title: Joint Optimization Framework for Learning with Noisy Labels

abstract: Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance degradation because DNNs easily overfit to noisy labels. To overcome this problem, we propose a joint optimization framework of learning DNN parameters and estimating true labels. Our framework can correct labels during training by alternating update of network parameters and labels. We conduct experiments on the noisy CIFAR-10 datasets and the Clothing1M dataset. The results indicate that our approach significantly outperforms other state-of-the-art methods.


title: Contested Groves: Forest Reserves and Land Conflict in the Eastern Amazon

abstract: Over the last thirty years, Amazonian deforestation has alarmed the world, and though many of the drivers of deforestation are well known, others are more novel. By combining accounts of conflict from the news media, remote sensing, and geographic information systems, I find that contentious social interaction through land conflict involves the forests of the Eastern Amazon, and involves forests as site and subject of conflict. The results of case studies and a regional deforestation analysis provide some implications for foreign investment to slow deforestation and also the ongoing deforestation monitoring program that Brazil has implemented over the past decade. Resumo: Ao longo dos ultimos trinta anos, o desmatamento da Amazonia alarmou o mundo, e embora muitas das causas do desmatamento sao bem conhecidas, outros sao mais novas. Ao combinar os relatos de conflito a partir de noticias na imprensa, sensoriamento remoto e sistemas de informacoes geograficas, descubro que a interacao social conflituosa nas disputas fundiarias envolve as florestas da Amazonia Oriental, e as envolve como local e objeto de conflito. Os resultados de estudos de caso e de uma analise de desmatamento regional fornecem algumas implicacoes para o investimento estrangeiro para retardar o desmatamento e tambem para o programa de vigilância de desmatamento que o Brasil implementou na ultima decada.


title: Finite Position Set-Phase Locked Loop for Sensorless Control of Direct-Driven Permanent-Magnet Synchronous Generators

abstract: This paper presents a novel finite position set-phase locked loop (FPS-PLL) for sensorless control of surface-mounted permanent-magnet synchronous generators (PMSGs) in variable-speed wind turbines. The proposed FPS-PLL is based on the finite control set-model predictive control concept, where a finite number of rotor positions are used to estimate the back electromotive force of the PMSG. Then, the estimated rotor position, which minimizes a certain cost function, is selected to be the optimal rotor position. This eliminates the need of a fixed-gain proportional-integral controller, which is commonly utilized in the conventional PLL. The performance of the proposed FPS-PLL has been experimentally investigated and compared with that of the conventional one using a 14.5 kW PMSG with a field-oriented control scheme utilized as the generator control strategy. Furthermore, the robustness of the proposed FPS-PLL is investigated against PMSG parameters variations.


title: Locality preserving indexing for document representation

abstract: Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in deriving such an indexing. LSI essentially detects the most representative features for document representation rather than the most discriminative features. Therefore, LSI might not be optimal in discriminating documents with different semantics. In this paper, a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing. Each document is represented by a vector with low dimensionality. In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic structure. We compare the proposed LPI approach with LSI on two standard databases. Experimental results show that LPI provides better representation in the sense of semantic structure.


title: Automated library recommendation

abstract: Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.894.


title: A topologically robust algorithm for Boolean operations on polyhedral shapes using approximate arithmetic

abstract: We present a topologically robust algorithm for Boolean operations on polyhedral boundary models. The algorithm can be proved always to generate a result with valid connectivity if the input shape representations have valid connectivity, irrespective of the type of arithmetic used or the extent of numerical errors in the computations or input data. The main part of the algorithm is based on a series of interdependent operations. The relationship between these operations ensures a consistency in the intermediate results that guarantees correct connectivity in the final result. Either a triangle mesh or polygon mesh can be used. Although the basic algorithm may generate geometric artifacts, principally gaps and slivers, a data smoothing post-process can be applied to the result to remove such artifacts, thereby making the combined process a practical and reliable way of performing Boolean operations.


title: Continual Reinforcement Learning with Complex Synapses

abstract: Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a complex network of interacting biochemical components that evolve at different timescales. In this paper, we show that by equipping tabular and deep reinforcement learning agents with a synaptic model that incorporates this biological complexity (Benna & Fusi, 2016), catastrophic forgetting can be mitigated at multiple timescales. In particular, we find that as well as enabling continual learning across sequential training of two simple tasks, it can also be used to overcome within-task forgetting by reducing the need for an experience replay database.


title: Scalable Logo Detection and Recognition with Minimal Labeling

abstract: In this paper we describe a new approach to detecting and locating brand logos in an image using machine learning methods and synthetic training data. Deep learning methods, particularly the use of Convolutional Neural Networks (CNN), have been very popular for extracting visual information, such as image shapes and objects, from images. A CNN has parameters and configuration information that are learned from training images. To obtain good accuracy usually a large amount of labeled (groundtruthed) images are required for training. Collecting the training images and labeling them can be expensive and time consuming. Methods that include data augmentation, image synthesis, and bootstrapping techniques provide useful alternatives to creating training images. In this paper, we present a logo detection method that requires minimum labeled images. First, we use synthetic images to train a CNN to detect logos. Then, this CNN is used to automatically detect and localize logos from images extracted from the web. Finally, these images are used to train a logo classifier. The combination of the logo detector and the classifier allows us to locate and classify multiple logos in a scene. While existing methods rely on manually labeled images, our method is fully trained with images obtained in an automated manner with minimal human supervision.


title: Data warehouse design from social media for opinion analysis: The case of Facebook and Twitter

abstract: In the recent years, the number of social media, such as Facebook, LinkedIn, Tumblr have been increasing regularly, have become ideal platforms for a large number of users at lower costs. However, social media helped companies get known by a wider public and collect the relevant information's about customers, their needs and their expectations. Therefore, several decision makers have worked on these media to get extra information about companies and customers. Moreover, the explosive growth of the amount of data makes their analysis and usage difficult. Therefore, the collection, analysis and the storage of these data in a data warehouse are extremely necessary. Thus, data warehouse modeling from social media should take into account aspects of social media. This paper proposes an approach for data warehouse construction from social media. Precisely, it deals with the conceptual modeling from social media, especially Facebook and Twitter. The proposed model can predict the success of the products prospects and improve the existing products.


title: Bridging Universal and Cultural Perspectives: A Vision for Developmental Psychology in a Global World

abstract: This article argues that now is an opportune time to draw together the accumulated bodies of knowl- edge in developmental and cultural psychology in order to build a new vision for developmental psychology scholar- ship that bridges universal and cultural perspectives. Such bridging requires rethinking (a) the entity of developmen- tal psychological analysis, (b) the scope and meaning of developmental psychology concepts, and (c) the nature of theoretical frameworks. This rethinking will render devel- opmental psychology more broadly valid across cultures and more applicable to local cultural conditions. This is imperative in an increasingly global world where diverse peoples interact more than ever. Although the present focus is on rethinking developmental psychology, conclu- sions about the implications of bridging universal and cul- tural perspectives may be of interest in other fields and disciplines addressing psychological thought and behav- ior.


title: Bad apples in bad barrels: A causal analysis of ethical decision-making behavior.

abstract: Les auteurs proposent et testent dans cet article un modele dans lequel l'apprentissage social, le stade du developpement moral cognitif et le locus of control sont supposes influencer la prise de decision ethique


title: Efficacy and Safety of Mifepristone for the Treatment of Psychotic Depression

abstract: AbstractOpen-label studies and randomized clinical trials have suggested that mifepristone may be effective for the treatment of major depression with psychotic features (psychotic depression). A recent study reported a correlation between mifepristone plasma concentration and clinical response.The


title: MUTE: Majority under-sampling technique

abstract: An application which operates on an imbalanced dataset loses its classification performance on a minority class, which is rare and important. There are a number of over-sampling techniques, which insert minority instances into a dataset, to adjust the class distribution. Unfortunately, these instances highly affect the computation of generating a classifier. In this paper, a new simple and effective under-sampling called MUTE is proposed. Its strategy is to get rid of noise majority instances which over-lap with minority instances. The removal majority instances are considered based on their safe levels relying on the Safe-Level-SMOTE concept. MUTE not only reduces the classifier construction time because of a downsizing dataset but also improves the prediction rate on a minority class. The experimental results show that MUTE improves F-measure by comparing to SMOTE techniques.


title: The Cracked Cookie Jar: HTTP Cookie Hijacking and the Exposure of Private Information

abstract: The widespread demand for online privacy, also fueled by widely-publicized demonstrations of session hijacking attacks against popular websites, has spearheaded the increasing deployment of HTTPS. However, many websites still avoid ubiquitous encryption due to performance or compatibility issues. The prevailing approach in these cases is to force critical functionality and sensitive data access over encrypted connections, while allowing more innocuous functionality to be accessed over HTTP. In practice, this approach is prone to flaws that can expose sensitive information or functionality to third parties. In this paper, we conduct an in-depth assessment of a diverse set of major websites and explore what functionality and information is exposed to attackers that have hijacked a user's HTTP cookies. We identify a recurring pattern across websites with partially deployed HTTPS, service personalization inadvertently results in the exposure of private information. The separation of functionality across multiple cookies with different scopes and inter-dependencies further complicates matters, as imprecise access control renders restricted account functionality accessible to non-session cookies. Our cookie hijacking study reveals a number of severe flaws, attackers can obtain the user's home and work address and visited websites from Google, Bing and Baidu expose the user's complete search history, and Yahoo allows attackers to extract the contact list and send emails from the user's account. Furthermore, e-commerce vendors such as Amazon and Ebay expose the user's purchase history (partial and full respectively), and almost every website exposes the user's name and email address. Ad networks like Doubleclick can also reveal pages the user has visited. To fully evaluate the practicality and extent of cookie hijacking, we explore multiple aspects of the online ecosystem, including mobile apps, browser security mechanisms, extensions and search bars. To estimate the extent of the threat, we run IRB-approved measurements on a subset of our university's public wireless network for 30 days, and detect over 282K accounts exposing the cookies required for our hijacking attacks. We also explore how users can protect themselves and find that, while mechanisms such as the EFF's HTTPS Everywhere extension can reduce the attack surface, HTTP cookies are still regularly exposed. The privacy implications of these attacks become even more alarming when considering how they can be used to deanonymize Tor users. Our measurements suggest that a significant portion of Tor users may currently be vulnerable to cookie hijacking.


title: Automated Summarization Evaluation with Basic Elements

abstract: As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.


title: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism

abstract: Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed. While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.


title: Creation and evaluation of large keyphrase extraction collections with multiple opinions

abstract: While several automatic keyphrase extraction (AKE) techniques have been developed and analyzed, there is little consensus on the definition of the task and a lack of overview of the effectiveness of different techniques. Proper evaluation of keyphrase extraction requires large test collections with multiple opinions, currently not available for research. In this paper, we (i) present a set of test collections derived from various sources with multiple annotations (which we also refer to as opinions in the remained of the paper) for each document, (ii) systematically evaluate keyphrase extraction using several supervised and unsupervised AKE techniques, (iii) and experimentally analyze the effects of disagreement on AKE evaluation. Our newly created set of test collections spans different types of topical content from general news and magazines, and is annotated with multiple annotations per article by a large annotator panel. Our annotator study shows that for a given document there seems to be a large disagreement on the preferred keyphrases, suggesting the need for multiple opinions per document. A first systematic evaluation of ranking and classification of keyphrases using both unsupervised and supervised AKE techniques on the test collections shows a superior effectiveness of supervised models, even for a low annotation effort and with basic positional and frequency features, and highlights the importance of a suitable keyphrase candidate generation approach. We also study the influence of multiple opinions, training data and document length on evaluation of keyphrase extraction. Our new test collection for keyphrase extraction is one of the largest of its kind and will be made available to stimulate future work to improve reliable evaluation of new keyphrase extractors.


title: ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

abstract: Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).


title: Parameterized Dynamic Tables

abstract: We analyze the amortized complexity of dynamic tables with arbitrary minimal and maximal load factors and arbitrary expansion and contraction factors.


ThingTalk : A Distributed Language for a Social Internet of Things


title: Adaptive algorithms for detecting community structure in dynamic social networks

abstract: Social networks exhibit a very special property: community structure. Understanding the network community structure is of great advantages. It not only provides helpful information in developing more social-aware strategies for social network problems but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments in cellular networks. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social activities and interactions are evolving rapidly. Can we quickly and efficiently identify the network community structure? Can we adaptively update the network structure based on previously known information instead of recomputing from scratch? In this paper, we present Quick Community Adaptation (QCA), an adaptive modularity-based method for identifying and tracing community structure of dynamic online social networks. Our approach has not only the power of quickly and efficiently updating network communities, through a series of changes, by only using the structures identified from previous network snapshots, but also the ability of tracing the evolution of community structure over time. To illustrate the effectiveness of our algorithm, we extensively test QCA on real-world dynamic social networks including ENRON email network, arXiv e-print citation network and Facebook network. Finally, we demonstrate the bright applicability of our algorithm via a realistic application on routing strategies in MANETs. The comparative results reveal that social-aware routing strategies employing QCA as a community detection core outperform current available methods.


title: The Process of Social Entrepreneurship: Creating Opportunities Worthy of Serious Pursuit

abstract: All acts of entrepreneurship start with the vision of an attractive opportunity. 1 For social entrepreneurs, an “attractive” opportunity is one that has sufficient potential for positive social impact to justify the investment of time, energy, and money required to pursue it seriously. Despite popular sayings, attractive entrepreneurial opportunities do not come knocking at the door fully formed. Nor are they out there, like lost treasures, simply waiting to be discovered by the lucky or observant. Rather, they have to be conceived, developed, and refined in a dynamic, creative, and thoughtful process. This note provides a framework to guide social entrepreneurs through the process of creating a worthwhile opportunity. It is designed to help increase the chances of success for anyone contemplating the journey of social entrepreneurship, and it may also be helpful for those considering investing in new social ventures. Our model breaks the opportunity creation process into two major steps (see Figure 1). First, a social entrepreneur generates a promising idea. Second, the social entrepreneur attempts to develop that idea into an attractive opportunity. It is natural to think of the generation step as an act of pure creativity and the development step as purely analytic and logical. However, both steps combine inspiration, insight, and imagination with research, logic, and analysis. Innovative ideas can be generated systematically, based on keen observation and reasoning, as well as creativity. 2 At the same time, converting a promising idea into a workable and attractive opportunity requires an on-going creative process working hand-in-hand with focused analysis, experimentation, and sometimes even launching the initial stages of a venture. We depict the development step as a funnel. Social entrepreneurs add the most significant value in this stage of the process, and few promising ideas make it through the development funnel to become opportunities worth pursuing in the long-term.


title: These aren't the droids you're looking for: retrofitting android to protect data from imperious applications

abstract: We examine two privacy controls for Android smartphones that empower users to run permission-hungry applications while protecting private data from being exfiltrated: (1) covertly substituting shadow data in place of data that the user wants to keep private, and (2) blocking network transmissions that contain data the user made available to the application for on-device use only. We retrofit the Android operating system to implement these two controls for use with unmodified applications. A key challenge of imposing shadowing and exfiltration blocking on existing applications is that these controls could cause side effects that interfere with user-desired functionality. To measure the impact of side effects, we develop an automated testing methodology that records screenshots of application executions both with and without privacy controls, then automatically highlights the visual differences between the different executions. We evaluate our privacy controls on 50 applications from the Android Market, selected from those that were both popular and permission-hungry. We find that our privacy controls can successfully reduce the effective permissions of the application without causing side effects for 66% of the tested applications. The remaining 34% of applications implemented user-desired functionality that required violating the privacy requirements our controls were designed to enforce; there was an unavoidable choice between privacy and user-desired functionality.


title: Criteria for reporting incidental findings in clinical exome sequencing – a focus group study on professional practices and perspectives in Belgian genetic centres

abstract: Incidental and secondary findings (IFs and SFs) are subject to ongoing discussion as potential consequences of clinical exome sequencing (ES). International policy documents vary on the reporting of these findings. Discussion points include the practice of unintentionally identified IFs versus deliberately pursued SFs, patient opt-out possibilities and the spectrum of reportable findings. The heterogeneity of advice permits a non-standardised disclosure but research is lacking on actual reporting practices. Therefore, this study assessed national reporting practices for IFs and SFs in clinical ES and the underlying professional perspectives. A qualitative focus group study has been undertaken, including professionals from Belgian centres for medical genetics (CMGs). Data were analysed thematically. All Belgian CMGs participated in this study. Data analysis resulted in six main themes, including one regarding the reporting criteria used for IFs. All CMGs currently use ES-based panel testing. They have limited experience with IFs in clinical ES and are cautious about the pursuit of SFs. Two main reporting criteria for IFs were referred to by all CMGs: the clinical significance of the IF (including pathogenicity and medical actionability) and patient-related factors (including the patient’s preference to know and patient characteristics). The consensus over the importance of these criteria contrasted with their challenging interpretation and application. Points of concern included IFs’ pathogenicity in non-symptomatic persons, IFs concerning variants of uncertain significance, the requirement and definition of medical actionability and patient opt-out possibilities. Finally, reporting decisions were guided by the interaction between the clinical significance of the IF and patient characteristics. This interaction questions the possible disclosure of findings with context-dependent and personal utility, such as IFs concerning a carrier status. To evaluate the IF’s final relevance, a professional and case-by-case deliberation was considered essential. The challenging application of reporting criteria for IFs results in diversified practices and policy perspectives within Belgian CMGs. This echoes international concerns and may have consequences for effective policy recommendations.


title: TCP and explicit congestion notification

abstract: This paper discusses the use of Explicit Congestion Notification (ECN) mechanisms in the TCP/IP protocol. The first part proposes new guidelines for TCP's response to ECN mechanisms (e.g., Source Quench packets, ECN fields in packet headers). Next, using simulations, we explore the benefits and drawbacks of ECN in TCP/IP networks. Our simulations use RED gateways modified to set an ECN bit in the IP packet header as an indication of congestion, with Reno-style TCP modified to respond to ECN as well as to packet drops as indications of congestion. The simulations show that one advantage of ECN mechanisms is in avoiding unnecessary packet drops, and therefore avoiding unnecessary delay for packets from low-bandwidth delay-sensitive TCP connections. A second advantage of ECN mechanisms is in networks (generally LANs) where the effectiveness of TCP retransmit timers is limited by the coarse granularity of the TCP clock. The paper also discusses some implementation issues concerning specific ECN mechanisms in TCP/IP networks.


title: cyNeo4j: connecting Neo4j and Cytoscape

abstract: We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have. © The Author 2015. Published by Oxford University Press. All rights reserved.


Semantic Deep Learning


title: DIAT: A Scalable Distributed Architecture for IoT

abstract: The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture—comprising smart control and actuation—has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.


title: Mind the Gap: a generative approach to interpretable feature selection and extraction

abstract: We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM's ability to help with dataset exploration.


title: Applied Nonlinear Control of Unmanned Vehicles with Uncertain Dynamics

abstract: The presented research concerns the control of unmanned vehicles. The results introduced in this dissertation provide a solid control framework for a wide class of nonlinear uncertain systems, with a special emphasis on issues related to implementation, such as control input amplitude and rate saturation, or partial state measurements availability. More specifically, an adaptive control framework, allowing to enforce amplitude and rate saturation of the command, is developed. The motion control component of this framework, which works in conjunction with a saturation algorithm, is then specialized to different types of vehicles. Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles are considered. A nonlinear control algorithm addressing the tracking problem for a class of underactuated, non-minimum phase marine vehicles is then introduced. This motion controller is extended, using direct and indirect adaptive techniques, to handle parametric uncertainties in the system model. Numerical simulations are used to illustrate the efficacy of the algorithms. Next, the output feedback control problem is treated, for a large class of nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer which uses output measurements and partial knowledge of the system’s dynamics to reconstruct the entire state for a wide class of nonlinear systems. The observer is then extended to operate in conjunction with a full state feedback control law and solve both the output feedback control problem and the state observation problem simultaneously. The resulting output feedback control algorithm is then adjusted to provide a high level of robustness to both parametric and structural model uncertainties. Finally, in a natural extension of these results from motion control of a single system to collaborative control of a group of vehicles, a cooperative control framework addressing limited communication issues is introduced.


title: Effectuation in SPWM technique for 3-Φ VSI using STM32F4 discovery board interfaced with MATLAB

abstract: In this paper, Design and development of control circuit algorithm of 3-Φ Voltage Source Inverter (VSI) using Unipolar Sinusoidal Pulse Width Modulation (SPWM) techniques is proposed using STM32F407VGTx discovery board interfaced with MATLAB Simulink Environment. For implementing 3-Φ VSI, this method is most useful as it will eliminate complexity of control circuit algorithm occurred due to analog circuit, reduces cost, size of filter required, and improved overall inverter efficiency. Pulse width modulated voltage source inverter is preferred for high performance AC drives. The proposed control scheme and the result obtained by embedded simulations interfaced with STM32F4 microcontroller are presented and discussed. A simple hardware implementation is proposed and experimental results of SPWM based VSI are also shown. The Simulation results of the proposed scheme have excellent performance and compared with hardware results.


title: A Biophysically-Based Model of the Optical Properties of Skin Aging

abstract: This paper presents a time-varying, multi-layered biophysically-based model of the optical properties of human skin, suitable for simulating appearance changes due to aging. We have identified the key aspects that cause such changes, both in terms of the structure of skin and its chromophore concentrations, and rely on the extensive medical and optical tissue literature for accurate data. Our model can be expressed in terms of biophysical parameters, optical parameters commonly used in graphics and rendering such as spectral absorption and scattering coefficients, or more intuitively with higher-level parameters such as age, gender, skin care or skin type. It can be used with any rendering algorithm that uses diffusion profiles, and it allows to automatically simulate different types of skin at different stages of aging, avoiding the need for artistic input or costly capture processes. While the presented skin model is inspired on tissue optics studies, we also provided a simplified version valid for non-diagnostic applications.


title: Body-centric interaction techniques for very large wall displays

abstract: We examine the design space of interaction techniques for very large wall displays by drawing from existing theory and practice for reality-based interfaces and whole-body interfaces. We also apply insights drawn from research in psychology about the human cognitive mechanisms that support sensorimotor operations in different coordinate spaces, as well as research in sociology examining how people manage coordination and privacy concerns in these spaces. Using guidelines obtained from these analyses, we designed and implemented a novel suite of body-centric interaction techniques. These were integrated into a map browsing and editing application for a very large (5m×3m) wall display. The application was then used to gather user feedback to guide the further development of the interaction techniques.


title: The tail at scale

abstract: Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.


title: On construct validity: issues of method and measurement.

abstract: Fifty years ago, L. J. Cronbach and P. E. Meehl (1955) advocated for the concept of construct validity, noting that psychologists study hypothetical, inferred entities and that validating measures of such entities involves basic theory testing. Three important developments in clinical assessment following that seminal article are noteworthy. First, clinical research has benefited from greater theoretical integration and subsequent differentiation among related constructs. Second, implementation of ongoing, critical evaluation of all aspects of the construct validity process, including theory development, hypothesis specification, research design, and empirical evaluation, has improved clinical assessment. Third, improvement in evaluating fit between hypotheses and observations has been sought. Improved means of evaluating multitrait, multimethod designs, and ways to increase their clinical representativeness, are one encouraging development. Ongoing efforts to improve the construct validity process reflect the legacy of L. J. Cronbach and P. E. Meehl.


title: The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation

abstract: Database fragmentation allows reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments. In this paper, we address multimedia database fragmentation to take into account the rich characteristics of multimedia objects. We particularly discuss multimedia primary horizontal fragmentation and focus on semantic-based textual predicates implication required as a pre-process in current fragmentation algorithms in order to partition multimedia data efficiently. Identifying semantic implication between similar queries (if a user searches for the images containing a car, he would probably mean auto, vehicle, van or sport-car as well) will improve the fragmentation process. Making use of the neighborhood concept in knowledge bases to identify semantic implications constitutes the core of our proposal. A prototype has been implemented to evaluate the performance of our approach.


title: A New Approach to Humanitarian Demining

abstract: Landmines can deprive whole areas of valuable resources, and continue to kill and cause injuries years after the end of armed conflicts. Armored vehicles are used for mine clearance, but with limited reliability. The final inspection of minefields is still performed by human deminers exposed to potentially fatal accidents. The aim of this research is to introduce automation as a way to improve the final level of humanitarian demining. This paper addresses mobility and manipulation, while sensing, communication and visualization shall be discussed in detail in a subsequent paper. After analyzing the merits and limitations of previous works, a new approach to tele-operated demining is considered, using off-road buggies equipped with combustion engines, and taking into account actual field requirements. Control of the automated buggies on rough terrain is also discussed, as well as the development of a new weight-balanced manipulator for landmine clearance operations.


title: Municipal solid waste management in Pudong New Area, China.

abstract: The increase in population, the rapid economic growth and the rise in community living standards accelerate municipal solid waste (MSW) generation in developing cities. This problem is especially serious in Pudong New Area, Shanghai, China. The daily amount of MSW generated in Pudong was about 1.11 kg per person in 2006. According to the current population growth trend, the solid waste quantity generated will continue to increase with the city's development. In this paper, we describe a waste generation and composition analysis and provide a comprehensive review of municipal solid waste management (MSWM) in Pudong. Some of the important aspects of waste management, such as the current status of waste collection, transport and disposal in Pudong, will be illustrated. Also, the current situation will be evaluated, and its problems will be identified.


title: Adaptive backoff synchronization techniques

abstract: Shared-memory multiprocessors commonly use shared variables for synchronization. Our simulations of real parallel applications show that large-scale cache-coherent multiprocessors suffer significant amounts of invalidation traffic due to synchronization. Large multiprocessors that do not cache synchronization variables are often more severely impacted. If this synchronization traffic is not reduced or managed adequately, synchronization references can cause severe congestion in the network. We propose a class of adaptive back-off methods that do not use any extra hardware and can significantly reduce the memory traffic to synchronization variables. These methods use synchronization state to reduce polling of synchronization variables. Our simulations show that when the number of processors participating in a barrier synchronization is small compared to the time of arrival of the processors, reductions of 20 percent to over 95 percent in synchronization traffic can be achieved at no extra cost. In other situations adaptive backoff techniques result in a tradeoff between reduced network accesses and increased processor idle time.


title: UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation

abstract: Parallel corpus is a valuable resource for cross-language information retrieval and data-driven natural language processing systems, especially for Statistical Machine Translation (SMT). However, most existing parallel corpora to Chinese are subject to in-house use, while others are domain specific and limited in size. To a certain degree, this limits the SMT research. This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese. The corpora constructed in this paper contain about 15 million English-Chinese (E-C) parallel sentences, and more than 2 million training data and 5,000 testing sentences are made publicly available. Different from previous work, the corpus is designed to embrace eight different domains. Some of them are further categorized into different topics. The corpus will be released to the research community, which is available at the NLPCT website.


title: Review of studies on online consumer trust

abstract: Online consumer trust (online trust) is consumer's trusting in the website and its operators in e-commerce. Based on previous literature on online trust, this paper reviewed the category of the online trust and its antecedents. From the previous studies, online trust is classified into initial trust and ongoing trust. Online consumers experience a trust development process from being short of trust to the initial trust and to the high level trust, that is, ongoing trust. The trust development process includes exploratory stage and commitment stage. And the two stages develop initial trust and ongoing trust respectively. Online trust's antecedents have five types: the knowledge-based trust, the cognition-based trust, the calculative-based trust, the institution-based trust and the personality-based trust; they have different effects on initial trust and ongoing trust. Through review of studies on online trust, this paper expects to offer theory basis on trust to website operators.


title: Review of Human-Computer Interaction Issues in Image Retrieval

abstract: Image retrieval is an active area of research, which is growing very rapidly. Indeed, stimulated by the rapid growth in storage capacity and processing speed, the number of images in electronic collections and the World Wide Web has considerably increased over the last few years. However, with this abundance of information, people are continuously looking for tools that help them find the image(s) they are looking for within a reasonable amount of time. These tools are image retrieval engines. When using an image retrieval engine, the user is continuously interacting with the machine. First, he1 uses the system’s interface to formulate a query that expresses his needs. Second, he provides feedback about the retrieved results at each search iteration. This allows the engine to provide more accurate results by using relevance feedback (RF) techniques. Third, he may be asked to assign a goodness score or weight to each image retrieved, which helps evaluating the system’s performance. In this chapter, we will review the main interactions between human and the machine in the context of image retrieval. We will address several issues, including: Query formulation: • How the user expresses his needs and what he is looking for • The different ways the query can be formulated: keywords-based, sentence-based, query by example image, query by sketch, query by feature values, composite queries, etc. • Query by region of interest (ROI) vs. global query. • Queries with positive example only vs. queries with both positive and negative examples. • Page zero problem: finding a good image to initiate a retrieval session. Relevance feedback: we will try to answer questions like: • Why do systems use relevance feedback? • How can the user express his needs during the relevance feedback process • How this information is exploited by the system to perform operations like feature selection or the identification of the sought image. 1 Note that the masculine gender has been used strictly to facilitate reading, and is to be understood to include the feminine. Advances in Human-Computer Interaction 216 • The different families of RF techniques. • Relevance feedback with retrieval memory, i.e., taking into account the value of old iteration queries when constructing the new one. • Whether it is useful for the system to create user profiles, and the challenges it has to face. • The number of RF iterations required to obtain satisfactory results. Viewing retrieval results: • Existing viewing techniques: 2D linear presentation, 3D-based presentation, etc. • Different ways the resulting images may be ordered and presented to the user: similarity-ordered, time-ordered, event-ordered, etc. Evaluation of the retrieval performance by the user: • How the user can express his satisfaction/dissatisfaction about the retrieved images • What about the ground truth in image retrieval evaluation? • System response time and its influence on user satisfaction. • The ease of use of the system’s interface. Other issues: • User’s needs: He may be looking for a specific image, for images that meet a given need (e.g. illustrate a concept) or simply browsing the collection looking for potentially “good” images.


title: The ecological impacts of marine debris: unraveling the demonstrated evidence from what is perceived

abstract: Anthropogenic debris contaminates marine habitats globally, leading to several perceived ecological impacts. Here, we critically and systematically review the literature regarding impacts of debris from several scientific fields to understand the weight of evidence regarding the ecological impacts of marine debris. We quantified perceived and demonstrated impacts across several levels of biological organization that make up the ecosystem and found 366 perceived threats of debris across all levels. Two hundred and ninety-six of these perceived threats were tested, 83% of which were demonstrated. The majority (82%) of demonstrated impacts were due to plastic, relative to other materials (e.g., metals, glass) and largely (89%) at suborganismal levels (e.g., molecular, cellular, tissue). The remaining impacts, demonstrated at higher levels of organization (i.e., death to individual organisms, changes in assemblages), were largely due to plastic marine debris (> 1 mm; e.g., rope, straws, and fragments). Thus, we show evidence of ecological impacts from marine debris, but conclude that the quantity and quality of research requires improvement to allow the risk of ecological impacts of marine debris to be determined with precision. Still, our systematic review suggests that sufficient evidence exists for decision makers to begin to mitigate problematic plastic debris now, to avoid risk of irreversible harm.


title: Correspondence patterns for ontology alignment

abstract: We introduce in this paper correspondence patternsas templates to model ontology alignments. Correspondence patterns capture regularities recurring when aligning ontologies. They come in complement of ontology matching algorithms and graphical mapping tools, and facilitate the task of the engineer building the alignment between a pair of ontologies. We develop an ontology mediation framework based on three ontology correspondence abstraction levels. We particularly detail the most abstract level: correspondence patterns.


title: Structure of a SusD homologue, BT1043, involved in mucin O-glycan utilization in a prominent human gut symbiont.

abstract: Mammalian distal gut bacteria have an expanded capacity to utilize glycans. In the absence of dietary sources, some species rely on host-derived mucosal glycans. The ability of Bacteroides thetaiotaomicron, a prominent human gut symbiont, to forage host glycans contributes to both its ability to persist within an individual host and to be transmitted naturally to new hosts at birth. The molecular basis of host glycan recognition by this species is still unknown, but likely occurs through an expanded suite of outermembrane glycan-binding proteins that are the primary interface between B. thetaiotaomicron and its environment. Presented here is the atomic structure of the B. thetaiotaomicron protein BT1043, an outermembrane lipoprotein involved in host glycan metabolism. Despite a lack of detectable amino acid sequence similarity, BT1043 is a structural homolog of the B. thetaiotaomicron starch-binding protein SusD. Both structures are dominated by tetratrico peptide repeats that may facilitate association with outer membrane β-barrel transporters required for glycan uptake. The structure of BT1043 complexed with N-acetyl lactosamine reveals that recognition in mediated via hydrogen-bonding interactions with the reducing end of β-N-acetyl glucosamine, suggesting a role in binding glycans liberated from the mucin polypeptide. This is in contrast to CBM 32 family members that target the terminal non-reducing galactose residue of mucin glycans. The highly articulated glycan-binding pocket of BT1043 suggests that ligand binding to BT1043 relies more upon interactions with the composite sugar residues than upon overall ligand conformation as previously observed for SusD. The diversity in amino acid sequence level likely reflects early divergence from a common ancestor while the unique and conserved α-helical fold the SusD family suggests a similar function in glycan uptake.


title: AR Furniture: Integrating Augmented Reality Technology to Enhance Interior Design using Marker and Markerless tracking

abstract: Purchasing products for interior design always has a problem that the purchased products may not satisfy customers because they cannot put them in their own place before buying. The purpose of this research is to study and develop an android application called 'AR Furniture' with the use of Augmented Reality technology for design and decoration that will help customers visualize how furniture pieces will look and fit (to scale) in their homes and also can provide details of products to support customer decision. This application is a prototype to find out factors affecting the design and tracking of AR applications. This paper presents three factors that are important for designing and tracking AR applications. The principle of the application is started with analyzing images from the rear camera of a smartphone or tablet using marker tracking technique for displaying product's details and markerless tracking technique for displaying 3D models, performing feature tracking, and calculating positions to display a 3D model over the real world image. The implementation of the application can be split into 2 parts: Part 1 Creating 3D Models using Autodesk 3Ds Max and Part 2 Developing the application using Unity3D and Kudan Augmented Reality SDK as an engine for image analysis, image processing and 3D model rendering. Then we performed three experiments to test the application, 1) Image analysis with marker tracking 2) Image analysis with markerless tracking and 3) User's satisfaction of using the application. The results show that image analysis with marker tracking works well using markers which their size should not be less than 200 x 200 pixels, the distance between the camera and the marker should not be far more than 60 cm. Image analysis with markerless tracking works well with surfaces having a lot of features and at light levels of 100--300 lux (indoor light levels) with 70% accuracy. The user experience evaluation shows that the weakness (2.86 out of 5 points) of the application is when a user found a problem in the application they would need time to solve it. The user experience evaluation shows that the strength (3.93 out of 5 points) of the application is the application can show 3D Object that meet user satisfaction. And the average overall user's satisfaction come up with 3.93 out of 5 point evaluation score. From the experiments, the application should be modified for better performance such as develop various maker patterns using QR code or barcode, distinguish walls and ceilings so that the application would show 3D objects on them properly, improve light robustness and make 3D models more realistic.


title: Experiences from using snowballing and database searches in systematic literature studies

abstract: Background: Systematic literature studies are commonly used in software engineering. There are two main ways of conducting the searches for these type of studies; they are snowballing and database searches. In snowballing, the reference list (backward snowballing - BSB) and citations (forward snowballing - FSB) of relevant papers are reviewed to identify new papers whereas in a database search, different databases are searched using predefined search strings to identify new papers. Objective: Snowballing has not been in use as extensively as database search. Hence it is important to evaluate its efficiency and reliability when being used as a search strategy in literature studies. Moreover, it is important to compare it to database searches. Method: In this paper, we applied snowballing in a literature study, and reflected on the outcome. We also compared database search with backward and forward snowballing. Database search and snowballing were conducted independently by different researchers. The searches of our literature study were compared with respect to the efficiency and reliability of the findings. Results: Out of the total number of papers found, snowballing identified 83% of the papers in comparison to 46% of the papers for the database search. Snowballing failed to identify a few relevant papers, which potentially could have been addressed by identifying a more comprehensive start set. Conclusion: The efficiency of snowballing is comparable to database search. It can potentially be more reliable than a database search however, the reliability is highly dependent on the creation of a suitable start set.


title: A Novel Integrated Framework for Learning both Text Detection and Recognition

abstract: In this paper, we propose a novel integrated framework for learning both text detection and recognition. For most of the existing methods, detection and recognition are treated as two isolated tasks and trained separately, since parameters of detection and recognition models are different and two models target to optimize their own loss functions during individual training processes. In contrast to those methods, by sharing model parameters, we merge the detection model and recognition model into a single end-to-end trainable model and train the joint model for two tasks simultaneously. The shared parameters not only help effectively reduce the computational load in inference process, but also improve the end-to-end text detection-recognition accuracy. In addition, we design a simpler and faster sequence learning method for the recognition network based on a succession of stacked convolutional layers without any recurrent structure, this is proved feasible and dramatically improves inference speed. Extensive experiments on different datasets demonstrate that the proposed method achieves very promising results.


title: IP based Wireless Sensor Networks: Performance Analysis using Simulations and Experiments

abstract: Wireless sensor networks are at the crux of the Internet of Things applications. At the current state, there exist several technologies competing against each other in the IoT space. These propriet ...


title: TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems

abstract: Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors.


title: Eye gaze estimation from a single image of one eye

abstract: We present a novel approach, called the "one-circle " algorithm, for measuring the eye gaze using a monocular image that zooms in on only one eye of a person. Observing that the iris contour is a circle, we estimate the normal direction of this iris circle, considered as the eye gaze, from its elliptical image. From basic projective geometry, an ellipse can be back-projected into space onto two circles of different orientations. However, by using an anthropometric property of the eyeball, the correct solution can be disambiguated. This allows us to obtain a higher resolution image of the iris with a zoom-in camera and thereby achieving higher accuracies in the estimation. The robustness of our gaze determination approach was verified statistically by the extensive experiments on synthetic and real image data. The two key contributions are that we show the possibility of finding the unique eye gaze direction from a single image of one eye and that one can obtain better accuracy as a consequence of this.


title: Conversational Databases: Explaining Structured Queries to Users

abstract: Many applications offer a form-based environment for na\"{\i}ve users for accessing databases without being familiar with the database schema or a structured query language. ::: Do-It-Yourself, database-driven web application platforms empower non-programmers to rapidly create applications. ::: Users interactions are translated to structured queries and executed. However, as a user is unlikely to know the underlying semantic connections among the fields presented in a form, it is often useful to provide her with ::: some feedback about the queries built without exposing her to the underlying query language, in order to assist her in forming queries correctly. ::: Explaining queries may be also useful for users who explicitly use a structured query language for verification or debugging purposes. ::: In this paper, we take a graph-based approach to the query translation problem. We represent various forms of structured queries as directed graphs and we annotate the graph edges with template labels using an extensible template mechanism. We present different graph traversal strategies for efficiently exploring these graphs and composing textual query descriptions. ::: Finally, we present experimental results for the efficiency and effectiveness of the proposed methods.


title: Sensing immersive 360° mobile interactive video

abstract: Video has the potential for a strong impact on viewers, their sense of presence and engagement, due to its immersive capacities. Multimedia sensing and the flexibility of mobility may be considered as options to further extend the video's immersive capacities. Mobile devices are becoming ubiquitous and the range of sensors and actuators they incorporate is ever increasing, which creates the potential to capture and display 360o video and metadata and to support more powerful and immersive video user experiences. In this paper, we explore the immersion potential of mobile interactive video augmented with visual, auditory and tactile multisensing. User evaluation revealed advantages in using a multisensory approach to increase immersion and user satisfaction. Also, several properties and parameters that worked better in different conditions were identified, which may help to inform design of future mobile immersive video environments.


title: User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest

abstract: Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or “mainstream” topics, but Reddit’s large userbase provides a key advantage in supporting niche topics.


title: Document Clustering Using Concept Space and Cosine Similarity Measurement

abstract: Document clustering is related to data clustering concept which is one of data mining tasks and unsupervised classification. It is often applied to the huge data in order to make a partition based on their similarity. Initially, it used for Information Retrieval in order to improve the precision and recall from query. It is very easy to cluster with small data attributes which contains of important items. Furthermore, document clustering is very useful in retrieve information application in order to reduce the consuming time and get high precision and recall. Therefore, we propose to integrate the information retrieval method and document clustering as concept space approach. The method is known as Latent Semantic Index (LSI) approach which used Singular Vector Decomposition (SVD) or Principle Component Analysis (PCA). The aim of this method is to reduce the matrix dimension by finding the pattern in document collection with refers to concurrent of the terms. Each method is implemented to weight of term-document in vector space model (VSM) for document clustering using fuzzy c-means algorithm. Besides reduction of term-document matrix, this research also uses the cosine similarity measurement as replacement of Euclidean distance to involve in fuzzy c-means. And as a result, the performance of the proposed method is better than the existing method with f-measure around 0.91 and entropy around 0.51.


title: Chimera: Collaborative Preemption for Multitasking on a Shared GPU

abstract: The demand for multitasking on graphics processing units (GPUs) is constantly increasing as they have become one of the default components on modern computer systems along with traditional processors (CPUs). Preemptive multitasking on CPUs has been primarily supported through context switching. However, the same preemption strategy incurs substantial overhead due to the large context in GPUs. The overhead comes in two dimensions: a preempting kernel suffers from a long preemption latency, and the system throughput is wasted during the switch. Without precise control over the large preemption overhead, multitasking on GPUs has little use for applications with strict latency requirements. In this paper, we propose Chimera, a collaborative preemption approach that can precisely control the overhead for multitasking on GPUs. Chimera first introduces streaming multiprocessor (SM) flushing, which can instantly preempt an SM by detecting and exploiting idempotent execution. Chimera utilizes flushing collaboratively with two previously proposed preemption techniques for GPUs, namely context switching and draining to minimize throughput overhead while achieving a required preemption latency. Evaluations show that Chimera violates the deadline for only 0.2% of preemption requests when a 15us preemption latency constraint is used. For multi-programmed workloads, Chimera can improve the average normalized turnaround time by 5.5x, and system throughput by 12.2%.


title: Efficient mining of frequent subgraphs in the presence of isomorphism

abstract: Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. We propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.


title: Improving the accuracy of estimated blood loss at obstetric haemorrhage using clinical reconstructions.

abstract: OBJECTIVES ::: Following the results of the Confidential Enquiries into Maternal Deaths report, which claims two maternal deaths annually in the UK from postpartum haemorrhage, our aim was to assess the accuracy of 'visual estimation of blood loss' and produce suitable pictorial and written algorithms to aid in the recognition and management of massive obstetric haemorrhage. ::: ::: ::: DESIGN ::: Observational study to determine discrepancy between actual blood loss (ABL) and estimated blood loss (EBL). ::: ::: ::: SETTING ::: Teaching hospital. ::: ::: ::: POPULATION ::: Hundred and three obstetricians, anaesthetists, midwives, nurses and healthcare assistants. ::: ::: ::: METHODS ::: Clinical scenarios were reproduced in the form of 12 Objective Structured Clinical Examination (OSCE) style stations augmented with known volumes of whole blood. Individual staff estimated the blood loss visually and recorded their results. Digital photographs were used to produce a pictorial 'algorithm' suitable for use as a teaching tool in labour ward. ::: ::: ::: MAIN OUTCOME MEASURES ::: Areas of greatest discrepancy between EBL and ABL. ::: ::: ::: RESULTS ::: Significant underestimation of the ABL occurred in 5 of the 12 OSCE stations: 500-ml (50-cm diameter) floor spill, 1000-ml (75-cm diameter) floor spill, 1500-ml (100-cm diameter) floor spill, 350-ml capacity of soaked 45- x 45-cm large swab and the 2-l vaginal postpartum haemorrhage on bed/floor. ::: ::: ::: CONCLUSIONS ::: Accurate visual estimation of blood loss is known to facilitate timely resuscitation, minimising the risk of disseminated intravascular coagulation and reducing the severity of haemorrhagic shock. Participation in clinical reconstructions may encourage early diagnosis and prompt treatment of postpartum haemorrhage. Written and pictorial guidelines may help all staff working in labour wards.


title: Online web video topic detection and tracking with semi-supervised learning

abstract: With the pervasiveness of online social media and rapid growth of web data, a large amount of multi-media data is available online. However, how to organize them for facilitating users' experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including labeled data and plenty of unlabeled data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adaptable to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking.


title: Narrative abstraction model for story-oriented video

abstract: TV program review services, especially drama review services, are one of the most popular video on demand services on the Web. In this paper, we propose a novel video abstraction model for a review service of story-oriented video such as dramas. In a drama review service, viewers want to understand the story in a short time and service providers want to provide video abstracts at minimum cost. The proposed model enables the automatic creation of a video abstract that still allows viewers to understand the overall story of the source video. Also, the model has a flexible structure so that the duration of an abstract can be adjusted depending on the requirements given by viewers. We get clues for human understanding of a story from scenario writing rules and editorial techniques which are popularly used in the process of video producing. We have implemented the proposed model and successfully applied it to several TV dramas.


title: Mining the peanut gallery: opinion extraction and semantic classification of product reviews

abstract: The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.


title: A Survey on the Contributions of Software-Defined Networking to Traffic Engineering

abstract: Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.


title: Automatic Road Crack Detection and Characterization

abstract: A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.


title: Bethe-ADMM for Tree Decomposition based Parallel MAP Inference

abstract: We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.


title: Recycling ambient microwave energy with broad-band rectenna arrays

abstract: This paper presents a study of reception and rectification of broad-band statistically time-varying low-power-density microwave radiation. The applications are in wireless powering of industrial sensors and recycling of ambient RF energy. A 64-element dual-circularly-polarized spiral rectenna array is designed and characterized over a frequency range of 2-18 GHz with single-tone and multitone incident waves. The integrated design of the antenna and rectifier, using a combination of full-wave electromagnetic field analysis and harmonic balance nonlinear circuit analysis, eliminates matching and filtering circuits, allowing for a compact element design. The rectified dc power and efficiency is characterized as a function of dc load and dc circuit topology, RF frequency, polarization, and incidence angle for power densities between 10/sup -5/-10/sup -1/ mW/cm/sup 2/. In addition, the increase in rectenna efficiency for multitone input waves is presented.


title: When Is PBL More Effective? A Meta-Synthesis of Meta-Analyses Comparing PBL to Conventional Classrooms.

abstract: Problem-based learning (PBL) has been utilized for over 40 years in a variety of diff erent disciplines. Although extensively researched, there is heated debate about the eff ectiveness of PBL. Several meta-analyses have been conducted that provide a synthesis of the eff ects of PBL in comparison to traditional forms of instruction. This study used a qualitative meta-synthesis approach to compare and contrast the assumptions and fi ndings of the meta-analytical research on the eff ectiveness of PBL. Findings indicated that PBL was superior when it comes to long-term retention, skill development and satisfaction of students and teachers, while traditional approaches were more eff ective for short-term retention as measured by standardized board exams. Implications are discussed.


title: Is there a lateralized category effect for color

abstract: According to the lateralized category effect for color, the influence of color category borders on color perception in fast reaction time tasks is significantly stronger in the right visual field than in the left. This finding has directly related behavioral category effects to the hemispheric lateralization of language. Multiple succeeding articles have built on these findings. We ran ten different versions of the two original experiments with overall 230 naive observers. We carefully controlled the rendering of the stimulus colors and determined the genuine color categories with an appropriate naming method. Congruent with the classical pattern of a category effect, reaction times in the visual search task were lower when the two colors to be discriminated belonged to different color categories than when they belonged to the same category. However, these effects were not lateralized: They appeared to the same extent in both visual fields.


title: Remote control of an ultrasonic motor by using a GSM mobile phone

abstract: In this study, remote control of an ultrasonic motor has been implemented by using a standard GSM mobile phone. To drive the ultrasonic motor a digitally controlled drive system has been designed. Then a tone decoder circuit and microcontroller have been added between output of a mobile phone and the drive system of the motor. This system is flexible to be controlled with both GSM and DTMF based phones. With the developed drive and control system the overall control of the ultrasonic motor has been achieved. The system has been tested for different speed, position and direction conditions successfully. The experimental results verify that the GSM controlled drive system is highly effective, reliable, proper and applicable to achieve remote control of the ultrasonic motor. This study gets novel and important point of view for GSM based remote control applications addition to the control of ultrasonic motors.


Finite Formulation of the Electromagnetic Field


title: 3-D micromachined hemispherical shell resonators with integrated capacitive transducers

abstract: We present a self-aligned fabrication method developed for three-dimensional (3-D) microscale hemispherical shell resonators with integrated capacitive transducers and a center post for electrical access to the shell. The self-aligned process preserves the axisymmetry for robust, balanced resonators that can potentially reach very high-Q due to suppressed anchor loss. High-Q operation of a thin polycrystalline silicon shell resonator is verified by exciting devices capacitively into a breathing resonance mode, with measured Q of 8,000 at 412 kHz in vacuum. This process can be further optimized to batch-fabricate micro-hemispherical resonator gyroscopes for portable inertial navigation.


title: Phase-domain all-digital phase-locked loop

abstract: A fully digital frequency synthesizer for RF wireless applications has recently been proposed. At its foundation lies a digitally controlled oscillator that deliberately avoids any analog tuning controls. When implemented in a digital deep-submicrometer CMOS process, the proposed architecture appears more advantageous over conventional charge-pump-based phase-locked loops (PLLs), since it exploits signal processing capabilities of digital circuits and avoids relying on the fine voltage resolution of analog circuits. An actual implementation of an all-digital PLL (ADPLL)-based local oscillator and transmitter used in a commercial 0.13-/spl mu/m CMOS single-chip Bluetooth radio has recently been disclosed. The conventional phase/frequency detector, charge pump and RC loop filter are replaced by a time-to-digital converter and a simple digital loop filter. Due to the lack of the correlational phase detection mechanism, the loop does not contribute to the reference spurs. The measured close-in phase noise of -86 dBc/Hz is adequate even for Global System for Mobile communications (GSM) applications. In this paper, we present the mathematical description and operational details of the phase-domain ADPLL.


title: Performance Improvement of a 6-Axis Force-torque Sensor via Novel Electronics and Cross-shaped Double-hole Structure

abstract: Performance of a force-torque sensor is affected significantly by an error signal that is included in the sensor signal. The error sources may be classified mainly into two categories: one is a structural error due to inaccuracy of sensor body, and the other is a noise signal existing in sensed information. This paper presents a principle of 6-axis force-torque sensor briefly, a double-hole structure to be able to improve a structural error, and then a signal conditioning to reduce the effect of a noise signal. The validity of the proposed method is investigated through experimental study, which shows that S/N ratio is improved significantly in our experimental setup, and the sensor can be implemented cheaply with reasonable performance.


title: The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

abstract: The unprecedented availability of large-scale human behavioral data is profoundly changing the world we live in. Researchers, companies, governments, financial institutions, non-governmental organizations and also citizen groups are actively experimenting, innovating and adapting algorithmic decision-making tools to understand global patterns of human behavior and provide decision support to tackle problems of societal importance. In this chapter, we focus our attention on social good decision-making algorithms, that is algorithms strongly influencing decision-making and resource optimization of public goods, such as public health, safety, access to finance and fair employment. Through an analysis of specific use cases and approaches, we highlight both the positive opportunities that are created through data-driven algorithmic decision-making, and the potential negative consequences that practitioners should be aware of and address in order to truly realize the potential of this emergent field. We elaborate on the need for these algorithms to provide transparency and accountability, preserve privacy and be tested and evaluated in context, by means of living lab approaches involving citizens. Finally, we turn to the requirements which would make it possible to leverage the predictive power of data-driven human behavior analysis while ensuring transparency, accountability, and civic participation.


title: The representing brain: Neural correlates of motor intention and imagery

abstract: This paper concerns how motor actions are neurally represented and coded. Action planning and motor preparation can be studied using a specific type of representational activity, motor imagery. A close functional equivalence between motor imagery and motor preparation is suggested by the positive effects of imagining movements on motor learning, the similarity between the neural structures involved, and the similar physiological correlates observed in both imaging and preparing. The content of motor representations can be inferred from motor images at a macroscopic level, based on global aspects of the action (the duration and amount of effort involved) and the motor rules and constraints which predict the spatial path and kinematics of movements. A more microscopic neural account calls for a representation of object-oriented action. Object attributes are processed in different neural pathways depending on the kind of task the subject is performing. During object-oriented action, a pragmatic representation is activated in which object affordances are transformed into specific motor schemas (independently of other tasks such as object recognition). Animal as well as human clinical data implicate the posterior parietal and premotor cortical areas in schema instantiation. A mechanism is proposed that is able to encode the desired goal of the action and is applicable to different levels of representational organization.


title: Wembedder: Wikidata entity embedding web service

abstract: I present a web service for querying an embedding of entities in the Wikidata knowledge graph. The embedding is trained on the Wikidata dump using Gensim's Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with the Wikidata API the web service exposes a multilingual resource for over 600'000 Wikidata items and properties.


title: Online Banking Information Systems Acceptance: An Empirical Examination of System Characteristics and Web Security

abstract: ABSTRACT Prior work on the technology acceptance model (TAM) is mainly devoted to the influence of TAM’s core motivational factors and their impact on behavioral intent toward IS acceptance. Relatively little research has focused on what specific system design characteristics motivate individuals toward IS acceptance. This article identified specific systems design factors and examined their impact on TAM’s motivational factors through the TAM. The findings will help designers to design and implement better user-accepted systems.


title: Wi-Fi Coordinated WiGig Concurrent Transmissions in Random Access Scenarios

abstract: About 60-GHz millimeter wave (mmWave) unlicensed frequency band is considered as a key enabler for future multi-Gbps WLANs. IEEE 802.11ad (WiGig) standard has been ratified for 60-GHz wireless local area networks (WLANs) by only considering the use case of peer to peer (P2P) communication coordinated by a single WiGig access point (AP). However, due to 60-GHz fragile channel, multiple number of WiGig APs should be installed to fully cover a typical target environment. Nevertheless, the exhaustive search beamforming training and the maximum received power-based autonomous users association prevent WiGig APs from establishing optimal WiGig concurrent links using random access. In this paper, we formulate the problem of WiGig concurrent transmissions in random access scenarios as an optimization problem, and then we propose a greedy scheme based on (2.4/5 GHz) Wi-Fi/(60 GHz) WiGig coordination to find out a suboptimal solution for it. In the proposed WLAN, the wide coverage Wi-Fi band is used to provide the control signalling required for launching the high date rate WiGig concurrent links. Besides, statistical learning using Wi-Fi fingerprinting is utilized to estimate the suboptimal candidate AP along with its suboptimal beam direction for establishing the WiGig concurrent link without causing interference to the existing WiGig data links while maximizing the total system throughput. Numerical analysis confirms the high impact of the proposed Wi-Fi/WiGig coordinated WLAN.


title: Differential Mode Input Filter Design for a Three-Phase Buck-Type PWM Rectifier Based on Modeling of the EMC Test Receiver

abstract: For a three-phase buck-type pulsewidth modulation rectifier input stage of a high-power telecommunications power supply module, a differential-mode (DM) electromagnetic compatibility (EMC) filter is designed for compliance to CISPR 22 Class B in the frequency range of 150 kHz-30 MHz. The design is based on a harmonic analysis of the rectifier input current and a mathematical model of the measurement procedure including the line impedance stabilization network (LISN) and the test receiver. Guidelines for a successful filter design are given, and components for a 5-kW rectifier prototype are selected. Furthermore, formulas for the estimation of the quasi-peak detector output based on the LISN output voltage spectrum are provided. The damping of filter resonances is optimized for a given attenuation in order to facilitate a higher stability margin for system control. Furthermore, the dependence of the filter input and output impedances and the attenuation characteristic on the inner mains impedance are discussed. As experimentally verified by using a three-phase common-/Differential-Mode separator, this procedure allows accurate prediction of the converter DM conducted emission levels and therefore could be employed in the design process of the rectifier system to ensure compliance to relevant EMC standards


title: Simultaneous Localization and Mapping for Forest Harvesters

abstract: A real-time SLAM (simultaneous localization and mapping) approach to harvester localization and tree map generation in forest environments is presented in this paper. The method combines 2D laser localization and mapping with GPS information to form global tree maps. Building an incremental map while also using it for localization is the only way a mobile robot can navigate in large outdoor environments. Until recently SLAM has only been confined to small-scale, mostly indoor, environments. We try to addresses the issues of scale for practical implementations of SLAM in extensive outdoor environments. Presented algorithms are tested in real outdoor environments using an all-terrain vehicle equipped with the navigation sensors and a DGPS receiver.


title: Constructing Bayesian networks by harvesting knowledge from online resources

abstract: In this paper, the development of a human-like intelligent system, named AKEOS (Automatic Knowledge Extraction from Online Sources), is introduced. AKEOS can automatically harvest knowledge from online resources to build a Bayesian network inference engine. Starting from a single event, the AKEOS system performs unsupervised knowledge extraction to convert unstructured text into structured knowledge. By performing repeated knowledge extraction for multiple similar events, AKEOS produces structured databases. Thus, various machine learning algorithms can be directly applied to explore relations between attributes, to discover patterns hidden in the data, or to build inference engines such as Bayesian networks for predictive and diagnostic reasoning. AKEOS is an end-to-end system with lists of events as input, structured databases as intermediate products, and inference engines as end products.


title: Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

abstract: To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.


title: Visual Diagnosis of Tree Boosting Methods

abstract: Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.


title: Attack scenarios and security analysis of MQTT communication protocol in IoT system

abstract: Various communication protocols are currently used in the Internet of Things (IoT) devices. One of the protocols that are already standardized by ISO is MQTT protocol (ISO / IEC 20922: 2016). Many IoT developers use this protocol because of its minimal bandwidth requirement and low memory consumption. Sometimes, IoT device sends confidential data that should only be accessed by authorized people or devices. Unfortunately, the MQTT protocol only provides authentication for the security mechanism which, by default, does not encrypt the data in transit thus data privacy, authentication, and data integrity become problems in MQTT implementation. This paper discusses several reasons on why there are many IoT system that does not implement adequate security mechanism. Next, it also demonstrates and analyzes how we can attack this protocol easily using several attack scenarios. Finally, after the vulnerabilities of this protocol have been examined, we can improve our security awareness especially in MQTT protocol and then implement security mechanism in our MQTT system to prevent such attack.