title: Improvement of post-thaw sperm motility in poor quality human semen*

abstract: Objective To find alternative cryopreservation methods to improve the post-thaw fertilizing capacity of poor quality human sperm. Design Controlled clinical study. Setting Fertility clinic of a teaching hospital. Patients Men with poor quality semen samples, i.e., asthenozoospermia ( 6 sperm/mL). Fertile sperm donors were used for comparison. Interventions Semen samples were divided into four aliquots and slowly diluted 1:1 with: [1] n-tris (hydroxymethyl) methyl-2-amino ethane sulfonic acid (TES) and tris (hydroxymethyl) aminomethane (Tris)-citric acid-egg yolk buffer with 12% glycerol (TEST), [2] TEST + CryoSeeds (Cell Systems, Ltd., Cambridge, UK), [3] TEST + 10 mM dithiothreitol (DTT), or [4] TEST + CryoSeeds + 10 mM DTT. Cryovials were frozen using slow staged cooling and static vapor freeze and stored at —196°C. Main Outcome Measure The frozen aliquots were randomly thawed and, after 15 minutes at 37°C, motion analysis was performed. Results The percent motility after freeze-thaw in TEST was significantly decreased to 42 ± 5% of prefreeze motility ( P P Conclusion The present results suggest that DTT, a reducing agent that prevents oxidation of sulfhydryl groups, protects poor quality spermatozoa from excessive cryodamage. Thus, DTT along with seeding may be a useful addition when long-term storage of poor quality semen is crucial for maintaining reproductive potential.


title: AuRA: Principles and Practice in Review

abstract: This paper reviews key concepts of the Autonomous Robot Architecture (AuRA). Its structure, strengths, and roots in biology are presented. AuRA is a hybrid deliberative/ reactive robotic architecture that has been developed and refined over the past decade. In this article, particular focus is placed on the reactive behavioural component of this hybrid architecture. Various real world robots that have been implemented using this architectural paradigm are discussed, including a case study of a multiagent robotic team that competed and won the 1994 AAAI Mobile Robot Competition.


A Hybrid Model of Particle Swarm Optimization ( PSO ) and Artificial Bee Colony ( ABC ) Algorithm for Test Case Optimization


Cooperation Under the Shadow of the Future: Experimental Evidence from Infinitely Repeated Games


Forecasting Bike Rental Demand


title: Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error

abstract: Neural network models offer a theoretical testbed for the study of learning at the cellular level. The only experimentally verified learning rule, Hebb's rule, is extremely limited in its ability to train networks to perform complex tasks. An identified cellular mechanism responsible for Hebbian-type long-term potentiation, the NMDA receptor, is highly versatile. Its function and efficacy are modulated by a wide variety of compounds and conditions and are likely to be directed by non-local phenomena. Furthermore, it has been demonstrated that NMDA receptors are not essential for some types of learning. We have shown that another neural network learning rule, the chemotaxis algorithm, is theoretically much more powerful than Hebb's rule and is consistent with experimental data. A biased random-walk in synaptic weight space is a learning rule immanent in nervous activity and may account for some types of learning -- notably the acquisition of skilled movement.


Kinematic Modelling and Simulation of a 2R Robot Using SolidWorks and Verification by MATLAB / Simulink Regular Paper


title: Coevolutionary Dynamics in a Minimal Substrate

abstract: One of the central difficulties of coevolutionary methods arises from 'intransitive superiority' – in a two-player game, for example, the fact that A beats B, and B beats C, does not exclude the possibility that C beats A. Such cyclic superiority in a coevolutionary substrate is hypothesized to cause cycles in the dynamics of the population such that it 'chases its own tail' - traveling through some part of strategy space more than once despite apparent improvement with each step. It is often difficult to know whether an application domain contains such difficulties and to verify this hypothesis in the failure of a given coevolutionary set-up. In this paper we wish to elucidate some of the issues and concepts in an abstract domain where the dynamics of coevolution can be studied simply and directly. We define three simple 'number games' that illustrate intransitive superiority and resultant oscillatory dynamics, as well as some other relevant concepts. These include the distinction between a player's perceived performance and performance with respect to an external metric, and the significance of strategies with a multidimensional nature. These features alone can also cause oscillatory behavior and coevolutionary failure.


title: A Practical Learning-Based Approach for Dynamic Storage Bandwidth Allocation

abstract: In this paper, we address the problem of dynamic allocation of storage bandwidth to application classes so as to meet their response time requirements. We present an approach based on reinforcement learning to address this problem. We argue that a simple learning-based approach may not be practical since it incurs significant memory and search space overheads. To address this issue, we use application-specific knowledge to design an efficient, practical learning-based technique for dynamic storage bandwidth allocation. Our approach can react to dynamically changing workloads, provide isolation to application classes and is stable under overload. We implement our techniques into the Linux kernel and evaluate it using prototype experimentation and trace-driven simulations. Our results show that (i) the use of learning enables the storage system to reduce the number of QoS violations by a factor of 2.1 and (ii) the implementation overheads of employing such techniques in operating system kernels is small.


title: Big data set privacy preserving through sensitive attribute-based grouping

abstract: There is a growing trend towards attacks on database privacy due to great value of privacy information stored in big data set. Public's privacy are under threats as adversaries are continuously cracking their popular targets such as bank accounts. We find a fact that existing models such as K-anonymity, group records based on quasi-identifiers, which harms the data utility a lot. Motivated by this, we propose a sensitive attribute-based privacy model. Our model is the early work of grouping records based on sensitive attributes instead of quasi-identifiers which is popular in existing models. Random shuffle is used to maximize information entropy inside a group while the marginal distribution maintains the same before and after shuffling, therefore, our method maintains a better data utility than existing models. We have conducted extensive experiments which confirm that our model can achieve a satisfying privacy level without sacrificing data utility while guarantee a higher efficiency.


title: On Coresets for $k$-Median and $k$-Means Clustering in Metric and Euclidean Spaces and Their Applications

abstract: We present new approximation algorithms for the $k$-median and $k$-means clustering problems. To this end, we obtain small coresets for $k$-median and $k$-means clustering in general metric spaces and in Euclidean spaces. In $\mathbb{R}^d$, these coresets are of size with polynomial dependency on the dimension $d$. This leads to $(1+\varepsilon)$-approximation algorithms to the optimal $k$-median and $k$-means clustering in $\mathbb{R}^d$, with running time $O(ndk+2^{(k/\varepsilon)^{O(1)}}d^2\log^{k+2}n)$, where $n$ is the number of points. This improves over previous results. We use those coresets to maintain a $(1+\varepsilon)$-approximate $k$-median and $k$-means clustering of a stream of points in $\mathbb{R}^d$, using $O(d^2k^2\varepsilon^{-2}\log^8n)$ space. These are the first streaming algorithms, for those problems, that have space complexity with polynomial dependency on the dimension.


title: FIRST: Fast Interactive Attributed Subgraph Matching

abstract: Attributed subgraph matching is a powerful tool for explorative mining of large attributed networks. In many applications (e.g., network science of teams, intelligence analysis, finance informatics), the user might not know what exactly s/he is looking for, and thus require the user to constantly revise the initial query graph based on what s/he finds from the current matching results. A major bottleneck in such an interactive matching scenario is the efficiency, as simply rerunning the matching algorithm on the revised query graph is computationally prohibitive. In this paper, we propose a family of effective and efficient algorithms (FIRST) to support interactive attributed subgraph matching. There are two key ideas behind the proposed methods. The first is to recast the attributed subgraph matching problem as a cross-network node similarity problem, whose major computation lies in solving a Sylvester equation for the query graph and the underlying data graph. The second key idea is to explore the smoothness between the initial and revised queries, which allows us to solve the new/updated Sylvester equation incrementally, without re-solving it from scratch. Experimental results show that our method can achieve (1) up to 16x speed-up when applying on networks with 6M$+$ nodes; (2) preserving more than 90% accuracy compared with existing methods; and (3) scales linearly with respect to the size of the data graph.


title: 10.1177/0022002705277545 JOURNAL Humphreys OF / NATURAL CONFLICT RESOURCES, RESOLUTION CONFLICT, AND CONFLICT RESOLUTION Natural Resources, Conflict, and Conflict Resolution UNCOVERING THE MECHANISMS

abstract: The interpretation of the resource-conflict link that has become most publicizedâ€”the rebel greed hypothesisâ€”depends on just one of many plausible mechanisms that could underlie a relationship between resource dependence and violence. The author catalogues a large range of rival possible mechanisms, highlights a set of techniques that may be used to identify these mechanisms, and begins to employ these techniques to distinguish between rival accounts of the resource-conflict linkages. The author uses finer natural resource data than has been used in the past, gathering and presenting new data on oil and diamonds production and on oil stocks. The author finds evidence that (1) conflict onset is more responsive to the impacts of past natural resource production than to the potential for future production, supporting a weak states mechanism rather than a rebel greed mechanism; (2) the impact of natural resources on conflict cannot easily be attributed entirely to the weak states mechanism, and in particular, the impact of natural resources is independent of state strength; (3) the link between primary commodities and conflict is driven in part by agricultural dependence rather than by natural resources more narrowly defined, a finding consistent with a â€œsparse networksâ€ mechanism; (4) natural resources are associated with shorter wars, and natural resource wars are more likely to end with military victory for one side than other wars. This is consistent with evidence that external actors have incentives to work to bring wars to a close when natural resource supplies are threatened. The author finds no evidence that resources are associated with particular difficulties in negotiating ends to conflicts, contrary to arguments that loot-seeking rebels aim to prolong wars.


title: A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services

abstract: In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.


title: AutoBlackTest: a tool for automatic black-box testing

abstract: In this paper we present AutoBlackTest, a tool for the automatic generation of test cases for interactive applications. AutoBlackTest interacts with the application though its GUI, and uses reinforcement learning techniques to understand the interaction modalities and to generate relevant testing scenarios. Early results show that the tool has the potential of automatically discovering bugs and generating useful system and regression test suites.


title: Dynamic programming for detecting, tracking, and matching deformable contours

abstract: The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images. >


title: Online Parameter Estimation and Adaptive Control of Permanent-Magnet Synchronous Machines

abstract: This paper focuses on the benefits of adaptive control for permanent-magnet synchronous machines; a novel method of online parameter estimation for such machines has been developed. Two recursive least square algorithm segments, a fast and a slow one, are uniquely combined in real time with rich enough data from the machine to estimate all four machine parameters instead of a subset of these. Simulation and experimental results demonstrate the effectiveness of the proposed method.


title: Analyzing the impact of social media on social movements: a computational study on Twitter and the occupy wall street movement

abstract: The extensive use of digital social media by social movement actors is an emerging trend that restructures the communication dynamics of social protest, and it is widely credited with contributing to the successful mobilizations of recent movements (e.g., Arab Spring, Occupy Wall Street). Yet, our understanding of both the roles played by social movement's use of social media and the extent of its impact is largely derived from anecdotal evidence, news reports, and a thin body of scholarly research on web-based technologies. In this research we explore several computational methods for measuring the impact of social media on a social movement. Inspired by methodologies originally developed for analyzing computer networks and other dynamic systems, these methods measure various static and dynamic aspects of social networks, and their relations to an underlying social movement. We demonstrated the feasibility and benefits of these measurement methods in the context of Twitter and the Occupying Wall Street movement (OWS). By analyzing tweets related to OWS, we demonstrated the link between the vitality of the movement and the volume of the related tweets over time. We show that there is a positive correlation between the dynamic of tweets and the short-term trend of OWS. The correlation makes it possible to forecast the short-term trend of a social movement using social media data. By ranking users based on the number of their OWS-related tweets and the durations of their tweeting, we are able to identify "buzz makers". Using a strategy similar to the page-rank algorithm, we define the influence of a user by the number of re-tweets that his/her original tweets incite. By tracing where OWS-related tweets are generated, we measure the geographic diffusion of OWS. By analyzing the percentage of OWS tweets generated from different sources, we show that smart phones and applications such as tweet deck had been used extensively for tweeting in the OWS movement. This indicates the involvement of a younger and more technology-inclined generation in OWS.


title: Improving a simulated soccer team's performance through a Memory-Based Collaborative Filtering approach

abstract: Collaborative filtering techniques have been used for some years, almost exclusively in Internet environments, helping users find items they are expected to like by using the user's past purchases to provide such recommendations. With this concept in mind, this research uses a collaborative filtering technique to automatically improve the performance of a simulated soccer team. Many studies have attempted to address this problem over the last years but none has shown meaningful improvements in the performance of the soccer team. Using a collaborative filtering technique based on nearest neighbors and the FC Portugal team as the test subject (in the context of the RoboCup 2D Simulation League), several simulations were run for matches against different teams with much better, better and worse performance than FC Portugal. The strategy used by FC Portugal was to combine 8 set-plays and 2 team formations. The simulation results revealed an improvement in performance between 32% and 384%. In the future, there are plans to expand this approach to other contexts, such as the 3D Simulation League.


title: Characterization of a fertile backcross progeny derived from inter-specific hybrid of Momordica dioica and M. subangulata subsp. renigera and its implications on improvement of dioecious Momordica spp

abstract: Abstract A fertile female backcross progeny was produced from an inter-specific hybrid [teasel gourd ( Momordica subangulata Blume subsp. renigera (G. Don) WJJ de Wilde) × spine gourd ( Momordica dioica Roxb)] by backcrossing the F 1 with the female parent. Agro morphological and biochemical traits of the first generation backcross (F 1 ♀ BC) confirmed interchanges of genetic material. The F 1 ♀ BC bear more resemblance to maternal parent than the paternal parent and exhibited substantial heterosis for number of flowers and yield per plant. In F 1 ♀ BC, fruit set was significantly higher when pollinated with teasel gourd (84%) while the fruit set was very low upon sibbing (36%) and pollination with spine gourd (64%). The fruits of F 1 ♀ BC had little variation for shape from teasel gourd when pollinated with teasel gourd pollen, while fruits obtained from sibbing and dusting of spine gourd pollen were deformed and smaller in size. The proximate and phytochemical analysis revealed that the F 1 ♀ BC was intermediate between the original parents in its biochemical traits and antioxidant activity while mineral content was found to be higher or similar to its parents. The present study shows the potential for improving Momordica species through inter-specific hybridization followed by restoration of fertility by backcrossing.


title: Development of a batteryless sensor transmitter

abstract: This paper presents a batteryless transmitter for wireless sensor systems. Unlike the conventional battery powered sensor transmitters, our proposed transmitter is powered from a cellular energy-harvesting circuit, a microwave energy transmission technique that has capacity to harvest 3.2 V in 5.5 hours at 0 dBm of incident cellular signals (2 GHz). The transmitter is built around a 2.4 GHz nRF24L01+ programmable radio chip, controlled by a PIC16F877A MCU via a Serial Peripheral Interface (SPI). A 5 min successful locked carrier transmit test was carried out at ISM band channels (2.402, 2.440, and 2.850 GHz). The cellular energy-harvesting circuit described in this work is rated 36.16 mW max, given 11.3 mA maximum transmit drain current.


title: Distributional Word Clusters vs. Words for Text Categorization

abstract: We study an approach to text categorization that combines distributional clustering of words and a Support Vector Machine (SVM) classifier. This word-cluster representation is computed using the recently introduced Information Bottleneck method, which generates a compact and efficient representation of documents. When combined with the classification power of the SVM, this method yields high performance in text categorization. This novel combination of SVM with word-cluster representation is compared with SVM-based categorization using the simpler bag-of-words (BOW) representation. The comparison is performed over three known datasets. On one of these datasets (the 20 Newsgroups) the method based on word clusters significantly outperforms the word-based representation in terms of categorization accuracy or representation efficiency. On the two other sets (Reuters-21578 and WebKB) the word-based representation slightly outperforms the word-cluster representation. We investigate the potential reasons for this behavior and relate it to structural differences between the datasets.


title: Robotic monitoring of power systems

abstract: Monitoring of electric power systems in real time for reliability, aging status, and presence of incipient faults requires distributed and centralized processing of large amounts of data from distributed sensor networks. To solve this task, cohesive multidisciplinary efforts are needed from such fields as sensing, signal processing, control, communications, optimization theory, and, more recently, robotics. This review paper focuses on one trend of power system monitoring, namely, mobile monitoring. The developments in robotic maintenance for power systems indicate significant potential of this technological approach. Authors discuss integration of several important relevant sensor technologies that are used to monitor power systems, including acoustic sensing, fringing electric field sensing, and infrared sensing.


title: Rotated dispersed dither: a new technique for digital halftoning

abstract: Rotated dispersed-dot dither is proposed as a new dither technique for digital halftoning. It is based on the discrete one-to-one rotation of a Bayer dispersed-dot dither array. Discrete rotation has the effect of rotating and splitting a significant part of the frequency impulses present in Bayer's halftone arrays into many low-amplitude distributed impulses. The halftone patterns produced by the rotated dither method therefore incorporate fewer disturbing artifacts than the horizontal and vertical components present in most of Bayer's halftone patterns. In grayscale wedges produced by rotated dither, texture changes at consecutive gray levels are much smoother than in error diffusion or in Bayer's dispersed-dot dither methods, thereby avoiding contouring effects. Due to its semi-clustering behavior at mid-tones, rotated dispersed-dot dither exhibits an improved tone reproduction behavior on printers having a significant dot gain, while maintaining the high detail rendition capabilities of dispersed-dot halftoning algorithms. Besides their use in black and white printing, rotated dither halftoning techniques have also been successfully applied to in-phase color reproduction on ink-jet printers.


title: Stable topological signatures for points on 3D shapes

abstract: Comparing points on 3D shapes is among the fundamental operations in shape analysis. To facilitate this task, a great number of local point signatures or descriptors have been proposed in the past decades. However, the vast majority of these descriptors concentrate on the local geometry of the shape around the point, and thus are insensitive to its connectivity structure. By contrast, several global signatures have been proposed that successfully capture the overall topology of the shape and thus characterize the shape as a whole. In this paper, we propose the first point descriptor that captures the topology structure of the shape as 'seen' from a single point, in a multiscale and provably stable way. We also demonstrate how a large class of topological signatures, including ours, can be mapped to vectors, opening the door to many classical analysis and learning methods. We illustrate the performance of this approach on the problems of supervised shape labeling and shape matching. We show that our signatures provide complementary information to existing ones and allow to achieve better performance with less training data in both applications.


title: Stock Optimization of Spare Parts with Genetic Algorithm

abstract: In this research, a new method is proposed for the optimization of warship spare parts stock with genetic algorithm. Warships should fulfill her duties in all circumstances. Considering the warships have more than a hundred thousand unique parts, it is a very hard problem to decide which spare parts should be stocked at warehouse aiming to use in case of failure. In this study, genetic algorithm that is a heuristic optimization method is used to solve this problem. The demand quantity, the criticality and the cost of parts is used for optimization. A genetic algorithm with very long chromosome is used, i.e. over 1000 genes in one chromosome. The outputs of the method is analyzed and compared with the Price Sensitive 0.5 FLSIP+ model, which is widely used over navies, and came to a conclusion that the proposed method is better.


title: Drawbridge: software-defined DDoS-resistant traffic engineering

abstract: End hosts in today's Internet have the best knowledge of the type of traffic they should receive, but they play no active role in traffic engineering. Traffic engineering is conducted by ISPs, which unfortunately are blind to specific user needs. End hosts are therefore subject to unwanted traffic, particularly from Distributed Denial of Service (DDoS) attacks. This research proposes a new system called DrawBridge to address this traffic engineering dilemma. By realizing the potential of software-defined networking (SDN), in this research we investigate a solution that enables end hosts to use their knowledge of desired traffic to improve traffic engineering during DDoS attacks.


title: The missing memristor found

abstract: Basic electronics textbooks list three fundamental passive circuit elements: resistors, capacitors and inductors. But nearly forty years ago, Leon Chua predicted the existence of a fourth, the memristor — in effect a nonlinear resistor with memory. A paper from the Hewlett-Packard research lab now reports that memristance arises naturally in nanoscale systems where solid-state electronic and ionic transport are coupled under an external bias voltage. This finding can help explain many examples of apparently anomalous hysteretic current–voltage behaviour observed in electronic devices during the past 50 years. Memristors may have a significant impact on future electronic circuits by dramatically increasing the functional density over that achieved by transistors. There are three fundamental passive circuit elements, resistors, capacitors, and inductors, but it was reasoned that there should be a fourth fundamental element, called a memristor, which has until now not been realized in a physical system. A fresh analysis of the concept shows that memristance arises naturally in nanoscale systems where solid state electronic and ionic transport are coupled under an external bias voltage. Anyone who ever took an electronics laboratory class will be familiar with the fundamental passive circuit elements: the resistor, the capacitor and the inductor. However, in 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor)1. Although he showed that such an element has many interesting and valuable circuit properties, until now no one has presented either a useful physical model or an example of a memristor. Here we show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current–voltage behaviour observed in many nanoscale electronic devices2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches20,21,22.


title: Recommendations for Integrating a P300-Based Brain Computer Interface in Virtual Reality Environments for Gaming

abstract: The integration of a P300-based brain–computer interface (BCI) into virtual reality (VR) environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and constraints engendered by the stimulation needed by the BCI. The main limitation is still the low transfer rate that can be achieved by current BCI technology. The goal of this paper is to review current limitations and to provide application creators with design recommendations in order to overcome them. We also overview current VR and BCI commercial products in relation to the design of video games. An essential recommendation is to use the BCI only for non-complex and non-critical tasks in the game. Also, the BCI should be used to control actions that are naturally integrated into the virtual world. Finally, adventure and simulation games, especially if cooperative (multi-user) appear the best candidates for designing an effective VR game enriched by BCI technology.


title: Students' learning style detection using tree augmented naive Bayes

abstract: Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past resear...


title: Developing an Ontology for Cyber Security Knowledge Graphs

abstract: In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.


title: Co-operative information system design : how multi-domain information system design takes place in UK organisations

abstract: The thesis focussed on the need to understand the nature of design processes in innovative, ::: multi-domain, organisational information systems design. A cross-disciplinary, interpretive ::: investigation of organisational IS design was based upon multiple literatures: information ::: system development and methodologies, human-computer interaction, situated action, social ::: psychology, psychology of programming, computer-supported co-operative work, computer ::: science, design 'rationale' and organisational behaviour. Three studies were performed: ::: 1. A case study of a user-centred design project, employing grounded theory analysis. ::: 2. A postal survey of IS development approaches in large UK companies. ::: 3. A longitudinal field study, involving participant observation over a period of 18 months in ::: a cross-domain design team, employing ethnography, discourse analysis and ::: hermeneutics. ::: The main contributions of this research were to provide rich insights into the interior nature of ::: IS design activity, situated in the context of the organisation (a perspective which is largely ::: missing from the literature); to provide conceptual models to explain the management of ::: meaning in design, and design framing activity; to produce a social action model of ::: organisational information system development which may form the basis for communicating ::: the situated nature of design in teaching; and to suggest elements of a process model of design ::: activity in multi-domain, organisational information system development. The implications of ::: the research findings for IS managers and developers are also considered a significant ::: contribution to practice. ::: Detailed findings from these studies relate to: ::: I. Disparities between the technology-centred view of organisational IS development found ::: in the literature and the business and organisation-based approaches reported in the ::: survey. ::: 2. The role of pre-existing 'investment in form' in shaping the meaning of design processes ::: and outcomes for other team members and its implications for the management of ::: expertise and for achieving double-loop leaming. ::: 3. The detailed processes by which design is framed at individual and group levels of ::: analysis. These findings indicated a mismatch between "top down" models of ::: organisational IS design and observed design "abstraction" processes, which were ::: grounded in concrete analogies and local exemplars; this finding has significant ::: implications for organisational design approaches, such as Business Process Redesign. ::: 4. The distributed nature of group design, which has implications for achieving a 'common ::: vision' of the design and for the division of labour in design groups. Intersubjectivity with ::: respect to process objectives may be more critical to design success than intersubjectivity ::: with respect to the products of design. - ::: 5. The political nature of design activity: it was concluded that an effective design process ::: must manage conflict between the exploration of organisational possibilities and ::: influential, external stakeholders' expectations of efficiency benefits. ::: 6. Design suffers from legitimacy problems related to the investigation of a "grey area" ::: between explicit system design goals and boundary and emergent definitions of design ::: goals and target system boundaries; this issue needs to be managed both internally to the ::: design-team and externally, in respect of stakeholders and influential decision-makers. ::: It is argued that the situated nature of design requires the teaching of design skills to be ::: achieved through simulated design contexts, rather than the communication of abstract ::: models. It is also suggested that the findings of this thesis have implications for knowledge ::: management and organisational innovation. If organisational problem-investigation processes ::: are seen as involving distributed knowledge, then the focus of organisational learning and ::: innovation shifts from sharing organisational knowledge to accessing distributed ::: organisational knowledge which is emergent and incomplete.


title: A Novel Dynamic Android Malware Detection System With Ensemble Learning

abstract: With the popularity of Android smartphones, malicious applications targeted Android platform have explosively increased. Proposing effective Android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through static and dynamic analysis in conjunction with machine learning algorithm have been the mainstream in large-scale malware identification. In general, static analysis becomes invalid in detecting applications which adopt sophisticated obfuscation techniques like encryption or dynamic code loading. However, dynamic analysis is suitable to deal with these evasion techniques. In this paper, we propose an effective dynamic analysis framework, called EnDroid, in the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication. In addition, EnDroid adopts feature selection algorithm to remove noisy or irrelevant features and extracts critical behavior features. Extracting behavior features through runtime monitor, EnDroid is able to distinguish malicious from benign applications with ensemble learning algorithm. Through experiments, we prove the effectiveness of EnDroid on two datasets. Furthermore, we find Stacking achieves the best classification performance and is promising in Android malware detection.


title: A survey of mobile cloud computing: architecture, applications, and approaches

abstract: Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.


Environment Mapping using the Lego Mindstorms NXT and leJOS NXJ


title: (s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences

abstract: Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present (s|qu)eries (pronounced "Squeries"), a visual query interface for creating queries on sequences (series) of data, based on regular expressions. (s|qu)eries is a touch-based system that exposes the full expressive power of regular expressions in an approachable way and interleaves query specification with result visualizations. Being able to visually investigate the results of different query-parts supports debugging and encourages iterative query-building as well as exploratory work-flows. We validate our design and implementation through a set of informal interviews with data scientists that analyze event sequences on a daily basis.


title: The relative cost of biomass energy transport.

abstract: Logistics cost, the cost of moving feedstock or products, is a key component of the overall cost of recovering energy from biomass. In this study, we calculate for small- and large-project sizes, the relative cost of transportation by truck, rail, ship, and pipeline for three biomass feedstocks, by truck and pipeline for ethanol, and by transmission line for electrical power. Distance fixed costs (loading and unloading) and distance variable costs (transport, including power losses during transmission), are calculated for each biomass type and mode of transportation. Costs are normalized to a common basis of a giga Joules of biomass. The relative cost of moving products vs feedstock is an approximate measure of the incentive for location of biomass processing at the source of biomass, rather than at the point of ultimate consumption of produced energy. In general, the cost of transporting biomass is more than the cost of transporting its energy products. The gap in cost for transporting biomass vs power is significantly higher than the incremental cost of building and operating a power plant remote from a transmission grid. The cost of power transmission and ethanol transport by pipeline is highly dependent on scale of project. Transport of ethanol by truck has a lower cost than by pipeline up to capacities of 1800 t/d. The high cost of transshipment to a ship precludes shipping from being an economical mode of transport for distances less than 800 km (woodchips) and 1500 km (baled agricultural residues).


title: Being Ethical When the Boss Is Not

abstract: A missing element in discussions of ethical leadership is: What happens to enforcement of ethics if it is the managers who are behaving unethically? In this article we address this question by describing a framework of upward ethical leadership. This framework expands conceptualizations of leadership beyond top-down models to a view that considers employees to be active participants in the leadership process. Upward ethical leadership is defined as leadership behavior displayed by individuals who take action to maintain ethical standards in the face of questionable moral behaviors by higher-ups. It is fostered when employees are encouraged to establish personal power bases and develop upward leadership skills that help reduce their sense of powerlessness when faced with choosing how to respond to ethical misconduct by managers. By developing employees who are more willing and capable to take a stand in the face of ethical violations, we hope to broaden the resource base for ethical leadership in organizations and bring much needed attention to the role of employees in maintaining ethical


title: Open production: scientific foundation for co-creative product realization

abstract: Globalization and the use of technology call for an adaptation of value creation strategies. As the potential for rationalization and achieving flexibility within companies is to the greatest possible extent exhausted, approaches to the corporate reorganization of value creation are becoming increasingly important. In this process, the spread and further development of information and communication technology often provide the basis for a reorganization of cross-company value nets and lead to a redistribution of roles and tasks between the actors involved in value creation. While cooperative, decentralized and self-organizing value creation processes are in fact being promoted, the associated potential for development and production engineering is being underestimated and hence not implemented sufficiently. This contribution will introduce a value creation taxonomy and then, using its notion and structure, describe the emerging transformations in value creation on the basis of case studies. Finally an adequate framework for analysing and configuring value creation will be presented.


title: A bridgeless totem-pole interleaved PFC converter for plug-in electric vehicles

abstract: This paper proposes a bidirectional bridgeless totem-pole interleaved power-factor-correction (PFC) converter using SiC MOSFETs as the front-end stage of an onboard charger for plug-in electric vehicles (PEVs). The proposed converter provides bidirectional operation enabling both grid-to-vehicle (G2V) charging and vehicle-to-grid (V2G) ancillary services. The converter is suitable for efficient G2V V2G onboard charging due to its superiorities in terms of bidirectional operation, smaller current ripple, lower EMI, lower conduction losses and switching losses. A 3.3kW PFC converter is designed and developed, using Silicon-Carbide MOSFETs with fast recovery body diodes, for validation of and V2G operating modes. Utilizing SiC MOSFETs enables continuous current mode (CCM) operation of the totem-pole PFC converter in high-power applications. The converter is capable of converting 85Vac-265Vac line voltages into a regulated dc voltage in the range of 300V to 600V. The maximum efficiency of converter reaches up to 99.2% with 0.99 power factor.


title: Identifying Quality, Novel, and Creative Ideas: Constructs and Scales for Idea Evaluation

abstract: Researchers and practitioners have an abiding interest in improving tools and methods to support idea generation. In studies that go beyond merely enumerating ideas, researchers typically select one or more of the following three constructs, which are often operationalized as the dependent variable(s): 1) idea quality, 2) idea novelty, which is sometimes referred to as rarity or unusualness, and 3) idea creativity. It has been chronically problematic to compare findings across studies because these evaluation constructs have been variously defined and the constructs have been sampled in different ways. For example, some researchers term an idea ‘creative’ if it is novel, while others consider an idea to be creative only if it is also applicable, effective, and implementable. This paper examines 90 studies on creativity and idea generation. Within the creativity studies considered here, the novelty of ideas was always measured, but in 1 Detmar Straub was the accepting senior editor. This paper was submitted on September, 3, 2004, and went through three revisions. Journal of the Association for Information Systems Vol. 7 No. 10, pp. 646-699/October 2006 646 Identifying Quality, Novel, and Creative Ideas/Dean et al. some cases the ideas had to also meet additional requirements to be considered creative. Some studies that examined idea quality also assessed novelty, while others measured different quality attributes, such as effectiveness and implementability, instead. This paper describes a method for evaluating ideas with regard to four dimensions—novelty, workability, relevance, and specificity—and has identified two measurable sub-dimensions for each of the four main dimensions. An action-research approach was used to develop ordinal scales anchored by clearly differentiable descriptions for each sub-dimension. Confirmatory factor analysis revealed high loadings among the sub-dimensions that comprise each dimension as well as high discriminant validity between dimensions. Application of this method resulted in high inter-rater reliability even when the method was applied by different raters to different problems and to ideas produced by both manual methods and group support systems (GSS).


title: Flexible dual-band LCP antenna for RFID applications

abstract: A dual-band antenna is developed on a flexible Liquid Crystal Polymer (LCP) substrate for simultaneous operation at 2.45 and 5.8 GHz in high frequency Radio Frequency IDentification (RFID) systems. The response of the low profile double T-shaped slot antenna is preserved when the antenna is placed on platforms such as wood and cardboard, and when bent to conform to a cylindrical plastic box. Furthermore, experiments show that the antenna is still operational when placed at a distance of around 5cm from a metallic surface.


title: A Longitudinal and Cross-Dataset Study of Internet Latency and Path Stability

abstract: We present a retrospective and longitudinal study of Internet latency and path stability using three large-scale traceroute datasets collected over several years: Ark and iPlane from 2008 to 2013 and a proprietary CDN’s traceroute dataset spanning 2012 and 2013. Using these different “lenses”, we revisit classical properties of Internet paths such as end-toend latency, stability, and of routing graph structure. Iterative data analysis at this scale is challenging given the idiosyncrasies of different collection tools, measurement noise, and the diverse analysis we desire. To this end, we leverage recent big-data techniques to develop a scalable data analysis toolkit, Hummus, that enables rapid and iterative analysis on large traceroute measurement datasets. Our key findings are: (1) overall latency seems to be decreasing; (2) some geographical regions still have poor latency; (3) route stability (prevalence and persistence) is increasing; and (4) we observe a mixture of effects in the routing graph structure with high-degree ASes rapidly increasing in degree and lowerdegree ASes forming denser “communities”.


title: Efficient HIK SVM Learning for Image Classification

abstract: Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.


title: Graph PCA Hashing for Similarity Search

abstract: This paper proposes a new hashing framework to conduct similarity search via the following steps: first, employing linear clustering methods to obtain a set of representative data points and a set of landmarks of the big dataset; second, using the landmarks to generate a probability representation for each data point. The proposed probability representation method is further proved to preserve the neighborhood of each data point. Third, PCA is integrated with manifold learning to lean the hash functions using the probability representations of all representative data points. As a consequence, the proposed hashing method achieves efficient similarity search (with linear time complexity) and effective hashing performance and high generalization ability (simultaneously preserving two kinds of complementary similarity structures, i.e., local structures via manifold learning and global structures via PCA). Experimental results on four public datasets clearly demonstrate the advantages of our proposed method in terms of similarity search, compared to the state-of-the-art hashing methods.


title: Entropy analysis of the EEG background activity in Alzheimer's disease patients

abstract: Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although a definite diagnosis is only possible by necropsy, a differential diagnosis with other types of dementia and with major depression should be attempted. The aim of this study was to analyse the electroencephalogram (EEG) background activity of AD patients to test the hypothesis that the regularity of the AD patients' EEG is higher than that of age-matched controls. We recorded the EEG from 19 scalp electrodes in 11 AD patients and 11 age-matched controls. Two different methods were used to estimate the regularity of the EEG background activity: spectral entropy (SpecEn) and sample entropy (SampEn). We did not find significant differences between AD patients and control subjects' EEGs with SpecEn. On the other hand, AD patients had significantly lower SampEn values than control subjects (p < 0.01) at electrodes P3, P4, O1 and O2. Our results show an increase of EEG regularity in AD patients. These findings suggest that nonlinear analysis of the EEG with SampEn could yield essential information and may contribute to increasing the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.


title: Comparing community structure identification

abstract: We compare recent approaches to community structure identification in terms of sensitivity and computational cost. The recently proposed modularity measure is revisited and the performance of the methods as applied to ad hoc networks with known community structure, is compared. We find that the most accurate methods tend to be more computationally expensive, and that both aspects need to be considered when choosing a method for practical purposes. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods.


title: Cross-Domain Adversarial Feature Learning for Sketch Re-identification

abstract: Under person re-identification (Re-ID), a query photo of the target person is often required for retrieval. However, one is not always guaranteed to have such a photo readily available under a practical forensic setting. In this paper, we define the problem of Sketch Re-ID, which instead of using a photo as input, it initiates the query process using a professional sketch of the target person. This is akin to the traditional problem of forensic facial sketch recognition, yet with the major difference that our sketches are whole-body other than just the face. This problem is challenging because sketches and photos are in two distinct domains. Specifically, a sketch is the abstract description of a person. Besides, person appearance in photos is variational due to camera viewpoint, human pose and occlusion. We address the Sketch Re-ID problem by proposing a cross-domain adversarial feature learning approach to jointly learn the identity features and domain-invariant features. We employ adversarial feature learning to filter low-level interfering features and remain high-level semantic information. We also contribute to the community the first Sketch Re-ID dataset with 200 persons, where each person has one sketch and two photos from different cameras associated. Extensive experiments have been performed on the proposed dataset and other common sketch datasets including CUFSF and QUML-shoe. Results show that the proposed method outperforms the state-of-the-arts.


title: Big Data Infrastructure for analyzing data generated by Wireless Sensor Networks

abstract: The rapid pace of technological advances in recentyears has enabled a significant evolution and deployment ofWireless Sensor Networks (WSN). These networks are a keyplayer in the so-called Internet of Things, generating exponentiallyincreasing amounts of data. Nonetheless, there are veryfew documented works that tackle the challenges related with thecollection, manipulation and exploitation of the data generated bythese networks. This paper presents a proposal for integrating BigData tools (in rest and in motion) for the gathering, storage andanalysis of data generated by a WSN that monitors air pollutionlevels in a city. We provide a proof of concept that combinesHadoop and Storm for data processing, storage and analysis,and Arduino-based kits for constructing our sensor prototypes.


title: Motivations and limitations on the use of information technology on statutory auditors' work: An exploratory study

abstract: Nowadays is conceivable to say that is almost impossible to accomplish auditors' tasks without using Information Technologies (IT). That discussion might be acceptable two decades ago but, now, is relevant to understand why are auditors using IT in their professional context and how could be possible, as stated in some authoritative bodies' guidance, to improve the efficacy and efficiency of statutory auditors' work. To accomplish previous objectives, an online survey was developed and applied to Portuguese Statutory Auditors. The motivations to use Computer-assisted Audit Tools (CAATs) will be discussed, either the constructs suggested by Unified Theory of Acceptance and Use of Technology (Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions) or new motivations' dimensions suggested from quantitative data analysis and open questions analyses. This paper contributes in three relevant areas: first, by understanding the big picture on the motivations to use CAATs; second, to recognize the limitations that auditors still feel in using CAATs; and third, to propose strategies to overcome those difficulties.


title: Linking Citizen Satisfaction with E-Government and Trust in Government

abstract: This article asks how Internet use, citizen satisfaction with e-government, and citizen trust in government are interrelated. We first review the literature on trust and explore how radical information technologies may work to alter the production or maintenance of trust. We then develop hypotheses about how citizens’ experience with e-government, satisfaction with e-government and government Web sites, and trust in government are interrelated. Moreover, the model for e-government and Web site satisfaction incorporates citizen perspectives on electronic transaction, transparency, and interactivity. Using data obtained from the Council on Excellence in Government, we then develop and test a twostage multiple-equation model that simultaneously predicts experience, satisfaction, and trust. Findings indicate that government Web site use is positively associated with e-government satisfaction and Web site satisfaction and that e-government satisfaction is positively associated with trust in government. We also find that while citizens are generally satisfied with the electronic provision of information (transparency), there is some dissatisfaction with the transaction and interactivity of Web sites. We conclude that electronic government strategies—transaction, transparency, and interactivity—are important factors that directly affect e-government satisfaction and indirectly affect trust. Individuals who use government Web sites are not only critical consumers but also demanding citizens.


title: Coherence analysis of research and education using topic modeling

abstract: Research and education are organically connected in that lectures convey the results of research, which is frequently initiated by inspiring lectures. As a result, the contents of lecture materials and research publications and the research capabilities of universities should be considered in the investigations of the relationships between research and teaching. We examine the relationship between research and teaching using automatic text analysis. In particular, we scrutinize the relatedness of the content of research papers with the content of lecture materials to investigate the association between teaching and research. We adopt topic modeling for the correlation analysis of research capabilities and the reflectiveness of research topics in lecture materials. We select the field of machine learning as a case study because the field is contemporary and because data related to teaching and research are easily accessible via the Internet. The results reveal interesting characteristics of lecture materials and research publications in the field of machine learning. The research capability of an institute is independent of the lecture materials. However, for introductory courses, teaching and research measures showed a weak negative relationship, and there is little relationship between the measures for advanced courses.


title: User profiling in personalization applications through rule discovery and validation

abstract: Gediminas Adomavicius New York University adomavic@cs.nyu.edu In many applications, ranging from recommender systems to one-to-one marketing to Web browsing, it is important to build personalized profiles of individual users from their transactional histories. These profiles describe individual behavior of users and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate customer profiles by separating “good” rules from the “bad.” This paper presents a method for validating such rules with an explicit participation of a human expert


title: Gender in Crime News: A Case Study Test of the Chivalry Hypothesis

abstract: This content analysis tested the chivalry hypothesis in 6 months of crime reporting in a local U.S. newspaper. The chivalry hypothesis posits that female criminals receive more lenient treatment in the criminal justice system and in news coverage of their crimes than their male counterparts. The study found partial support for the chivalry hypothesis and prompts a more nuanced formulation of the hypothesis-here termed patriarchal chivalry. This study also produced evidence that news coverage is harsher when men and women collaborate in crime than when men and women act independently of each other in criminal pursuit. The authors called it the Bonnie-and-Clyde effect.


title: Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents

abstract: Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a cost-effective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users' preference and varying external information. The DER management coordinates operations of micro combined heat and power systems ($\boldsymbol {\mu }$ CHPs), and vanadium redox battery (VRB) according to DR decisions. A two-level shared cost-led $\boldsymbol {\mu }$ CHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user's preference.


title: Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.

abstract: Variance reduction stochastic gradient descent methods enable minimization of model fitting problems involving big datasets with low iteration complexity and fast asymptotic convergence rates. However, they scale poorly in distributed settings. In this paper, we propose a highly parallel variance reduction method, CentralVR, with performance that scales linearly with the number of worker nodes. We also propose distributed versions of popular variance reduction methods that support a high degree of parallelization. Unlike existing distributed stochastic gradient schemes, CentralVR exhibits linear performance gains up to thousands of cores for massive datasets.


title: Localized Edge Detection in Sensor Fields

abstract: A wireless sensor network that studies relatively widespread phenomena (such as a contaminant flow or a seismic disturbance) may be called upon to provide a description of the boundary of the phenomenon (either a contour or some bounding box). In such cases, it may be necessary for each node to locally determine whether it lies at (or near) the edge of the phenomenon. In this paper, we show that such localized edge detection techniques are non-trivial to design in an arbitrarily deployed sensor network. We define the notion of an edge and develop performance metrics for evaluating localized edge detection algorithms. We propose three different approaches for localized edge detection and present one example scheme for each. In all our approaches, each sensor gathers information from its local neighborhood and determines whether or not it is an edge sensor. We evaluate the performance! of each of the example schemes and compare them with respect to the developed metrics.


title: Are the effects ofUnrealviolent video games pronounced when playing with a virtual reality system?

abstract: This study was conducted to analyze the short-term effects of violent electronic games, played with or without a virtual reality (VR) device, on the instigation of aggressive behavior. Physiological arousal (heart rate (HR)), priming of aggressive thoughts, and state hostility were also measured to test their possible mediation on the relationship between playing the violent game (VG) and aggression. The participants-148 undergraduate students-were randomly assigned to four treatment conditions: two groups played a violent computer game (Unreal Tournament), and the other two a non-violent game (Motocross Madness), half with a VR device and the remaining participants on the computer screen. In order to assess the game effects the following instruments were used: a BIOPAC System MP100 to measure HR, an Emotional Stroop task to analyze the priming of aggressive and fear thoughts, a self-report State Hostility Scale to measure hostility, and a competitive reaction-time task to assess aggressive behavior. The main results indicated that the violent computer game had effects on state hostility and aggression. Although no significant mediation effect could be detected, regression analyses showed an indirect effect of state hostility between playing a VG and aggression. Language: en


title: Signal Characteristics on Sensor Data Compression in IoT - An Investigation

abstract: In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.


title: Model-Agnostic Private Learning

abstract: We design differentially private learning algorithms that are agnostic to the learning model assuming access to limited amount of unlabeled public data. First, we give a new differentially private algorithm for answering a sequence of m online classification queries (given by a sequence of m unlabeled public feature vectors) based on a private training set. Our private algorithm follows the paradigm of subsample-and-aggregate, in which any generic non-private learner is trained on disjoint subsets of the private training set, then for each classification query, the votes of the resulting classifiers ensemble are aggregated in a differentially private fashion. Our private aggregation is based on a novel combination of distance-to-instability framework \cite{ST13} and the sparse-vector technique \cite{DNRRV09,HR10}. We show that our algorithm makes a conservative use of the privacy budget. In particular, if the underlying non-private learner yields classification error at most α∈(0,1), then our construction answers more queries than what is implied by a straightforward application of advanced composition theorems from differential privacy; and by at least a factor of 1/α in some cases. Next, we apply the knowledge transfer technique to construct a private learner that outputs a classifier, which can be used to answer unlimited number of queries. In the (agnostic) PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases. As in non-private sample complexity, our bounds are completely characterized by the VC dimension of the concept class.


title: Arc Stability Control of a High-Power Thyristor Rectifier System in a DC Arc Furnace

abstract: Fundamental features of the arc stability in a dc arc furnace of 720 V/100 kA/72 MW have been investigated. The Cassie-Mayr arc model has been employed and applied for the target dc arc furnace. In order to characterize the parameters of the Cassie-Mayr arc model and the behavior of unstable arc dynamics, the advanced arc simulations of magneto-hydrodynamics (MHD) have been performed. From the results of MHD simulation, the dc arc dynamic resistance is proposed to be an effective arc stability function reflecting the instability of dynamic arc behavior. A control strategy of the 12-pulse thyristor rectifier system to regulate the arc stability function is also proposed in this paper. The simulation and experimental results confirm the usefulness of the proposed dynamic arc resistance as the arc stability function along with the active control strategy. The proposed arc stability function can be regarded as an effective criterion for the overall power conversion system to maintain highly stable arcing operation leading to better productivity and reliability in a dc arc furnace.


title: Techniques to Identify Themes

abstract: Theme identification is one of the most fundamental tasks in qualitative research. It also is one of the most mysterious. Explicit descriptions of theme discovery are rarely found in articles and reports, and when they are, they are often relegated to appendices or footnotes. Techniques are shared among small groups of social scientists, but sharing is impeded by disciplinary or epistemological boundaries. The techniques described here are drawn from across epistemological and disciplinary boundaries. They include both observational and manipulative techniques and range from quick word counts to laborious, in-depth, line-by-line scrutiny. Techniques are compared on six dimensions: (1) appropriateness for data types, (2) required labor, (3) required expertise, (4) stage of analysis, (5) number and types of themes to be generated, and (6) issues of reliability and validity.


title: FOG: A Fast Out-of-Core Graph Processing Framework

abstract: In this paper we present FOG, an open source graph processing framework designed for out-of-core (external memory) graph processing (https://github.com/mrshawcode/fog). FOG provides a set of programming interfaces that break down update functions of vertices to their incident edges so as to process the functions with edge-centric manner. By these, FOG gives intuitive and productive programming interfaces, and achieves high main memory utilization rate and processing efficiency at the same time. Moreover, FOG proposes an in-place update shuffling mechanism to improve the performance by dramatically reducing disk I/Os during computing. By extensive evaluations on typical graph algorithms and large real-world graphs, we show that FOG outperforms existing out-of-core graph processing systems, including GraphChi, X-Stream and TurboGraph. By comparing the performances of FOG and those of state-of-art distributed graph processing frameworks, we show that only by using just a commodity PC, FOG achieves comparable or even better performance than the best distributed graph processing framework that uses an Amazon EC2 cluster with 128 nodes.


title: Deployment of a Wireless Ultrasonic Sensor Array for Psychological Monitoring

abstract: The deployment of a wireless sensor network to monitor subjects’ locations and relative distances during psychological experiments is discussed. As its primary function, an overhead array of ultrasound sensors automatically tracks a parent, child and stranger over a \(4.45~\text{m} \times 4.23~\text{m}\) observation area. The array of ultrasonic-equipped motes can resolve two point targets separated by at least 73 cm. Each sensor can detect the range to the nearest object with 20 cm resolution. Challenges with setting up the 6 x 6 ultrasonic imaging array and with establishing a Kalman tracking filter are also detailed. This WiPsy (Wireless sensors for Psychology research) system provides accurate, real-time quantitative metrics for psychological evaluation in lieu of traditional qualitative manual coding. Moreover, tracking subjects using ultrasound sensors is less error-prone than existing methods that track based on human coding of video. Overall, WiPsy strives to streamline data acquisition, processing, and analysis by providing previously unavailable assessment parameters.


title: Scalable agent alignment via reward modeling: a research direction

abstract: One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user's intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.


title: Non-orthogonal multiple access in LTE heterogeneous networks with system-level evaluation

abstract: Non-orthogonal multiple access (NOMA) has been regarded as a promising technology to meet challenges in future network. NOMA based on power-domain multiplexing and successive interference cancellation (SIC) receiver is proved to be effective in enhancing system spectrum efficiency (SE). In this paper, we propose an efficient predefined user grouping and group-wise power allocation for NOMA in LTE heterogeneous networks (HetNets). For performance evaluation, we also construct a system-level simulator for LTE with NOMA based on 3GPP specifications. It is verified that the NOMA achieves higher network throughput and SE compared to the orthogonal multiple access (OMA) scheme. Besides, the performance gain by NOMA is shown to be more pronounced with an increasing number of users.


title: Human Action Recognition Based on Action Forests Model Using Kinect Camera

abstract: Human action recognition is one of the most important issues in computer vision. In this paper, the main idea is to design a general approach to recognize the human behavior. This approach is based on a pre-collected action database, which extracted by the depth images and forms the sequences of skeletons, and trained by the proposed Action Forests (AF) model. AF extends the random forest algorithm by using different decision functions to fit the skeletal features in 3D space. The system achieves the real-time classification result without the limitation of background and camera position. In the experiments, we collected several human behavior with single-character actions and two-character interactions to train the AF model. The skeleton features were retrieved from the depth sensor Kinect. We investigated the effect of several training parameters in AF. In conclusion, AF can learn the skeletal features efficiently and runs at 30 frames per second on action classification with high accuracy.


title: Generative adversarial networks for anomaly detection in images

abstract: Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissues


title: Cost-effective low-loss flexible optical engine with microlens-imprinted film for high-speed on-board optical interconnection

abstract: There is a strong demand for optical interconnection technology to overcome bandwidth bottlenecks in high-end server systems. The interconnection speed in present systems is approaching 10 Gb/s, and higher-speed interconnections over 25 Gb/s are being discussed. To achieve such optical interconnections in commercial production, it is necessary to develop lower-cost and higher-speed optical transceiver modules. We propose a flexible printed circuit optical engine (FPC-OE) with a microlens-imprinted film and a polymer waveguide to achieve low-cost and high-speed operation. The microlens-imprinted film can be produced at low cost by using nanoimprint technology and can drastically reduce the optical loss of the FPC-OE with polymer waveguide. We successfully demonstrated error-free operation at 25 Gb/s with the fabricated optical transceiver that contains an FPC-OE, microlens-imprinted film, and a polymer waveguide.


SOME ATYPICAL ATRIAL SEPTAL DEFECTS AND HAEMODYNAMICALLY RELATED ANOMALIES.


title: Social context and perceived agency affects empathy for pain: An event-related fMRI investigation

abstract: Abstract Studying of the impact of social context on the perception of pain in others is important for understanding the role of intentionality in interpersonal sensitivity, empathy, and implicit moral reasoning. Here we used an event-related fMRI with pain and social context (i.e., the number of individuals in the stimuli) as the two factors to investigate how different social contexts and resulting perceived agency modulate the neural response to the perception of pain in others. Twenty-six healthy participants were scanned while presented with short dynamic visual stimuli depicting painful situations accidentally caused by or intentionally caused by another individual. The main effect of perception of pain was associated with signal increase in the aMCC, insula, somatosensory cortex, SMA and PAG. Importantly, perceiving the presence of another individual led to specific hemodynamic increase in regions involved in representing social interaction and emotion regulation including the temporoparietal junction, medial prefrontal cortex, inferior frontal gyrus, and orbitofrontal cortex. Furthermore, the functional connectivity pattern between the left amygdala and other brain areas was modulated by the perceived agency. Our study demonstrates that the social context in which pain occurs modulate the brain response to other's pain. This modulation may reflect successful adaptation to potential danger present in a social interaction. Our results contribute to a better understanding of the neural mechanisms underpinning implicit moral reasoning that concern actions that can harm other people.


title: Reading values in electrical meter using image processing techniques

abstract: This paper proposes the method for extracting the series numbers in the electrical meter. Initially the portion of the meter where the reading is actually located is captured by placing the camera in a fixed position in front of the meter. The image of the meter is captured in certain series of time interval. The input image converted to grayscale image and it is enhanced using image enhancement techniques. The image is then converted to the binary image by removing the connected components by using Adaptive Thresholding (AT), for the grayscale image. Then the Morphological operation on binary image is performed as to produce an enhanced binary image. The image is scanned horizontally until the white pixel is encountered and the resultant image is stored in an array matrix. Then the resultant image is segmented by Vertical Edge Detection Algorithm and each segmented image is stored in an individual array matrix. Each image is then compared with the actual template and the result is stored in the text file. It compared with the Vertical Edge Detection Algorithm to the Sobel operator in terms of accuracy, algorithm complexity, and processing time. The results show accurate edge detection performance and faster processing than Sobel by five to nine times.


title: Structure, microparameters and properties of crosslinked DGEBA/MTHPA: A molecular dynamics simulation

abstract: Investigating the relationship between microstructure and macroscopic properties of epoxy resin (EP) materials for high-voltage insulation at the molecular level can provide theoretical guidance for the synthetic design of EP. Here, using diglycidyl ether (DGEBA) as the resin matrix and methyl tetrahydrophthalic anhydride (MTHPA) as the curing agent, a set of crosslinked EP molecular models at different curing stages were constructed based on the proposed crosslinking method. We studied the influences of crosslinking density on micro-parameters and macro-properties employing molecular dynamics (MD) simulations. The results indicate that crosslinking of DGEBA/MTHPA is a contraction and exothermic process. The structural parameters and macroscopic properties are closely related to the degree of crosslinking. With the increase of crosslinking density, the mean square displacement (MSD) of the system decreases, and the segment motion in the models is weakened gradually, while, the fractional free volume (FFV) first decreases and then increases. In addition, the thermal and mechanical properties of DGEBA/MTHPA have a significant dependence on the crosslinking density. Increasing crosslinking density can improve the glass transition temperature (Tg), reduce the coefficient of thermal expansion (CTE), and enhances the static mechanical properties of DGEBA/MTHPA system. Furthermore, the relationship between microparameters and properties has been fully investigated. Free volume is an important factor that causes thermal expansion of DGEBA/MTHPA. Moreover, there is a negative correlation between MSD and mechanical moduli. By elevating temperature, the decline in mechanical moduli may be due to the exacerbated thermal motion of the molecules and the increasing MSD values.Investigating the relationship between microstructure and macroscopic properties of epoxy resin (EP) materials for high-voltage insulation at the molecular level can provide theoretical guidance for the synthetic design of EP. Here, using diglycidyl ether (DGEBA) as the resin matrix and methyl tetrahydrophthalic anhydride (MTHPA) as the curing agent, a set of crosslinked EP molecular models at different curing stages were constructed based on the proposed crosslinking method. We studied the influences of crosslinking density on micro-parameters and macro-properties employing molecular dynamics (MD) simulations. The results indicate that crosslinking of DGEBA/MTHPA is a contraction and exothermic process. The structural parameters and macroscopic properties are closely related to the degree of crosslinking. With the increase of crosslinking density, the mean square displacement (MSD) of the system decreases, and the segment motion in the models is weakened gradually, while, the fractional free volume (FFV)...


title: Invariance Principles Allowing of Non-Lyapunov Functions for Estimating Attractor of Discrete Dynamical Systems

abstract: This technical note establishes several versions of invariance principles for describing the eventual dynamical behaviors of discrete dynamical systems. Instead of the requirement of the so-called Lyapunov functions in the classical LaSalle invariance principle, some more relaxed conditions are imported. The established invariance principles thus can be applied to a more general class of discrete dynamical systems for classifying their orbits into two categories based on the eventual dynamical behaviors, and the proposed classification scheme is suitable for theoretically and numerically estimating the local or global attractors produced by the discrete dynamical systems. The practical usefulness of the analytical results is verified by systematically investigating several representative discrete systems.


title: Vowel Effects Towards Dental Arabic Consonants Based on Spectrogram

abstract: This paper discussed the effect of vowel (fatha, kasra and damma) in Arabic consonants. These vowels are added to the basic consonants with three simple diacritics using the utterances of every dental consonant concerned by Malaysian children. The dental consonants refer to the consonants utter using dental medium. It is called the place of articulation at labiodentals, dental and interdental. The formant frequencies produced for each place of articulation are study based on their spectrogram. The visualization of the spectrogram makes the formants easily being identified by normal human being’s vision. The formants (F1, F2 and F3) are averaged and the results show the different increment and decrement compare to utterances of single phoneme. F1 is decreased for all observed consonants. Meanwhile F2 and F3 changes according to its manner of articulation, where in this study the tabulation are plosive and fricative.


title: Variability of Phthalate Monoester Levels in Daily First-Morning Urine from Adult Women: A Pilot Study

abstract: Phthalate exposure is ubiquitous and may affect child and adolescent health through both in utero exposure and direct exposure during childhood. Variability in exposure within women is not well documented. We analyzed 90 first-morning urine samples collected by ten reproductive-age women for phthalate metabolites and creatinine. Monoethyl [122 ng/mL (geometric mean concentration = 139 microg/g creatinine)], monobutyl [85.4 ng/mL (97.0 microg/g creatinine)], monobenzyl [37.2 ng/mL (42.2 microg/g creatinine)], and mono-2-ethylhexyl phthalate [9.4 ng/mL (10.7 microg/g creatinine)] were detected in most (94.4%) samples. The concentrations ranged from 23.8-1090 ng/mL, 43-437 ng/mL, 12.4-186 ng/mL, and 1.3-31.1 ng/mL, respectively. We observed considerable variation in phthalate concentrations by day for individual women. The intraclass correlation coefficient, indicating the proportion of variance explained by differences between subjects, ranged from 0.40 (monobutyl) to 0.68 (monoethyl). Monobenzyl and monoethyl phthalates showed higher levels on weekends as compared with weekdays (p = .01 for both). We found no significant difference between monoester levels from different menstrual cycles. Phthalate concentrations vary considerably for an individual and may require multiple samples for accurate assessment


title: OS-level virtualization for industrial automation systems: are we there yet?

abstract: Virtualization is entering the world of real-time embedded systems. Industrial automation systems, in particular, can benefit from what virtualization has to offer: flexible consolidation of applications on different hardware types and scales, extending the life-time of legacy code or decoupling software and hardware lifecycles. However, such systems require a light-weight virtualization technology in order to be able to maintain real-time behavior while dealing with real-time data. This paper sets out to investigate the applicability of container-based OS-level virtualization technology to industrial automation systems. To this end, we provide insights into the capabilities of containers to achieve flexible consolidation and easy migration of industrial automation applications as well as into the container technology readiness with respect to the fundamental requirement of industrial automation systems, namely performing timely control actions based on real-time data. Moreover, we provide an empirical study of the performance overhead introduced by containers based on micro-benchmarks that capture the characteristics of targeted industrial automation applications.


Compare and Contrast : Learning Prominent Visual Differences ( Supplementary )


title: CNN: Single-label to Multi-label

abstract: Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) no explicit hypothesis label is required; 4) the shared CNN may be well pre-trained with a large-scale single-label image dataset, e.g. ImageNet; and 5) it may naturally output multi-label prediction results. Experimental results on Pascal VOC2007 and VOC2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 84.2% by HCP only and 90.3% after the fusion with our complementary result in [47] based on hand-crafted features on the VOC2012 dataset, which significantly outperforms the state-of-the-arts with a large margin of more than 7%.


title: A Survey of Wireless Power Transfer and a Critical Comparison of Inductive and Capacitive Coupling for Small Gap Applications

abstract: Inductive power transfer (IPT) and capacitive power transfer (CPT) are the two most pervasive methods of wireless power transfer (WPT). IPT is the most common and is applicable to many power levels and gap distances. Conversely, CPT is only applicable for power transfer applications with inherently small gap distances due to constraints on the developed voltage. Despite limitations on gap distance, CPT has been shown to be viable in kilowatt power level applications. This paper provides a critical comparison of IPT and CPT for small gap applications, wherein the theoretical and empirical limitations of each approach are established. A survey of empirical WPT data across diverse applications in the last decade using IPT and CPT technology graphically compares the two approaches in power level, gap distance, operational frequency, and efficiency, among other aspects. The coupler volumetric power density constrained to small gap sizes is analytically established through theoretical physical limitations of IPT and CPT. Finally, guidelines for selecting IPT or CPT in small gap systems are presented.


title: A Basic Recurrent Neural Network Model

abstract: We present a model of a basic recurrent neural network (or bRNN) that includes a separate linear term with a slightly "stable" fixed matrix to guarantee bounded solutions and fast dynamic response. We formulate a state space viewpoint and adapt the constrained optimization Lagrange Multiplier (CLM) technique and the vector Calculus of Variations (CoV) to derive the (stochastic) gradient descent. In this process, one avoids the commonly used re-application of the circular chain-rule and identifies the error back-propagation with the co-state backward dynamic equations. We assert that this bRNN can successfully perform regression tracking of time-series. Moreover, the "vanishing and exploding" gradients are explicitly quantified and explained through the co-state dynamics and the update laws. The adapted CoV framework, in addition, can correctly and principally integrate new loss functions in the network on any variable and for varied goals, e.g., for supervised learning on the outputs and unsupervised learning on the internal (hidden) states.


title: OpenJIT: an open-ended, reflective JIT compiler framework for Java

abstract: OpenJIT is an open-ended, reflective JIT compiler framework for Java being researched and developed in a joint project by Tokyo Inst. Tech. and Fujitsu Ltd. Although in general self-descriptive systems have been studied in various contexts such as reflection and interpreter/compiler bootstrapping, OpenJIT is a first system we know to date that offers a stable, full-fledged Java JIT compiler that plugs into existing monolithic JVMs, and offer competitive performance to JITs typically written in C or C++. This is in contrast to previous work where compilation did not occur in the execution phase, customized VMs being developed ground-up, performance not competing with existing optimizing JIT compilers, and/or only a subset of the Java language being supported. The main contributions of this paper are, 1) we propose an architecture for a reflective JIT compiler on a monolithic VM, and identify the technical challenges as well as the techniques employed, 2) We define an API that adds to the existing JIT compiler APIs in classic JVM to allow reflective JITs to be constructed, 3) We show detailed benchmarks of run-time behavior of OpenJIT to demonstrate that, while being competitive with existing JITs the time- and space-overheads of compiler metaobjects that exist in the heap are small and manageable. Being an object-oriented compiler framework, OpenJIT can be configured to be small and portable or fully-fledged optimizing compiler framework in the spirit of SUIF. It is fully JCK compliant, and runs all large Java applications we have tested to date including HotJava. We are currently distributing OpenJIT for free to foster further research into advanced compiler optimization, compile-time reflection, advanced run-time support for languages, as well as other areas such as embedded computing, metacomputing, and ubiquitous computing.


DESIGN AND COMPARISON OF LOW POWER & HIGH SPEED 4-BIT ALU


title: Learning to Classify Documents with Only a Small Positive Training Set

abstract: Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set Pis small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in Pand the hidden positive examples in Uwere not drawn from the same distribution.


title: Implementation of an incampus fire alarm system using ZigBee

abstract: Nearly all our buildings and workspaces are protected against fire breaks, which may occur due to some fault in the electric circuitries and power sources. The immediate alarming and aid to extinguish the fire in such situations of fire breaks are provided using embedded systems installed in the buildings. But as the area being monitored against such fire threats becomes vast, these systems do not provide a centralized solution. For the protection of such a huge area, like a college campus or an industrial park, a centralized wireless fire control system using Wireless sensor network technology is developed. The system developed connects the five dangers prone zones of the campus with a central control room through a ZigBee communication interface such that in case of any fire break in any of the building, a direct communication channel is developed that will send an immediate signal to the control room. In case if any of the emergency zone lies out of reach of the central node, multi hoping technique is adopted for the effective transmitting of the signal. The five nodes maintains a wireless interlink among themselves as well as with the central node for this purpose. Moreover a hooter is attached along with these nodes to notify the occurrence of any fire break such that the persons can leave the building immediately and with the help of the signal received in the control room, the exact building where the fire break occurred is identified and fire extinguishing is done. The real time system developed is implemented in Atmega32 with temperature, fire and humidity sensors and ZigBee module.


title: Vehicle Lateral Dynamics Control Through AFS/DYC and Robust Gain-Scheduling Approach

abstract: In this paper, we investigate the combined active front-wheel steering/direct yaw-moment control for the improvement of vehicle lateral stability and vehicle handling performance. A more practical assumption in this work is that the longitudinal velocity is not constant but varying within a range. Both the nonlinear tire model and the variation of longitudinal velocity are considered in vehicle system modeling. A linear-parameter-varying model with norm-bounded uncertainties is obtained. To track the system reference, a generalized proportional-integral (PI) control law is proposed. Since it is difficult to get the analytic solution for the PI gains, an augmented system is developed, and the PI control is then converted into the state-feedback control for the augmented system. Both the stability and the energy-to-peak performance of the augmented system are explored. Based on the analysis results, the controller-gain tuning method is proposed. The proposed control law and controller design method are illustrated via an electric vehicle model.