title: A Self-Triggered Pulsed-Mode Flyback Converter for Electric-Field Energy Harvesting

abstract: Electric-field energy harvesting (EFEH) from medium-voltage (MV) power lines is an emerging technology to energize the low-cost distributed electronic measurement systems that are needed to monitor vital line parameters for smart grid systems. The challenge with EFEH is to extract energy without requiring a direct connection to the power line conductor. This severely constrains the amount of continuous energy that can be obtained because of the weak capacitive coupling between the power line and the energy harvesting system. This paper addresses this challenge, by using a self-triggered discontinuously operating flyback converter to transform a high-voltage dc bus rectified from the weakly coupled ac supply down to useful low-voltage levels. The results show that over 20 mW of continuous power can be extracted from a 12.7-kV MV line using this approach. Detailed circuit analysis and matching experimental results are presented to validate the concept.


Investigating LSTMs for Joint Extraction of Opinion Entities and Relations


title: Ethical issues in preparing and publishing systematic reviews.

abstract: The medical evidence base, or ‘literature’, forms the basis for clinical and policy decisions, so those who contribute to it have a responsibility to ensure that it is as accurate and unbiased as possible. Since publications are also used to judge the productivity of individuals and departments, and to select candidates for academic positions, it is important that those who did the work receive fair credit. Preparing a systematic review is a form of research, and should therefore be undertaken in a responsible manner to ensure integrity and avoid misconduct. This paper sets out practical and ethical issues to be considered when preparing and publishing a systematic review. It is written primarily for authors involved in The Cochrane Collaboration, but has broader relevance. Cochrane Review Groups, which are responsible for the quality of reviews that they publish, have highlighted concerns about unclear or inappropriate authorship, copying of material from other sources, and dual or duplicate publication. Various journals and editors’ organizations have produced publication guidelines and instructions for potential authors. One of the best known is the Uniform Requirements for Manuscripts Submitted to Biomedical Journals, produced by the International Committee of Medical Journal Editors (1). Guidance is also available in the Cochrane Handbook (2). However, many of the requirements and conventions are scattered, so it may be hard for researchers to get a complete picture of best practices. Another problem facing those preparing systematic reviews is that general guidance, designed mainly for preparing primary reports of research, may not be applicable or may not cover the special issues that arise with systematic reviews. This paper therefore aims to help review authors avoid ethical problems by providing an introduction and overview of good practice in preparing and publishing systematic reviews.


Acceleration of FPGA Fault Injection Through Multi-Bit Testing.


title: Performance evaluation of twisted strings driven robotic finger

abstract: Actuation and control of robotic hands is one of the problems in engineering that still needs to be satisfactorily solved. In this paper we present a robotic finger actuated by twisted strings mechanism that we call Twist Drive. A principle of the proposed actuation is described and its basic characteristics are presented. The structure of a developed robotic finger with the proposed actuation method is explained. Forward kinematics of a finger with non-linearly coupled joints was solved and the obtained Jacobian was used for two directional force control on the finger's tip. Experimental results of the force control and measured frequency characteristics of the force control are presented in the paper. The results show that the developed robotic finger has capabilities similar to that of a human finger. Additionally, because there is no gear reducer used in the transmission, the finger is of lightweight, low-cost, and provides zero mechanical impedance when the strings are untwisted.


title: Spotting opinion spammers using behavioral footprints

abstract: Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.


title: A distributed auction algorithm for the assignment problem

abstract: The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problems? size and the cost for global computation and information can be highly prohibitive, gives rise to the need for local solutions that dynamically assign distinct agents to distinct tasks, while maximizing the total assignment benefit. In this paper, we consider the linear assignment problem in the context of networked systems, where the main challenge is dealing with the lack of global information due to the limited communication capabilities of the agents. We address this challenge by means of a distributed auction algorithm, where the agents are able to bid for the task to which they wish to be assigned. The desired assignment relies on an appropriate selection of bids that determine the prices of the tasks and render them more or less attractive for the agents to bid for. Up to date pricing information, necessary for accurate bidding, can be obtained in a multi-hop fashion by means of local communication between adjacent agents. Our algorithm is an extension to the parallel auction algorithm proposed by Bertsekas et al to the case where only local information is available and it is shown to always converge to an assignment that maximizes the total assignment benefit within a linear approximation of the optimal one.


title: This ain't your dose: sensor spoofing attack on medical infusion pump

abstract: Sensors measure physical quantities of the environment for sensing and actuation systems, and are widely used in many commercial embedded systems such as smart devices, drones, and medical devices because they offer convenience and accuracy. As many sensing and actuation systems depend entirely on data from sensors, these systems are naturally vulnerable to sensor spoofing attacks that use fabricated physical stimuli. As a result, the systems become entirely insecure and unsafe. ::: ::: In this paper, we propose a new type of sensor spoofing attack based on saturation. A sensor shows a linear characteristic between its input physical stimuli and output sensor values in a typical operating region. However, if the input exceeds the upper bound of the operating region, the output is saturated and does not change as much as the corresponding changes of the input. Using saturation, our attack can make a sensor to ignore legitimate inputs. To demonstrate our sensor spoofing attack, we target two medical infusion pumps equipped with infrared (IR) drop sensors to control precisely the amount of medicine injected into a patients' body. Our experiments based on analyses of the drop sensors show that the output of them could be manipulated by saturating the sensors using an additional IR source. In addition, by analyzing the infusion pumps' firmware, we figure out the vulnerability in the mechanism handling the output of the drop sensors, and implement a sensor spoofing attack that can bypass the alarm systems of the targets. As a result, we show that both over-infusion and under-infusion are possible: our spoofing attack can inject up to 3.33 times the intended amount of fluid or 0.65 times of it for a 10 minute period.


title: Measuring usability as quality of use

abstract: The conventional assumption that quality is an attribute of a product is misleading, as the attributes required for quality will depend on how the product is used. Quality of use is therefore defined as the extent to which a product satisfies stated and implied needs when used under stated conditions. Quality of use can be used to measure usability as the extent to which specific goals can be achieved with effectiveness, efficiency and satisfaction by specified users carrying out specified tasks in specified environments. Practical and reliable methods of measuring quality of use have been developed by the MUSiC project. These provide criteria for usability which can be incorporated into a quality system. A description is given of the MUSiC methods for specifying the context of use and measuring effectiveness, efficiency, and satisfaction.


title: A Divide-and-Conquer Solver for Kernel Support Vector Machines

abstract: The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples. In this paper, we propose and analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the division step, we partition the kernel SVM problem into smaller subproblems by clustering the data, so that each subproblem can be solved independently and efficiently. We show theoretically that the support vectors identified by the subproblem solution are likely to be support vectors of the entire kernel SVM problem, provided that the problem is partitioned appropriately by kernel clustering. In the conquer step, the local solutions from the subproblems are used to initialize a global coordinate descent solver, which converges quickly as suggested by our analysis. By extending this idea, we develop a multilevel Divide-and-Conquer SVM algorithm with adaptive clustering and early prediction strategy, which outperforms state-of-the-art methods in terms of training speed, testing accuracy, and memory usage. As an example, on the covtype dataset with half-a-million samples, DC-SVM is 7 times faster than LIBSVM in obtaining the exact SVM solution (to within 10-6 relative error) which achieves 96.15% prediction accuracy. Moreover, with our proposed early prediction strategy, DC-SVM achieves about 96% accuracy in only 12 minutes, which is more than 100 times faster than LIBSVM.


title: A RF mixer using 0.7μm CMOS for intra-corporal communication devices

abstract: This paper presents an up/down converter double-balanced Gilbert cell mixer for frequencies of 433, 870 and 928 MHz in the ISM band (industrial, scientific and medical). This mixer was designed with the on-semiconductor (former AMIS) 0.7μm CMOS technology. The Gilbert cell topology was selected due to its good figure of merit (FOM), which takes into account parameters such as the conversion gain (CG), isolation between the input ports, linearity and the noise figure. This mixer provides frequency translation by means of a true multiplication on four quadrants. The simulations showed a total power consumption of 3mW from a power supply of 5V, a CG of 10.5dB, an input referred third-order input intercept point (IIP3) of 29.72dBm and an input referred 1-dB compression point (IP1dB) of 13.15dBm. This paper also presents a comparison for the three bands in order to confirm the scalability and suitability of the mixer implemented. The target application of this mixer is to provide communication to/from intra-corporal communication devices.


title: A Hierarchical Neural Autoencoder for Paragraphs and Documents

abstract: Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Longshort term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization1.


title: Assessment, enhancement, and verification determinants of the self-evaluation process.

abstract: The 3 major self-evaluation motives were compared: self-assessment (people pursue accurate self-knowledge), self-enhancement (people pursue favorable self-knowledge), and self-verification (people pursue highly certain self-knowledge). Ss considered the possession of personality traits that were either positive or negative and either central or peripheral by asking themselves questions that varied in diagnosticity (the extent to which the questions could discriminate between a trait and its alternative) and in confirmation value (the extent to which the questions confirmed possession of a trait)


title: A novel framework for semantic segmentation with generative adversarial network

abstract: Semantic segmentation plays an important role in a series of high-level computer vision applications. However, the performance of Convolutional Neural Network (CNN) based segmentation models is currently influenced by higher order inconsistencies, which are mainly caused by the CNNs built-in invariance to spatial transformations and the independent prediction for each of pixel. In this paper, a novel framework, consisting of a segmentation network and a Generative Adversarial Network (GAN), is proposed to tackle this challenging problem by enforcing long-range spatial label contiguity. With the help of fully connected layers in the discriminator and adversarial training, the GAN model can evaluate the higher-order potentials loss. The motivation is that the GAN model provides an auxiliary higher-order potentials loss to the segmentation model, thus the segmentation model have the ability of correcting higher order inconsistencies. Extensive experiments on public benchmarking database demonstrate the effectiveness of the proposed method.


title: White-Box Traceable CP-ABE for Cloud Storage Service: How to Catch People Leaking Their Access Credentials Effectively

abstract: Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.


title: Learning to Navigate for Fine-grained Classification

abstract: Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.


R.: OpenPEX: An Open Provisioning and EXecution System for Virtual Machines


title: Scaled Agile Framework: Presentation and real world example

abstract: This case focuses on the applicability of the Scaled Agile Framework (SAFe) founded by Dean Leffingwell. Modern organizations often work with agile software engineering teams using traditional single team-level methods like Scrum, but multiple teams and the program or portfolio level are not part of methods like Scrum. SAFe tries to apply agile methodologies to the whole organisation. The real world example focuses on a key element of SAFe, the Program Increment (PI) planning meeting and how it can improve multiple team collaboration.


title: Improving the Testability of Mixed-Signal Integrated Circuits

abstract: The author presents a discussion on several methods that can be used to improve the testability of mixed-signal integrated circuits. He begins by outlining the role of test, and its impact on product cost and qualify. A brief look is taken at the pending test crises for mixed-signal circuits. Subsequently, the author outlines several common test strategies, and their corresponding test setups for verifying the function of the analog portion of a mixed-signal circuit. In the remainder of the paper he describes several analog test buses and circuits for built-in self-test applications.


title: Learned D-AMP: Principled Neural-Network-based Compressive Image Recovery

abstract: Compressive image recovery is a challenging problem that requires fast and accurate algorithms. Recently, neural networks have been applied to this problem with promising results. By exploiting massively parallel GPU processing architectures and oodles of training data, they can run orders of magnitude faster than existing techniques. However, these methods are largely unprincipled black boxes that are difficult to train and often-times specific to a single measurement matrix. It was recently demonstrated that iterative sparse-signal-recovery algorithms can be ``unrolled’' to form interpretable deep networks. Taking inspiration from this work, we develop a novel neural network architecture that mimics the behavior of the denoising-based approximate message passing (D-AMP) algorithm. We call this new network {\em Learned} D-AMP (LDAMP). The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance. Most importantly, it outperforms the state-of-the-art BM3D-AMP and NLR-CS algorithms in terms of both accuracy and run time. At high resolutions, and when used with sensing matrices that have fast implementations, LDAMP runs over $50\times$ faster than BM3D-AMP and hundreds of times faster than NLR-CS.


Summarization Algorithms for Record Linkage


title: Tamper Resistance -- a Cautionary Note

abstract: An increasing number of systems from pay-TV to electronic purses, rely on the tamper resistance of smartcards and other security processors. We describe a number of attacks on such systems -- some old, some new and some that are simply little known outside the chip testing community. We conclude that trusting tamper resistance is problematic; smartcards are broken routinely, and even a device that was described by a government signals agency as 'the most secure processor generally available' turns out to be vulnerable. Designers of secure systems should consider the consequences with care.


title: Music preferences, personality style, and developmental issues of adolescents.

abstract: The purpose of this study was to examine the personality characteristics and developmental issues of 3 groups of adolescent music listeners: those preferring light qualities of music, those preferring heavy qualities of music, and those who had eclectic preferences for music qualities. One hundred sixty-four adolescents completed an age-appropriate personality inventory and a systematic measure of music listening preference. The findings indicate that each of the 3 music preference groups is inclined to demonstrate a unique profile of personality dimensions and developmental issues. Those preferring heavy or light music qualities indicated at least moderate difficulty in negotiating several distinct domains of personality and/or developmental issues; those with more eclectic music preferences did not indicate similar difficulty. Thus, there was considerable support for the general hypothesis that adolescents prefer listening to music that reflects specific personalities and the developmental issues with which they are dealing.


title: Electrophysiological evidence that ethanol alters function of medial septal area without affecting lateral septal function.

abstract: Evidence is provided in this manuscript that ethanol acts directly on neurons in the medial septal area (MSA). Initially, the electrophysiological characteristics of MSA neurons in freely moving rats were characterized and found similar to that observed in rats anesthetized with urethane, but not chloral hydrate. Therefore, urethane was used to evaluate the effects of ethanol in anesthetized rats. The conclusion that ethanol influences neural function in the MSA is based on electrophysiological data that ethanol (0.75-3.0 g/kg i.p.) suppresses neural firing of medial septal cells in urethane-anesthetized as well as in unanesthetized rats in a dose-related fashion. Concurrent with the suppression of firing rate, the rhythmic bursting pattern of activity of MSA neurons is disrupted by ethanol. The changes observed in the MSA could not be attributed to an indirect action of ethanol on afferents from the lateral septum to the MSA, because ethanol did not alter neural activity of cells in the lateral septum. These data indicate that ethanol does not have a common action on all neurons. Neural activity in the MSA recovered from the acute action of ethanol at a time when blood ethanol levels were near maximal, indicating an acute tolerance to this effect of ethanol. The time course of change in neural activity in the MSA was highly correlated with the time course of a measure of behavioral sedation, but not the hypothermia produced by ethanol. Thus, the work in this manuscript supports the view that ethanol has selective actions on MSA neurons in the rat septal area and that these actions may influence the behavioral sedation induced by ethanol.


title: Why people buy virtual items in virtual worlds with real money

abstract: Virtual worlds, such as Second Life and Everquest, have grown into virtual game communities that have economic potential. In such communities, virtual items are bought and sold between individuals for real money. The study detailed in this paper aims to identify, model and test the individual determinants for the decision to purchase virtual items within virtual game communities. A comprehensive understanding of these key determinants will enable researchers to further the understanding of player behavior towards virtual item transactions, which are an important aspect of the economic system within virtual games and often raise one of the biggest challenges for game community operators. A model will be developed via a mixture of new constructs and established theories, including the theory of planned behavior (TPB), the technology acceptance model (TAM), trust theory and unified theory of acceptance and use of technology (UTAUT). For this purpose the research uses a sequential, multi-method approach in two phases: combining the use of inductive, qualitative data from focus groups and expert interviews in phase one; and deductive, quantitative survey data in phase two. The final model will hopefully provide an impetus to further research in the area of virtual game community transaction behavior. The paper rounds off with a discussion of further research challenges in this area over the next seven years.


Quo Vadis, Atlas-based Segmentation?


title: IPA: A New Class of Power Attacks

abstract: We present Inferential Power Analysis (IPA), a new class of attacks based on power analysis. An IPA attack has two stages: a profiling stage and a key extraction stage. In the profiling stage, intratrace differencing, averaging, and other statistical operations are performed on a large number of power traces to learn details of the implementation, leading to the location and identification of key bits. In the key extraction stage, the key is obtained from a very few power traces; we have successfully extracted keys from a single trace. Compared to differential power analysis, IPA has the advantages that the attacker does not need either plaintext or ciphertext, and that, in the key extraction stage, a key can be obtained from a small number of traces.


title: Effect of Peanut Butter Manufacture on Vitamin E

abstract: : The effect of peanut butter manufacture on vitamin E originating from raw peanuts (Arachis hypogaea L., runner-type) was determined. Tocopherols were quantified by normal-phase high-performance liquid chromatography. No significant differences were observed in tocopherol (T) values between 1998 and 1999 crop raw peanuts or between raw peanuts and peanut butter except for γ-T (P > 0.05). Oil and stabilizer added to the roasted peanuts during peanut butter processing provided 4% of α-T in the finished peanut butter. Retention of total tocopherols during peanut butter manufacture was 95%. Mean α-T values (mg/100 g) of commercial peanut products ranged from 12.3 (peanut oil) to 4.1 (dry roasted peanuts).


title: Towards Universal Serial Item Names

abstract: The Universal Serial Item Name (USIN) scheme is proposed as a framework for a single global namespace of articles and other contributions published in organized serial collections. Requirements for USINs are analysed with an emphasis on the use of USINs in scholarly communication. A uniform naming model is described based on the hierarchical naming of serial publications and the hierarchical numbering of serial items. A number of concrete design ideas for USIN syntax are presented. A USIN Global Registry and a USIN Global Database are proposed and analysed in terms of specific architectural features that interact to meet the requirements of publishers, librarians and scholars. Applications of the USIN concept to literature research, document retrieval, bibliography preparation and addressing the 'broken links' problem of the World Wide Web are considered.


title: A Field Model for Repairing 3D Shapes

abstract: This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.


title: Improving multi-step prediction of learned time series models

abstract: Most typical statistical and machine learning approaches to time series modeling optimize a single-step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d assumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction. Our insight is to formulate the problem as imitation learning; the training data serves as a "demonstrator" by providing corrections for the errors made during multi-step prediction. By this reduction of multi-step time series prediction to imitation learning, we establish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DAD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction.


title: The question of animal culture

abstract: In this paper I consider whether traditional behaviors of animals, like traditions of humans, are transmitted by imitation learning. Review of the literature on problem solving by captive primates, and detailed consideration of two widely cited instances of purported learning by imitation and of culture in free-living primates (sweet-potato washing by Japanese macaques and termite fishing by chimpanzees), suggests that nonhuman primates do not learn to solve problems by imitation. It may, therefore, be misleading to treat animal traditions and human culture as homologous (rather than analogous) and to refer to animal traditions as cultural.


title: On-line creativity and conceptual change in science

abstract: This chapter reports an investigation of "On-line Creativity." I present a new account of the cognitive and social mechanisms underlying complex thinking of creative scientists as they work on significant problems in contemporary science. I lay out an innovative methodology that I have developed for investigating creative and complex thinking in a realworld context. Using this method, I have discovered that there are a number of strategies that are used in contemporary science that increase scientists' likelihood of making discoveries. The findings reported in this chapter provide new insights into complex scientific thinking and will dispel many of the myths surrounding the generation of new concepts and scientific discoveries.


title: A survey of cryptographic approaches to securing big-data analytics in the cloud

abstract: The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals. We then survey three cryptographic techniques - homomorphic encryption, verifiable computation, and multi-party computation - that can be used to achieve these goals. We describe the cryptographic techniques in the context of our cloud model and highlight the differences in performance cost associated with each.


title: HTTPi for Practical End-to-End Web Content Integrity

abstract: Widespread growth of open wireless hotspots has made it easy to carry out man-in-the-middle attacks and impersonate web sites. Although HTTPS can be used to prevent such attacks, its universal adoption is hindered by its performance cost and its inability to leverage caching at intermediate servers (such as CDN servers and caching proxies) while maintaining end-to-end security. To complement HTTPS, we revive an old idea from SHTTP, a protocol that offers end-to-end web integrity without confidentiality. We name the protocol HTTPi and give it an efficient design that is easy to deploy for today’s web. In particular, we tackle several previously-unidentified challenges, such as supporting progressive page loading on the client’s browser, handling mixed content, and defining access control policies among HTTP, HTTPi, and HTTPS content from the same domain. Our prototyping and evaluation experience show that HTTPi incurs negligible performance overhead over HTTP, can leverage existing web infrastructure such as CDNs or caching proxies without any modifications to them, and can make many of the mixed-content problems in existing HTTPS web sites easily go away. Based on this experience, we advocate browser and web server vendors to adopt HTTPi.


title: Instructable intelligent personal agent

abstract: Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. ::: ::: We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures defining sensors and effectors of the agent. Given a natural language command that LIA does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also specified in natural language. A novel lexicon induction algorithm enables LIA to generalize across taught commands, e.g., having been taught how to "forward an email to Alice," LIA can correctly interpret the command "forward this email to Bob." A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands significantly reduce task completion time. These results demonstrate the potential of natural language instruction as a significant, under-explored paradigm for machine learning.


title: Event driven analog modeling for the verification of PLL frequency synthesizers

abstract: The focus of this work is to provide a efficient modeling approach for the functional verification of complex analog frequency synthesizers. The event driven analog modeling approach uses the double precision data type wreal (supported by VerilogAMS), that enables analog accuracy in the digital simulation domain. It is therefore possible to separate high frequency signal paths in the frequency synthesizers from the analog domain, in order to archive higher simulation efficiency for fast verification purposes. The modeling approach and an investigation of the necessary accuracy requirements for the verification including phase noise performances of the analog frequency synthesizers is presented. The proposed approach is demonstrated on baseof a sub micron CMOS Fractional-N frequency synthesizer and compared with different traditional modeling approaches, like phase model and pure digital model. The paper concludes with a proposal of a verification approach for RF mixed signal systems.


title: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

abstract: Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled ‘seed’ image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.


title: A real-time Twitter sentiment analysis using an unsupervised method

abstract: With the evolution of Social Networks (SNs) such as Twitter, millions of users can interacting, sharing interests, activities, contents or exchanging experiences and opinions. Sharing opinions is an active research topic in the framework of sentiment analysis and opinion mining. In this paper, we present a real-time implementation of a system that can discover and track opinions on Twitter using Apache Storm tool. Our system provides multiple opinions' representations through dynamic graphic visualizations. We use an unsupervised machine learning technique, to analyze opinions and detect tweets polarity. By doing so, we can recommend relevant keywords regarding the main topic of interest. Results using SemEval Datasets, show that our solution outperforms the existing ones, by recording better scores.


title: Application of a non-invasive reference headband and a surgical splint for intraoperative paediatric navigation

abstract: The key to the success of surgical navigation based on computer-aided design and computer-aided manufacturing (CAD/CAM) planning is the registration process. This has to be precise and adapted to the surgical needs. However, the application of a conventional rigid skull-fixed navigation star for accurate registration is limited for use in the paediatric population, because of the risk of unstable fixation, dural perforation, and intracranial bleeding. The authors describe their experience with a non-invasive reference headband that was used in combination with a custom-made acrylic resin dental registration splint for resection of a rare infraorbital zygomatic desmoplastic fibroma in a 2-year old patient. This approach appears not to have been reported in the literature to date.


title: Document Management System using Wireless RFID Technology for Intelligent Healthcare Operations

abstract: It has been estimated that in the UK National Health Service (NHS) 33% of nurses spend at least an hour in a shift on ::: non-productive activities, equals 40 hours a month and costs the NHS approximately £900 million per year. This includes ::: searching for equipment and patient records, a situation which will become more acute as the ageing population is ::: expected to rise to 18.5% in the UK by 2020. The paper presents the use of emerging Radio Frequency Identification ::: (RFID) technology for application in tracking and tracing document such as patient records in a hospital environment. ::: The system developed uses low-cost passive tags to provide real time non intervention information using a TCP/IP ::: protocol allow monitored across the organisation to track and trace and reduce un-productive time and prevent loss of ::: documentation. This information can be linked to intelligent systems such as work flow management, simulation and data ::: mining techniques etc. to provide intelligent decision making and what-if analysis. The paper discusses the development ::: of Electronic Patient Record (EPR) and its issues and what our proposed system can offer and enhance EPR system.


title: Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

abstract: Abstract Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms “learn” a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set (“brain reading”). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.


title: Detecting Buildings in Aerial Images *

abstract: Abstract Detecting building structures in aerial images is a task of importance for many applications. Low-level segmentation rarely gives a complete outline of the desired structures. We use a generic model of the shapes of the structures we are looking for — that they are rectangular or composed of rectangular components. We also use shadows cast by buildings to confirm their presence and to estimate their height. Our techniques have been tested on images with density typical of suburban areas.


title: Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks

abstract: Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. For all five, we show high agreement between Mechanical Turk non-expert annotations and existing gold standard labels provided by expert labelers. For the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. We propose a technique for bias correction that significantly improves annotation quality on two tasks. We conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.


title: TRAJECTORY GENERATION FOR CAR-LIKE ROBOTS USING CUBIC CURVATURE POLYNOMIALS

abstract: Curvature polynomials of cubic order are ideal primitive trajectories for car-like robots. Unlike the clothoids, which are linear curvature polynomials, cubic curves can be used to determine a unique trajectory to an arbitrary target posture using a single continuous primitive. Such curves are also the lowest order curves which are continuous in the torque applied to steering mechanisms, so they generate trajectories which are relatively easily tracked by a real vehicle. Like the clothoids, cubic curvature polynomials are relatively difficult to compute but are easy to execute. A real-time numerical method to compute them is described.


title: Why we use and abandon smart devices

abstract: Smart devices are becoming increasingly commercially available. However, uptake of these devices has been slow and abandonment swift, which indicates that smart devices may not currently meet the needs of users. To advance an understanding of the ways users benefit from, are challenged by, and abandon smart devices, we asked a group of users to purchase smart sensing devices to advance themselves towards a personal, self-defined goal. We found that participants abandoned devices because they did not fit with the their conceptions of themselves, the data collected by devices were perceived to not be useful, and device maintenance became unmanageable. Participants used devices because they had developed routines and because devices were useful, satisfied curiosity, and held hope for potential benefit to them. We propose ways to reduce barriers, motivate use, and argue for envisioning an additional function of these devices for short-term interventions, in addition to standard long-term use.


title: Wilms tumor arising in a child with X-linked nephrogenic diabetes insipidus

abstract: We report on a child with X-linked nephrogenic diabetes insipidus (NDI) who developed Wilms tumor (WT). Nephrogenic diabetes insipidus is caused by mutations of the arginine vasopressin receptor (AVPR2) or aquaporin-II (AQP2) genes. Wilms tumor is also genetically heterogeneous and is associated with mutations of WT1 (15–20%), WTX (20–30%) and other loci. The boy presented at 5 months with failure to thrive, polyuria, hypernatremia and abdominal mass. Analysis of leukocyte DNA showed a novel missense mutation (Q174H) of the AVPR2 gene, which was not present in his mother. In cells (WitS) isolated from the tumor, WTX mRNA expression and coding sequence were intact. However, we identified a 44-kb homozygous deletion of the WT1 gene spanning exons 4 to 10. The WT1 deletion was not present in leukocyte DNA from the patient or his mother. We also noted strong β-catenin (CTNNB1) expression in the tumor cells and identified a heterozygote missense Ser45Cys mutation of exon 3 of CTNNB1. However, the mutation was absent both in the constitutional DNA of the patient and his mother. The concurrence of WT and NDI has not been previously reported and may be unrelated. Nevertheless, this case nicely illustrates the sequence of events leading to sporadic Wilms tumor.


title: Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

abstract: Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R 2 = 0.80−0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R 2 = 0.74 for four selected species, R 2 = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.


title: Battery management system and control strategy for hybrid and electric vehicle

abstract: Battery management system (BMS) is an integral part of any electrical vehicle, which ensures that the batteries are not subjected to conditions outside their specified safe operating conditions. Thus the safety of the battery as well as of the passengers depend on the design of the BMS. In the present work a preliminary work is carried out to simulate a typical BMS for hybrid electrical vehicle. The various functional blocks of the BMS are implemented in SIMULINK toolbox of MATLAB. The BMS proposed is equipped with a battery model in which SOC is used as one of the states to overcome the limitation of stand-alone coulomb counting method for SOC estimation. The parameters of the battery are extracted from experimental results and incorporated in the model. The simulation results are validated by experimental results.


title: Fast Priority Queues for Cached Memory

abstract: The cache hierarchy prevalent in todays high performance processors has to be taken into account in order to design algorithms that perform well in practice. This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on k-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs.


The Problem of Trusted Third Party in Authentication and Digital Signature Protocols 1


title: A Survey on applications of Pneumatic Artificial Muscles

abstract: The aim of this article is to present a survey on applications of Pneumatic Artificial Muscles (PAMs). PAMs are highly non-linear pneumatic actuators where their elongation is proportional to the interval pressure. During the last decade, there has been a significant increase in the industrial and scientific utilization of PAMs due to their advantages such as high strength and small weight, while various types of PAMs with different technical characteristics have been appeared in the relative scientific literature. This article will summarize the key enabling applications in PAMs that are focusing in the following areas: a) Biorobotic, b) Medical, c) Industrial, and d) Aerospace applications.


title: Coefficient alpha and related internal consistency reliability coefficients.

abstract: The author studied the conditions under which coefficient alpha and 10 related internal consistency reliability coefficients underestimate the reliability of a measure. Simulated data showed that alpha, though reasonably robust when computed on n components in moderately heterogeneous data, can under certain conditions seriously underestimate the reliability of a measure. Consequently, alpha, when used in corrections for attenuation, can result in nontrivial overestimation of the corrected correlation. Most of the coefficients studied, including lambda 2, did not improve the estimate to any great extent when the data were heterogeneous. The exceptions were stratified alpha and maximal reliability, which performed well when the components were grouped into two subsets, each measuring a different factor, and maximized lambda 4, which provided the most consistently accurate estimate of the reliability in all simulations studied.


title: Wearable Internet of Things - from human activity tracking to clinical integration

abstract: Wearable devices for human activity tracking have been emerging rapidly. Most of them are capable of sending health statistics to smartphones, smartwatches or smart bands. However, they only provide the data for individual analysis and their data is not integrated into clinical practice. Leveraging on the Internet of Things (IoT), edge and cloud computing technologies, we propose an architecture which is capable of providing cloud based clinical services using human activity data. Such services could supplement the shortage of staff in primary healthcare centers thereby reducing the burden on healthcare service providers. The enormous amount of data created from such services could also be utilized for planning future therapies by studying recovery cycles of existing patients. We provide a prototype based on our architecture and discuss its salient features. We also provide use cases of our system in personalized and home based healthcare services. We propose an International Telecommunication Union based standardization (ITU-T) for our design and discuss future directions in wearable IoT.


title: Understanding users' budgets for recommendation with hierarchical Poisson factorization

abstract: People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several realworld datasets. The results show the advantage of uncovering users' budgets for recommendation.


title: One-shot learning for fine-grained relation extraction via convolutional siamese neural network

abstract: Extracting fine-grained relations between entities of interest is of great importance to information extraction and large-scale knowledge graph construction. Conventional approaches on relation extraction require an existing knowledge graph to start with or sufficient observed samples from each relation type in the training process. However, such resources are not always available, and fine-grained manual labeling is extremely time-consuming and requires extensive expertise for specific domains such as healthcare and bioinformatics. Additionally, the distribution of fine-grained relations is often highly imbalanced in practice. We tackle this label scarcity and distribution imbalance issue from a one-shot classification perspective via a convolutional siamese neural network which extracts discriminative semantic-aware features to verify the relations between a pair of input samples. The proposed siamese network effectively extracts uncommon relations with only limited observed samples on the tasks of 1-shot and few-shot classification, demonstrating significant benefits to domain-specific information extraction in practical applications.


title: Emotion Regulation and Psychopathology: The Role of Gender

abstract: This review addresses three questions regarding the relationships among gender, emotion regulation, and psychopathology: (a) are there gender differences in emotion regulation strategies, (b) are emotion regulation strategies similarly related to psychopathology in men and women, and (c) do gender differences in emotion regulation strategies account for gender differences in psychopathology? Women report using most emotion regulation strategies more than men do, and emotion regulation strategies are similarly related to psychopathology in women and men. More rumination in women compared to men partially accounts for greater depression and anxiety in women compared to men, while a greater tendency to use alcohol to cope partially accounts for more alcohol misuse in men compared to women. The literature on emotion regulation is likely missing vital information on how men regulate their emotions. I discuss lessons learned and questions raised about the relationships between gender differences in emotion regu...


title: mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud

abstract: Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices’ proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.


title: Substrate integrated antenna conformal to 3D-printing cone

abstract: In this paper, a substrate integrated conformal antenna based on a 3D-printing conical pedestal is demonstrated. This array is mounted on a cone with a radius on the top of 15 mm and a radius on the bottom of 35 mm. It is synthesized at the center frequency of 35 GHz. It is not an easy task to fabricate a non-planar structure, thus the 3D printing technology is used to fabricate the cone. This method is much cheaper and simpler compared with the conventional machining. A waveguide feeding structure is employed to excite the substrate integrated waveguide (SIW) conformal antenna and fabricated through the 3D printing technology as well. In simulation, it has a −21dB side-lobe level (SLL) in H-plane at 35 GHz. At last, experimental results of an antenna prototype has been reported, demonstrating that due to the process and measurement error the SLL is −16dB.


title: Performance evaluation of similarity-based link prediction schemes for social network

abstract: Social network is a platform where people can share their information, make new connections, and explore the information about the different events occurring in society. In recent year, prediction for new link in the social network has been attracted many researchers. Link Prediction in social network refers to finding new connections that will occur between people in the future. It finds the absence and presence of edge in a social network. In this paper, we have explored well known similarity based link prediction algorithms and discuss their performance in terms of accuracy, precision, and recall. The experimental results show that common neighbours and Jaccard coefficient based algorithms perform better than other algorithms in term of accuracy.


title: Criminal investigation and forensic tools for smartphones

abstract: The technological capability of mobile devices, in particular Smartphones, makes their use valuable to the criminal community as a data terminal in the facilitation of organised crime or terrorism. The provision of best evidence admissible in a Court of Law requires a thorough understanding of the legal precepts of law under which such admissible evidence may be obtained and proffered. Equally, in the digital age, this evidence must be obtained and presented in a manner which not only ensures admissibility but is also irrefutable. The paper is a technical overview of the currently available forensic software tools that are developed to carry out forensic investigation of mobile devices and terminal/device examination procedures.


title: Water-Hyacinth (Eichhornia Crassipes) in Ruminant Nutrition

abstract: In a pilot experiment, ten sheep were used to study the possibility of using water hyacinth as ruminant feed. The mean live weight of the animals was 40.5 kg and they were allocated in individual pens. Water hyacinth were fed (4.0 kg/d) once a day plus 300 g of a concentrate mixture. The mean intake was 3.6 kg of fresh water hyacinth per animal and the chemical analyses of dried material showed respectively, 10.00%, 15.96%, 16.28%, 2.65%, 47.29% and 17.84% for DM, CP, CF, EE, NNE and MM * The dry matter remaining in nylon bags after 48 hours incubation in the rumen of a steer was 55.29%. It was concluded that water hyacinth has potential as roughage source for ruminants, but further investigation needs to be done on harvesting and storage of fresh material.


title: Hedonic and Instrumental Motives in Anger Regulation

abstract: What motivates individuals to regulate their emotions? One answer, which has been highlighted in emotion-regulation research, is that individuals are motivated by short-term hedonic goals (e.g., the motivation to feel pleasure). Another answer, however, is that individuals are motivated by instrumental goals (e.g., the motivation to perform certain behaviors). We suggest that both answers have merit. To demonstrate the role instrumental goals may play in emotion regulation, we pitted short-term hedonic motives and instrumental motives against each other, by testing whether individuals were motivated to experience a potentially useful, albeit unpleasant, emotion. We found that (a) individuals preferred activities that would increase their level of anger (but not their level of excitement) when they were anticipating confrontational, but not nonconfrontational, tasks and that (b) anger improved performance in a confrontational, but not a non-confrontational, task. These findings support a functional view of...


title: A Kronecker-factored approximate Fisher matrix for convolution layers

abstract: Second-order optimization methods such as natural gradient descent have the potential to speed up training of neural networks by correcting for the curvature of the loss function. Unfortunately, the exact natural gradient is impractical to compute for large models, and most approximations either require an expensive iterative procedure or make crude approximations to the curvature. We present Kronecker Factors for Convolution (KFC), a tractable approximation to the Fisher matrix for convolutional networks based on a structured probabilistic model for the distribution over backpropagated derivatives. Similarly to the recently proposed Kronecker-Factored Approximate Curvature (K-FAC), each block of the approximate Fisher matrix decomposes as the Kronecker product of small matrices, allowing for efficient inversion. KFC captures important curvature information while still yielding comparably efficient updates to stochastic gradient descent (SGD). We show that the updates are invariant to commonly used reparameterizations, such as centering of the activations. In our experiments, approximate natural gradient descent with KFC was able to train convolutional networks several times faster than carefully tuned SGD. Furthermore, it was able to train the networks in 10-20 times fewer iterations than SGD, suggesting its potential applicability in a distributed setting.


title: A Linear Precoding Scheme for Downlink Multiuser MIMO Precoding Systems

abstract: In this letter, we propose a low complexity linear precoding scheme for downlink multiuser MIMO precoding systems where there is no limit on the number of multiple antennas employed at both the base station and the users. In the proposed algorithm, we can achieve the precoder in two steps. In the first step, we balance the multiuser interference (MUI) and noise by carrying out a novel channel extension approach. In the second step, we further optimize the system performance assuming parallel SU MIMO channels. Simulation results show that the proposed algorithm can achieve elaborate performance while offering lower computational complexity.


title: Service Discovery and Composition in Smart Cities

abstract: The ongoing digitalization trend has given rise to the concept of smart cities, targeting the interconnection of city infrastructure and services over a digital layer for innovative technological solutions as well as improvements on existing facilities in cities. This article investigates the critical information system constituents of smart cities that facilitate the holistic integration of its ecosystem and resources with the aim to foster dynamic and adaptive software. We identify three main enablers in this direction: (i) semantic functional description of city objects, representing physical devices or abstract services, (ii) a distributed service directory that embodies available city services for service lookup and discovery, (iii) planning tools for selecting and chaining basic services to compose new complex services. We provide an overview of the approach adopted in our ongoing smart city project for each of these three dimensions.


title: The History of Software Architecture - In the Eye of the Practitioner

abstract: Software architecture (SA) is celebrating 25 years. This is so if we consider the seminal papers establishing SA as a distinct discipline and scientific publications that have identified cornerstones of both research and practice, like architecture views, architecture description languages, and architecture evaluation. With the pervasive use of cloud provisioning, the dynamic integration of multi-party distributed services, and the steep increase in the digitalization of business and society, making sound design decisions encompasses an increasingly-large and complex problem space. The role of SA is essential as never before, so much so that no organization undertakes `serious' projects without the support of suitable architecture practices. But, how did SA practice evolve in the past 25 years? and What are the challenges ahead? There have been various attempts to summarize the state of research and practice of SA. Still, we miss the practitioners' view on the questions above. To fill this gap, we have first extracted the top-10 topics resulting from the analysis of 5,622 scientific papers. Then, we have used such topics to design an online survey filled out by 57 SA practitioners with 5 to 20+ years of experience. We present the results of the survey with a special focus on the SA topics that SA practitioners perceive, in the past, present and future, as the most impactful. We finally use the results to draw preliminary takeaways.


title: Efficient mining of understandable patterns from multivariate interval time series

abstract: We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM). We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data. The patterns have a hierarchical structure, with levels corresponding to the temporal concepts duration, coincidence, and partial order. The patterns are very compact, but offer details for each element on demand. In comparison with related approaches, the TSKR is shown to have advantages in robustness, expressivity, and comprehensibility. The search for coincidence and partial order in interval data can be formulated as instances of the well known frequent itemset problem. Efficient algorithms for the discovery of the patterns are adapted accordingly. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. Human interaction is used during the mining to analyze and validate partial results as early as possible and guide further processing steps. The efficacy of the methods is demonstrated using two real life data sets. In an application to sports medicine the results were recognized as valid and useful by an expert of the field.


title: Softmax GAN

abstract: Softmax GAN is a novel variant of Generative Adversarial Network (GAN). The key idea of Softmax GAN is to replace the classification loss in the original GAN with a softmax cross-entropy loss in the sample space of one single batch. In the adversarial learning of $N$ real training samples and $M$ generated samples, the target of discriminator training is to distribute all the probability mass to the real samples, each with probability $\frac{1}{M}$, and distribute zero probability to generated data. In the generator training phase, the target is to assign equal probability to all data points in the batch, each with probability $\frac{1}{M+N}$. While the original GAN is closely related to Noise Contrastive Estimation (NCE), we show that Softmax GAN is the Importance Sampling version of GAN. We futher demonstrate with experiments that this simple change stabilizes GAN training.


title: AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

abstract: In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.


title: SIW Pillbox Antenna for Monopulse Radar Applications

abstract: A multibeam pillbox antenna system incorporating monopulse phase comparison technique is proposed in the 24-GHz band. This low-profile antenna architecture combines the scanning capabilities of pillbox configurations and enhanced resolution of two-quadrant monopulse technique; this approach avoids mechanical orientation of the antenna system for tracking applications. The radiating panel consists of two subarrays of 23 slotted waveguides, with 8 slots per waveguide. The beam is scanned in E-plane over a field of view of $\pm {\bf 40}^\circ $ , and sum/difference patterns are generated in H-plane. Around the design frequency, ${\bf f} = {\bf 24}.{\bf 15}\,{\bf GHz}$ , the antenna gain varies between 25 and 21.5 dBi for the central and extreme beams, respectively. The measured null depth is better than $- {\bf 16}\;\text{dB}$ for all difference beam patterns. The antenna bandwidth for ${\bf VSWR} ( ${\textbf{S}_{\textbf{ii}}} ) and isolation better than ${\bf 10}\;{\bf dB}$ ( ${\textbf{S}_{\textbf{ij}}} ) is equal to 4.5%.


title: A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting

abstract: Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the performance of probabilistic forecasting. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation, and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach.


title: Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information.

abstract: We reviewed the literature on Indoor Air Quality (IAQ), ventilation, and building-related health problems in schools and identified commonly reported building-related health symptoms involving schools until 1999. We collected existing data on ventilation rates, carbon dioxide (CO2) concentrations and symptom-relevant indoor air contaminants, and evaluated information on causal relationships between pollutant exposures and health symptoms. Reported ventilation and CO2 data strongly indicate that ventilation is inadequate in many classrooms, possibly leading to health symptoms. Adequate ventilation should be a major focus of design or remediation efforts. Total volatile organic compounds, formaldehyde (HCHO) and microbiological contaminants are reported. Low HCHO concentrations were unlikely to cause acute irritant symptoms (<0.05 ppm), but possibly increased risks for allergen sensitivities, chronic irritation, and cancer. Reported microbiological contaminants included allergens in deposited dust, fungi, and bacteria. Levels of specific allergens were sufficient to cause symptoms in allergic occupants. Measurements of airborne bacteria and airborne and surface fungal spores were reported in schoolrooms. Asthma and 'sick building syndrome' symptoms are commonly reported. The few studies investigating causal relationships between health symptoms and exposures to specific pollutants suggest that such symptoms in schools are related to exposures to volatile organic compounds (VOCs), molds and microbial VOCs, and allergens.


title: Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention

abstract: A critical function in both machine vision and biological vision systems is attentional selection of scene regions worthy of further analysis by higher-level processes such as object recognition. Here we present the first model of spatial attention that (1) can be applied to arbitrary static and dynamic image sequences with interactive tasks and (2) combines a general computational implementation of both bottom-up (BU) saliency and dynamic top-down (TD) task relevance; the claimed novelty lies in the combination of these elements and in the fully computational nature of the model. The BU component computes a saliency map from 12 low-level multi-scale visual features. The TD component computes a low-level signature of the entire image, and learns to associate different classes of signatures with the different gaze patterns recorded from human subjects performing a task of interest. We measured the ability of this model to predict the eye movements of people playing contemporary video games. We found that the TD model alone predicts where humans look about twice as well as does the BU model alone; in addition, a combined BU*TD model performs significantly better than either individual component. Qualitatively, the combined model predicts some easy-to-describe but hard-to-compute aspects of attentional selection, such as shifting attention leftward when approaching a left turn along a racing track. Thus, our study demonstrates the advantages of integrating BU factors derived from a saliency map and TD factors learned from image and task contexts in predicting where humans look while performing complex visually-guided behavior.


title: Rough sets and bayes factor

abstract: We present a novel approach to understanding the concepts of the theory of rough sets in terms of the inverse probabilities derivable from data. It is related to the Bayes factor known from the Bayesian hypothesis testing methods. The proposed Rough Bayesian model (RB) does not require information about the prior and posterior probabilities in case they are not provided in a confirmable way. We discuss RB with respect to its correspondence to the original Rough Set model (RS) introduced by Pawlak and Variable Precision Rough Set model (VPRS) introduced by Ziarko. We pay a special attention on RB’s capability to deal with multi-decision problems. We also propose a method for distributed data storage relevant to computational needs of our approach.


title: Ellipsoidal Cube Maps for Accurate Rendering of Planetary-Scale Terrain Data

abstract: Advances in sensor technology lead to a rapidly growing number of terrain data sets with very high spatial resolution. To allow reliable visual analysis of this data, terrain data for planetary objects needs to be rendered with accurate reproduction of every detail. This combination of very large scale and very fine detail is challenging for multiple reasons: the numerical accuracy of typical data types is not sufficient, simple spherical planet models fail to accurately represent the data, and distortions in map projections used for data storage lead to sampling problems. In this paper, we propose the Ellipsoidal Cube Map model to address these problems. We demonstrate the possibilities of the model using a simple renderer implementation that achieves interactive frame rates for a variety of data sets for Earth, Moon, and Mars.


title: Clustering WordNet word senses

abstract: This paper presents the results of a set of methods to cluster WordNet word senses. The methods rely on different information sources: confusion matrixes from Senseval-2 Word Sense Disambiguation systems, translation similarities, hand-tagged examples of the target word senses and examples obtained automatically from the web for the target word senses. The clustering results have been evaluated using the coarsegrained word senses provided for the lexical sample in Senseval-2. We have used Cluto, a general clustering environment, in order to test different clustering algorithms. The best results are obtained for the automatically obtained examples, yielding purity values up to 84% on average over 20 nouns.


title: Data Gloves for Sign Language Recognition System

abstract: Communication between deaf-dumb and a normal person have always been a challenging task .About 9 billion people in the world come into this category which is quite large number to be ignored. As deaf-dumb people use sign language for their communication which is difficult to understand by the normal people. This paper aims at eradicating the communication barrier between them by developing an embedded system which will translate the hand gestures into synthesized textual and vocal format without any requirement of special sign language interpreter. This system consists of a glove that will be worn by a dumb person to facilitate the communication with the normal person. it translates the hand gestures to corresponding words using flex sensors and 3-axis accelerometer. The signals are converted to digital data using comparator circuits and ADC of microcontroller ARM LPC 2138.the microcontroller matches the binary combinations with the data given in the look up table of the databases and produces the speech signal. The output of the system is displayed using the speaker and LCD.


title: Non-Photorealistic Rendering from Stereo

abstract: Abstract : A new paradigm for automatic non-photorealistic rendering is introduced in this paper. Non-photorealistic rendering (NPR) provides an alternative way to render complex scenes by emphasizing high level or salient perceptual features. Particularly, the pen-and-ink rendering style produces sketchy-like drawings that can effectively communicate shape and geometry. This is achieved by combining drawing primitives that mimic ink patterns used by artists. Existing NPR approaches can be categorized into two groups depending on the type of input they use: image based and object based. Image-based NPR techniques use 2D images to produce the renderings. Object-based techniques work directly on given 3D models and make use of the full volumetric representation. In this paper, the authors propose to enjoy the best of both worlds by developing a hybrid model that simultaneously uses information from the image and object domains. These two sources of information are provided by a calibrated stereoscopic system. Given a pair of stereo images and the calibration data, they solve the stereo problem to extract the normal and principal direction fields, which are fundamental for guiding a texture synthesis algorithm that generates the NPR renderings. In particular, normals guide tonal variations, while principal directions determine the orientation of stroke-like texture patterns. They describe a particular, fully automatic implementation of these ideas and present a number of examples.


title: Modeling Sentences in the Latent Space

abstract: Sentence Similarity is the process of computing a similarity score between two sentences. Previous sentence similarity work finds that latent semantics approaches to the problem do not perform well due to insufficient information in single sentences. In this paper, we show that by carefully handling words that are not in the sentences (missing words), we can train a reliable latent variable model on sentences. In the process, we propose a new evaluation framework for sentence similarity: Concept Definition Retrieval. The new framework allows for large scale tuning and testing of Sentence Similarity models. Experiments on the new task and previous data sets show significant improvement of our model over baselines and other traditional latent variable models. Our results indicate comparable and even better performance than current state of the art systems addressing the problem of sentence similarity.


title: The ConceptMapper Approach to Named Entity Recognition

abstract: ConceptMapper is an open source tool we created for classifying mentions in an unstructured text document based on concept terminologies and yielding named entities as output. It is implemented as a UIMA 1 (Unstructured Information Management Architecture (IBM, 2004)) annotator, and concepts come from standardised or proprietary terminologies. ConceptMapper can be easily configured, for instance, to use different search strategies or syntactic concepts. In this paper we will describe ConceptMapper, its configuration parameters and their trade-offs, in terms of precision and recall in identifying concepts in a collection of clinical reports written in English. ConceptMapper is available from the Apache UIMA Sandbox, using the Apache Open Source license.


title: Hierarchical multi-label classification of social text streams

abstract: Hierarchical multi-label classification assigns a document to multiple hierarchical classes. In this paper we focus on hierarchical multi-label classification of social text streams. Concept drift, complicated relations among classes, and the limited length of documents in social text streams make this a challenging problem. Our approach includes three core ingredients: short document expansion, time-aware topic tracking, and chunk-based structural learning. We extend each short document in social text streams to a more comprehensive representation via state-of-the-art entity linking and sentence ranking strategies. From documents extended in this manner, we infer dynamic probabilistic distributions over topics by dividing topics into dynamic "global" topics and "local" topics. For the third and final phase we propose a chunk-based structural optimization strategy to classify each document into multiple classes. Extensive experiments conducted on a large real-world dataset show the effectiveness of our proposed method for hierarchical multi-label classification of social text streams.


title: Better Text Understanding Through Image-To-Text Transfer

abstract: Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in limitations of their ability to generalize. In this paper, we explore models that incorporate visual information into the text representation. Based on comprehensive ablation studies, we propose a conceptually simple, yet well performing architecture. It outperforms previous multimodal approaches on a set of well established benchmarks. We also improve the state-of-the-art results for image-related text datasets, using orders of magnitude less data.


title: Ubiquitous Computing: Smart Devices, Environments and Interactions

abstract: List of Figures. List of Tables. Preface. Acknowledgements. 1 Ubiquitous Computing: Basics and Vision. 1.1 Living in a Digital World. 1.2 Modelling the Key Ubiquitous Computing Properties. 1.3 Ubiquitous System Environment Interaction. 1.4 Architectural Design for UbiCom Systems: Smart DEI Model. 1.5 Discussion. Exercises. References. 2 Applications and Requirements. 2.1 Introduction. 2.2 Example Early UbiCom Research Projects. 2.3 Everyday Applications in the Virtual, Human and Physical World. 2.4 Discussion. Exercises. References. 3 Smart Devices and Services. 3.1 Introduction. 3.2 Service Architecture Models. 3.3 Service Provision Life-Cycle. 3.4 Virtual Machines and Operating Systems. Exercises. References. 4 Smart Mobiles, Cards and Device Networks. 4.1 Introduction. 4.2 Smart Mobile Devices, Users, Resources and Code. 4.3 Operating Systems for Mobile Computers and Communicator Devices. 4.4 Smart Card Devices. 4.5 Device Networks. Exercises. References. 5 Human-Computer Interaction. 5.1 Introduction. 5.2 User Interfaces and Interaction for Four Widely Used Devices. 5.3 Hidden UI Via Basic Smart Devices. 5.4 Hidden UI Via Wearable and Implanted Devices. 5.5 Human-Centred Design (HCD). 5.6 User Models: Acquisition and Representation. 5.7 iHCI Design. Exercises. References. 6 Tagging, Sensing and Controlling. 6.1 Introduction. 6.2 Tagging the Physical World. 6.3 Sensors and Sensor Networks. 6.4 Micro Actuation and Sensing: MEMS. 6.5 Embedded Systems and Real-Time Systems. 6.6 Control Systems (for Physical World Tasks). 6.7 Robots. Exercises. References. 7 Context-Aware Systems. 7.1 Introduction. 7.2 Modelling Context-Aware Systems. 7.3 Mobility Awareness. 7.4 Spatial Awareness. 7.5 Temporal Awareness: Coordinating and Scheduling. 7.6 ICT System Awareness. Exercises. References. 8 Intelligent Systems (IS). 8.1 Introduction. 8.2 Basic Concepts. 8.3 IS Architectures. 8.4 Semantic KB IS. 8.5 Classical Logic IS. 8.6 Soft Computing IS Models. 8.7 IS System Operations. Exercises. References. 9 Intelligent System Interaction. 9.1 Introduction. 9.2 Interaction Multiplicity. 9.3 Is Interaction Design. 9.4 Some Generic Intelligent Interaction Applications. Exercises. References. 10 Autonomous Systems and Artificial Life. 10.1 Introduction. 10.2 Basic Autonomous Intra-Acting Systems. 10.3 Reflective and Self-Aware Systems. 10.4 Self-Management and Autonomic Computing. 10.5 Complex Systems. 10.6 Artificial Life. Exercises. References. 11 Ubiquitous Communication. 11.1 Introduction. 11.2 Audio Networks. 11.3 Data Networks. 11.4 Wireless Data Networks. 11.5 Universal and Transparent Audio, Video and Alphanumeric Data. 11.6 Ubiquitous Networks. 11.7 Further Network Design Issues. Exercises. References. 12 Management of Smart Devices. 12.1 Introduction. 12.2 Managing Smart Devices in Virtual Environments. 12.3 Managing Smart Devices in Human User-Centred Environments. 12.4 Managing Smart Devices in Physical Environments. Exercises. References. 13 Ubiquitous System: Challenges and Outlook. 13.1 Introduction. 13.2 Overview of Challenges. 13.3 Smart Devices. 13.4 Smart Interaction. 13.5 Smart Physical Environment Device Interaction. 13.6 Smart Human-Device Interaction. 13.7 Human Intelligence Versus Machine Intelligence. 13.8 Social Issues: Promise Versus Peril. 13.9 Final Remarks. Exercises. References. Index.


title: High-Quality Single Phase Power Conversion by Reconsidering the Magnetic Components in the Output Stage - Building a Better Half Bridge

abstract: A novel half-bridge switching topology is presented which is based on using a split-wound series-connected coupled- winding inductor, interleaved switching, and zero-deadtime operation to achieve multi-level PWM output with a reduced part-count, reduced current ripple, and improved THD performance. This new topology is presented to highlight the design process for low power, high switching frequency designs requiring high performance, where circuit size and complexity are normally limiting factors. The design process demonstrated includes considering the current performance characteristics of the inverter and the design of the coupled-winding inductor. Design tradeoffs that are unique to this topology are explored that allow optimized designs for electrical performance, magnetic component size, or system losses. The benefit of the new topology is demonstrated using a case design for a class-D audio power amplifier, which shows improvement in open-loop THD from 2% to 0.2%.


title: Are People Really Social on Porn 2.0

abstract: Social Web 2.0 features have become a vital component in a variety of multimedia systems, e.g., Last.fm, Flickr and Spotify. Interestingly, adult video websites are also starting to adopt these Web 2.0 principles, giving rise to the term ``Porn 2.0''. This paper examines a large Porn 2.0 social network, through data covering 563k users. We explore a number of unusual behavioural aspects that set this apart from more traditional multimedia social networks, including differences in browsing activity, social communications and relationship creation. We also analyse the nature and behaviour of content sharing, highlighting the role it plays in the Porn 2.0 community, as well as the preferences that users have when deciding what to consume. We particularly explore the impact that gender and sexuality have on these issues, showing their vital importance for aspects such as profile popularity.


title: Type checking annotation-based product lines

abstract: Software product line engineering is an efficient means of generating a family of program variants for a domain from a single code base. However, because of the potentially high number of possible program variants, it is difficult to test them all and ensure properties like type safety for the entire product line. We present a product-line-aware type system that can type check an entire software product line without generating each variant in isolation. Specifically, we extend the Featherweight Java calculus with feature annotations for product-line development and prove formally that all program variants generated from a well typed product line are well typed. Furthermore, we present a solution to the problem of typing mutually exclusive features. We discuss how results from our formalization helped implement our own product-line tool CIDE for full Java and report of our experience with detecting type errors in four existing software product line implementations.


title: Design of a quasi-hemispherical UWB antenna

abstract: This paper proposes a design of three-dimensional antenna for ultrawideband applications. The antenna has dimensions of 58 mm × 58 mm × 76.5 mm and is composed of two identical arms. Each arm consists of a hollow quasi-hemisphere and an inner for it tube. In the simulation, a 50 Ω coaxial cable is employed to feed the antenna in order to assess the change of the antenna performance due to the cable effect. The simulated results show that the antenna is capable of operating in the frequency band from 7 to 32.5 GHz with S 11 below −10 dB, while maintaining an omnidirectional radiation pattern. The simulated total efficiency is above 88 % over the entire impedance bandwidth which indicates a good antenna performance. The realized gain varies from 0.8 to 3.1 dBi with frequency.


title: Using Topic Modeling and Similarity Thresholds to Detect Events

abstract: This paper presents a Retrospective Event Detection algorithm, called Eventy-Topic Detection (ETD), which automatically generates topics that describe events in a large, temporal text corpus. Our approach leverages the structure of the topic modeling framework, specifically the Latent Dirichlet Allocation (LDA), to generate topics which are then later labeled as Eventy-Topics or non-Eventy-Topics. The system first runs daily LDA topic models, then calculates the cosine similarity between the topics of the daily topic models, and then runs our novel Bump-Detection algorithm. Similar topics labeled as an Eventy-Topic are then grouped together. The algorithm is demonstrated on two Terabyte sized corpuses - a Reuters News corpus and a Twitter corpus. Our method is evaluated on a human annotated test set. Our algorithm demonstrates its ability to accurately describe and label events in a temporal text corpus.


title: A Survey of OCR Applications

abstract: Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and search text from electronic documents or to publish the text on a website. The paper presents a survey of applications of OCR in different fields and further presents the experimentation for three important applications such as Captcha, Institutional Repository and Optical Music Character Recognition. We make use of an enhanced image segmentation algorithm based on histogram equalization using genetic algorithms for optical character recognition. The paper will act as a good literature survey for researchers starting to work in the field of optical character recognition.


title: A Framework for Ocean Satellite Image Classification Based on Ontologies

abstract: In this paper we present a framework for ocean image classification based on ontologies. With this aim, we will describe how low and high level content of ocean satellite images can be modeled with an ontology. In addition, we will show how the image classification can be modeled with the ontology in which decision tree based classifiers and rule-based expert systems are represented. Particularly, the rule based expert systems include rules about low-level features (called training and labeling rules), and rules defined from the labeling (called human expert rules). The modeling with the ontology provides an extensible framework in which accommodate several methods of image classification. One of the main aims of our proposal is to provide a mechanism to share data about image classification between applications. We have developed an extensible Protege plugin to classify images.


title: Relationship between Motivation and Satisfaction of Online Computer Games: Evidence from Adolescent Players Using Wireless Service in Taiwan

abstract: This study provides insight into the relationship between leisure motivation and leisure satisfaction with wireless service among teenage online gamers. A systematic sampling produced 134 usable questionnaires for the final data analysis. Findings revealed that teenagers playing online games with wireless service for intellectual motivations report educational leisure satisfaction. With wireless service, online game players with social motivations report educational, social and relaxation leisure satisfaction. Players with stimulus-avoidance motivation report greater psychological, educational, relaxation, physiological and aesthetic leisure satisfaction. Finally, this work discusses implications for leisure practice and further research.