistic structures originally produced. We argue that even if one extends the domain of locality of CFGs to trees, using only substitution does not give the freedom to choose the head of each structure. We show how adjunction allows us to 'lexicalize' a CFG freely.We then show how a 'lexicalized' grammar naturally follows from the extended domain of locality of TAGs and present some of the linguistic advantages of our approach.A novel general parsing strategy for 'lexicalized' grammars is discussed. In a first stage, the parser builds a set structures corresponding to the input sentence and in a second stage, the sentence is parsed with respect to this set. The strategy is independent of the linguistic theory adopted and of the underlying grammar formalism. However, we focus our attention on TAGs. Since the set of trees needed to parse an input sentence is supposed to be finite, the parser can use in principle any search strategy. Thus, in particular, a top-down strategy can be used since problems due to recursive structures are eliminated. The parser is also able to use non-local information to guide the search.We then explain how the Earley-type parser for TAGs can be modified to take advantage of this approach.


title: Urban sensing: out of the woods

abstract: Embedded networked sensing, having successfully shifted from the lab to the environment, is primed for a more contentious move to the city to where citizens will likely be the target of data collection. This transition will warrant careful study and touch on issues that go far beyond the scientific realm.


title: Management of the Deviated Nasal Dorsum

abstract: The deviated nasal dorsum veers off the ideal straight vertical orientation at midline. Deviations in the dorsum lead to functional and aesthetic consequences that frequently prompt the patient to seek consultation with a rhinoplasty surgeon. Inability to breathe through the nose and self-image perception significantly detracts from the patient's quality of life. Correction of the deviated nasal dorsum represents a challenge for the rhinoplasty surgeon. Anatomic correction of deviations is the goal. Straightening a deviated nasal dorsum will require maneuvers to realign the nose distinct from traditional aesthetic rhinoplasty techniques. The nasal dorsum is formed by the three-dimensional structures of the septum, the bony nasal pyramid, and the cartilaginous nasal midvault. Restoring the position of the septum at midline is the first step in providing adequate support to the nasal architecture. Extracorporeal septoplasty and anterior septal transplant are often necessary techniques to correct the septum and achieve dorsal correction. Subsequently, asymmetric maneuvers to bony dorsum and midvault are performed to restore symmetry. Asymmetric hump reduction and nasal osteotomies are often necessary. Supporting the midvault to avoid nasal collapse often requires asymmetric maneuvers to the upper lateral cartilages and asymmetric spreader grafts. Finally, camouflaging grafts to the nasal dorsum may be necessary. Significant rigidity and memory of the native tissues must be overcome to successfully straighten a nose. The surgeon who can master the deviated dorsum will significantly improve the appearance and quality of life of the patients he or she treats.


title: A 0.45-to-1.2-V Fully Digital Low-Dropout Voltage Regulator With Fast-Transient Controller for Near/Subthreshold Circuits

abstract: A low quiescent current digital low-dropout (D-LDO) voltage regulator with fast-transient response time is proposed for self-powered wireless sensor applications operating at near/subthreshold supply voltage. The D-LDO regulator incorporates both hill-climbing and binary search algorithms (HCBS) in the control logic, thus leveraging on each other's strengths to minimize the output voltage's ripple and the quiescent current during the steady-state period as well as output voltage's spike and response time during the transition period. Additional features such as hysteresis mode control and freeze mode control are incorporated into the system to improve the performance of the D-LDO regulator. A dynamic comparator is proposed for the near/subthreshold supply voltage operation, which minimizes the voltage error and improves the maximum operating frequency. Fabricated in 130-nm CMOS technology, the D-LDO regulator regulates the output voltage ${V}_{{\rm OUT}}$ from 350 to 1150 mV, while the input supply voltage ${V}_{\text{IN}}$ ranges from 450 to 1200 mV. At a ${V}_{{\rm OUT}}$ of 450 mV, ${V}_{{\rm IN}}$ of 500 mV and an operating frequency of 10 MHz, the regulator delivers 1500- ${\mu }$ A load current with $I_{\text{QUIESCENT}}$ of 8.9 ${\mu }$ A and a transient response time of 1.6 ${\mu }$ s. The maximum current and power efficiencies reach 99.9% and 89.9%, respectively. The measured line regulation and load regulation are 1.6 and 0.6 mV/mA, respectively.


title: Review of underwater SLAM techniques

abstract: SLAM (Simultaneous Localization and Mapping) for underwater vehicles is a challenging research topic due to the limitations of underwater localization sensors and error accumulation over long-term operations. Furthermore, acoustic sensors for mapping often provide noisy and distorted images or low-resolution ranging, while video images provide highly detailed images but are often limited due to turbidity and lighting. This paper presents a review of the approaches used in state-of-the-art SLAM techniques: Extended Kalman Filter SLAM (EKF-SLAM), FastSLAM, GraphSLAM and its application in underwater environments.


title: Abstract Design Automation for Customized Apparel Products

abstract: This paper presents solution techniques for a three-dimensional Automatic Made-to-Measure scheme for apparel products. Freeform surface is adopted to represent the complex geometry models of apparel products. When designing the complex surface of an apparel product, abstractions are stored in conjunction with the models using a non-manifold data structure. Apparel products are essentially designed with reference to human body features, and thus share a common set of features as the human model. Therefore, the parametric feature-based modeling enables the automatic generation of fitted garments on differing body shapes. In our approach, different apparel products are each represented by a specific feature template preserving its individual characteristics and styling. When the specific feature template is encoded as the equivalent human body feature template, it automates the generation of made-to-measure apparel products. The encoding process is performed in 3D, which fundamentally solves the fitting problems of the 2D tailoring and pattern-making process. This paper gives an integrated solution scheme all above problems. In detail, a non-manifold data structure, a constructive design method, four freeform modification tools, and a detail template encoding/decoding method are developed for the design automation of customized apparel products.


title: Real-time wireless vibration monitoring system using LabVIEW

abstract: Vibration analysis provides relevant information about abnormal working condition of machine parts. Vibration measurement is prerequisite for vibration analysis which is used for condition monitoring of machinery. Also, wireless vibration monitoring has many advantages over wired monitoring. This Paper presents, implementation of a reliable and low cost wireless vibration monitoring system. Vibration measurement has been done using 3-Axis digital output MEMS Accelerometer sensor. This sensor can sense vibrations in the range 0.0156g to 8g where, 1g is 9.81m/s2. Accelerometer Sensor is interfaced with Arduino-derived microcontroller board having Atmel's AT-mega328p microcontroller. The implemented system uses ZigBee communication protocol i.e. standard IEEE 802.15.4, for wireless communication between Sensor Unit and Vibration Monitoring Unit. The wireless communication has been done using XBee RF modules. National Instruments's LabVIEW software has been used for development of graphical user interface, data-logging and alarm indication on the PC. Experimental results show continuous real-time monitoring of machine's vibrations on charts. These results, along with data-log file have been used for vibration analysis. This analysis is used to ensure safe working condition of machinery and used in predictive maintenance.


title: Percentage and Relative Error Measures in Forecast Evaluation

abstract: Properties of two large families of scale-free forecast accuracy measures that include popular measures such as mean absolute percentage error, relative error, and squared percentage error, are examined in this paper. We describe the optimal reports when forecasts are evaluated using these measures. We also provide analytic expressions for the optimal Bayes’ act associated with these measures under a general power transformation for several well-known probability distributions. We then show that using measures from these two families may inadvertently provide incentives for either pessimism or optimism among forecasters, i.e., rewarding underforecasts or overforecasts relative to some reference measure of central tendency. As an illustration of these concepts, we examine the use of these measures for model selection in a forecast aggregation example using stock price forecasts derived from the Thomson Reuters Institutional Brokers’ Estimate System. This example illustrates how aggregation methods that always yield lower estimates relative to the mean or median generally exhibit better scores using percentage error-based measures, while those that yield higher estimates compared to the mean or median will effectively rank higher when relative error-based measures are used.


title: Improving Stateless Hash-Based Signatures

abstract: We present several optimizations to SPHINCS, a stateless hash-based signature scheme proposed by Bernstein et al. in (2015): PORS, a more secure variant of the HORS few-time signature scheme used in SPHINCS; secret key caching, to speed-up signing and reduce signature size; batch signing, to amortize signature time and reduce signature size when signing multiple messages at once; mask-less constructions to reduce the key size and simplify the scheme; and Octopus, a technique to eliminate redundancies from authentication paths in Merkle trees. Based on a refined analysis of the subset resilience problem, we show that SPHINCS’ parameters can be modified to reduce the signature size while retaining a similar security level and computation time. We then propose Gravity-SPHINCS, our variant of SPHINCS embodying the aforementioned tricks. Gravity-SPHINCS has shorter keys (32 and 64 bytes instead of \({\approx }1\,\text {KB}\)), shorter signatures (\({\approx }30\,\text {KB}\) instead of 41 KB), and faster signing and verification for the same security level as SPHINCS.


title: Training CNNs with Low-Rank Filters for Efficient Image Classification

abstract: We propose a new method for creating computationally efficient convolutional neural networks (CNNs) by using low-rank representations of convolutional filters. Rather than approximating filters in previously-trained networks with more efficient versions, we learn a set of small basis filters from scratch; during training, the network learns to combine these basis filters into more complex filters that are discriminative for image classification. To train such networks, a novel weight initialization scheme is used. This allows effective initialization of connection weights in convolutional layers composed of groups of differently-shaped filters. We validate our approach by applying it to several existing CNN architectures and training these networks from scratch using the CIFAR, ILSVRC and MIT Places datasets. Our results show similar or higher accuracy than conventional CNNs with much less compute. Applying our method to an improved version of VGG-11 network using global max-pooling, we achieve comparable validation accuracy using 41% less compute and only 24% of the original VGG-11 model parameters; another variant of our method gives a 1 percentage point increase in accuracy over our improved VGG-11 model, giving a top-5 center-crop validation accuracy of 89.7% while reducing computation by 16% relative to the original VGG-11 model. Applying our method to the GoogLeNet architecture for ILSVRC, we achieved comparable accuracy with 26% less compute and 41% fewer model parameters. Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.


title: A Planar Low-Profile Meander Antenna (PLMA) Design for Wireless Terminal Achieving Between Intrasystem EMC and Isolation in Multiantenna Systems

abstract: In this paper, a meander line internal antenna used for wireless terminal is proposed. The current of this antenna is mostly distributed on the antenna radiator itself, rather than on the main board of the wireless device. As a result, the chance of having intrasystem electromagnetic compatibility issues, which usually result in receiver desensitization in wireless radios, can be significantly reduced. The antenna has good radiation performance in the vertical polarization with a low physical profile, compared to the existing antenna designs for typical wireless terminals. The antenna has efficiency similar to the monopole antenna with much less reference/ground plane dependence, which is important for high sensitivity radio design. Further, the mutual coupling (i.e., isolation) between the two such antennas is studied and the envelope correlation coefficient between the two such antennas when they are placed in one system is found to be low, which is important for better throughput for a multiple input and multiple output system. Both simulation and measurement results demonstrate the proposed design.


title: Locally Differentially Private Heavy Hitter Identification

abstract: The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequent oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments on both synthetic and real-world datasets demonstrate the advantage of our proposed protocol.


Protein Microarrays: A New Tool for the Study of Autoantibodies in Immunodeficiency


title: Ambulatory assessment of human body kinematics and kinetics

abstract: Traditional human movement analysis systems consist of an optical position measurement system with one or more 6D force plates mounted in a laboratory. Although clinically accepted as `the golden standard' for the assessment of human movement, the restriction to a laboratory environment with its limited measurement volume prevents these systems to be used in an ambulatory environment. To overcome this limitation, this thesis proposes two measurement systems. First, an instrumented shoe is proposed that is used to assess the 3D ground reaction force, the center of pressure trajectory, the center of mass trajectory, and the movement of the foot. The instrumented shoe consists of an orthopedic sandal with two 6D force/moment sensors beneath the heel and the forefoot, and two inertial sensors rigidly attached to the force/moment sensors. Second, a measurement system is proposed which fuses inertial and magnetic sensing to estimate relative positions and orientations on the human body. Fusion is achieved using a realtime Extended Kalman Filter (EKF) with a tightly coupled structure. Change of position and orientation is predicted by integration of acceleration and angular velocity measured by an inertial sensor. Magnetic updates are used to reduce the inherent integration drift due to noise and a fluctuating offset. To minimize energy consumption, the magnetic system only actuates if the uncertainty associated with the position and orientation exceeds a predefined threshold. Moreover, only the coil delivering most information for the reduction of the uncertainty is actuated.


Survey on Spatio-Temporal Clustering


title: Enterprise 2.0: the dawn of emergent collaboration

abstract: There is a new wave of business communication tools including blogs, wikis and group messaging software ? which the author has dubbed, collectively, Enterprise 2.0 ? that allow for more spontaneous, knowledge-based collaboration. These new tools, the author contends, may well supplant other communication and knowledge management systems with their superior ability to capture tacit knowledge, best practices and relevant experiences from throughout a company and make them readily available to more users. This article offers a paradigm that highlights the salient characteristics of these new technologies, which the author refers to as SLATES (search, links, authoring, tags, extensions, signals). The resulting organizational communication patterns can lead to highly productive and highly collaborative environments by making both the practices of knowledge work and its outputs more visible. Drawing on case studies and survey data, the article offers managers a set of ground rules for implementing the new technologies. First, it is necessary to create a receptive culture in order to prepare the way for new practices. Second, a common platform must be created to allow for a collaboration infrastructure. Third, an informal rollout of the technologies may be preferred to a more formal procedural change. And fourth, managerial support and leadership is crucial. Even when implanted and implemented well, these new technologies will certainly bring with them new challenges. These tools may well reduce management?s ability to exert unilateral control and to express some level of negativity. Whether a company?s leaders really want this to happen and will be able to resist the temptation to silence dissent is an open question. Leaders will have to play a delicate role if they want Enterprise 2.0 technologies to succeed.


title: Web-based learning object selection software using analytical hierarchy process

abstract: The concepts of sustainability and reusability have great importance in engineering education. In this context, metadata provides reusability and the effective use of Learning Objects (LOs). In addition, searching the huge LO Repository with metadata requires too much time. If the selection criteria do not exactly match the metadata values, it is not possible to find the most appropriate LO. When this situation arises, the multi-criteria decision making (MCDM) method can meet the requirements. In this study, the SDUNESA software was developed and this software allows for the selection of a suitable LO from the repository by using an analytical hierarchy process MCDM method. This web-based SDUNESA software is also used to store, share and select a suitable LO in the repository. To meet these features, the SDUNESA software contains Web 2.0 technologies such as AJAX, XML and SOA Web Services. The SDUNESA software was especially developed for computer engineering education. Instructors can use this software to select LOs with defined criteria. The parameters of the web-based SDUNESA learning object selection software that use the AHP method are defined under the computer education priorities. The obtained results show that the AHP method selects the most reliable learning object that meets the criteria.


title: A Mandarin-English Code-Switching Corpus

abstract: Generally the existing monolingual corpora are not suitable for large vocabulary continuous speech recognition (LVCSR) of codeswitching speech. The motivation of this paper is to study the rules and constraints code-switching follows and design a corpus for code-switching LVCSR task. This paper presents the development of a Mandarin-English code-switching corpus. This corpus consists of four parts: 1) conversational meeting speech and its data; 2) project meeting speech data; 3) student interviews speech; 4) text data of on-line news. The speech was transcribed by an annotator and verified by Mandarin-English bilingual speakers manually. We propose an approach for automatically downloading from the web text data that contains code-switching. The corpus includes both intra-sentential code-switching (switch in the middle of a sentence) and inter-sentential code-switching (switch at the end of the sentence). The distribution of part-of-speech (POS) tags and code-switching reasons are reported.


title: From goods to service ( s ) : Divergences and convergences of logics

abstract: Abstract There are two logics or mindsets from which to consider and motivate a transition from goods to service(s). The first, “goods-dominant (G-D) logic”, views services in terms of a type of (e.g., intangible) good and implies that goods production and distribution practices should be modified to deal with the differences between tangible goods and services. The second logic, “service-dominant (S-D) logic”, considers service – a process of using ones resources for the benefit of and in conjunction with another party – as the fundamental purpose of economic exchange and implies the need for a revised, service-driven framework for all of marketing. This transition to a service-centered logic is consistent with and partially derived from a similar transition found in the business-marketing literature — for example, its shift to understanding exchange in terms value rather than products and networks rather than dyads. It also parallels transitions in other sub-disciplines, such as service marketing. These parallels and the implications for marketing theory and practice of a full transition to a service-logic are explored.


title: Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons

abstract: We present a novel approach to RTE that exploits a structure-oriented sentence representation followed by a similarity function. The structural features are automatically acquired from tree skeletons that are extracted and generalized from dependency trees. Our method makes use of a limited size of training data without any external knowledge bases (e.g. WordNet) or handcrafted inference rules. We have achieved an accuracy of 71.1% on the RTE-3 development set performing a 10-fold cross validation and 66.9% on the RTE-3 test data.


title: Tailoring Gamification to Requirements Elicitation: A Stakeholder-Centric Motivation Concept

abstract: Involving stakeholders in requirements elicitation is a cornerstone of successful requirements engineering (RE). With the recent technological advances, the number of stakeholders of a system has significantly increased. Major stakeholders, end-users in particular, are increasingly difficult to reach, because they may be globally distributed and outside organizational reach, i.e., they are no members of the organizations that are involved in the development of a system. Online elicitation platforms allow to elicit requirements collaboratively from a large number of distributed stakeholders. However, such platforms are not sufficient for motivating stakeholders outside organizational reach to contribute voluntarily. Gamification is a potential means for creating and sustaining such motivation. However, there is little research on stakeholder engagement with gamification so far. Current approaches particularly do not consider that stakeholders learn during elicitation and that their motivational factors also change. In this paper, we address this gap with a motivation concept that is inspired by the theories of experiential learning and need satisfaction. Our contribution is threefold. First, we suggest how to characterize these stakeholders despite not knowing who they are. Second, we show the role of experiential learning and need satisfaction with respect to gamification in the context of requirements elicitation. Third, we present a three-dimensional concept of how to motivate these stakeholders towards requirements elicitation over the whole period of requirements elicitation.


title: Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes

abstract: This paper presents a new method to learn online policies in continuous state, continuous action, model-free Markov decision processes, with two properties that are crucial for practical applications. First, the policies are implementable with a very low computational cost: once the policy is computed, the action corresponding to a given state is obtained in logarithmic time with respect to the number of samples used. Second, our method is versatile: it does not rely on any a priori knowledge of the structure of optimal policies. We build upon the Fitted Q-iteration algorithm which represents the $Q$-value as the average of several regression trees. Our algorithm, the Fitted Policy Forest algorithm (FPF), computes a regression forest representing the Q-value and transforms it into a single tree representing the policy, while keeping control on the size of the policy using resampling and leaf merging. We introduce an adaptation of Multi-Resolution Exploration (MRE) which is particularly suited to FPF. We assess the performance of FPF on three classical benchmarks for reinforcement learning: the "Inverted Pendulum", the "Double Integrator" and "Car on the Hill" and show that FPF equals or outperforms other algorithms, although these algorithms rely on the use of particular representations of the policies, especially chosen in order to fit each of the three problems. Finally, we exhibit that the combination of FPF and MRE allows to find nearly optimal solutions in problems where $\epsilon$-greedy approaches would fail.


title: Sonification for Process Monitoring

abstract: This chapter looks at a range of auditory display and sonification applications that have tackled the problem of monitoring real-time data streams and concludes with some recommendations that further research should be informed by semiotic and aesthetic thinking and should explore the use of soundscapes, steganographic embedding, model-based sonification, and spatialization as profitable techniques for sonifying monitoring data.


title: AN ANALYTICAL METHOD TO FIND WORKSPACE OF A ROBOTIC MANIPULATOR

abstract: The workspace of a Robot is determined by an analytical method. The method is applicable to kinematic chains that can be modeled using the Denavit-Hartenberg representation for serial kinematic chains. This method is based upon analytical criteria for determining singular behavior of the mechanism. By manipulating the Jacobian of the robot by the row rank deficiency condition, the singularities are computed. Then these singularities are substituted into the constraint equations to parameterize singular surfaces. The boundary conditions of the joints are substituted to obtain the other set of singularities. These singularities are substituted in the wrist vector to obtain the range of motion of the robot wrist in three dimensional space, which is the workspace of the Robot. These singularities are plotted in Matlab to develop all the surfaces enveloping the workspace of the Robot. The priactical examples of RV-M1 MITSIBUSHI ROBOT and 3 DOF spatial manipulator are treated with this method. Key Words: Jacobian; Workspace; Singularities; Degree of freedom DOI: 10.3329/jme.v41i1.5359 Journal of Mechanical Engineering , Vol. ME 41, No. 1, June 2010 25-30


title: Towards a Multi-hypothesis Road Representation for Automated Driving

abstract: The process of perception inevitably involves uncertainty. In the field of automated driving, however, uncertainty about the roadway -- and especially about the understanding of the roadway -- is yet mostly neglected. In this paper it is argued that this uncertainty should be represented and considered in the process of behavior generation. A road representation capable of representing multiple hypotheses about the roadway is presented. The representation language allows to express where and how hypotheses differ and to infer this information in an efficient way. The focus of this paper is on the qualitative representation of uncertainty.


title: The EMOTE model for effort and shape

abstract: Human movements include limb gestures and postural attitude. Although many computer animation researchers have studied these classes of movements, procedurally generated movements still lack naturalness. We argue that looking only at the psychological notion of gesture is insufficient to capture movement qualities needed by animated charactes. We advocate that the domain of movement observation science, specifically Laban Movement Analysis (LMA) and its Effort and Shape components, provides us with valuable parameters for the form and execution of qualitative aspects of movements. Inspired by some tenets shared among LMA proponents, we also point out that Effort and Shape phrasing across movements and the engagement of the whole body are essential aspects to be considered in the search for naturalness in procedurally generated gestures. Finally, we present EMOTE (Expressive MOTion Engine), a 3D character animation system that applies Effort and Shape qualities to independently defined underlying movements and thereby generates more natural synthetic gestures.


title: Succinate Dehydrogenase Supports Metabolic Repurposing of Mitochondria to Drive Inflammatory Macrophages

abstract: Activated macrophages undergo metabolic reprogramming, which drives their pro-inflammatory phenotype, but the mechanistic basis for this remains obscure. Here, we demonstrate that upon lipopolysaccharide (LPS) stimulation, macrophages shift from producing ATP by oxidative phosphorylation to glycolysis while also increasing succinate levels. We show that increased mitochondrial oxidation of succinate via succinate dehydrogenase (SDH) and an elevation of mitochondrial membrane potential combine to drive mitochondrial reactive oxygen species (ROS) production. RNA sequencing reveals that this combination induces a pro-inflammatory gene expression profile, while an inhibitor of succinate oxidation, dimethyl malonate (DMM), promotes an anti-inflammatory outcome. Blocking ROS production with rotenone by uncoupling mitochondria or by expressing the alternative oxidase (AOX) inhibits this inflammatory phenotype, with AOX protecting mice from LPS lethality. The metabolic alterations that occur upon activation of macrophages therefore repurpose mitochondria from ATP synthesis to ROS production in order to promote a pro-inflammatory state.


title: Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

abstract: Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.


RoboGlove - A Robonaut Derived Multipurpose Assistive Device


title: Android anomaly detection system using machine learning classification

abstract: Android is one of the most popular open-source smartphone operating system and its access control permission mechanisms cannot detect any malware behavior. In this paper, new software behavior-based anomaly detection system is proposed to detect anomaly caused by malware. It works by analyzing anomalies on power consumption, battery temperature and network traffic data using machine learning classification algorithm. The result shows that this method can detect anomaly with 85.6% accuracy.


title: Infrared Dim and Small Target Detection Based on Stable Multisubspace Learning in Heterogeneous Scene

abstract: Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background.


title: A Probabilistic Approach to WLAN User Location Estimation

abstract: We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.


title: Multiuser MIMO Downlink Beamforming Design Based on Group Maximum SINR Filtering

abstract: In this paper we aim to solve the multiuser multi-input multi-output (MIMO) downlink beamforming problem where one multi-antenna base station broadcasts data to many users. Each user is assigned multiple data streams and has multiple antennas at its receiver. Efficient solutions to the joint transmit-receive beamforming and power allocation problem based on iterative methods are proposed. We adopt the group maximum signal-to-interference-plus-noise-ratio (SINR) filter bank (GSINR-FB) as our beamformer which exploits receiver diversity through cooperation between the data streams of a user. The data streams for each user are subject to an average SINR constraint, which has many important applications in wireless communication systems and serves as a good metric to measure the quality of service (QoS). The GSINR-FB also optimizes the average SINR of its output. Based on the GSINR-FB beamformer, we find an SINR balancing structure for optimal power allocation which simplifies the complicated power allocation problem to a linear one. Simulation results verify the superiority of the proposed algorithms over previous works with approximately the same complexity.


title: An optimized 3D-stacked memory architecture by exploiting excessive, high-density TSV bandwidth

abstract: Memory bandwidth has become a major performance bottleneck as more and more cores are integrated onto a single die, demanding more and more data from the system memory. Several prior studies have demonstrated that this memory bandwidth problem can be addressed by employing a 3D-stacked memory architecture, which provides a wide, high frequency memory-bus interface. Although previous 3D proposals already provide as much bandwidth as a traditional L2 cache can consume, the dense through-silicon-vias (TSVs) of 3D chip stacks can provide still more bandwidth. In this paper, we contest that we need to re-architect our memory hierarchy, including the L2 cache and DRAM interface, so that it can take full advantage of this massive bandwidth. Our technique, SMART-3D, is a new 3D-stacked memory architecture with a vertical L2 fetch/write-back network using a large array of TSVs. Simply stated, we leverage the TSV bandwidth to hide latency behind very large data transfers. We analyze the design trade-offs for the DRAM arrays, careful enough to avoid compromising the DRAM density because of TSV placement. Moreover, we propose an efficient mechanism to manage the false sharing problem when implementing SMART-3D in a multi-socket system. For single-threaded memory-intensive applications, the SMART-3D architecture achieves speedups from 1.53 to 2.14 over planar designs and from 1.27 to 1.72 over prior 3D designs. We achieve similar speedups for multi-program and multi-threaded workloads on multi-core and multi-socket processors. Furthermore, SMART-3D can even lower the energy consumption in the L2 cache and 3D DRAM for it reduces the total number of row buffer misses.


title: Bricks: laying the foundations for graspable user interfaces

abstract: We introduce the concept of Graspable User Interfaces that allow direct control of electronic or virtual objects through physical handles for control. These physical artifacts, which we call "bricks," are essentially new input devices that can be tightly coupled or “attached” to virtual objects for manipulation or for expressing action (e.g., to set parameters or for initiating processes). Our bricks operate on top of a large horizontal display surface known as the "ActiveDesk." We present four stages in the development of Graspable UIs: (1) a series of exploratory studies on hand gestures and grasping; (2) interaction simulations using mock-ups and rapid prototyping tools; (3) a working prototype and sample application called GraspDraw; and (4) the initial integrating of the Graspable UI concepts into a commercial application. Finally, we conclude by presenting a design space for Bricks which lay the foundation for further exploring and developing Graspable User Interfaces.


title: School food standards in the UK: implementation and evaluation.

abstract: OBJECTIVE ::: To outline the evolution of school food standards and their implementation and evaluation in each of the four countries of the UK since 2000. ::: ::: ::: DESIGN ::: Review of relevant policies, surveys and evaluations, including country-specific surveys and regional evaluations. ::: ::: ::: SETTING ::: UK: England, Wales, Scotland and Northern Ireland. ::: ::: ::: SUBJECTS ::: Primary and secondary schools and schoolchildren. ::: ::: ::: RESULTS ::: By September 2013 standards will have been introduced in all primary and secondary schools in the UK. Evaluations have varied in their scope and timing, relating to government forward planning, appropriate baselines and funding. Where standards have been implemented, the quality and nutritional value of food provided have improved. Emerging evidence shows improved overall diet and nutrient intake by school-aged children as a result. ::: ::: ::: CONCLUSIONS ::: The re-introduction of school food standards in the UK has not been centrally coordinated, but by September 2013 will be compulsory across all four countries in the UK, except in England where academies are now exempt. Provision of improved school food has had a demonstrable impact on diet and nutrition beyond the school dining room and the school gate, benefiting children from all socio-economic groups. Improved school food and dining environments are associated with higher levels of school lunch take up. Implementation of school food standards requires investment. It is critical to policy development that the value of this investment is measured and protected using planned, appropriate, robust and timely evaluations. Where appropriate, evaluations should be carried out across government departments and between countries.


title: Proxemic Transitions: Designing Shape-Changing Furniture for Informal Meetings

abstract: The field of Shape-Changing Interfaces explores the qualities of physically dynamic artifacts. At furniture-scale, such artifacts have the potential of changing the ways we collaborate and engage with interiors and physical spaces. Informed by theories of proxemics, empirical studies of informal meetings and design work with shape-changing furniture, we develop the notion of proxemic transitions. We present three design aspects of proxemic transitions: transition speed, stepwise reconfiguration, and radical shifts. The design aspects focus on how to balance between physical and digital transformations in designing for proxemic transitions. Our contribution is three-fold: 1) the notion of proxemic transitions, 2) three design aspects to consider in designing for proxemic transitions, and 3) initial exploration of how these design aspects might generate designs of dynamic furniture. These contributions outline important aspects to consider when designing shape-changing furniture for informal workplace meetings.


title: Provenance of e-Science Experiments - Experience from Bioinformatics

abstract: Like experiments performed at a laboratory bench, the data associated with an e-Science experiment are of reduced value if other scientists are not able to identify the origin, or provenance, of those data. Provenance information is essential if experiments are to be validated and verified by others, or even by those who originally performed them. In this article, we give an overview of our initial work on the provenance of bioinformatics e-Science experiments within myGrid. We use two kinds of provenance: the derivation path of information and annotation. We show how this kind of provenance can be delivered within the myGrid demonstrator WorkBench and we explore how the resulting Webs of experimental data holdings can be mined for useful information and presentations for the e-Scientist.


title: Unilateralization of MMIC Distributed Amplifiers

abstract: This paper presents an unilateralization technique for distributed amplifiers (DAs) based on the transformer coupling between the gate and drain lines. Theoretical analysis of the DA indicates that the voltage waves in the gate and drain lines can be described by a system of linear partial differential equations. The transformer coupling between the lines allows for cancellation of the reverse transmission coefficient of the system. There is an optimal value for the coupling coefficient between the lines that unilateralizes the DA. This optimal coupling coefficient is derived in terms of the gate-drain capacitance and capacitances of the gate and drain lines. Using the proposed technique, two monolithic microwave integrated circuit DAs are designed and implemented in a 0.1- μm GaAs pHEMT process. The first DA provides average gain of 10 dB and 3-dB bandwidth (BW) of 39 GHz. The amplifier exhibits the minimum noise figure (NF) of 2.3 dB and the maximum output 1-dB compression point ( P1 dB) of 14.0 dBm, while consuming 80 mW from a 2-V supply. The second amplifier, composed of two cascaded coupled-line DAs, provides average gain of 19.2 dB, 3-dB BW of 37.5 GHz, the minimum NF of 2.3 dB, and the maximum P1 dB of 12.6 dBm. It consumes 131 mW from a 2-V supply. The designed amplifiers can operate with supply voltages as low as 0.5 V to reduce power consumption, while their gain, BW, and NF are preserved. The maximum ratio of gain-bandwidth product to power consumption GBW/Pdc of 11.9 GHz/mW is provided by the cascaded DA.


title: Active Video Games to Promote Physical Activity in Children and Youth

abstract: OBJECTIVES ::: To systematically review levels of metabolic expenditure and changes in activity patterns associated with active video game (AVG) play in children and to provide directions for future research efforts. ::: ::: ::: DATA SOURCES ::: A review of the English-language literature (January 1, 1998, to January 1, 2010) via ISI Web of Knowledge, PubMed, and Scholars Portal using the following keywords: video game, exergame, physical activity, fitness, exercise, energy metabolism, energy expenditure, heart rate, disability, injury, musculoskeletal, enjoyment, adherence, and motivation. ::: ::: ::: STUDY SELECTION ::: Only studies involving youth (< or = 21 years) and reporting measures of energy expenditure, activity patterns, physiological risks and benefits, and enjoyment and motivation associated with mainstream AVGs were included. Eighteen studies met the inclusion criteria. Articles were reviewed and data were extracted and synthesized by 2 independent reviewers. MAIN OUTCOME EXPOSURES: Energy expenditure during AVG play compared with rest (12 studies) and activity associated with AVG exposure (6 studies). ::: ::: ::: MAIN OUTCOME MEASURES ::: Percentage increase in energy expenditure and heart rate (from rest). ::: ::: ::: RESULTS ::: Activity levels during AVG play were highly variable, with mean (SD) percentage increases of 222% (100%) in energy expenditure and 64% (20%) in heart rate. Energy expenditure was significantly lower for games played primarily through upper body movements compared with those that engaged the lower body (difference, -148%; 95% confidence interval, -231% to -66%; P = .001). ::: ::: ::: CONCLUSIONS ::: The AVGs enable light to moderate physical activity. Limited evidence is available to draw conclusions on the long-term efficacy of AVGs for physical activity promotion.


Textmarker: A tool for rule-based information extraction


title: Dynamic channel equalization for IEEE 802.11p waveforms in the vehicle-to-vehicle channel

abstract: The IEEE 802.11p standard (or Dedicated Short Range Communication, DSRC) [1] has been proposed to be the standard for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Since the IEEE 802.11a-based DSRC standard is not fully customized for outdoor, highly mobile channels, the performance of DSRC standard might degrade in a challenging V2V channel. In this paper, we develop several equalization schemes that are able to closely track the V2V channel dynamics and thus improve performance at the physical layer. Through a set of empirical experiments, we show that the performance (in terms of Packet Error Rate) can be significantly improved from 41% (using a simple Least Square Estimator) to 19% (using a Spectral Temporal Averaging Estimator).