title: Analysis and performances of a new emitter-switching bipolar transistor device suitable for high-voltage applications

abstract: This paper deals with the analysis and experimental investigation of a new monolithic cascode, named the emitter-switching bipolar transistor (ESBT), which is suitable for high-voltage medium-frequency applications. The performances of the device are compared with those of a power MOSFET, having equal blocking voltage and equal rated current. The experimental investigation has been carried out in an actual application using a flyback scheme as workbench. The main advantages and drawbacks of the two devices, with special reference to the switching characteristics and the forward voltage, are hence shown, thus providing useful information to the power converter designers looking for a good exploiting of the ESBT. The correlation between the physical structure of the ESBT and its electrical characteristics is explained aiming to understand some interesting features belonging to the device family. Finally, analysis and discussion on the ESBT driving requirements are done, while several circuit topologies, which are devoted to energize the four-terminal device, are proposed.


HSTDEK: Developing a methodology for construction of large-scale, multi-use knowledge bases


title: Hierarchical Category Detector for Clothing Recognition from Visual Data

abstract: Clothing detection is an important step for retrieving similar clothing items, organizing fashion photos, artificial intelligence powered shopping assistants and automatic labeling of large catalogues. Training a deep learning based clothing detector requires pre-defined categories (dress, pants etc) and a high volume of annotated image data for each category. However, fashion evolves and new categories are constantly introduced in the marketplace. For example, consider the case of jeggings which is a combination of jeans and leggings. Detection of this new category will require adding annotated data specific to jegging class and subsequently relearning the weights for the deep network. In this paper, we propose a novel object detection method that can handle newer categories without the need of obtaining new labeled data and retraining the network. Our approach learns the visual similarities between various clothing categories and predicts a tree of categories. The resulting framework significantly improves the generalization capabilities of the detector to novel clothing products.


title: An efficient Search Tool for an Anti-Money Laundering Application of an Multi-national Bank's Dataset

abstract: Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliche of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight fraud activities. However, the AML systems are so complicated that simple query tools provided by current DBMS may produce incorrect and ambiguous results and they are also very time-consuming due to the complexity of the database system architecture. In this paper, we present a new approach for identifying customers quickly and easily as part of an AML application. This will help AML experts to identify quickly customers who are managed independently across separate databases of the organization. This approach is tested on large and real-world financial datasets. Some preliminary experimental results show that this new approach is efficient and effective.


title: Novel Vertical-Stacked-Array-Transistor (VSAT) for ultra-high-density and cost-effective NAND Flash memory devices and SSD (Solid State Drive)

abstract: A novel 3-D NAND flash memory device, VSAT (Vertical-Stacked-Array-Transistor), has successfully been achieved. The VSAT was realized through a cost-effective and straightforward process called PIPE (planarized-Integration-on-the-same-plane). The VSAT combined with PIPE forms a unique 3-D vertical integration method that may be exploited for ultra-high-density Flash memory chip and solid-state-drive (SSD) applications. The off-current level in the polysilicon-channel transistor dramatically decreases by five orders of magnitude by using an ultra-thin body of 20 nm thick and a double-gate-in-series structure. In addition, hydrogen annealing improves the subthreshold swing and the mobility of the polysilicon-channel transistor.


title: Analysis of resnet model for malicious code detection

abstract: In this study, we have used the ResNet model to detect the new type of malware. ResNet model is investigated and tested which belongs to Microsoft. We have used two types of datasets to train and test the model. We collected dataset from Microsoft Malware Classification Challenge which contains 10868 Binary files, these binary files are further divided in nine different malware families and second dataset is benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset from Microsoft Malware Classification Challenge were initially .exe files which were converted in to opcode and then converted in to image files. We obtained a testing accuracy of 87.98% on ResNet model.


title: Traveling salesman problem heuristics: Leading methods, . . .

abstract: Heuristics for the traveling salesman problem (TSP) have made remarkable advances in recent years. We survey the leading methods and the special components responsible for their successful implementations, together with an experimental analysis of computational tests on a challenging and diverse set of symmetric and asymmetric TSP benchmark problems. The foremost algorithms are represented by two families, deriving from the Lin-Kernighan (LK) method and the stem-and-cycle (S&C) method. We show how these families can be conveniently viewed within a common ejection chain framework which sheds light on their similarities and differences, and gives clues about the nature of potential enhancements to today's best methods that may provide additional gains in solving large and difficult TSPs.


title: Cloud Docs: Secure Scalable Document Sharing on Public Clouds

abstract: Secure cloud storage solutions such as Trust Store, Sec Cloud, HPI Secure, and Twin Cloud have primarily focused on securing persistent data while storing it in public cloud services. Though data sharing has been recognized as an important security feature, these storage solutions mostly focus on three key properties: confidentiality, integrity and availability. Modern enterprise applications demand data is able to be shared within or across organizations. The challenge is how to securely share data in public clouds without increasing data movement and computation costs. This problem has been addressed in recent times by utilizing or developing new data encryption techniques such as identity-based encryption, attribute-based encryption and proxy-re-encryption. However, these techniques suffer from scalability and flexibility problems when dealing with big data and support for dynamic access control rules. This paper presents a novel architecture and corresponding protocols to provide secure sharing of documents on public cloud services: Cloud Docs. This system uses AES for data encryption to achieve scalability and supports identity based access control rules using private-public key pairs to provide flexibility.


title: An LTCC superstrate patch antenna for 60-GHz package applications

abstract: The increasing capabilities of high speed silicon germanium (SiGe) and complementary metal–oxide–semiconductor (CMOS) technologies have made millimeter wave (mmWave) frequencies attractive for low-cost applications. An overview of the capabilities can be found in [1]. Promising applications could be high data rate wireless personal-area networks (WPAN) at 60 GHz [2], automotive radars at 76–77 GHz or 78–81 GHz [3], and imaging at 94 GHz [4]. These applications require small, low-profile, but high-performance packages at moderate to low cost. Such mmWave systems require not only highly-integrated radio frequency integrated circuits (RFICs), but also high-performance antennas. However, integrating an antenna into a package is not an easy task. The realization of a robust, efficient and broadband mmWave antenna within a plastic or multilayer organic (MLO) package is challenging due to assembly difficulties, limited material selection and manufacturing tolerances. In [5] we indicated that our cavity-backed folded dipole superstrate antenna has potential for integration with our 60-GHz SiGe chipset [1] in a plastic land-grid array (LGA) package [6]. This package concept includes a fused-silica substrate with antenna structures, a metal frame forming the cavity and providing the support for the fused- silica substrate, a package carrier to carry the radio frequency (RF) chip and the antenna assembly, and glob-top to protect the chip and the antenna. Although this package shows excellent performance, the assembly is still complicated and expensive. In [7], we proposed an aperture-coupled superstrate patch antenna with an embedded air cavity and an open cavity for antenna evaluation (possibly also for holding an RFIC chip). The antenna, based on printed circuit board (PCB) or MLO technology, provides high efficiency and wide bandwidth.


title: Millimeter-wave mobile broadband with large scale spatial processing for 5G mobile communication

abstract: The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3–300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10–100 times better than the current 20MHz LTE-Advanced systems.


Reinforcement Learning for Atari Breakout


title: The global occlusion map: a new occlusion culling approach

abstract: Occlusion culling is an important technique to speed up the rendering process for walkthroughs in a complex environment. In this paper, we present a new approach for occlusion culling with respect to a view cell. A compact representation, the Global Occlusion Map (GOM), is proposed for storing the global visibility information of general 3D models with respect to the view cell. The GOM provides a collection of Directional Visibility Barriers (DVB), which are virtual occluding planes aligned with the main axes of the world coordinates that act as occluders to reject invisible objects lying behind them in every direction from a view cell. Since the GOM is a two-dimensional array, its size is bounded, depending only on the number of the sampled viewing directions. Furthermore, it is easy to conservatively compress the GOM by treating it as a depth image. Due to the axial orientations of the DVBs, both the computational and storage costs for occlusion culling based on the GOM is minimized. Our implementation shows the Global Occlusion Map is effective and efficient in urban walkthrough applications.


title: A Wideband Differentially Fed Dual-Polarized Antenna with Stable Radiation Pattern for Base Stations

abstract: A new wideband differentially fed dual-polarized antenna with stable radiation pattern for base stations is proposed and studied. A cross-shaped feeding structure is specially designed to fit the differentially fed scheme and four parasitic loop elements are employed to achieve a wide impedance bandwidth. A stable antenna gain and a stable radiation pattern are realized by using a rectangular cavity-shaped reflector instead of a planar one. A detailed parametric study was performed to optimize the antenna’s performances. After that, a prototype was fabricated and tested. Measured results show that the antenna achieves a wide impedance bandwidth of 52% with differential standing-wave ratio $\approx 8$ dBi) and a stable radiation pattern with 3-dB beamwidth of 65° ±5° were also found over the operating frequencies. Moreover, the proposed antenna can be easily built by using printed circuit board fabrication technique due to its compact and planar structure.


title: CamStyle: A Novel Data Augmentation Method for Person Re-Identification

abstract: Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle .


title: Optical Flow-based 3D Human Motion Estimation from Monocular Video

abstract: This paper presents a method to estimate 3D human pose and body shape from monocular videos. While recent approaches infer the 3D pose from silhouettes and landmarks, we exploit properties of optical flow to temporally constrain the reconstructed motion. We estimate human motion by minimizing the difference between computed flow fields and the output of our novel flow renderer. By just using a single semi-automatic initialization step, we are able to reconstruct monocular sequences without joint annotation. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.


title: Schizotypal personality traits and problematic use of massively-multiplayer online role-playing games (MMORPGs)

abstract: A link between maladaptive personality traits and an excessive use of massively multiplayer online role-playing games (MMORPGs) has been documented. However, the role of schizotypal personality traits in MMORPG use is understudied. The aim of this study was to explore the potential links between schizotypal traits, motivations for playing MMORPGs, and symptoms of problematic MMORPG use. Eighty-three MMORPG gamers were enrolled in the study. They filled out questionnaires measuring schizotypal personality traits and an adapted version of the same scale designed to measure in-game schizotypal traits. All participants also filled out questionnaires assessing motivations for gaming and disordered use of MMORPGs. Results of the study showed that the disorganized and interpersonal traits of schizotypy decreased when participants were thinking about themselves in the virtual world. Schizotypal traits, together with achievement and immersion motives, predicted problematic use of MMORPGs. The findings of this study may suggest that schizotypal traits and motivations for playing can interact and play a pivotal role in the onset and maintenance of problematic gaming.


title: Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

abstract: A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.


title: Social relationships between adult male and female mountain gorillas in the wild

abstract: Abstract Social relationships between adult males and females in two groups of wild mountain gorilla are described. Each group contained about five, largely unrelated sub-adult and adult females, a peripheral near-adult male, and one fully mature male. Females were clearly attracted to the dominant male, and those with young offspring spent the most time near him. Also, when a female came into oestrus her time near the dominant male increased temporarily. Not all females engaged in grooming with this male, and those that did were judged to have the less stable, i.e. less potentially long-lasting, relationships with him. The relevance of affinitive behaviour to acquisition of benefits and to establishment and maintenance of relationships, plus the influence of competition on maintenance of relationships are discussed.


title: Mining Console Logs for Large-Scale System Problem Detection

abstract: The console logs generated by an application contain messages that the application developers believed would be useful in debugging or monitoring the application. Despite the ubiquity and large size of these logs, they are rarely exploited in a systematic way for monitoring and debugging because they are not readily machine-parsable. In this paper, we propose a novel method for mining this rich source of information. First, we combine log parsing and text mining with source code analysis to extract structure from the console logs. Second, we extract features from the structured information in order to detect anomalous patterns in the logs using Principal Component Analysis (PCA). Finally, we use a decision tree to distill the results of PCA-based anomaly detection to a format readily understandable by domain experts (e.g. system operators) who need not be familiar with the anomaly detection algorithms. As a case study, we distill over one million lines of console logs from the Hadoop file system to a simple decision tree that a domain expert can readily understand; the process requires no operator intervention and we detect a large portion of runtime anomalies that are commonly overlooked.


title: A quantum chemical and statistical study of flavonoid compounds with anti-HIV activity

abstract: The molecular orbital semi-empirical method AM1 is employed to calculate a set of molecular properties (variables) of 14 biflavonoid compounds with anti-HIV-1 activity. Pattern recognition techniques, principal component analysis (PCA) and hierarchical cluster analysis (HCA), and stepwise discriminant analysis (SDA) were employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying the biflavonoid compounds according to their degree of anti-HIV-1 activity. The PCA, HCA and SDA studies showed that the variables HOMO (the energy of the highest occupied molecular orbital), LUMO (the energy of the lowest unoccupied molecular orbital) and A (surface area) are responsible for the separation between compounds with higher and lower anti-HIV-1 activity.


title: Interaction between Software Engineering and Artificial Intelligence- A Review

abstract: Software engineering and artificial intelligence is the two field of the computer science. During the last decades, the disciplines of Artificial Intelligence and Software Engineering have developed separately without the much exchange of research outcomes. However, both fields of computer science have different characteristics, benefits and limitations. This statement opens many possibilities and ideas for research. One idea is that the researcher applies the available methods, tools and techniques of Artificial Intelligence to Software Engineering and Software Engineering to Artificial Intelligence in a manner that good things, feature, characteristic and advantages of the both fields is taken up, and the limitations will reduces. During applicability, an intersection area is found between AI and SE, which forms the relation between AI and SE. The work in this paper discusses the factor that come while communicating between AI and SE such as Communication, objective, Problem and reasons for adopting. This work explores the framework of interaction on which both fields are communicate with each other. This framework has four major classes of interaction such as software support environment, AI tools and techniques in conventional software, Use of conventional software technology and Methodological considerations. This paper introduces the relation between AI and SE, and various techniques evolved while merging.


title: Amnestic Forgery: an Ontology of Conceptual Metaphors

abstract: This paper presents Amnestic Forgery, an ontology for metaphor semantics , based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frame and role mappings. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint.


title: State of the Field Emerging in a Digital World : A Decade Review of Media Use , Effects , and Gratifications in Emerging Adulthood

abstract: This article reviews the recent literature on uses, effects, and gratifications of media during emerging adulthood. We examine traditional media forms, including television, films, video games, music, and books, and also newer media, such as cell phones, social networking sites, and other Internet use. We find that emerging adults spend more time using the media than they spend doing any other activity, with the most time being spent on the Internet and listening to music. We also find that exposure to certain types of media content can influence both positive and negative outcomes in emerging adulthood, including, aggressive and prosocial behavior, body image, sexual behavior, friendship quality, and academic achievement. We also show that emerging adults use the media to gratify certain needs; key among these are for autonomy, identity, and intimacy needs. Finally, we discuss areas for future research involving media and emerging adulthood.


title: Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis

abstract: Some users of social media are spreading racist, sexist, and otherwise hateful content. For the purpose of training a hate speech detection system, the reliability of the annotations is crucial, but there is no ::: universally agreed-upon definition. We collected potentially hateful messages and asked two groups of internet users to determine ::: whether they were hate speech or not, whether they should be banned or not and to rate their degree of offensiveness. One of the groups was shown a definition prior to completing the survey. We aimed to assess whether hate speech can be annotated reliably, and the extent to which existing definitions are in accordance with subjective ratings. Our results indicate that showing users a definition caused them to partially align their own opinion with the definition but did not improve reliability, which was very low overall. We conclude that the presence of hate speech should perhaps ::: not be considered a binary yes-or-no decision, and raters need more detailed instructions for the annotation.


title: Customer Loyalty: Toward an Integrate Conceptual Framework

abstract: Customer loyalty is viewed as the strength of the relationship between an individual’s relative attitude and repeat patronage. The relationship is seen as mediated by social norms and situational factors. Cognitive, affective, and conative antecedents of relative attitude are identified as contributing to loyalty, along with motivational, perceptual, and behavioral consequences. Implications for research and for the management of loyalty are derived.


title: Blind deconvolution through digital signal processing

abstract: This paper addresses the problem of deconvolving two signals when both are unknown. The authors call this problem blind deconvolution. The discussion develops two related solutions which can be applied through digital signal processing in certain practical cases. The case of reverberated and resonated sound forms the center of the development. The specific problem of restoring old acoustic recordings provides an experimental test. The important effects of noise and non-stationary signals lead to the detailed part of the presentation. In addition, the paper presents results for the case of images degraded by some common forms of blur.


Individualized versus standardized risk assessment in patients at high risk for adverse drug reactions (IDrug) – study protocol for a pragmatic randomized controlled trial


title: ExCuSe: Robust Pupil Detection in Real-World Scenarios

abstract: The reliable estimation of the pupil position is one the most important prerequisites in gaze-based HMI applications. Despite the rich landscape of image-based methods for pupil extraction, tracking the pupil in real-world images is highly challenging due to variations in the environment (e.g. changing illumination conditions, reflection, etc.), in the eye physiology or due to variations related to further sources of noise (e.g., contact lenses or mascara). We present a novel algorithm for robust pupil detection in real-world scenarios, which is based on edge filtering and oriented histograms calculated via the Angular Integral Projection Function. The evaluation on over 38,000 new, hand-labeled eye images from real-world tasks and 600 images from related work showed an outstanding robustness of our algorithm in comparison to the state-of-the-art. Download link (algorithm and data): https://www.ti.uni-tuebingen.de/Pupil-detection.1827.0.html?&L=1.


title: Organisational sustainability modelling—An emerging service and analytics model for evaluating Cloud Computing adoption with two case studies

abstract: We develop a model, Organisational sustainability modelling (OSM), to evaluate Cloud Computing to analyse the status of risk and return.OSM is an Emerging Services and Analytics for Cloud Computing. We explain the supporting theory, how to use OSM and how to process lots of data.We compare OSM with Capital Asset Pricing Model for data processing and analysis.We present two OSM case studies and explain inputs, data collection, data analysis and results.We explain how OSM can be useful and efficient for organisations using Cloud Computing and justify our research contributions. Cloud Computing is an emerging technology which promises to bring with it great benefits to all types of computing activities including business support. However, the full commitment to Cloud Computing necessary to gain the full benefit is a major project for any organisation, since it necessitates adoption of new business processes and attitudes to computing services in addition to the immediately obvious systems changes. Hence the evaluation of a Cloud Computing project needs to consider the balance of benefits and risks to the organisation in the full context of the environment in which it operates; it is not sufficient or appropriate to examine technical considerations alone.In this paper, we consider the application of CAPM, a well established approach used for the analysis of risks and benefits of commercial projects to Cloud adoption projects and propose a revised and improved technique, OSM. To support the validity of OSM, two full case studies are presented. In the first, we describe an application of the approach to the iSolutions Group at University of Southampton, which focuses on evaluations of Cloud Computing service improvement. We then illustrate the use of OSM for measuring learning satisfaction of two cohort groups at the University of Greenwich. The results confirm the advantages of using OSM. We conclude that OSM can analyse the risk and return status of Cloud Computing services and help organisations that adopt Cloud Computing to evaluate and review their Cloud Computing projects and services. OSM is an emerging service and analytics model supported by several case studies.


title: Porous silicon ethanol vapor sensor

abstract: A novel very-large-scale-integrable porous Si based ethanol vapor sensor will be presented. It has simple structure, high sensitivity, fast and stable response for ethanol vapor sensing at room environment. To explain the sensor results, a model based on the interaction of adsorbed ethanol molecules and the porous Si layer will also be proposed.


title: Stereoselective synthesis of a Podophyllum lignan core by intramolecular reductive nickel-catalysis

abstract: A Ni-catalyzed reductive cascade to a diastereocontrolled construction of THN[2,3-c]furan, is developed. The mild reaction conditions led to the tolerance of broad functional groups that can be placed in almost every position of this skeleton with good yields. The conformational control for the observed trans- or cis-fused selectivity during this tandem cyclization-coupling is also proposed.


title: DBpedia: A Nucleus for a Web of Open Data

abstract: DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.


title: Design of Novel Wideband Circularly Polarized Antenna Based on Vivaldi Antenna Structure

abstract: This letter presents a novel compact wideband circularly polarized antenna by utilizing Vivaldi antenna structure as the design concept. The proposed antenna consists of four exponentially tapered patches, a circular metal wall and a feed network. Four tapered patches are sequentially rotated and vertically located in the circular metal wall which acts as a vertical ground plane. Slot-coupled feed technique is applied to feed the antenna. Feeding by a corporate feed network, wide impedance bandwidth and axial ratio bandwidth are achieved. Parametric studies are made to optimize the antenna. The experimental results show that the antenna exhibits a 3-dB AR bandwidth of 41.4% (3.45–5.25 GHz) and a 10-dB impedance bandwidth of 57.3% (3.06–5.52 GHz).


title: Effects of Glyphosate-Based Herbicide (Excel Mera 71) on Digestive Enzymes and Recovery Pattern in Teleostean Fishes

abstract: Present study was aimed to note the effects of glyphosate-based herbicide, Excel Mera 71 (17.20 mg/L) on digestive enzyme activity in teleostean fishes, Anabas testudineus (Bloch) and Heteropneustes fossilis (Bloch) and their subsequent recovery responses under Liv.52 for a period of 30 days. Amylase activity was raised highly in intestine of A. testudineus (674.07%) and was lowest in liver of H. fossilis (180.14%). Lipase activity showed highest elevation in liver of H. fossilis (182.14%) and lowest in intestine of A. testudineus (70.77%). Highest elevation of protease activity was observed in intestine of H. fossilis (376.92%) but was lowest in liver of A. testudineus (138.46%). During depuration phase, these enzyme activities significantly came down towards the control value which indicated compensatory responses against herbicidal toxicosis. Therefore, it can also be inferred that these responses of A. testudineus and H. fossilis to Excel Mera 71 and Liv.52 exposures could be considered as biomarkers against herbicidal toxicosis in aquatic ecosystem, although it needs further studies on mechanistic pathway to investigate the detailed mechanism involved in recovery responses.


title: Improving the grid power quality using virtual synchronous machines

abstract: This paper presents the improvement of power quality and grid stability for distributed generation using the virtual synchronous machine (VISMA) which embodies a hysteresis controlled three phase inverter with a synchronous machine model on an embedded control computer to calculate the reference currents. Currently the conventional grid-connected inverters are predominantly designed to transmit electrical energy to the grid discounting the maintenance of frequency and voltage and also its transient stability. However, the VISMA is able to regulate both the active and reactive power separately and bidirectionally by setting the virtual torque and virtual excitation to meet the power system requirements. Furthermore, a virtual rotating mass is implemented in the VISMA in order to increase the inertia in the grid and improve the transient frequency stability in analogy to the conventional synchronous generator. Additionally, the virtual damping of the VISMA can reduce the frequency and power oscillation in the grid. All these properties mentioned above have been verified in simulations and measurements in an experimental micro grid.


title: Automatic generation and detection of highly reliable fiducial markers under occlusion

abstract: This paper presents a fiducial marker system specially appropriated for camera pose estimation in applications such as augmented reality and robot localization. Three main contributions are presented. First, we propose an algorithm for generating configurable marker dictionaries (in size and number of bits) following a criterion to maximize the inter-marker distance and the number of bit transitions. In the process, we derive the maximum theoretical inter-marker distance that dictionaries of square binary markers can have. Second, a method for automatically detecting the markers and correcting possible errors is proposed. Third, a solution to the occlusion problem in augmented reality applications is shown. To that aim, multiple markers are combined with an occlusion mask calculated by color segmentation. The experiments conducted show that our proposal obtains dictionaries with higher inter-marker distances and lower false negative rates than state-of-the-art systems, and provides an effective solution to the occlusion problem. HighlightsWe propose an algorithm for generating configurable marker dictionaries.We derive the maximum theoretical inter-marker distance.A method for automatically detecting the markers and correcting errors is proposed.A solution to the occlusion problem in augmented reality applications is shown.


title: Evolutionary Algorithms, Fuzzy Logic and Artificial Immune Systems applied to Cryptography and Cryptanalysis: State-of-the-art review

abstract: The need and importance of secure information communication, particularly text, image and video transmission has made cryptography to take a larger space in current research era. Improvement of pictorial information for betterment of human perception involves de-blurring, de-noising and safe transmission. These applications extend over several fields such as satellite imaging, medical imaging etc. Specifically we would like to elaborate our research on the significance of computational intelligence as one of the domains which finds application in cryptography and information security, and then the relevance of cryptography is indeed unavoidable. This paper deals with the study of the requirements for strong cryptography and various computational intelligence techniques that find use in cryptography. Cryptanalysis is a well-studied problem with many important applications in the areas of error-free and secure data transmission. In this paper, we have reviewed the improved algorithms used for cryptography optimization; the algorithms under reviews integrate genetic algorithms, immune computing, crossover and mutation operators and fuzzy systems to maintain the diversity and optimization of candidate objects. These computational intelligence techniques have seen to greatly improve the fitness levels of candidate groups in cryptography. Lastly, we make detailed comparison between these methods and those cryptography techniques without computational intelligence.


title: Borderline over-sampling for imbalanced data classification

abstract: Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through over-sampling at the borderline between the minority class and the majority class of the data set. A Support Vector Machines (SVMs) classifier then is trained to predict new unknown instances. Compared to other over-sampling methods, the proposed method focuses only on the minority class instances lying around the borderline due to the fact that this area is most crucial for establishing the decision boundary. Furthermore, new instances will be generated in such a manner that minority class area will be expanded further toward the side of the majority class at the places where there appear few majority class instances. Experimental results show that the proposed method can achieve better performance than some other over-sampling methods, especially with data sets having low degree of overlap due to its ability of expanding minority class area in such cases.


title: A Classification for Community Discovery Methods in Complex Networks

abstract: Many real-world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection. Since network representation can be very complex and can contain different variants in the traditional graph model, each algorithm in the literature focuses on some of these properties and establishes, explicitly or implicitly, its own definition of community. According to this definition, each proposed algorithm then extracts the communities, which typically reflect only part of the features of real communities. The aim of this survey is to provide a ‘user manual’ for the community discovery problem. Given a meta definition of what a community in a social network is, our aim is to organize the main categories of community discovery methods based on the definition of community they adopt. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) this review paper is designed to provide a set of approaches that researchers could focus on. The proposed classification of community discovery methods is also useful for putting into perspective the many open directions for further research. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 512–546, 2011 © 2011 Wiley Periodicals, Inc.


title: Constraint modeling for curriculum planning and validation

abstract: Curricula authoring is a complex process, involving different actors and different kinds of knowledge. Learners aim at acquiring expertise about some topic of their own interest, and need to perceive that the curriculum they attend will lead them toward their goal; when this does not happen, they become demotivated. Learners are all different, not only in their aims but also in their background knowledge and skills; curricula must carefully be tailored to the learner's individual traits: when this does not happen, curricula are not effective from a pedagogical perspective. On the other hand, it is not possible to leave learners alone in the design of a curriculum because this activity involves both knowledge about thetopics to teach, and knowledge about teaching itself. It is one of the tasks of the school to support curricula authoring so as to guarantee the correctness of the result w.r.t. the teaching goals and to pedagogical strategies. In this article, we face the problem of authoring personalized cu...


title: DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions

abstract: The Database of Interacting Proteins (DIP: http:// dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein–protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs ∼11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein–protein interactions available in the DIP database.


title: Fast Channel Tracking for Terahertz Beamspace Massive MIMO Systems

abstract: The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes.


title: An Android Communication App Forensic Taxonomy

abstract: Due to the popularity of Android devices and applications (apps), Android forensics is one of the most studied topics within mobile forensics. Communication apps, such as instant messaging and Voice over IP (VoIP), are one popular app category used by mobile device users, including criminals. Therefore, a taxonomy outlining artifacts of forensic interest involving the use of Android communication apps will facilitate the timely collection and analysis of evidentiary materials from such apps. In this paper, 30 popular Android communication apps were examined, where a logical extraction of the Android phone images was collected using XRY, a widely used mobile forensic tool. Various information of forensic interest, such as contact lists and chronology of messages, was recovered. Based on the findings, a two-dimensional taxonomy of the forensic artifacts of the communication apps is proposed, with the app categories in one dimension and the classes of artifacts in the other dimension. Finally, the artifacts identified in the study of the 30 communication apps are summarized using the taxonomy. It is expected that the proposed taxonomy and the forensic findings in this paper will assist forensic investigations involving Android communication apps.


title: Adaptive Manycore Architectures for Big Data Computing

abstract: This work presents a cross-layer design of an adaptive manycore architecture to address the computational needs of emerging big data applications within the technological constraints of power and reliability. From the circuits end, we present links with reconfigurable repeaters that allow single-cycle traversals across multiple hops, creating fast single-cycle paths on demand. At the microarchitecture end, we present a router with bi-directional links, unified virtual channel (VC) structure, and the ability to perform self-monitoring and self-configuration around faults. We present our vision for self-aware manycore architectures and argue that machine learning techniques are very appropriate to efficiently control various configurable on-chip resources in order to realize this vision. We provide concrete learning algorithms for core and NoC reconfiguration; and dynamic power management to improve the performance, energy-efficiency, and reliability over static designs to meet the demands of big data computing. We also discuss future challenges to push the state-of-the-art on fully adaptive manycore architectures.


title: Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network

abstract: Wafer maps provide important information for engineers in identifying root causes of die failures during semiconductor manufacturing processes. We present a method for wafer map defect pattern classification and image retrieval using convolutional neural networks (CNNs). Twenty eight thousand six hundred synthetic wafer maps for 22 defect classes are generated theoretically and used for CNN training, validation, and testing. The overall classification accuracy for the 6600 test dataset is 98.2%. One thousand one hundred and ninety one real wafer maps are used for CNN performance evaluation for the same model trained by synthetic wafer maps. We demonstrate that by using only synthetic data for network training, real wafer maps can be classified with high accuracy. For image retrieval, a binary code for each wafer map is generated from an output of a fully connected layer with sigmoid activation. A retrieval error rate is 0.36% for the test dataset and 3.7% for the real wafers. Image retrieval takes 0.13 s per wafer map from the 18 000 wafer map library.


title: Singular point detection based on orientation filed regularization and poincaré index in fingerprint images

abstract: Detection of singular points (SPs) in fingerprint images is an important task in fingerprint recognition. In this paper, we propose a novel technique for SPs detection using orientation field regularization and the Poincare Index (PI) technique. The squared orientation field is first extracted from a fingerprint image. In order to distinguish the local orientation patterns of genuine SPs from that of spurious SPs, a novel technique based on the Discrete Hodge Helmholtz Decomposition (DHHD) is proposed to reconstruct a regular orientation field of the fingerprint. Based on the regular orientation field, the PI technique is then applied to extract the SPs. Experimental results on the public fingerprint database FVC2002 show that, the proposed technique is rather accurate and robust in identifying SPs.


title: Robust GaN MMIC Chipset for T/R Module Front-End Integration

abstract: In this contribution, a set of robust GaN MMIC T/R switches and low-noise amplifiers, all based on the same GaN process, is presented. The target operating bandwidths are the X-band and the 2-18 GHz bandwidth. Several robustness tests on the fabricated MMICs demonstrate state-of-the-art survivability to CW input power levels. The development of high-power amplifiers, robust low-noise amplifiers and T/R switches on the same GaN monolithic process will bring to the next generation of fully-integrated T/R module front-end MMICs. © 2014 IAMOT.


title: Time-frequency Techniques in Biomedical Signal Analysis

abstract: OBJECTIVES ::: This review outlines the methodological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. ::: ::: ::: METHODS ::: The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville distribution (WVD) serves as an example of the 'quadratic transforms' class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches. ::: ::: ::: RESULTS ::: Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the interference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals. ::: ::: ::: CONCLUSIONS ::: The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered 'optimal'. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.


title: Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching

abstract: We report about the design and test of an image processing algorithm for the localization of the optic disk (OD) in low-resolution (about 20 micro/pixel) color fundus images. The design relies on the combination of two procedures: 1) a Hausdorff-based template matching technique on edge map, guided by 2) a pyramidal decomposition for large scale object tracking. The two approaches are tested against a database of 40 images of various visual quality and retinal pigmentation, as well as of normal and small pupils. An average error of 7% on OD center positioning is reached with no false detection. In addition, a confidence level is associated to the final detection that indicates the "level of difficulty" the detector has to identify the OD position and shape.


title: A secure authentication scheme based on elliptic curve cryptography for IoT and cloud servers

abstract: The Internet of Things (IoT) is now a buzzword for Internet connectivity which extends to embedded devices, sensors and other objects connected to the Internet. Rapid development of this technology has led to the usage of various embedded devices in our daily life. However, for resource sharing and communication among these devices, there is a requirement for connecting these embedded devices to a large pool of resources like a cloud. The promising applications of IoT in Government and commercial sectors are possible by integrating cloud servers with these embedded devices. But such an integration of technologies involves security issues like data privacy and authentication of devices whenever information is exchanged between them. Recently, Kalra and Sood proposed an authentication scheme based on elliptic curve cryptography (ECC) for IoT and cloud servers and claimed that their scheme satisfies all security requirements and is immune to various types of attacks. However, in this paper, we show that Kalra and Sood scheme is susceptible to offline password guessing and insider attacks and it does not achieve device anonymity, session key agreement, and mutual authentication. Keeping in view of the shortcomings of Kalra and Sood’s scheme, we have proposed an authentication scheme based on ECC for IoT and cloud servers. In the proposed scheme in this paper, we have formally analyzed the security properties of the designed scheme by the most widely accepted and used Automated Validation of Internet Security Protocols and Applications tool. Security and performance analysis show that when compared with other related schemes, the proposed scheme is more powerful, efficient, and secure with respect to various known attacks.


title: 5G air interface design based on Universal Filtered (UF-)OFDM

abstract: In this paper we discuss 5G air interface design with respect to waveforms, multiple access and frame structure. We start by introducing the 5G system level requirements and expected scenarios. 5G will be driven by supporting very heterogeneous service and device classes. A unified frame structure for handling those heterogeneous traffic types is presented. Multiple access for 5G will make use of strict synchronicity where it is justifiable and will drop it where signalling overhead and energy consumption will demand for. In order to serve this unified frame structure best, the choice of the underlying waveform is discussed. CP-OFDM has its limitations in spectral properties and in conjunction with relaxed time-frequency alignment. The most discussed contender so far is Filter-Bank based Multi-Carrier (FBMC), with better spectral properties but new drawbacks introduced by offset-QAM and long filter lengths. Hence, a new alternative is required: Universal-Filtered OFDM (UF-OFDM), also known as Universal Filtered Multi-Carrier (UFMC), is a recent technology close to OFDM. UF-OFDM, according to encouraging results so far, summarized in this paper, fits best to the 5G system requirements. A further feature of the Unified Frame Structure is the usage of multiple signal layers. Here, users can be separated e.g. based on their interleavers, as done in Interleave-Division Multiple-Access (IDMA). This will introduce an additional degree of freedom for the system, improve robustness against crosstalk and helps to exploit the capacity of the multiple access channel (MAC). Altogether, the proposed new concepts offer an emboldening approach for dealing with the new challenges, faced by 5G wireless system designers.


title: Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras

abstract: We present a real-time RGB-D based multiperson detection and tracking system suitable for mobile robots and head-worn cameras. Our approach combines RGB-D visual odometry estimation, region-of-interest processing, ground plane estimation, pedestrian detection, and multi-hypothesis tracking components into a robust vision system that runs at more than 20fps on a laptop. As object detection is the most expensive component in any such integration, we invest significant effort into taking maximum advantage of the available depth information. In particular, we propose to use two different detectors for different distance ranges. For the close range (up to 5-7m), we present an extremely fast depth-based upper-body detector that allows video-rate system performance on a single CPU core when applied to Kinect sensors. In order to cover also farther distance ranges, we optionally add an appearance-based full-body HOG detector (running on the GPU) that exploits scene geometry to restrict the search space. Our approach can work with both Kinect RGB-D input for indoor settings and with stereo depth input for outdoor scenarios. We quantitatively evaluate our approach on challenging indoor and outdoor sequences and show state-of-the-art performance in a large variety of settings. Our code is publicly available.


title: Eletronic Health Records using Blockchain Technology

abstract: Data privacy refers to ensuring that users keep control over access to information, whereas data accessibility refers to ensuring that information access is unconstrained. Conflicts between privacy and accessibility of data are natural to occur, and healthcare is a domain in which they are particularly relevant. In the present article, we discuss how blockchain technology, and smart contracts, could help in some typical scenarios related to data access, data management and data interoperability for the specific healthcare domain. We then propose the implementation of a large-scale information architecture to access Electronic Health Records (EHRs) based on Smart Contracts as information mediators. Our main contribution is the framing of data privacy and accessibility issues in healthcare and the proposal of an integrated blockchain based architecture.


title: Cell-Surface Marker Signatures for the Isolation of Neural Stem Cells, Glia and Neurons Derived from Human Pluripotent Stem Cells

abstract: Background ::: Neural induction of human pluripotent stem cells often yields heterogeneous cell populations that can hamper quantitative and comparative analyses. There is a need for improved differentiation and enrichment procedures that generate highly pure populations of neural stem cells (NSC), glia and neurons. One way to address this problem is to identify cell-surface signatures that enable the isolation of these cell types from heterogeneous cell populations by fluorescence activated cell sorting (FACS). ::: ::: ::: Methodology/Principal Findings ::: We performed an unbiased FACS- and image-based immunophenotyping analysis using 190 antibodies to cell surface markers on naive human embryonic stem cells (hESC) and cell derivatives from neural differentiation cultures. From this analysis we identified prospective cell surface signatures for the isolation of NSC, glia and neurons. We isolated a population of NSC that was CD184+/CD271−/CD44−/CD24+ from neural induction cultures of hESC and human induced pluripotent stem cells (hiPSC). Sorted NSC could be propagated for many passages and could differentiate to mixed cultures of neurons and glia in vitro and in vivo. A population of neurons that was CD184−/CD44−/CD15LOW/CD24+ and a population of glia that was CD184+/CD44+ were subsequently purified from cultures of differentiating NSC. Purified neurons were viable, expressed mature and subtype-specific neuronal markers, and could fire action potentials. Purified glia were mitotic and could mature to GFAP-expressing astrocytes in vitro and in vivo. ::: ::: ::: Conclusions/Significance ::: These findings illustrate the utility of immunophenotyping screens for the identification of cell surface signatures of neural cells derived from human pluripotent stem cells. These signatures can be used for isolating highly pure populations of viable NSC, glia and neurons by FACS. The methods described here will enable downstream studies that require consistent and defined neural cell populations.


title: A 9b 100MS/s 1.46mW SAR ADC in 65nm CMOS

abstract: A 9b 100MS/s successive approximation register (SAR) ADC has been implemented in 65nm CMOS, with an active area of 0.012mm2. A tri-level based charge redistribution technique improves DAC switching energy efficiency and settling time, which is achieved by connecting bottom plates of differential capacitor arrays. The ADC achieves an SNDR of 53.1dB (8.53 ENOB) and consumes 1.46mW from a 1.2V supply, resulting in an FOM of 39fJ/conversion-step.


title: Development and Implementation of a Real-Time See-Through-Wall Radar System Based on FPGA

abstract: This paper presents the development of a low-cost real-time ultrawideband (UWB) see-through-wall (STW) imaging radar system. The designs of the microwave front end, the UWB data acquisition, and the system integration are discussed in detail. As for the most challenging task, the UWB data acquisition, we introduce a custom low-cost module based on commercial field-programmable gate array (FPGA) boards and low-speed analog-to-digital converters. The introduced module does not require a custom implementation of high-speed wideband mixed-signal circuitry but only depends on the FPGA firmware design, which favors a rapid system prototyping. The data acquisition module accomplishes a 100-ps equivalent-time sampling resolution at 100-Msamples/s real-time rate, while the developed STW system provides a 2-D real-time view of motion with a 1.5-ms speed behind walls. The system allows for an easy reconfiguration to support multiple operating frequency ranges, pulse sampling resolutions, and array deployments, thus providing a tremendous experimental flexibility. Our studies indicate that utilizing available technologies and off-the-shelf components could produce a practical stand-alone STW system at reasonable design effort and cost, which will lead to a better understanding of the challenging problems associated with STW technology.


title: An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks

abstract: Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.


title: Linguistic Analysis of Toxic Behavior in an Online Video Game

abstract: In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and warn players who may become toxic and thus insulate potential victims from toxic playing in advance.


title: Balanced Parallel Frequent Pattern Mining over Massive Data Stream

abstract: Frequent pattern mining is playing an increasingly important role in a growing number of real-time data flow scenarios, such as large-scale order stream data, network traffic monitoring, web accessing record stream, and so on. The continuous, unbounded and high speed characteristics of massive data stream are a huge challenge for the current frequent pattern mining approach. The main challenge is that, as data stream continuously arriving, the non frequent patterns discarded can possibly become frequent again. In this paper, aimed at the characteristics of real-time data stream, we propose a compact data structure, called CPS-tree to maintain and operate the full information of data stream. Compared to current related works, our algorithm can dynamically support large-scale data stream with one-pass scan which can be easily applied to other data stream processing environments, Moreover, the load imbalance in the current frequent pattern mining is a pretty common problem. We analysis the features of data stream, and propose a depth-based strategy to solve the imbalance problem in our parallel algorithm. In conclusion, we propose the BPFPMS algorithm, a balanced parallel frequent pattern mining over massive data stream, to dynamically and efficiently mine frequent patterns over large scale data stream. Our experiments show that our algorithm can achieve a good speedup and a good degree of balance among each node with different degree of parallelism.


title: Software Engineering Aspects of Green and Sustainable Software: A Systematic Mapping Study

abstract: Green and sustainable software engineering is an emerging research field which aims at creating, using, and disposing the energy-efficient software in an environment friendly manner with less negative impacts. The research community strongly believes that the energy efficiency and sustainability of the software can be improved by modifying the existing software engineering methods. This systematic mapping study identifies and map such methods for green and sustainable software development. Especially, this study identifies the research types, research goals, software engineering research topics, accepted validation methods and publication fora that are used in the field of green and sustainable software engineering. This study was conducted with 7 research questions and analyzed 82 relevant studies. We have used snowballing reading to find out the relevant studies that were published from 2010 to May, 2016. One of the important finding of this study is, there are less number of contributions on software design and construction. In future, sufficient research works and tools support must be provided to make this research field more matured. The main contribution of this study is to summarize the body of knowledge in the field of green and sustainable software engineering and provides a platform to conduct future research.


Conceptualizing IT Resilience : An Explorative Approach


title: Static lumped parameter model for nested PZT cellular actuators with exponential strain amplification mechanisms

abstract: A static lumped parameter model is proposed for the design and analysis of nested piezoelectric cellular actuators with exponential strain amplification mechanisms. Piezoelectric ceramic material, such as Lead Zirconate Titanate (PZT), has large stress and bandwidth, but its extremely small strain, i.e. only 0.1%, has been a major bottleneck for broad applications. We have proposed a "nested rhombus" multi-layer mechanism for PZT actuators, which increases strain exponentially through its hierarchical cellular structure, for over 20% effective strain. To drive a large load, however, care must be taken in the design of the strain amplification structure. Through kinematic and static analysis this paper addresses how the output force and displacement are attenuated by the joint stiffness and beam compliance involved in the strain amplification mechanism. An insightful lumped parameter model is developed to quantify the performance degradation and facilitate design trade-offs. A prototype nested PZT cellular actuator that weighs only 15 g has produced 21% effective strain (2.49 mm displacement from 12 mm actuator length) and 1.7 N blocking force.


title: Stepped-Impedance Coupled Resonators for Implementation of Parallel Coupled Microstrip Filters With Spurious Band Suppression

abstract: A parallel-coupled microstrip bandpass filter based on the stepped-impedance coupled resonator (SICR) for suppression of harmonic spurious response is presented. The SICR relies upon the incorporation of different stepped impedances for the even and odd propagation modes of the coupled microstrip lines. This is to align the modal parallel resonance frequencies, and thereby effectively equalize the modal phase velocities. Its structural simplicity enables closed-form analytical formulation, and the derived equations are effectively utilized to explicitly guide parameters selection based on classical synthesis methodology of coupled microstrip filters. Practical feasibility of the SICR and the integrity of the analysis are demonstrated via designs and implementations of two third-order 0.01-dB ripple Chebyshev bandpass filters with a passband center frequency of 2.0 GHz and 3-dB fractional bandwidths of 15% and 25%. Good agreement between design, simulation, and measurement is obtained, where suppressions of the first harmonic spurious responses by more than 40 dB are achieved in both filters.


title: On board eco-driving system for varying road-traffic environments using model predictive control

abstract: This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.


title: Gazetteer Preparation for Named Entity Recognition in Indian Languages

abstract: This paper describes our approaches for the preparation of gazetteers for named entity recognition (NER) in Indian languages. We have described two methodologies for the preparation of gazetteers 1 . Since the relevant gazetteer lists are more easily available in English we have used a transliteration based approach to convert available English name lists to Indian languages. The second approach is a context pattern induction based domain specific gazetteer preparation. This approach uses a domain specific raw corpus and a few seed entities to learn context patterns and then the corresponding name lists are generated by using bootstrapping.


title: Evaluating computer-supported cooperative work: models and frameworks

abstract: Evaluating distributed CSCW applications is a difficult endeavor. Frameworks and methodologies for structuring this type of evaluation have become a central concern for CSCW researchers. In this paper we describe the problems involved in evaluating remote collaborations, and we review some of the more prominent conceptual frameworks of group interaction that have driven CSCW evaluation in the past. A multifaceted evaluation framework is presented that approaches the problem from the relationships underlying joint awareness, communication, collaboration, coordination, and work coupling. Finally, recommendations for carrying out multifaceted evaluations of remote interaction are provided.


title: Detecting controversy on the web

abstract: A useful feature to facilitate critical literacy would alert users when they are reading a controversial web page. This requires solving a binary classification problem: does a given web page discuss a controversial topic? We explore the feasibility of solving the problem by treating it as supervised k-nearest-neighbor classification. Our approach (1) maps a webpage to a set of neighboring Wikipedia articles which were labeled on a controversiality metric; (2) coalesces those labels into an estimate of the webpage's controversiality; and finally (3) converts the estimate to a binary value using a threshold. We demonstrate the applicability of our approach by validating it on a set of webpages drawn from seed queries. We show absolute gains of 22% in F_0.5 on our test set over a sentiment-based approach, highlighting that detecting controversy is more complex than simply detecting opinions.


title: A 0.45 V, Nano-Watt 0.033% Line Sensitivity MOSFET-Only Sub-Threshold Voltage Reference With no Amplifiers

abstract: An ultra-low voltage, low power, low line sensitivity MOSFET-only sub-threshold voltage reference with no amplifiers is presented. The low sensitivity is realized by the difference between two complementary currents and second-order compensation improves the temperature stability. The bulk-driven technique is used and most of the transistors work in the sub-threshold region, which allow a remarkable reduction in the minimum supply voltage and power consumption. Moreover, a trimming circuit is adopted to compensate the process-related reference voltage variation while the line sensitivity is not affected. The proposed voltage reference has been fabricated in the 0.18 μm 1.8 V CMOS process. The measurement results show that the reference could operate on a 0.45 V supply voltage. For supply voltages ranging from 0.45 to 1.8 V the power consumption is 15.6 nW, and the average temperature coefficient is 59.4 ppm/°C across a temperature range of -40 to 85 °C and a mean line sensitivity of 0.033%. The power supply rejection ratio measured at 100 Hz is -50.3 dB. In addition, the chip area is 0.013 mm2.


title: 3D Ear Segmentation and Classification Through Indexing

abstract: Current growth trends in different biometrics applications present challenges to researchers. To address these challenges, we need new data storage and retrieval techniques to make the recognition process time efficient. This paper presents a system for time efficient 3D ear biometrics from a large biometrics database. The proposed system has two components that are primarily responsible for: 1) automatic 3D ear segmentation and 2) hierarchical categorization of the 3D ear database using the shape information and surface depth information, respectively. We use an active contour algorithm along with a tree-structured graph to segment the ear region from the 3D profile images. The segmented 3D ear database is then categorized based on the geometrical feature values, computed from the ear shape, into oval, round, rectangular, and triangular categories. For the categorization based on the depth information, the feature space is partitioned using tree-based indexing techniques. We used indexing techniques with balanced split (k-dimensional (KD) tree) and unbalanced split (pyramid tree) data structures to categorize the database separately and then compared their retrieval efficiency. Experiments are conducted to compare the average computation time per query when performing recognition through hierarchical categorization with the average computation time when recognition is based on sequential search. Experimental results without indexing conducted on the University of Notre Dame Collection J2 data set yielded a rank-one recognition rate of 98.5%. We applied the indexing technique to compute the rank-one recognition accuracy with a 10%–50% search space reduction using a 10% step size. With 10%, 20%, 30%, 40%, and 50% search space reduction the rank-one recognition accuracy gracefully degrades to 96.87%, 96.14%, 95.18%, 94.21%, and 93.49%, respectively, while performing nearly 3, 3.3, 4, 4.2, and 5 times faster than the state-of-the-art technique that uses sequential search.


title: Parametric analysis for adaptive computation offloading

abstract: Many programs can be invoked under different execution options, input parameters and data files. Such different execution contexts may lead to strikingly different execution instances. The optimal code generation may be sensitive to the execution instances. In this paper, we show how to use parametric program analysis to deal with this issue for the optimization problem of computation offloading.Computation offloading has been shown to be an effective way to improve performance and energy saving on mobile devices. Optimal program partitioning for computation offloading depends on the tradeoff between the computation workload and the communication cost. The computation workload and communication requirement may change with different execution instances. Optimal decisions on program partitioning must be made at run time when sufficient information about workload and communication requirement becomes available.Our cost analysis obtains program computation workload and communication cost expressed as functions of run-time parameters, and our parametric partitioning algorithm finds the optimal program partitioning corresponding to different ranges of run-time parameters. At run time, the transformed program self-schedules its tasks on either the mobile device or the server, based on the optimal program partitioning that corresponds to the current values of run-time parameters. Experimental results on an HP IPAQ handheld device show that different run-time parameters can lead to quite different program partitioning decisions.


title: Camera-Trap Images Segmentation using Multi-Layer Robust Principal Component Analysis

abstract: The segmentation of animals from camera-trap images is a difficult task. To illustrate, there are various challenges due to environmental conditions and hardware limitation in these images. We proposed a multi-layer robust principal component analysis (multi-layer RPCA) approach for background subtraction. Our method computes sparse and low-rank images from a weighted sum of descriptors, using color and texture features as case of study for camera-trap images segmentation. The segmentation algorithm is composed of histogram equalization or Gaussian filtering as pre-processing, and morphological filters with active contour as post-processing. The parameters of our multi-layer RPCA were optimized with an exhaustive search. The database consists of camera-trap images from the Colombian forest taken by the Instituto de Investigacion de Recursos Biologicos Alexander von Humboldt. We analyzed the performance of our method in inherent and therefore challenging situations of camera-trap images. Furthermore, we compared our method with some state-of-the-art algorithms of background subtraction, where our multi-layer RPCA outperformed these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average fine-grained F-measure for color and infrared sequences, respectively. To our best knowledge, this paper is the first work proposing multi-layer RPCA and using it for camera-trap images segmentation.


title: A High-Dynamic Range Current Source Gate Driver for Switching-Loss Reduction of High-Side Switch in Buck Converter

abstract: In this letter, a high-dynamic range current source gate driver (HD-CSD) circuit is proposed to reduce the switching loss of the high-side switch in buck converter with wide variation of the gate resistance. Hard switching loss is the major loss in high-side switch and limits the high switching-frequency application of dc-dc converter. Comparing with conventional voltage source gate driver (VSD) and the reported four switches CSD (4S-CSD), the proposed HD-CSD behaves more like the ideal current source driver which can realize the fast switching of power switches to reduce the switching loss. In addition, with proposed HD-CSD, impact of gate resistance that limits the switching speed of the power switch can be greatly reduced. Experimental results are presented to show the power efficiency improvement of buck converter with HD-CSD high-side driver comparing with VSD and 4S-CSD high-side drivers at switching frequency of 1 MHz.


title: A new Voronoi-based surface reconstruction algorithm

abstract: Author(s): Amenta, Nina; Bern, Marshall; Kamvysselis, Manolis | Abstract: We describe our experience with a new algorithm for the reconstruction of surfaces from unorganized sample points in IR3. The algorthim is the first for this problem with provable guarantees. Given a ''good sample'' from a smooth surface, the output is guaranteed to be topologically correct and convergent to the original surface as the sampling sensity increases. The definition of a good sample is itself interesting: the required sampling density varies locally, rigorously capturing the intuitive notion that featureless areas can be reconstructed from fewer samples. The output mesh interpolates, rather than approximates, the input points. Our algorithm is based on the three-dimensional Voronoi diagram. Given a good program for thsi fundamental subroutine, the algorithm is quite easy to implement.


title: Images of Success and the Preference for Luxury Brands

abstract: This research examines the impact of media depictions of success (or failure) on consumers’ desire for luxury brands. In a pilot study and three additional studies, we demonstrate that reading a story about a similar/successful other, such as a business major from the same university, increases consumers’ expectations about their own future wealth, which in turn increases their desire for luxury brands. However, reading about a dissimilar successful other, such as a biology major, lowers consumers’ preferences for luxury brands. Furthermore, we examine the role of ease of imagining oneself in the narrative as a mediator of the relation between direction of comparison, similarity, and brand preference.


title: Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms?

abstract: Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naive bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.


title: Substrate Integrated Waveguide-Based Periodic Backward-to-Forward Scanning Leaky-Wave Antenna With Low Cross-Polarization

abstract: For many leaky-wave antennas (LWAs), it is challenging to realize beam scanning through broadside. A problem is the presence of an open stopband (OSB), which restricts radiation in the broadside direction. In this paper, a novel substrate integrated waveguide (SIW)-based LWA is described to overcome the OSB problem and provide beam scanning continuously from the backward to the forward direction from a conventional periodic LWA. It is demonstrated that the $n =-1$ spatial harmonic can be excited efficiently from an SIW LWA and enables broadside radiation. However, it was found in our initial design that when the beam scans through the broadside, the cross-polarization level increases significantly compared to the beam close to the backfire direction. A technique is developed to reduce the cross-polarization level. As a result, a new antenna configuration is created. The antenna design has been realized and measured to validate the concept. The measured beam scanning range of the prototype is from −74° to +45° (119° of beam scanning) when the frequency sweeps from 7.625 to 11 GHz, and the measured cross-polarization level is 20.8 dB low at the main beam direction for the broadside beam.


abstract: The growing ubiquity of algorithms in society raises a number of fundamental questions concerning governance of data, transparency of algorithms, legal and ethical frameworks for automated algorithmic decision-making and the societal impacts of algorithmic automation itself. This article, an introduction to the discussion meeting issue of the same title, gives an overview of current challenges and opportunities in these areas, through which accelerated technological progress leads to rapid and often unforeseen practical consequences. These consequences-ranging from the potential benefits to human health to unexpected impacts on civil society-are summarized here, and discussed in depth by other contributors to the discussion meeting issue.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.


title: Learning by Stimulation Avoidance: A Principle to Control Spiking Neural Networks Dynamics

abstract: Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle “Learning by Stimulation Avoidance” (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.


title: Image auto-annotation via tag-dependent random search over range-constrained visual neighbours

abstract: The quantity setting of visual neighbours can be critical for the performance of many previously proposed visual-neighbour-based (VNB) image auto-annotation methods. And in those methods, each candidate tag of a to-be-annotated image would be better to have its own trustworthy part of visual neighbours for score prediction. Hence in this paper we propose to use a constrained range rather than an identical and fixed number of visual neighbours for VNB methods to allow more flexible choices of neighbours, and then put forward a novel tag-dependent random search process to estimate the tag-dependent trust degrees of visual neighbours for each candidate tag. We further propose an effective image auto-annotation method termed TagSearcher based on a widely-used conditional probability model for auto-annotation, considering image-dependent weights of visual neighbours, tag-dependent trust degrees of visual neighbours and votes for a candidate tag from visual neighbours. Extensive experiments conducted on both a benchmark dataset and real-world web images present that the proposed TagSearcher can yield inspiring annotation performance and also reduce the performance sensitivity to the quantity setting of visual neighbours.


title: Crowdsourcing Multiple Choice Science Questions

abstract: We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at this http URL). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.


title: Crime prediction and forecasting in Tamilnadu using clustering approaches

abstract: Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. In this work, we use various clustering approaches of data mining to analyse the crime data of Tamilnadu. The crime data is extracted from National Crime Records Bureau (NCRB) of India. It consists of crime information about six cities namely Chennai, Coimbatore, Salem, Madurai, Thirunelvelli and Thiruchirapalli from the year 2000–2014 with 1760 instances and 9 attributes to represent the instances. K-Means clustering, Agglomerative clustering and Density Based Spatial Clustering with Noise (DBSCAN) algorithms are used to cluster crime activities based on some predefined cases and the results of these clustering are compared to find the best suitable clustering algorithm for crime detection. The result of K-Means clustering algorithm is visualized using Google Map for interactive and easy understanding. The K-Nearest Neighbor (KNN) classification is used for crime prediction. The performance of each clustering algorithms are evaluated using the metrics such as precision, recall and F-measure, and the results are compared. This work helps the law enforcement agencies to predict and detect crimes in Tamilnadu with improved accuracy and thus reduces the crime rate.


title: Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices

abstract: Robust and reliable detection of falls is crucial especially for elderly activity monitoring systems. In this letter, we present a fall detection system using wearable devices, e.g., smartphones, and tablets, equipped with cameras and accelerometers. Since the portable device is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may travel, as opposed to static sensors installed in certain rooms. Moreover, a camera provides an abundance of information, and the results presented here show that fusing camera and accelerometer data not only increases the detection rate, but also decreases the number of false alarms compared to only accelerometer-based or only camera-based systems. We employ histograms of edge orientations together with the gradient local binary patterns for the camera-based part of fall detection. We compared the performance of the proposed method with that of using original histograms of oriented gradients (HOG) as well as a modified version of HOG. Experimental results show that the proposed method outperforms using original HOG and modified HOG, and provides lower false positive rates for the camera-based detection. Moreover, we have employed an accelerometer-based fall detection method, and fused these two sensor modalities to have a robust fall detection system. Experimental results and trials with actual Samsung Galaxy phones show that the proposed method, combining two different sensor modalities, provides much higher sensitivity, and a significant decrease in the number of false positives during daily activities, compared to accelerometer-only and camera-only methods.


title: Energy-Aware Ant Colony Based Workload Placement in Clouds

abstract: With increasing numbers of energy hungry data centers energy conservation has now become a major design constraint. One traditional approach to conserve energy in virtualized data centers is to perform workload (i.e., VM) consolidation. Thereby, workload is packed on the least number of physical machines and over-provisioned resources are transitioned into a lower power state. However, most of the workload consolidation approaches applied until now are limited to a single resource (e.g., CPU) and rely on simple greedy algorithms such as First-Fit Decreasing (FFD), which perform resource-dissipative workload placement. Moreover, they are highly centralized and known to be hard to distribute. In this work, we model the workload consolidation problem as an instance of the multi-dimensional bin-packing (MDBP) problem and design a novel, nature-inspired workload consolidation algorithm based on the Ant Colony Optimization (ACO). We evaluate the ACO-based approach by comparing it with one frequently applied greedy algorithm (i.e., FFD). Our simulation results demonstrate that ACO outperforms the evaluated greedy algorithm as it achieves superior energy gains through better server utilization and requires less machines. Moreover, it computes solutions which are nearly optimal. Finally, the autonomous nature of the approach allows it to be implemented in a fully distributed environment.


title: Social media marketing: literature review and future research directions

abstract: In a decade's time, the social media has not only brought about drastic changes in the mode of communication but has also become an important marketing tool, making it an area of eminent interest for the academicians as well as the industrialists. This study provides a taxonomic classification of the social media research articles published in the marketing journals listed on the website of the Academy of Marketing Science, from 2004 to March 2015. In addition to it a framework depicting various antecedents, mediators, moderators and consequences has been developed. This is presumably the first exhaustive review of the literature on the social media for the period 2004-March 2015, comprising both classification of the literature and various constructs adopted in the social media research. It contributes to the existing body of literature by providing more comprehensive knowledge of the trends in, and the cause-and-effect relationship between the constructs used in the social media research.


title: TED-LIUM: an Automatic Speech Recognition dedicated corpus

abstract: This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4%. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.


title: Stages of Small-Group Development Revisited

abstract: The purpose of this review was to examine published research on small-group development done in the last ten years that would constitute an empirical test of Tuckman's (1965) hypothesis that groups go through the stages of "forming," "storming," "norming," and "performing." Of the twenty-two studies reviewed, only one set out to directly test this hypothesis, although many of the others could be related to it. Following a review of these studies, a fifth stage, "adjourning," was added to the hypothesis, and more empirical work was recommended.