title: Analysis of a microstrip patch array fed cylindric lens antenna for 77GHz automotive radar

abstract: Regarding gain and SLL, the new lens antenna concept has better focusing abilities compared to the column antenna. Pattern perturbations due to lens edge effects were analyzed with regard to digital array processing. They can be confined either with an appropriate choice for the lens length L and the xlscr,i of the radar sensorpsilas receiving ULA or with narrower feeding beams in the H-plane.


Music Composition using Recurrent Neural Networks


title: Name Translation in Statistical Machine Translation - Learning When to Transliterate

abstract: We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we developed and trained a transliterator on a bitext of 7 million sentences and Google’s English terabyte ngrams and achieved better name translation accuracy than 3 out of 4 professional translators. The paper also includes a discussion of challenges in name translation evaluation.


title: Communication issues in requirements elicitation: a content analysis of stakeholder experiences

abstract: Abstract The gathering of stakeholder requirements comprises an early, but continuous and highly critical stage in system development. This phase in development is subject to a large degree of error, influenced by key factors rooted in communication problems. This pilot study builds upon an existing theory-based categorisation of these problems through presentation of a four-dimensional framework on communication. Its structure is validated through a content analysis of interview data, from which themes emerge, that can be assigned to the dimensional categories, highlighting any problematic areas. The paper concludes with a discussion on the utilisation of the framework for requirements elicitation exercises.


title: Automatic Algorithm Selection for Complex Simulation Problems

abstract: To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.


title: Privacy and Behavioral Advertising: Towards Meeting Users' Preferences

abstract: Online advertisers track Internet users’ activities to deliver targeted ads. To study how dierent factors aect users’ attitudes towards this practice, we conducted a betweensubjects online study (n=1,882). We elicited participants’ comfort with sharing commonly collected types of information in scenarios with varying online advertisers’ data practices. Quantitative analysis showed that participants’ willingness to share information with online advertisers is not only based on the sensitivity of the information, but also on the scope of collection and use, relevance for advertising, and perceived benets of disclosing specic data types. Qualitative analysis showed nuanced and individualized preferences, which suggest that personalized privacy agents have the potential to better assist users to control how advertising networks collect and use their information.


title: A Robust SRAM-PUF Key Generation Scheme Based on Polar Codes

abstract: Physical unclonable functions (PUFs) are relatively new security primitives used for device authentication and device-specific secret key generation. In this paper we focus on SRAM- PUFs. The SRAM-PUFs enjoy uniqueness and randomness properties stemming from the intrinsic randomness of SRAM memory cells, which is a result of manufacturing variations. This randomness can be translated into the cryptographic keys thus avoiding the need to store and manage the device cryptographic keys. Therefore these properties, combined with the fact that SRAM memory can be often found in today's IoT devices, make SRAM-PUFs a promising candidate for securing and authentication of the resource-constrained IoT devices. PUF observations are always affected by noise and environmental changes. Therefore secret- generation schemes with helper data are used to guarantee reliable regeneration of the PUF-based secret keys. Error correction codes (ECCs) are an essential part of these schemes. In this work, we propose a practical error correction construction for PUF-based secret generation that are based on polar codes. The resulting scheme can generate 128-bit keys using 1024 SRAM-PUF bits and 896 helper data bits and achieve a failure probability of 10^{-9} or lower for a practical SRAM-PUFs setting with bit error probability of 15%. The method is based on successive cancellation combined with list decoding and hash-based checking that makes use of the hash that is already available at the decoder. In addition, an adaptive list decoder for polar codes is investigated. This decoder increases the list size only if needed.


title: Towards a scalable HDFS architecture

abstract: Cloud computing infrastructures allow corporations to reduce costs by outsourcing computations on-demand. One of the areas cloud computing is increasingly being utilized for is large scale data processing. Apache Hadoop is one of these large scale data processing projects that supports data-intensive distributed applications. Hadoop applications utilize a distributed file system for data storage called Hadoop Distributed File System (HDFS). HDFS architecture, by design, has only a single master node called ame ode, which manages and maintains the metadata of storage nodes, called Datanodes, in its RAM. Hence, HDFS Datanodes' metadata is restricted by the capacity of the RAM of the HDFS's single-point-of-failure ame ode. This paper proposes a fault tolerant, highly available and widely scalable HDFS architecture. The proposed architecture provides a distributed ame ode space eliminating the drawbacks of the current HDFS architecture. This is achieved by integrating the Chord protocol into the HDFS architecture.


title: Transposition effects on bundle proximity losses in high-speed PM machines

abstract: Proximity effects caused by uneven distribution of current among the insulated wire strands of stator multi-strand windings can contribute significant bundle-level proximity losses in permanent magnet (PM) machines operating at high speeds. Three-dimensional finite element analysis is used to investigate the effects of transposition of the insulated strands in stator winding bundles on the copper losses in high-speed machines. The investigation confirms that the bundle proximity losses must be considered in the design of stator windings for high-speed machines, and the amplitude of these losses decreases monotonically as the level of transposition is increased from untransposed to fully-transposed (360°) wire bundles. Analytical models are introduced to estimate the currents in strands in a slot for a high-speed machine.


title: Robust Textual Inference Via Learning and Abductive Reasoning

abstract: We present a system for textual inference (the task of inferring whether a sentence follows from another text) that uses learning and a logical-formula semantic representation of the text. More precisely, our system begins by parsing and then transforming sentences into a logical formula-like representation similar to the one used by (Harabagiu et al., 2000). An abductive theorem prover then tries to find the minimum "cost" set of assumptions necessary to show that one statement follows from the other. These costs reflect how likely different assumptions are, and are learned automatically using information from syntactic/semantic features and from linguistic resources such as WordNet. If one sentence follows from the other given only highly plausible, low cost assumptions, then we conclude that it can be inferred. Our approach can be viewed as combining statistical machine learning and classical logical reasoning, in the hope of marrying the robustness and scalability of learning with the preciseness and elegance of logical theorem proving. We give experimental results from the recent PASCAL RTE 2005 challenge competition on recognizing textual inferences, where a system using this inference algorithm achieved the highest confidence weighted score.


title: COMPARATIVE STUDY ON DIFFERENT CLASSIFICATION TECHNIQUES FOR BREAST CANCER DATASET

abstract: Breast cancer is one of the most common cancers among women in the world. Early detection of breast cancer is essential in reducing their life losses. Data mining is the process of analyzing massive data and summarizing it into useful knowledge discovery and the role of data mining approaches is growing rapidly especially classification techniques are very effective way to classifying the data, which is essential in decision-making process for medical practitioners. This study presents the different data mining classifiers on the database of breast cancer, by using classification accuracy with and without feature selection techniques. Feature selection increases the accuracy of the classifier because it eliminates irrelevant attributes. The experiment shows that the feature selection enhances the accuracy of all three different classifiers, reduces the Mean Standard Error (MSE) and increase Receiver Operating Characteristics (ROC).


title: FlexPro MD, a Mixture of Krill Oil, Astaxanthin, and Hyaluronic Acid, Suppresses Lipopolysaccharide-Induced Inflammatory Cytokine Production Through Inhibition of NF-κB

abstract: FlexPro MD® (FP-MD), a novel multi-ingredient dietary supplement formulation, has been demonstrated to relieve knee joint pain in humans. However, the mechanisms of action responsible for the activity of FP-MD have not been elucidated. In this study, we show the anti-inflammatory effects of FP-MD in RAW264.7 macrophage cells and mice challenged with lipopolysaccharide (LPS). FP-MD significantly inhibited the mRNA levels of pro-inflammatory cytokines, including interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and IL-1β. In contrast, it elevated the mRNA levels of anti-inflammatory cytokine IL-10 in LPS-stimulated RAW264.7 cells. FP-MD markedly reduced LPS-induced phosphorylation levels of nuclear factor-κB (NF-κB) p65 and inhibitor of κB-α (IκB-α). Importantly, the anti-inflammatory effects of FP-MD were demonstrated in mice with LPS-induced inflammatory arthritis in which FP-MD significantly reduced the expression levels of pro-inflammatory cytokines and inflammatory markers. Thus, this study suggests that FP-MD has anti-inflammatory effects by inhibiting NF-κB that may offer a molecular basis for its pain relief property.


title: Identification of Design Elements for a Maturity Model for Interorganizational Integration: A Comparative Analysis

abstract: Maturity models in the domain of IOS have been developed and used for organizational design and analysis. Nevertheless, they typically provide a heterogeneous and inconsistent picture of relevant problem spaces (e.g. Supply Chain Management, interoperability problems, etc.). This effect is aggravated by the lack of a thorough model or method for self-appraisal with respect to an organization’s capabilities on technical, organizational and institutional levels to implement interorganizational processes. Our research attempts to fill this gap by constructing a maturity model for interorganizational integration that addresses the shortcomings of previous approaches. We analysed 23 maturity models in the domain of IOS to identify critical design elements for our own design process. We selected the model of Fraser et al. (2002) as our starting configuration that includes levels, characteristics, dimensions, elements and activities. From the analysis of alternative models we identified four additional design elements that we incorporated, namely class of entity, benefit, barrier and product. These extend the previously defined elements by Fraser et al. (2002).


title: Graph-based point drift: Graph centrality on the registration of point-sets

abstract: The problem of point-set registration often arises in Pattern Recognition whenever one needs to match information available in images, such as feature locations, landmarks, or points representing a surface of an object. It is a challenging task and a widely explored topic in stereo vision, image alignment, medical imaging, and other fields. Many of those problems have been addressed using graph theory by taking advantage of the structural information available in graphs. In this paper, graph centralities are explored in the point-set registration problem for the first time. We propose a variant of the Coherent Point Drift (CPD) by integrating the degree, betweenness, closeness, eigenvector, and pagerank centralities. The centrality values bring topological information used during the computation of correspondence between points. We analyse the performance on several datasets and our results indicate that the registration can converge faster when the centrality is combined with the spatial information in the traditional probabilistic framework. Our novel contribution introduces the social network centralities as a good source of prior information for the registration problem and it demonstrates how one can take advantage of such information. HighlightsWe introduce the graph centrality into the point-set registration problem.Integrating centrality with spatial information can improve the convergence speed.We discuss the importance of the graph edges for the registration.We compare the performance of five centrality variants with the CPD algorithm.


title: Studies of Integration Readiness Levels: Case Shared Maritime Situational Awareness System

abstract: The research question of this study is: How Integration Readiness Level (IRL) metrics can be understood and realized in the domain of border control information systems. The study address to the IRL metrics and their definition, criteria, references, and questionnaires for validation of border control information systems in case of the shared maritime situational awareness system. The target of study is in improvements of ways for acceptance, operational validation, risk assessment, and development of sharing mechanisms and integration of information systems and border control information interactions and collaboration concepts in Finnish national and European border control domains.


title: Big Data Security: Survey on Frameworks and Algorithms

abstract: Technology today has progressed to an extent wherein collection of data is possible for every granular aspect of a business, in real time. Electronic devices, power grids and modern software all generate huge volumes of data, which is in the form of petabytes, exabytes and zettabytes. It is important to secure existing big data environments due to increasing threats of breaches and leaks from confidential data and increased adoption of cloud technologies due to the ability of buying processing power and storage on-demand. This is exposing traditional and new data warehouses and repositories to the outside world and at the risk of being compromised to hackers and malicious outsiders and insiders. In this paper the current big data scenario has been summarized along with challenges faced and security issues that need attention. Also some existing approaches have been described to illustrate current and standard directions to solving the issues.


title: Multi-target tracking by learning local-to-global trajectory models

abstract: The multi-target tracking problem is challenging when there exist occlusions, tracking failures of the detector and severe interferences between detections. In this paper, we propose a novel detection based tracking method that links detections into tracklets and further forms long trajectories. Unlike many previous hierarchical frameworks which split the data association into two separate optimization problems (linking detections locally and linking tracklets globally), we introduce a unified algorithm that can automatically relearn the trajectory models from the local and global information for finding the joint optimal assignment. In each temporal window, the trajectory models are initialized by the local information to link those easy-to-connect detections into a set of tracklets. Then the trajectory models are updated by the reliable tracklets and reused to link separated tracklets into long trajectories. We iteratively update the trajectory models by more information from more frames until the result converges. The iterative process gradually improves the accuracy of the trajectory models, which in turn improves the target ID inferences for all detections by the MRF model. Experiment results revealed that our proposed method achieved state-of-the-art multi-target tracking performance. HighlightsA unified framework to online learn local-to-global trajectory models is proposed.The iterative algorithm can alternately update the trajectory models.We solve inferences of target IDs for all the detections by using the MRF model.Our method has low complexity compared with most state-of-the-art methods.


title: Automated Blood Count Analysis by Trimodal Size Distribution of Leukocytes with the SYSMEX E-5000

abstract: The automated haematology analyser, SYSMEX E-5000, measures and computes quantitative haematological parameters, and determines the size distribution of blood cells and platelets. After partial lysis, the analyser classifies the leukocytes into 3 populations: small cells (lymphocytes), intermediate sized cells (basophils, eosinophils, monocytes) and large cells (neutrophils, including band cells). One thousand blood samples from inpatients and outpatients were analysed automatically in the SYSMEX as well as being submitted to microscopic blood smear differentiation, and the results were compared. The trimodal size distribution of the automated analysis revealed 1.8% false normal results. Ten cases of eosinophilia of between 6.6 and 12.5% remained undetected by the automated method, which also failed to detect 7 cases of left shift with normal leukocyte count, as well as a single sample containing 2% of myelocytes. Both diagnostic sensitivity and diagnostic specificity were high, i.e. 97.1% and 81.8%, respectively. The predictive values were also high for both pathological and normal results. Since certain changes in blood cell morphology are not detected by the SYSMEX, certain clinical indications still call for a microscopic blood smear examination. With due regard to these limitations, the apparatus yields reliable results and economizes considerably the routine laboratory work load. In the present study, 31% of the microscopic blood cell differential counts were saved by using the SYSMEX E-5000.


title: Low-dose CT denoising with convolutional neural network

abstract: To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.


title: The Restless Engram: Consolidations Never End

abstract: Memory consolidation is the hypothetical process in which an item in memory is transformed into a long-term form. It is commonly addressed at two complementary levels of description and analysis: the cellular/synaptic level (synaptic consolidation) and the brain systems level (systems consolidation). This article focuses on selected recent advances in consolidation research, including the reconsolidation of long-term memory items, the brain mechanisms of transformation of the content and of cue-dependency of memory items over time, as well as the role of rest and sleep in consolidating and shaping memories. Taken together, the picture that emerges is of dynamic engrams that are formed, modified, and remodified over time at the systems level by using synaptic consolidation mechanisms as subroutines. This implies that, contrary to interpretations that have dominated neuroscience for a while, but similar to long-standing cognitive concepts, consolidation of at least some items in long-term memory may never r...


title: Abundance and Diversity of Bacterial Nitrifiers and Denitrifiers and Their Functional Genes in Tannery Wastewater Treatment Plants Revealed by High-Throughput Sequencing

abstract: Biological nitrification/denitrification is frequently used to remove nitrogen from tannery wastewater containing high concentrations of ammonia. However, information is limited about the bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants (WWTPs) due to the low-throughput of the previously used methods. In this study, 454 pyrosequencing and Illumina high-throughput sequencing, combined with molecular methods, were used to comprehensively characterize structures and functions of nitrification and denitrification bacterial communities in aerobic and anaerobic sludge of two full-scale tannery WWTPs. Pyrosequencing of 16S rRNA genes showed that Proteobacteria and Synergistetes dominated in the aerobic and anaerobic sludge, respectively. Ammonia-oxidizing bacteria (AOB) amoA gene cloning revealed that Nitrosomonas europaea dominated the ammonia-oxidizing community in the WWTPs. Metagenomic analysis showed that the denitrifiers mainly included the genera of Thauera, Paracoccus, Hyphomicrobium, Comamonas and Azoarcus, which may greatly contribute to the nitrogen removal in the two WWTPs. It is interesting that AOB and ammonia-oxidizing archaea had low abundance although both WWTPs demonstrated high ammonium removal efficiency. Good correlation between the qPCR and metagenomic analysis is observed for the quantification of functional genes amoA, nirK, nirS and nosZ, indicating that the metagenomic approach may be a promising method used to comprehensively investigate the abundance of functional genes of nitrifiers and denitrifiers in the environment.


title: Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline

abstract: This Guideline is an official statement of the European Society of Gastrointestinal Endoscopy (ESGE). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system 1 2 was adopted to define the strength of recommendations and the quality of evidence. Main recommendations ::: 1 ESGE recommends endoscopic en bloc resection for superficial esophageal squamous cell cancers (SCCs), excluding those with obvious submucosal involvement (strong recommendation, moderate quality evidence). Endoscopic mucosal resection (EMR) may be considered in such lesions when they are smaller than 10 mm if en bloc resection can be assured. However, ESGE recommends endoscopic submucosal dissection (ESD) as the first option, mainly to provide an en bloc resection with accurate pathology staging and to avoid missing important histological features (strong recommendation, moderate quality evidence). 2 ESGE recommends endoscopic resection with a curative intent for visible lesions in Barrett’s esophagus (strong recommendation, moderate quality evidence). ESD has not been shown to be superior to EMR for excision of mucosal cancer, and for that reason EMR should be preferred. ESD may be considered in selected cases, such as lesions larger than 15 mm, poorly lifting tumors, and lesions at risk for submucosal invasion (strong recommendation, moderate quality evidence). 3 ESGE recommends endoscopic resection for the treatment of gastric superficial neoplastic lesions that possess a very low risk of lymph node metastasis (strong recommendation, high quality evidence). EMR is an acceptable option for lesions smaller than 10 – 15 mm with a very low probability of advanced histology (Paris 0-IIa). However, ESGE recommends ESD as treatment of choice for most gastric superficial neoplastic lesions (strong recommendation, moderate quality evidence). 4 ESGE states that the majority of colonic and rectal superficial lesions can be effectively removed in a curative way by standard polypectomy and/or by EMR (strong recommendation, moderate quality evidence). ESD can be considered for removal of colonic and rectal lesions with high suspicion of limited submucosal invasion that is based on two main criteria of depressed morphology and irregular or nongranular surface pattern, particularly if the lesions are larger than 20 mm; or ESD can be considered for colorectal lesions that otherwise cannot be optimally and radically removed by snare-based techniques (strong recommendation, moderate quality evidence).


title: SPANStore: cost-effective geo-replicated storage spanning multiple cloud services

abstract: By offering storage services in several geographically distributed data centers, cloud computing platforms enable applications to offer low latency access to user data. However, application developers are left to deal with the complexities associated with choosing the storage services at which any object is replicated and maintaining consistency across these replicas. In this paper, we present SPANStore, a key-value store that exports a unified view of storage services in geographically distributed data centers. To minimize an application provider's cost, we combine three key principles. First, SPANStore spans multiple cloud providers to increase the geographical density of data centers and to minimize cost by exploiting pricing discrepancies across providers. Second, by estimating application workload at the right granularity, SPANStore judiciously trades off greater geo-distributed replication necessary to satisfy latency goals with the higher storage and data propagation costs this entails in order to satisfy fault tolerance and consistency requirements. Finally, SPANStore minimizes the use of compute resources to implement tasks such as two-phase locking and data propagation, which are necessary to offer a global view of the storage services that it builds upon. Our evaluation of SPANStore shows that it can lower costs by over 10x in several scenarios, in comparison with alternative solutions that either use a single storage provider or replicate every object to every data center from which it is accessed.


title: The effect of education on health among US residents in relation to country of birth

abstract: This research explores the impact of education on health in relation to an individual's country of birth using the US National Health and Nutrition Examination Surveys for 2001–2004. We analyze health equations that relate health to education and other variables. Health is measured in terms of self-reported overall health, an index of biological risk factors, and body mass index. The primary hypothesis tested is whether education has a greater impact on immigrants' productive and allocative efficiency, because of their need to learn about how to remain healthy and access appropriate health care in a new environment. The empirical results indicate that for US residents, who were foreign‐born, education is associated with a greater beneficial effect on every health outcome compared to those born in the United States. More education is related to an even greater positive effect on health for immigrants from Mexico, the origin of most immigrants, than from other countries. These results provide additional support for the portions of the 2007 Immigration Reform Act rejected by the US Congress, which placed a higher priority on education and job skills than current law. Since increased education and improved health are associated, such policy reform would help reduce the demands on the US health‐care system. Copyright (C) 2009 John Wiley & Sons, Ltd.


title: Modeling and Control of a Doubly-Fed Induction Generator (DFIG) Wind Power Generation System for Real-time Simulations

abstract: This paper presents a study of a DFIG wind power generation system for real-time simula- tions. For real-time simulations, the Real-Time Digital Simulator (RTDS) and its user friendly interface simulation software RSCAD are used. A 2.2MW grid-connected variable speed DFIG wind power generation system is modeled and analyzed in this study. The stator-flux oriented vector control scheme is applied to the stator/rotor side converter control, and the back-to-back PWM converters are implemented for the decoupled control. The real-wind speed signal extracted by an anemometer is used for a realistic, reliable and accurate simulation analysis. Block diagrams, a mathematical presenta- tion of the DFIG and a control scheme of the stator/rotor-side are introduced. Real-time simulation cases are carried out and analyzed for the validity of this work.


title: Facilitating Time Critical Information Seeking in Social Media

abstract: Social media plays a major role in helping people affected by natural calamities. These people use social media to request information and help in situations where time is a critical commodity. However, generic social media platforms like Twitter and Facebook are not conducive for obtaining answers promptly. Algorithms to ensure prompt responders for questions in social media have to understand and model the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to identify users who can provide timely and relevant responses to questions posted on social media. We first draw from these theories to model the future availability and past response behavior of the candidate responders and integrate these criteria with user relevance. We propose a learning algorithm from these criteria to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media and use them to evaluate our framework. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media.


title: Security assessment of computer networks -an ethical hacker's perspective

abstract: Internet has been used for Critical Infrastructure and E-commerce business activities along with communication. The Information and Communication Technology (ICT) sector, finds itself confronted with sizeable Network Security problems. Computer Networks Security is of primary concern in the world over today. Hackers has been using various innovative techniques to steal the crucial information and accomplishing their tasks. Modern world people have been using cyber space for many day to day activities and dependency on the cyber space is very high. Whether the attack is focused on stealing business and technology secrets, disrupting financial systems or worse, the threat is real. This paper presents a rigorous yet practical system of Security Assessment of Computer Networks using open source tools to identify and fix the security lapses. The security assessment process from network, web and client security perspective and discussed in real time scenarios. The work is supplemented by wide spread Information Security awareness i.e. the user will be provided with adequate Security to Computer Networks and equip their computer networks to proactively deal with Network Security threats with security accountability. This work finds applicable for user community in the face of cyber surveillance and cyber warfare conflicts.


title: Experimental study on population-based incremental learning algorithms for dynamic optimization problems

abstract: Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBIL’s adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.


Lagrange Resolvents


title: A Brief History of Serious Games

abstract: Serious Games are now an established field of study. In this field most would attribute the rise of Serious Games to Clark C Abt’s creation of the term in 1970, or indeed Ben Sawyer’s popularization of it in 2002. However, considering the rich history of purposing non-digital games, itself preceded by discussions of purposing play that are traceable to the work of Plato, it can be said that Serious Games is a contemporary manifestation of centuries old theories and practices. In this chapter, we explore the pre-history of Serious Games, beginning with the suggested purpose, and purposing of play. Throughout this historical review we identify key in research and practice that are apparent in the contemporary Serious Games field.


title: Diverse Few-Shot Text Classification with Multiple Metrics

abstract: We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further study.


title: Bot Detection Based on Social Interactions in MMORPGs

abstract: The objective of this work is to detect the use of automated programs, known as game bots, based on social interactions in MMORPGs. Online games, especially MMORPGs, have become extremely popular among internet users in the recent years. Not only the popularity but also security threats such as the use of game bots and identity theft have grown manifold. As bot players can obtain unjustified assets without corresponding efforts, the gaming community does not allow players to use game bots. However, the task of identifying game bots is not an easy one because of the velocity and variety of their evolution in mimicking human behavior. Existing methods for detecting game bots have a few drawbacks like reducing immersion of players, low detection accuracy rate, and collision with other security programs. We propose a novel method for detecting game bots based on the fact that humans and game bots tend to form their social network in contrasting ways. In this work we focus particularly on the in game mentoring network from amongst several social networks. We construct a couple of new features based on eigenvector centrality to capture this intuition and establish their importance for detecting game bots. The results show a significant increase in the classification accuracy of various classifiers with the introduction of these features.


title: A system for indoor 3D mapping and virtual environments

abstract: The key to navigate in a 3-D environment or designing autonomous vehicles that can successfully maneuver and manipulate objects in their environment is the ability to create, maintain, and use effectively a 3-D digital model that accurately represents its physical counterpart. Virtual exploring of real places and environments, either for leisure, engineering design, training and simulation, or tasks in remote or hazardous environments, is more effective and useful if geometrical relationships and dimensions in the virtual model are accurate. A system which can rapidly, reliably, remotely and accurately perform measurements in the three-dimensional space for the mapping of indoor environments is needed for many applications. In this paper we present a mobile mapping system that is designed to generate a geometrically precise three-dimensional model of an unknown indoor environment. The same general design concept can be used for environments ranging from simple office hallways to long winding underground mine tunnels. Surfaces and features can be accurately mapped from images acquired by a unique configuration of different types of optical imaging sensor and dead reckoning positioning device. This configuration guarantees that all the information required to create the three-dimensional model of the environment is included in the collected data. Sufficient overlaps between two-dimensional intensity images, in combination with information from three-dimensional range images, insure that the complete environment can be accurately reconstructed when all the data is simultaneously processed. The system, the data collection and processing procedure, test results, the modeling and display at our virtual environment facility are described.


title: Automatic fuzzy ontology generation for semantic Web

abstract: Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed


title: 3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts

abstract: Effective extraction of road boundaries plays a significant role in intelligent transportation applications, including autonomous driving, vehicle navigation, and mapping. This paper presents a new method to automatically extract 3-D road boundaries from mobile laser scanning (MLS) data. The proposed method includes two main stages: supervoxel generation and 3-D road boundary extraction. Supervoxels are generated by selecting smooth points as seeds and assigning points into facets centered on these seeds using several attributes (e.g., geometric, intensity, and spatial distance). 3-D road boundaries are then extracted using the $\alpha $ -shape algorithm and the graph cuts-based energy minimization algorithm. The proposed method was tested on two data sets acquired by a RIEGL VMX-450 MLS system. Experimental results show that road boundaries can be robustly extracted with an average completeness over 95%, an average correctness over 98%, and an average quality over 94% on two data sets. The effectiveness and superiority of the proposed method over the state-of-the-art methods is demonstrated.


title: Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality

abstract: Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.


title: Thermoelastic Dissipation in Micromachined Birdbath Shell Resonators

abstract: Many MEMS gyroscopes rely on micro mechanical resonators to measure angular rotation. Maximizing their quality factor ( $Q$ ) will help improve accuracy. There are several energy dissipation mechanisms that limit $Q$ . This paper studies the role of thermoelastic dissipation (TED) in micro birdbath shell resonators. Fully coupled thermo-mechanical equations of physical behavior are solved for these shells using a finite-element method. Furthermore, an analytical model is developed to predict TED. The effects of material properties, shell geometry, edge chipping around the shell rim, trimming approaches, thin-film coatings, and operating temperature on thermoelastic $Q$ ( $Q_{{TED}})$ are studied. It is found that the shell material properties and rim thickness have significant impact on $Q_{{TED}}$ . However, edge chipping and most of the shell geometrical parameters do not have large impact. Additionally, this paper shows that some trimming approaches, such as forming grooves along the rim, can improve $Q_{{TED}}$ . A study of the effect of metal coatings on the resonator on $Q$ shows that the coating thickness and material are important factors affecting $Q$ and $Q_{{TED}}$ . The results presented in this paper provide guidelines for the design of other similar high- $Q$ resonators. [2016–0027]


RV-IOV : Tethering RISC-V Processors via Scalable I / O Virtualization


Chapter 2 GIS and Remote Sensing-Based Forest Resource Assessment , Quanti fi cation , and Mapping in Amhara Region , Ethiopia


title: Analyzing Factors Influencing Continuance Intention of E-Payment Adoption Using Modified UTAUT 2 Model

abstract: Online business of Indonesia continuous to grow rapidly. This rapid growth encourages developments for electronic payment systems. Moreover, Bank Indonesia is running a program to increase the awareness of society to become cash-less. One of the e-payment that emerging in Indonesia is Go-Pay, a Go-Jek payment system. Go-Pay successfully becomes one of the fifth largest e-money in Indonesia less than a year of its operation. Therefore, Bank Indonesia awarded Go-Jek as the most active company that increase society's awareness in conducting non-cash transactions in 2017. The successful of Go-Pay creates opportunities to upgrade the positions of Go-Pay service by knowing factors that influence its customers in continuing using Go-Pay services. This study intended to analyze factors influencing continuance intention of Go-Pay adoption in Indonesia by using a Modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) Model with Trust as a new variable. The data were gathered from 507 valid respondents in Indonesia chosen by purposive sampling technique. The result revealed that the factors influence continuance intention adoption of Go-Pay from the highest to the lowest respectively are Habit, Trust, Social Influence, Price Saving Orientation, Hedonic Motivation, and Performance Expectancy. The model can predict strongly the continuance intention of consumers towards Go-Pay services in Indonesia since the R2 is 72.8%. This model can be used by Go-Pay management in making decisions to maintain the continuance intention of consumers towards Go-Pay adoption by paying attention to those factors and their indicators.


title: An Integrated Driving/Charging Switched Reluctance Motor Drive Using Three-Phase Power Module

abstract: This paper develops an integrated driving/charging switched reluctance motor (SRM) drive for electric vehicles using off-the-shelf three-phase intelligent power modules (IPMs). Five legs of two IPMs are employed to construct the four-phase SRM modified Miller converter. Moreover, its front-end dc/dc boost converter is formed by the remaining one leg to boost the dc-link voltage from a battery. Proper current and speed control schemes are designed to yield satisfactory driving performance. In particular, the performance under higher speed is enhanced by the boosted dc-link voltage and the advanced shift of commutation instant. In idle case, some imbedded IPM power devices and SRM windings are arranged to form a buck-boost type or a buck-type switch-mode rectifier (SMR) for charging the battery from a utility with good charging control characteristics and a line drawn power quality. The rating derivations, performance analyses, and controller designs for these two SMRs are performed in detail. All constituted circuit components of the charger are naturally placed onboard; only the insertion of a power cable to the ac source socket is needed. A common digital signal processor is used to realize all control schemes fully digitally, and their control performances are demonstrated experimentally.


title: Robust Image Segmentation Using Contour-Guided Color Palettes

abstract: The contour-guided color palette (CCP) is proposed for robust image segmentation. It efficiently integrates contour and color cues of an image. To find representative colors of an image, color samples along long contours between regions, similar in spirit to machine learning methodology that focus on samples near decision boundaries, are collected followed by the mean-shift (MS) algorithm in the sampled color space to achieve an image-dependent color palette. This color palette provides a preliminary segmentation in the spatial domain, which is further fine-tuned by post-processing techniques such as leakage avoidance, fake boundary removal, and small region mergence. Segmentation performances of CCP and MS are compared and analyzed. While CCP offers an acceptable standalone segmentation result, it can be further integrated into the framework of layered spectral segmentation to produce a more robust segmentation. The superior performance of CCP-based segmentation algorithm is demonstrated by experiments on the Berkeley Segmentation Dataset.


title: Hot Under the Collar: Mapping Thermal Feedback to Dimensional Models of Emotion

abstract: There are inherent associations between temperature and emotion in language, cognition and subjective experience [22,42]. However, there exists no systematic mapping of thermal feedback to models of emotion that could be used by designers and users to convey a range of emotions in HCI. A common way of classifying emotions and quantifying emotional experience is through ratings along valence and arousal dimensions, originating from Russell's circumplex model [32]. Therefore, the research in this paper mapped subjective ratings of a range of thermal stimuli to the circumplex model to understand the range of emotions that might be conveyed through thermal feedback. However, as the suitability of the model varies depending on the type of emotional stimuli [31], we also compared the goodness of fit of ratings between the circumplex and vector [8,31] models of emotion. The results showed that thermal feedback was interpreted as representing a limited range of emotions concentrated in just two quadrants or categories of the circumplex: high valence, low arousal and low valence, high arousal. Warm stimuli were perceived as more pleasant/positive than cool stimuli and altering either the rate or extent of temperature change affected both valence and arousal axes simultaneously. The results showed a significantly better fit to a vector model than to the circumplex.


title: Feature-based aggregation and deep reinforcement learning: a survey and some new implementations

abstract: In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller “ aggregate ” Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural network-based reinforcement learning, thereby potentially leading to more effective policy improvement.


title: Deep Collective Classification in Heterogeneous Information Networks

abstract: Collective classification has attracted considerable attention in the last decade, where the labels within a group of instances are correlated and should be inferred collectively, instead of independently. Conventional approaches on collective classification mainly focus on exploiting simple relational features (such as count and exists aggregators on neighboring nodes). However, many real-world applications involve complex dependencies among the instances, which are obscure/hidden in the networks. To capture these dependencies in collective classification, we need to go beyond simple relational features and extract deep dependencies between the instances. In this paper, we study the problem of deep collective classification in Heterogeneous Information Networks (HINs), which involves different types of autocorrelations, from simple to complex relations, among the instances. Different from conventional autocorrelations, which are given explicitly by the links in the network, complex autocorrelations are obscure/hidden in HINs, and should be inferred from existing links in a hierarchical order. This problem is highly challenging due to the multiple types of dependencies among the nodes and the complexity of the relational features. In this study, we proposed a deep convolutional collective classification method, called GraphInception, to learn the deep relational features in HINs. The proposed method can automatically generate a hierarchy of relational features with different complexities. Extensive experiments on four real world networks demonstrate that our approach can improve the collective classification performance by considering deep relational features in HINs.


title: Multimodal Unsupervised Image-to-Image Translation

abstract: Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT


title: Adaptive Control of an Inverted Pendulum Using Adaptive PID Neural Network

abstract: Abstract- In this paper, a new method for adaptive control of nonlinear systems using neural networks and Proportional-Integral-Derivative (PID) methodology is proposed. In this method, a PID control and adaptive linear neural network is used to control a inverted pendulum with uncertainities and external disturbances. The system consists of an inverted pole hinged on a cart which is free to move in the x direction. The performance of the proposed method is demonstrated via simulations.


title: Recognition of Car Makes and Models From a Single Traffic-Camera Image

abstract: This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.


title: Towards Robot Self-consciousness (I): Brain-Inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-recognition

abstract: Mirror Self-Recognition is a well accepted test to identify whether an animal is with self-consciousness. Mirror neuron system is believed to be one of the most important biological foundation for Mirror Self-Recognition. Inspired by the biological mirror neuron system of the mammalian brain, we propose a Brain-inspired Robot Mirror Neuron System Model (Robot-MNS-Model) and we apply it to humanoid robots for mirror self-recognition. This model evaluates the similarity between the actual movements of robots and their visual perceptions. The association for self-recognition is supported by STDP learning which connects the correlated visual perception and motor control. The model is evaluated on self-recognition mirror test for 3 humanoid robots. Each robot has to decide which one is itself after a series of random movements facing a mirror. The results show that with the proposed model, multiple robots can pass the self-recognition mirror test at the same time, which is a step forward towards robot self-consciousness.


title: Neural network ensemble strategies for financial decision applications

abstract: Considerable research effort has been expended to identify more accurate models for decision support systems in financial decision domains including credit scoring and bankruptcy prediction. The focus of this earlier work has been to identify the "single best" prediction model from a collection that includes simple parametric models, nonparametric models that directly estimate data densities, and nonlinear pattern recognition models such as neural networks. Recent theories suggest this work may be misguided in that ensembles of predictors provide more accurate generalization than the reliance on a single model. This paper investigates three recent ensemble strategies: crossvalidation, bagging, and boosting. We employ the multilayer perceptron neural network as a base classifier. The generalization ability of the neural network ensemble is found to be superior to the single best model for three real world financial decision applications.


title: Crowdroid: behavior-based malware detection system for Android

abstract: The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue. In this paper we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the Android platform. The detector is embedded in a overall framework for collection of traces from an unlimited number of real users based on crowdsourcing. Our framework has been demonstrated by analyzing the data collected in the central server using two types of data sets: those from artificial malware created for test purposes, and those from real malware found in the wild. The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware. This shows the potential for avoiding the spreading of a detected malware to a larger community.


title: Signal propagation techniques for wireless underground communication networks

abstract: Wireless Underground Communication Networks (WUCNs) consist of wireless devices that operate below the ground surface. These devices are either (i) buried completely under dense soil, or (ii) placed within a bounded open underground space, such as underground mines and road/subway tunnels. The main difference between WUCNs and the terrestrial wireless communication networks is the communication medium. In this paper, signal propagation characteristics are described in these constrained areas. First, a channel model is described for electromagnetic (EM) waves in soil medium. This model characterizes not only the propagation of EM waves, but also other effects such as multipath, soil composition, water content, and burial depth. Second, the magnetic induction (MI) techniques are analyzed for communication through soil. Based on the channel model, the MI waveguide technique for communication is developed to address the high attenuation challenges of MI waves through soil. Furthermore, a channel model, i.e., the multimode model, is provided to characterize the wireless channel for WUCNs in underground mines and road/subway tunnels. The multimode model can characterize two cases for underground communication, i.e., the tunnel channel and the room-and-pillar channel. Finally, research challenges for the design communication protocols for WUCNs in both underground environments are discussed based on the analysis of the signal propagation.


title: Direct methods for 3D reconstruction and visual SLAM

abstract: The reconstruction of the 3D world from camera images has a tradition of over 100 years. Nevertheless, we have witnessed over the last few years a dramatic boost in performance of reconstruction algorithms. An important innovation underlying this performance boost is the development of direct methods to estimate the 3D structure and the camera motion. Some of these developments shall be reviewed in the following.


title: Modular active curiosity-driven discovery of tool use

abstract: This article studies algorithms used by a learner to explore high-dimensional structured sensorimotor spaces such as in tool use discovery. In particular, we consider goal babbling architectures that were designed to explore and learn solutions to fields of sensorimotor problems, i.e. to acquire inverse models mapping a space of parameterized sensorimotor problems/effects to a corresponding space of parameterized motor primitives. However, so far these architectures have not been used in high-dimensional spaces of effects. Here, we show the limits of existing goal babbling architectures for efficient exploration in such spaces, and introduce a novel exploration architecture called Model Babbling (MB). MB exploits efficiently a modular representation of the space of parameterized problems/effects. We also study an active version of Model Babbling (the MACOB architecture). These architectures are compared in a simulated experimental setup with an arm that can discover and learn how to move objects using two tools with different properties, embedding structured high-dimensional continuous motor and sensory spaces.


FRnet-DTI: Convolutional Neural Networks for Drug-Target Interaction


title: Dynamic Multi-Level Multi-Task Learning for Sentence Simplification

abstract: Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.


title: ‘teen Mental Health First Aid’: a description of the program and an initial evaluation

abstract: Many adolescents have poor mental health literacy, stigmatising attitudes towards people with mental illness, and lack skills in providing optimal Mental Health First Aid to peers. These could be improved with training to facilitate better social support and increase appropriate help-seeking among adolescents with emerging mental health problems. teen Mental Health First Aid (teen MHFA), a new initiative of Mental Health First Aid International, is a 3 × 75 min classroom based training program for students aged 15–18 years. An uncontrolled pilot of the teen MHFA course was undertaken to examine the feasibility of providing the program in Australian secondary schools, to test relevant measures of student knowledge, attitudes and behaviours, and to provide initial evidence of program effects. Across four schools, 988 students received the teen MHFA program. 520 students with a mean age of 16 years completed the baseline questionnaire, 345 completed the post-test and 241 completed the three-month follow-up. Statistically significant improvements were found in mental health literacy, confidence in providing Mental Health First Aid to a peer, help-seeking intentions and student mental health, while stigmatising attitudes significantly reduced. teen MHFA appears to be an effective and feasible program for training high school students in Mental Health First Aid techniques. Further research is required with a randomized controlled design to elucidate the causal role of the program in the changes observed.


title: Lane Detection Method Based on Improved RANSAC Algorithm

abstract: Lane detection based on computer vision is a key technology of Automatic Drive System for intelligent vehicles. In this paper, we propose a real-time and efficient lane detection algorithm that can detect lanes appearing in urban streets and highway roads under complex background. In order to enhance lane boundary information and to be suitable for various light conditions, we adopt canny algorithm for edge detection to get good feature points. We use the generalized curve lane parameter model, which can describe both straight and curved lanes. We propose an improved random sample consensus (RANSAC) algorithm combined with the least squares technique to estimate lane model parameters based on feature extraction. Experiments are conducted on both real road lane videos captured by Tongji University and Caltech Lane Datasets. The experimental results show that our algorithm is can meet the real time requirement and fit lane boundaries well in various challenging road conditions.


title: Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

abstract: Object detection in videos is an important task in computer vision for various applications such as object tracking, video summarization and video search. Although great progress has been made in improving the accuracy of object detection in recent years due to the rise of deep neural networks, the state-of-the-art algorithms are highly computationally intensive. In order to address this challenge, we make two important observations in the context of videos: (i) Objects often occupy only a small fraction of the area in each video frame, and (ii) There is a high likelihood of strong temporal correlation between consecutive frames. Based on these observations, we propose Pack and Detect (PaD), an approach to reduce the computational requirements of object detection in videos. In PaD, only selected video frames called anchor frames are processed at full size. In the frames that lie between anchor frames (inter-anchor frames), regions of interest (ROIs) are identified based on the detections in the previous frame. We propose an algorithm to pack the ROIs of each inter-anchor frame together into a reduced-size frame. The computational requirements of the detector are reduced due to the lower size of the input. In order to maintain the accuracy of object detection, the proposed algorithm expands the ROIs greedily to provide additional background around each object to the detector. PaD can use any underlying neural network architecture to process the full-size and reduced-size frames. Experiments using the ImageNet video object detection dataset indicate that PaD can potentially reduce the number of FLOPS required for a frame by 4×. This leads to an overall increase in throughput of 1.25× on a 2.1 GHz Intel Xeon server with a NVIDIA Titan X GPU at the cost of 1.1% drop in accuracy.


Improved image-based classification of Alzheimer ’ s disease using multimodality brain imaging data


title: Full-Range Soft-Switching-Isolated Buck-Boost Converters With Integrated Interleaved Boost Converter and Phase-Shifted Control

abstract: A new method for deriving isolated buck-boost (IBB) converter with single-stage power conversion is proposed in this paper and novel IBB converters based on high-frequency bridgeless-interleaved boost rectifiers are presented. The semiconductors, conduction losses, and switching losses are reduced significantly by integrating the interleaved boost converters into the full-bridge diode-rectifier. Various high-frequency bridgeless boost rectifiers are harvested based on different types of interleaved boost converters, including the conventional boost converter and high step-up boost converters with voltage multiplier and coupled inductor. The full-bridge IBB converter with voltage multiplier is analyzed in detail. The voltage multiplier helps to enhance the voltage gain and reduce the voltage stresses of the semiconductors in the rectification circuit. Hence, a transformer with reduced turns ratio and parasitic parameters, and low-voltage rated MOSFETs and diodes with better switching and conduction performances can be applied to improve the efficiency. Moreover, optimized phase-shift modulation strategy is applied to the full-bridge IBB converter to achieve isolated buck and boost conversion. What's more, soft-switching performance of all of the active switches and diodes within the whole operating range is achieved. A 380-V output prototype is fabricated to verify the effectiveness of the proposed IBB converters and its control strategies.


The Effect of Mineral Supplements on the Availability of Manganese


title: Video games and urban simulation: new tools or new tricks?

abstract: Video games allow complex systems modelling, revealing retroaction loops, replicating self-organization and the emergence of hierarchical organization, functional differentiation and social segregation through multi-level interactions. Recent trends focus on improving modelling tools’ graphic quality and interface attractiveness and on using video games to facilitate urban studies teaching and research. This apparent convergence between simulation and video games is addressed through a selection of strategy and city builder video games. Comparisons reveal that simulations and video games point to similar results:, they both allow the simulation of complex urban processes, like hierarchical urban networks or urban segregation. Games even seem to allow going one step further, being often more comprehensive simulations. However, the main limitation of video games emerges from their didactic power: video games and simulation software implement rules and models in almost opposite means. Games induce players to learn the model but not to challenge or to produce new knowledge.


title: An original face anti-spoofing approach using partial convolutional neural network

abstract: Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. Then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. The experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods.


title: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

abstract: The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between the words receptionist and female, while maintaining desired associations such as between the words queen and female. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.


title: Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance

abstract: Chronic infection with hepatitis C virus (HCV) affects 170 million people worldwide and is the leading cause of cirrhosis in North America. Although the recommended treatment for chronic infection involves a 48-week course of peginterferon-alpha-2b (PegIFN-alpha-2b) or -alpha-2a (PegIFN-alpha-2a) combined with ribavirin (RBV), it is well known that many patients will not be cured by treatment, and that patients of European ancestry have a significantly higher probability of being cured than patients of African ancestry. In addition to limited efficacy, treatment is often poorly tolerated because of side effects that prevent some patients from completing therapy. For these reasons, identification of the determinants of response to treatment is a high priority. Here we report that a genetic polymorphism near the IL28B gene, encoding interferon-lambda-3 (IFN-lambda-3), is associated with an approximately twofold change in response to treatment, both among patients of European ancestry (P = 1.06 x 10(-25)) and African-Americans (P = 2.06 x 10(-3)). Because the genotype leading to better response is in substantially greater frequency in European than African populations, this genetic polymorphism also explains approximately half of the difference in response rates between African-Americans and patients of European ancestry.


title: Cyclodextrin-Based “Green” Wrinkle-Free Finishing of Cotton Fabrics

abstract: This study presents a cyclodextrin-based “green”, inexpensive cross-linker to enhance the wrinkle-recovery angle (WRA) of fabrics. Cyclodextrin (CD) was oxidized using hydrogen peroxide (H2O2) and carboxylated with malic acid to introduce multiple carboxylic acid groups. The presence of carboxylic acid groups on CD was confirmed using attenuated total reflectance Fourier transform infrared (ATR-FTIR) and 13C NMR. Carboxylated CD (C-CD) was used to cross-link cotton fibers via multiple ester bond formation. Cross-linking was confirmed using ATR-FTIR and an increase in WRA. The impact of cross-linking on color change and tensile properties such as strength, strain, and tear strength of the fabrics was characterized. The wrinkle resistance of the treated fabrics was found to be durable for up to 25 laundry washings. Shelf-life of C-CD was found to be stable for 6 weeks as it did not show a reduction in WRA of fabrics. C-CD would be an excellent green alternative for industrial scale-up to replace dimethylol ...


title: Psychopathy and the five-factor model of personality: A replication and extension

abstract: It has recently been argued that psychopathy can be understood and represented using common dimensions of personality taken from the Five-factor model (FFM). In this research, we examined this possibility by using the Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992) to assess psychopathy in an undergraduate sample. Specifically, we matched individuals' NEO-PI-R profiles with an expert-generated psychopathy prototype to yield a psychopathy score. These scores were correlated with self-reports of drug use, delinquency, risky sex, aggression, and several laboratory tasks. FFM psychopathy was significantly related to all forms of deviance, although the effects tended to be small in size. Moreover, individuals who more closely resembled the prototypic FFM psychopath were more aggressive in a laboratory aggression task, less willing to delay gratification in a time discounting task, and demonstrated a preference for aggressive responses in a social information-processing paradigm.


title: Overview of Maximum Power Point Tracking Control Methods for PV Systems

abstract: Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.


title: A System-Level Simulation Framework for UWB Localization

abstract: In this paper we present a comprehensive simulation framework that can accurately simulate our indoor UWB positioning system including the analog front-ends, digital back-ends, and channel effects. Experimental results from our UWB positioning system are compared to simulated results to validate the accuracy of the simulation framework. Simulated results illustrate the design decisions made in optimizing overall system performance. Multiple peak detection algorithms are presented, and the robustness of our leading-edge detection algorithm is shown even in dense multipath environments. Geometric effects are examined by simulating various base station configurations. This comprehensive simulation framework provides an in-depth analysis of our UWB positioning system and also provides a general framework for rapid prototyping and design of complex wireless systems.


title: Kinect vs. Low-cost Inertial Sensing for Gesture Recognition

abstract: In this paper, we investigate efficient recognition of human gestures / movements from multimedia and multimodal data, including the Microsoft Kinect and translational and rotational acceleration and velocity from wearable inertial sensors. We firstly present a system that automatically classifies a large range of activities (17 different gestures) using a random forest decision tree. Our system can achieve near real time recognition by appropriately selecting the sensors that led to the greatest contributing factor for a particular task. Features extracted from multimodal sensor data were used to train and evaluate a customized classifier. This novel technique is capable of successfully classifying various gestures with up to 91 % overall accuracy on a publicly available data set. Secondly we investigate a wide range of different motion capture modalities and compare their results in terms of gesture recognition accuracy using our proposed approach. We conclude that gesture recognition can be effectively performed by considering an approach that overcomes many of the limitations associated with the Kinect and potentially paves the way for low-cost gesture recognition in unconstrained environments.


title: Finger Vein Identification Based on Minutiae Feature Extraction with Spurious Minutiae Removal

abstract: Finger vein technology is the newest biometric technology which utilizes the vein pattern which is hidden under the human finger for identification. As these patterns are hidden under the skin surface, they provide a huge privacy consideration, and are hence extremely difficult to forge. An approach to perform finger vein identification based on extracting minutiae features and the spurious minutiae removal is presented in this work. Minutiae feature extraction includes the extraction of end points and bifurcation points from the skeletal patterns of vein and the removal of spurious or false minutiae, makes the identification more accurate.


title: Employee Participation and Productivity

abstract: Abstract This paper measures the productivity impact of shop-floor employee involvement. On the basis of a representative German establishment data set, the study finds that the introduction of teamwork and autonomous work groups, and a reduction of hierarchies in 1996/1997 significantly increased average establishment productivity in 1997–2000. The estimation strategy controls for unobserved invariant establishment heterogeneity by using a two-step system GMM panel regression approach. It simultaneously takes account of endogeneity of participative work organization by instrument variable regressions. It is also shown that the productivity effect of shop-floor employee involvement is stronger in establishments with works councils.


title: Metformin prevents an adaptive increase in GSH and induces apoptosis under the conditions of GSH deficiency in H4IIE cells.

abstract: The antidiabetic effect of metformin is mediated by activation of AMP-activated kinase (AMPK). This study investigated whether metformin at a high pharmacologic concentration alters the levels of cellular GSH in H4IIE hepatocytes and if so, whether the agent affects cell viability under GSH-deficient conditions. Treatment of cells with either metformin or 5-aminoimidazole-4-carboxamide riboside (AICAR) increased dichlorofluorescein oxidation, as did tert-butylhydroxyquinone (t-BHQ). Metformin or AICAR treatment blocked a rebound increase in GSH produced by t-BHQ and decreased GSH content below that of control. Exposure of cells to metformin or metformin + t-BHQ for 24 hr did not produce cell death. However, metformin treatment in combination with t-BHQ for a prolonged period of time (48 hr) at the concentrations, at which each agent was non-toxic, produced apoptosis. Treatment of AICAR with t-BHQ resulted in similar effects. Induction of apoptosis by the combination treatment was evidenced by changes in mitochondrial cytochrome c content, BCl(xl) expression, poly(ADP-ribose)polymerase (PARP) cleavage and caspase-3 activation. Compound C, an AMPK inhibitor, reversed apoptosis and changes in the apoptotic markers, suggesting a role of AMPK activation by metformin in the apoptotic process. Similarly, metformin treatment, when combined with buthionine sulfoximine or doxorubicin, induced apoptosis. Our data indicated that metformin prevents an adaptive increase in cellular GSH in H4IIE cells, and therefore induces apoptosis under the conditions of GSH deficiency.


title: Keeping and Using Reflective Journals in the Qualitative Research Process

abstract: The problem of bias in qualitative research particularly is still debated in methodology texts and there is a lack of agreement on how much researcher influence is acceptable, whether or not it needs to be "controlled," and how it might be accounted for. Denzin (1994) refers to this as "the interpretive crisis" (p. 501). I chose to make my experiences, opinions, thoughts, and feelings visible and an acknowledged part of the research process through keeping reflective journals and using them in writing up the research. The aim of this paper is to show how reflective journals were used in engaging with the notion of creating transparency in the research process, and explore the impact of critical self-reflection on research design. Key Words: Self-reflection, Qualitative Research, and Research Journals Introduction A reflexive approach to the research process is now widely accepted in much qualitative research. Researchers are urged to talk about themselves, "their presuppositions, choices, experiences, and actions during the research process" (Mruck & Breuer, 2003, p. 3). Reflective practice such as this aims to make visible to the reader the constructed nature of research outcomes, a construction that "originates in the various choices and decisions researchers undertake during the process of researching" (Mruck & Breuer, p. 3). Increasingly qualitative research, particularly that which is situated within feminist, critical, and poststructuralist paradigms is, ... presented in ways that make it clear how the researcher's own experiences, values, and positions of privilege in various hierarchies have influenced their research interests, the way they choose to do their research, and the ways they choose to represent their research findings. (Harrison, MacGibbon, & Morton, 2001, p. 325) Rather than attempting to control researcher values through method or by bracketing assumptions, the aim is to consciously acknowledge those values. Keeping self-reflective journals is a strategy that can facilitate reflexivity, whereby researchers use their journal to examine "personal assumptions and goals" and clarify "individual belief systems and subjectivities" (Ahern as cited in Russell & Kelly, 2002, p. 2). Whilst keeping a reflective journal is a common practice in qualitative research, particularly reflexive research (Etherington, 2004), there is relatively little literature on the use of reflective journals in the research process, and limited guidance for novice researchers as to the purposes of keeping a reflective journal from a methodological perspective and how to use their reflections as an integral part of the research process. This aim of this paper is to show the reader how reflective journals were used in engaging with the notion of creating transparency in the research process, and how keeping a reflective journal can have concrete effects on the research design. The goal is to provide a research "trail" of gradually altering methodologies and reshaping analysis. My target audience is novice researchers, perhaps doctoral students, who have been advised to keep a research journal, but are not sure about the purpose of keeping such a journal or how they might use it in their research. The purpose is to share my experiences with, and uses of, reflective research journals so that novice researchers can more consciously engage in journaling and make it part of their research from the beginning, and also, to illustrate in a descriptive way how journals might be written and how they might be used. The project in which I used reflective journals was a doctoral research study that explored how tertiary supervisors understand and practise assessment of the early childhood practicum. Participants were tertiary supervisors who supervised and assessed students enrolled in pre-service early childhood education courses offered by universities and/or institutes of Technical and Further Education (TAFE) in Australia. …


title: Improved design of Vivaldi antenna with loading resistance for wideband applications

abstract: With the development of high-speed integrated circuit, the Ultra Wideband (UWB) communication system has been developed toward the direction of miniaturization, integration, which will necessarily promote UWB antenna also be developed toward the direction of miniaturization, integration, etc. This paper proposes an improved structure of Vivaldi antenna, which loads resistance at the bottom of exponential type antenna to improve Voltage Standing Wave Ratio (VSWR) at low frequency, and which opens three symmetrical unequal rectangular slots in the antenna radiation part to increase the gain. The improved Vivaldi antenna size is 150 mm * 150 mm, and the working frequency is 0.8–3.8 GHz (measured VSWR<2).The experimental results show that the antenna has good directional radiation characteristics within the scope of bandwidth.


title: Design principles for sensemaking support systems in environmental sustainability transformations

abstract: AbstractThis paper reports on the results of a design science research (DSR) study that develops design principles for information systems (IS) that support organisational sensemaking in environmen...


title: Bidirectional transformations: A cross-discipline perspective

abstract: The GRACE International Meeting on Bidirectional Transformations was held in December 2008 near Tokyo, Japan. The meeting brought together researchers and practitioners from a variety of sub-disciplines of computer science to share research efforts and help create a new community. In this report, we survey the state of the art and summarize the technical presentations delivered at the meeting. We also describe some insights gathered from our discussions and introduce a new effort to establish a benchmark for bidirectional transformations.


title: KVM/ARM: the design and implementation of the linux ARM hypervisor

abstract: As ARM CPUs become increasingly common in mobile devices and servers, there is a growing demand for providing the benefits of virtualization for ARM-based devices. We present our experiences building the Linux ARM hypervisor, KVM/ARM, the first full system ARM virtualization solution that can run unmodified guest operating systems on ARM multicore hardware. KVM/ARM introduces split-mode virtualization, allowing a hypervisor to split its execution across CPU modes and be integrated into the Linux kernel. This allows KVM/ARM to leverage existing Linux hardware support and functionality to simplify hypervisor development and maintainability while utilizing recent ARM hardware virtualization extensions to run virtual machines with comparable performance to native execution. KVM/ARM has been successfully merged into the mainline Linux kernel, ensuring that it will gain wide adoption as the virtualization platform of choice for ARM. We provide the first measurements on real hardware of a complete hypervisor using ARM hardware virtualization support. Our results demonstrate that KVM/ARM has modest virtualization performance and power costs, and can achieve lower performance and power costs compared to x86-based Linux virtualization on multicore hardware.


title: Using Goal Modeling to Capture Competency Questions in Ontology-based Systems

abstract: Most of the available ontology engineering methodologies presuppose the existence of a set of questions which provide the objective and scope of the ontology under development. However, these so-called competency questions are not always clear from start. In this sense, such competency questions are comparable to system requirements, elicited and modeled during the requirements engineering stage of a software development process. Having this in mind, this work proposes the use of the Tropos methodology to help the ontology engineer reason and model competency questions. For that, he should follow the traditional Tropos process, starting with the early requirements activity to analyze the objectives of an organization. This initial stage is followed by the late requirements activity, in which the competency question are captured and linked to the organization’s goals, following the goal modeling process. In this way, we aim at providing a methodology which will consistently drive the ontology engineer in developing an ontology from scratch.


title: An Approach Based on Chain Key Predistribution against Sybil Attack in Wireless Sensor Networks

abstract: In wireless sensor networks (WSN), Sybil attack can destroy the routing and data distributed storage mechanisms through fabricating identity information of legitimate nodes. This paper presents a chain key predistribution based approach against Sybil attack. To enhance the security of common keys between neighboring nodes, during the chain key predistribution phase, our approach uses a lightweight hash function to generate several chain keys by hashing the unique identity information of every node sequentially in the trusted base station. These chain keys construct a pool of chain keys. During the pairwise key authentication establishment phase, a node-to-node chain key based authentication and exchange (CK-AE) protocol is proposed, by which each node can share the unique pairwise key with its neighboring node. The CK-AE protocol is provably secure in the universally composable security model (UCSM). Finally, we analyze our approach from the resilience of network and the performance overhead, and the results show that our approach can not only enhance the ability of resilience to Sybil attack, but also reduce the communication overhead significantly at the cost of a certain amount of computational overhead.


title: Biconvex Relaxation for Semidefinite Programming in Computer Vision

abstract: Semidefinite programming is an indispensable tool in computer vision, but general-purpose solvers for semidefinite programs are often too slow and memory intensive for large-scale problems. We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity. Our approach, referred to as biconvex relaxation (BCR), transforms a general SDP into a specific biconvex optimization problem, which can then be solved in the original, low-dimensional variable space at low complexity. The resulting biconvex problem is solved using an efficient alternating minimization (AM) procedure. Since AM has the potential to get stuck in local minima, we propose a general initialization scheme that enables BCR to start close to a global optimum - this is key for our algorithm to quickly converge to optimal or near-optimal solutions. We showcase the efficacy of our approach on three applications in computer vision, namely segmentation, co-segmentation, and manifold metric learning. BCR achieves solution quality comparable to state-of-the-art SDP methods with speedups between 4X and 35X. At the same time, BCR handles a more general set of SDPs than previous approaches, which are more specialized.


title: Deep Leaf Segmentation Using Synthetic Data

abstract: Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We train a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a combination of real and synthetic images of Arabidopsis plants. Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC). Our approach also achieves 81% mean performance over all five test datasets.


title: Gated Fusion Network for Joint Image Deblurring and Super-Resolution

abstract: Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.


title: The Influence of Exercise on Cognitive Abilities

abstract: Scientific evidence based on neuroimaging approaches over the last decade has demonstrated the efficacy of physical activity improving cognitive health across the human lifespan. Aerobic fitness spares age-related loss of brain tissue during aging, and enhances functional aspects of higher order regions involved in the control of cognition. More active or higher fit individuals are capable of allocating greater attentional resources toward the environment and are able to process information more quickly. These data are suggestive that aerobic fitness enhances cognitive strategies enabling to respond effectively to an imposed challenge with a better yield in task performance. In turn, animal studies have shown that exercise has a benevolent action on health and plasticity of the nervous system. New evidence indicates that exercise exerts its effects on cognition by affecting molecular events related to the management of energy metabolism and synaptic plasticity. An important instigator in the molecular machinery stimulated by exercise is brain-derived neurotrophic factor, which acts at the interface of metabolism and plasticity. Recent studies show that exercise collaborates with other aspects of lifestyle to influence the molecular substrates of cognition. In particular, select dietary factors share similar mechanisms with exercise, and in some cases they can complement the action of exercise. Therefore, exercise and dietary management appear as a noninvasive and effective strategy to counteract neurological and cognitive disorders.