title: Effect of calcium supplementation on bone density and parathyroid function in elderly subjects.

abstract: The effect of calcium supplementation on secondary hyperparathyroidism of old age depends on the intestinal absorption of the calcium preparation used. In order to test the bioavailability and clinical usefulness of a new calcium preparation, heated oyster shell-seaweed calcium (HOSS Ca), a randomized, prospective, double-blind comparison of HOSS Ca, calcium carbonate and placebo was carried out in 58 hospitalized women with a mean age of 82. Group A received 900 mg Ca/day Ca as HOSS Ca, group B 900 mg Ca/day as CaCO3 and group C placebo in addition to the basic hospital diet containing approximately 600 mg Ca/day. After 18 months, lumbar spine bone mineral density (BMD) measured by DPX was 106.1 +/- 3.5% (mean +/- SEM) of the pretrial basal value in group A, 99.8 +/- 3.8% in group B and 90.9 +/- 3.4% in group C (A bu not B significantly higher than C). Midradial BMD measured by DPX was 99.3 +/- 1.3% in A, 94.8 +/- 4.0% in B and 85.5 +/- 6.2% in C. The ratio of whole body calcium content between the 12th and 18th month was 96.5 +/- 1.7% in A, 90.1 +/- 2.7% in B and 89.8 +/- 1.3% in C (A but not B significantly smaller than C). Final/baseline ratio of urinary Ca/creatinine was 91.7 +/- 11.1 in A, 111.4 +/- 18.9 in B and 125.2 +/- 12.6 in C.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Electron-Hole Bilayer TFET: Experiments and Comments

abstract: We investigate Si/Si ::: 0.85 ::: Ge ::: 0.15 ::: fully depleted-SOI tunnel FET (TFET) devices operated in the electron-hole bilayer (EHB) mode. The application of negative bias on front gate and positive bias on back gate results in confined hole and electron layers that are expected to enable vertical band-to-band tunneling (BTBT). The idea of the EHB-TFET device is to enhance the tunneling current by expanding the BTBT generation area from the narrow lateral source/channel junction to the entire channel region. Our systematic measurements on a variety of TFETs with variable geometry and channel materials do not offer support to this attractive concept. Self-consistent simulations confirm that the vertical BTBT transitions do not produce an appreciable current in our devices, due to size-and bias-induced quantization, effective mass anisotropy, and incomplete formation of the bilayer. We examine the conditions for efficient vertical BTBT to occur and show that they cannot be met simultaneously, at least in Si or Si/SiGe devices.


title: Latent Predictor Networks for Code Generation

abstract: Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.


Learning from Disunity


title: Active database systems

abstract: Active database systems support mechanisms that enable them to respond automatically to events that are taking place either inside or outside the database system itself. Considerable effort has been directed towards improving understanding of such systems in recent years, and many different proposals have been made and applications suggested. This high level of activity has not yielded a single agreed-upon standard approach to the integration of active functionality with conventional database systems, but has led to improved understanding of active behavior description languages, execution models, and architectures. This survey presents the fundamental characteristics of active database systems, describes a collection of representative systems within a common framework, considers the consequences for implementations of certain design decisions, and discusses tools for developing active applications.


title: Brand communication through digital influencers: Leveraging blogger engagement

abstract: The growing power of bloggers to influence their connected network has emerged as a new communication venue for brands. This study elaborates upon the role of bloggers in brand communication, and reveals how brands can engage with bloggers, currently considered as online opinion leaders, from the perspective of the two-step flow theory. Following clarification of the aims of the study, we report on in-depth interviews with 17 brand and digital agency representatives, selected because they regard communication with bloggers as an important strategy in increasing the influence of their brands among online communities. This exploratory study reflects current blogger communication implementations, and concludes with a discussion of seven major issues arising from the literature review and interviews (definition of bloggers, blogger selection criteria, digital integration, power of bloggers, long-term relationship building with bloggers, measurement, and budgetary issues in blogger communication). These areas represent relatively unexplored areas of blogger engagement from both an academic and managerial perspective. Based on the findings of the interviews, we propose a model which traces the influencer role of bloggers from the two-step flow theory perspective. This model is named as the brand communication through digital influencers model.


title: A Fast x86 Implementation of Select

abstract: Rank and select are fundamental operations in succinct data structures, that is, data structures whose space consumption approaches the information-theoretic optimal. The performance of these primitives is central to the overall performance of succinct data structures. ::: Traditionally, the select operation is the harder to implement efficiently, and most prior implementations of select on machine words use 50--80 machine instructions. (In contrast, rank on machine words can be implemented in only a handful of instructions on machines that support POPCOUNT.) However, recently Pandey et al. gave a new implementation of machine-word select that uses only four x86 machine instructions; two of which were introduced in Intel's Haswell CPUs. ::: In this paper, we investigate the impact of this new implementation of machine-word select on the performance of general bit-vector-select. We first compare Pandey et al.'s machine-word select to the state-of-the-art implementations of Zhou et al. (which is not specific to Haswell) and Gog et al. (which uses some Haswell-specific instructions). We exhibit a speedup of 2X to 4X. ::: We then study the impact of plugging Pandey et al.'s machine-word select into two state-of-the-art bit-vector-select implementations. Both Zhou et al.'s and Gog et al.'s select implementations perform a single machine-word select operation for each bit-vector select. We replaced the machine-word select with the new implementation and compared performance. Even though there is only a single machine- word select operation, we still obtained speedups of 20% to 68%. We found that the new select not only reduced the number of instructions required for each bit-vector select, but also improved CPU instruction cache performance and memory-access parallelism.


title: Trends in Automotive Communication Systems

abstract: The use of networks for communications between the electronic control units (ECU) of a vehicle in production cars dates from the beginning of the 1990s. The specific requirements of the different car domains have led to the development of a large number of automotive networks such as Local Interconnect Network, J1850, CAN, TTP/C, FlexRay, media-oriented system transport, IDB1394, etc. This paper first introduces the context of in-vehicle embedded systems and, in particular, the requirements imposed on the communication systems. Then, a comprehensive review of the most widely used automotive networks, as well as the emerging ones, is given. Next, the current efforts of the automotive industry on middleware technologies, which may be of great help in mastering the heterogeneity, are reviewed. Finally, we highlight future trends in the development of automotive communication systems.


title: A Longitudinal Assessment of Hormonal and Physical Alterations during Normal Puberty in Boys . V . Rising Leptin Levels May Signal the Onset of Puberty *

abstract: Leptin, the product of the ob gene, is an adipocyte-derived hormone that signals the amount of adipose tissue energy stores to the brain and exerts major effects on energy homeostasis and neuroendocrine function. Leptin has recently been shown to affect reproductive function in leptin-deficient and normal rodents. As puberty, the process of sexual maturation and acquisition of reproductive competence, has been proposed to be triggered by the attainment of a critical amount and/or distribution of fat, we examined whether changes in circulating leptin levels could represent the hormonal signal responsible for triggering the onset of puberty in humans. Eight prepubertal boys (Tanner genital stage 1 or early stage 2 at the initiation of the study) were evaluated longitudinally for 2.5–5.1 yr depending on when Tanner stage 5 of genital development was achieved. Sera for the determination of leptin, testosterone, and dehydroepiandrosterone sulfate were obtained every 4 months during the period of the study. Com...


title: Biometric image processing and recognition

abstract: Biometric-based identification and verification systems are poised to become a key technology, with applications including controlling access to buildings and computers, reducing fraudulent transactions in electronic commerce, and discouraging illegal immigration. There are at least eight image-based biometrics that are being actively considered. In image-based biometrics, the biometrie signature is acquired as an image and the image is processed using techniques from computer vision, image understanding, and pattern recognition. We consider two promising image-based biometrics, faces and fingerprints. For each, we provide a critical assessment of the state of the art, suggest future research directions, and identify technological challenges.


title: Design of fractional-order proportional-integral-derivative controller: Hardware realization

abstract: The aim of this paper is to present the implementation of Fractional-Order Proportional-Integral-Derivative (FOPID) to control the step response of the first-order circuit. With FOPID control, we obtain a satisfying result. The FOPID controller outperforms the classical integer PID, wherein both of them are optimized with the Nelder-Mead method. The FOPID controller succeeds to regulate the output of the system to our desired set point with better settling time and rise time than the classical one. In addition, hardware realization is presented with Arduino Uno.


title: Towards a Viewpoint-specific Metamodel for Model-driven Development of Microservice Architecture

abstract: Microservice Architecture (MSA) is a service-based architectural style with a strong emphasis on high cohesion and loose coupling. It is commonly regarded as a descendant of Service-oriented Architecture (SOA) and thus might draw on existing findings of SOA research. This paper presents a metamodel for Model-driven Development (MDD) of MSA, which is deduced from existing SOA modeling approaches, but also incorporates MSA-specific modeling concepts. It is divided into the three viewpoints Data, Service and Operation, each of which encapsulates concepts related to a certain aspect of MSA. The metamodel aims to support DevOps-based MSA development and automatic transformation of metamodel instances into MSA implementations.


title: Planar Substrate-Integrated Endfire Antenna With Wide Beamwidth for Q-Band Applications

abstract: According to the IEEE 802.11aj (45 GHz) standard, a millimeter-wave planar substrate-integrated endfire antenna with wide beamwidths in both E - and H -planes, and good impedance matching over 42.3–48.4 GHz is proposed for the Q-band wireless local area network (WLAN) system. The proposed antenna comprises a printed angled dipole with bilateral symmetrical directors for generating wide-angle radiation, and the beamwidth in both E - and H -planes can be easily adjusted. The antenna is prototyped using the conventional printed circuit board process with a size of 6 × 26 × 0.508 mm3, and achieves beamwidths greater than 120° in two main planes, ${S}$ 11 of less than –12.5 dB, and peak gain of 3.67–5.2 dBi over 42.3–48.4 GHz. The measurements are in good agreement with simulations, which shows that the proposed antenna is very promising for Q-band millimeter-wave WLAN system access-point applications.


title: A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads

abstract: Discrete surface defects impact the riding quality and safety of a railway system. However, it is a challenge to inspect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This paper puts forward a real-time visual inspection system (VIS) for discrete surface defects. VIS first acquires a rail image by the image acquisition system, and then, it cuts the subimage of rail track by the track extraction algorithm. Subsequently, VIS enhances the contrast of the rail image using the local normalization (LN) method, which is nonlinear and illumination independent. At last, VIS detects defects using the defect localization based on projection profile (DLBP), which is robust to noise and very fast. Our experimental results demonstrate that VIS detects the Type-II defects with a recall of 93.10% and Type-I defects with a recall of 80.41%, and the proposed LN method and DLBP algorithm are better than the related well-established approaches. Furthermore, VIS is very fast with a linear computational time complexity, and it can be in real time to run on a 216-km/h test train under our experimental setup.


title: A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges

abstract: The Internet of Things (IoT) is a promising technology which tends to revolutionize and connect the global world via heterogeneous smart devices through seamless connectivity. The current demand for machine-type communications (MTC) has resulted in a variety of communication technologies with diverse service requirements to achieve the modern IoT vision. More recent cellular standards like long-term evolution (LTE) have been introduced for mobile devices but are not well suited for low-power and low data rate devices such as the IoT devices. To address this, there is a number of emerging IoT standards. Fifth generation (5G) mobile network, in particular, aims to address the limitations of previous cellular standards and be a potential key enabler for future IoT. In this paper, the state-of-the-art of the IoT application requirements along with their associated communication technologies are surveyed. In addition, the third generation partnership project cellular-based low-power wide area solutions to support and enable the new service requirements for Massive to Critical IoT use cases are discussed in detail, including extended coverage global system for mobile communications for the Internet of Things, enhanced machine-type communications, and narrowband-Internet of Things. Furthermore, 5G new radio enhancements for new service requirements and enabling technologies for the IoT are introduced. This paper presents a comprehensive review related to emerging and enabling technologies with main focus on 5G mobile networks that is envisaged to support the exponential traffic growth for enabling the IoT. The challenges and open research directions pertinent to the deployment of massive to critical IoT applications are also presented in coming up with an efficient context-aware congestion control mechanism.


title: Automatic and robust breadcrumb system deployment for indoor firefighter applications

abstract: Breadcrumb systems (BCS) have been proposed to aid firefighters inside buildings by communicating their physiological parameters to base stations outside the buildings. In this paper, we describe the design, implementation and evaluation of an automatic and robust breadcrumb system for firefighter applications. Our solution includes a breadcrumb dispenser with an optimized link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time. Experimental results from our study show that compared to the state of the art solution [14], our breadcrumb system achieves 200% link redundancy with only 23% additional deployed nodes. Our deployed crumb-chain can achieve 90% probability of end-to-end connectivity when one node fails in the crumb-chain and over 50% probability of end-to-end connectivity when up to 3 nodes fail in the crumb-chain. In addition, by applying adaptive transmission power control at each node after the crumb-chain deployment, we solve the link quality variation problem by avoiding significant variations in packet reception ratio (PRR) and maintain PRR of over 90% at the link level.


title: Adaptive filters for continuous queries over distributed data streams

abstract: We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Significant communication overhead is incurred in the presence of rapid update streams, and we propose a new technique for reducing the overhead. Users register continuous queries with precision requirements at the central stream processor, which installs filters at remote data sources. The filters adapt to changing conditions to minimize stream rates while guaranteeing that all continuous queries still receive the updates necessary to provide answers of adequate precision at all times. Our approach enables applications to trade precision for communication overhead at a fine granularity by individually adjusting the precision constraints of continuous queries over streams in a multi-query workload. Through experiments performed on synthetic data simulations and a real network monitoring implementation, we demonstrate the effectiveness of our approach in achieving low communication overhead compared with alternate approaches.


title: Electronic trading in order-driven markets: efficient execution

abstract: In this paper, we address the importance of efficient execution in electronic markets. Due to intense competition for profit opportunities, trading costs can represent a significant portion of overall return. They must be taken into account both when a specific trade is being executed, and when a general investment strategy is being designed. We empirically demonstrate that by combining market orders (which offer immediate execution regardless of price) and limit orders (which offer uncertain execution at a specified price), we are able to obtain a superior average price than by using market orders only. Our analysis highlights the trade-off between expected price improvement from limit orders and the risk of non-execution. We show how to determine the optimal limit order price in a simplified setting and suggest how this approach can be generalized to a complete solution. All of our experimental results are obtained on an extensive collection of NASDAQ limit order data.


title: Extended Object and Group Tracking: AComparison of Random Matrices and Random Hypersurface Models

abstract: Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany †Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany ‡Data Fusion Algorithms & Software, EADS Deutschland GmbH, Ulm, Germany marcus.baum@kit.edu, {michael.feldmann |wolfgang.koch}@fkie.fraunhofer.de dietrich.fraenken@eads.com, uwe.hanebeck@ieee.org


title: Understanding and Designing around Users' Interaction with Hidden Algorithms in Sociotechnical Systems

abstract: While today many online platforms employ complex algorithms to curate content, these algorithms are rarely highlighted in interfaces, preventing users from understanding these algorithms' operation or even existence. Here, we study how knowledgeable users are about these algorithms, showing that providing insight to users about an algorithm's existence or functionality through design facilitates rapid processing of the underlying algorithm models and increases users' engagement with the system. We also study algorithmic systems that might introduce bias to users' online experience to gain insight into users' behavior around biased algorithms. We will leverage these insights to build an algorithm-aware design that shapes a more informed interaction between users and algorithmic systems.


title: The Complexity of Enumeration and Reliability Problems

abstract: The class of $# P$-complete problems is a class of computationally eqivalent counting problems (defined by the author in a previous paper) that are at least as difficult as the $NP$-complete problems. Here we show, for a large number of natural counting problems for which there was no previous indication of intractability, that they belong to this class. The technique used is that of polynomial time reduction with oracles via translations that are of algebraic or arithmetic nature.


title: Incorporating Consistency Verification into Neural Data-to-Document Generation

abstract: Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts. However, large proportions of generated texts do not actually conform to the input data. To address this issue, we propose a new training framework which attempts to verify the consistency between the generated texts and the input data to guide the training process. To measure the consistency, a relation extraction model is applied to check information overlaps between the input data and the generated texts. The non-differentiable consistency signal is optimized via reinforcement learning. Experimental results on a recently released challenging dataset ROTOWIRE show improvements from our framework in various metrics.


title: KEEL : a software tool to assess evolutionary algorithms for data mining problems

abstract: This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.


title: Test Blueprints - Exposing Side Effects in Execution Traces to Support Writing Unit Tests

abstract: Writing unit tests for legacy systems is a key maintenance task. When writing tests for object-oriented programs, objects need to be set up and the expected effects of executing the unit under test need to be verified. If developers lack internal knowledge of a system, the task of writing tests is non-trivial. To address this problem, we propose an approach that exposes side effects detected in example runs of the system and uses these side effects to guide the developer when writing tests. We introduce a visualization called Test Blueprint, through which we identify what the required fixture is and what assertions are needed to verify the correct behavior of a unit under test. The dynamic analysis technique that underlies our approach is based on both tracing method executions and on tracking the flow of objects at runtime. To demonstrate the usefulness of our approach we present results from two case studies.


title: Open government and e-government: democratic challenges from a public value perspective

abstract: We consider open government (OG) within the context of e-government and its broader implications for the future of public administration. We argue that the current US Administration's Open Government Initiative blurs traditional distinctions between e-democracy and e-government by incorporating historically democratic practices, now enabled by emerging technology, within administrative agencies. We consider how transparency, participation, and collaboration function as democratic practices in administrative agencies, suggesting that these processes are instrumental attributes of administrative action and decision making, rather than the objective of administrative action, as they appear to be currently treated. We propose alternatively that planning and assessing OG be addressed within a "public value" framework. The creation of public value is the goal of public organizations; through public value, public organizations meet the needs and wishes of the public with respect to substantive benefits as well as the intrinsic value of better government. We extend this view to OG by using the framework as a way to describe the value produced when interaction between government and citizens becomes more transparent, participative, and collaborative, i.e., more democratic.


title: “It’s Really No Big Deal”: The Role of Social Supply Networks in Normalizing Use of Cannabis by Students at Canadian Universities

abstract: ABSTRACTCannabis (marijuana) has undergone a normalizing process as indicated by high use rates, social tolerance, and broader cultural acceptance of its use in many countries. Users also maintain access through extended friendship networks that facilitate the cultural diffusion of the practice. The social nature of supply is herein theorized in terms of Goffman’s understanding of activities that function to preserve a sense of normalcy as a collective achievement enabling predictable constructions of reality. Based on in-depth interviews with undergraduate students, we explore how social networks of supply—characterized by casual access, reciprocity, and sharing—contribute to shared meanings about using marijuana as an unremarkable or “normal” thing to do.


title: The role of culture in families' treatment decisions for children with autism spectrum disorders

abstract: There is little information available about how and why parents of children with autism spectrum disorders (ASD) make decisions regarding which of the many available treatments to implement with their children. Given the lack of available information regarding treatment efficacy, it is likely that parents' beliefs about child development, interpretation of the symptoms of ASD, its etiology and course, and their experiences with the health system influence treatment decisions. This article addresses these issues within the context of cultural influences. We review the small body of existing literature regarding cultural influences on decisions regarding ASD and draw implications for the study and treatment of ASD from the larger body of literature on culture and other health conditions of childhood. In addition to examining the potential for differences in clinical presentation by culture and different experiences with the healthcare system, we use Kleinman's framework of questions for understanding the role of culture in the interpretation and treatment of ASD. These questions address interpretation of symptoms and beliefs about their cause, course, and treatment. Finally, we present specific language for clinicians to use in discussion with families with different cultural beliefs about the use of less traditional treatment strategies. © 2005 Wiley-Liss, Inc. MRDD Research Reviews 2005;11:110–115.


title: SeededFuzz: Selecting and Generating Seeds for Directed Fuzzing

abstract: As an improvement on traditional random fuzzing, directed fuzzing utilizes dynamic taint analysis to locate regions ofseed inputs which can influence security-sensitive program points, and focuses on mutating these identified regions to generate errorrevealingtest cases. The seed inputs are of great importance todirected fuzzing, because they essentially determine the numberof security-sensitive program points we can test. In this paper, we present a seed selection method complementing with a seedgeneration method for directed fuzzing. Using static analysis, dynamic monitoring and symbolic execution, our approach canprovide directed fuzzing with seeds that can cover more securitysensitiveprogram points in a cost-effective way. We implementeda prototype called SeededFuzz, and applied it to five real-worldapplications. Experimental results show that starting directedfuzzing with our carefully selected and generated seeds, SeededFuzzcan test more critical program sites and detect more bugs.


title: Intrusion Detection for Grid and Cloud Computing

abstract: Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. The Grid and Cloud Computing Intrusion Detection System integrates knowledge and behavior analysis to detect intrusions.


title: The communication of emotion via touch

abstract: The study of emotional communication has focused predominantly on the facial and vocal channels but has ignored the tactile channel. Participants in the current study were allowed to touch an unacquainted partner on the whole body to communicate distinct emotions. Of interest was how accurately the person being touched decoded the intended emotions without seeing the tactile stimulation. The data indicated that anger, fear, disgust, love, gratitude, and sympathy were decoded at greater than chance levels, as well as happiness and sadness, 2 emotions that have not been shown to be communicated by touch to date. Moreover, fine-grained coding documented specific touch behaviors associated with different emotions. The findings are discussed in terms of their contribution to the study of emotion-related communication.


title: Analysis of a Location-Based Social Network

abstract: Location-based Social Networks (LSNs) allow users to see where their friends are, to search location-tagged contentwithin their social graph, and to meet others nearby. The recent availability of open mobile platforms, such as Apple iPhones and Google Android phones, makes LSNs much more accessible to mobile users.To study how users share their location in real world, wecollected traces from a commercial LSN service operated by astartup company. In this paper, we present results of data analysis over user profiles, update activities, mobility characteristics, social graphs, and attribute correlations. To the best of our knowledge, this study is the first large-scale quantitative analysis of a real-world commercial LSN service.


title: Strain Measurements of Composite Laminates with Embedded Fibre Bragg Gratings: Criticism and Opportunities for Research

abstract: Embedded optical fibre sensors are considered for structural health monitoring purposes in numerous applications. In fibre reinforced plastics, embedded fibre Bragg gratings are found to be one of the most popular and reliable solutions for strain monitoring. Despite of their growing popularity, users should keep in mind their shortcomings, many of which are associated with the embedding process. This review paper starts with an overview of some of the technical issues to be considered when embedding fibre optics in fibrous composite materials. Next, a monitoring scheme is introduced which shows the different steps necessary to relate the output of an embedded FBG to the strain of the structure in which it is embedded. Each step of the process has already been addressed separately in literature without considering the complete cycle, from embedding of the sensor to the internal strain measurement of the structure. This review paper summarizes the work reported in literature and tries to fit it into the big picture of internal strain measurements with embedded fibre Bragg gratings. The last part of the paper focuses on temperature compensation methods which should not be ignored in terms of in-situ measurement of strains with fibre Bragg gratings. Throughout the paper criticism is given where appropriate, which should be regarded as opportunities for future research.


title: Evaluating Automated Facial Age Estimation Techniques for Digital Forensics

abstract: In today's world, closed circuit television, cellphone photographs and videos, open-source intelligence (i.e., social media/web data mining), and other sources of photographic evidence are commonly used by police forces to identify suspects and victims of both online and offline crimes. Human characteristics, such as age, height, weight, gender, hair color, etc., are often used by police officers and witnesses in their description of unidentified suspects. In certain circumstances, the age of the victim can result in the determination of the crime's categorization, e.g., child abuse investigations. Various automated machine learning-based techniques have been implemented for the analysis of digital images to detect soft biometric traits, such as age and gender, and thus aid detectives and investigators in progressing their cases. This paper documents an evaluation of existing cognitive age prediction services. The evaluative and comparative analysis of the various services was conducted to identify trends and issues inherent to their performance. One significant contributing factor impeding the accurate development of the services investigated is the notable lack of sufficient sample images in specific age ranges, i.e., underage and elderly. To overcome this issue, a dataset generator was developed, which harnesses collections of several unbalanced datasets and forms a balanced, curated dataset of digital images annotated with their corresponding age and gender.


title: Regret Minimization for Partially Observable Deep Reinforcement Learning

abstract: Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial observations by using finite length observation histories or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to an advantage-like function and is robust to partially observed state. We demonstrate that this new algorithm can substantially outperform strong baseline methods on several partially observed reinforcement learning tasks: learning first-person 3D navigation in Doom and Minecraft, and acting in the presence of partially observed objects in Doom and Pong.


title: ndmaSNF: cancer subtype discovery based on integrative framework assisted by network diffusion model

abstract: Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) [1], we proposed a new integrative method named ndmaSNF (network diffusion model assisted SNF), which can be used for cancer subtype discovery with the advantage of making use of somatic mutation data and other discrete data. Firstly, we incorporate network diffusion model on mutation data to make it smoothed and adaptive. Then, the mutation data along with other data types are utilized in the SNF framework by constructing patient-by-patient similarity networks for each data type. Finally, a fused patient network containing all the information from different input data types is obtained by using a nonlinear iterative method. The fused network can be used for cancer subtype discovery through the clustering algorithm. Experimental results on four cancer datasets showed that our ndmaSNF method can find subtypes with significant differences in the survival profile and other clinical features.


title: A Novel Control for a Cascaded Buck–Boost PFC Converter Operating in Discontinuous Capacitor Voltage Mode

abstract: This paper presents a new dual-switch control structure for an ac/dc noninverting cascaded buck–boost power factor correction converter. The proposed converter operates at a discontinuous capacitor voltage mode providing an inherent high-power factor and a zero voltage turn-off switching. Additionally, the proposed control structure enables for a nondistorted sinusoidal current for a wide range of output voltage levels. Unlike the conventional methods, a mode detector is not required and consequently there is no hard transition between buck and boost modes. Although both converter switches are controlled, only one feedback control loop is required to obtain the desired power flow at a unity power factor. The principle of operation, theoretical analysis, simulation, and experimental results of a 1.6-kW prototype grid-connected converter are presented. The results confirmed the validity of the proposed system under various operating conditions.


title: Spinal Muscular Atrophy

abstract: Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by loss of motor neurons in the anterior horn of the spinal cord and resultant weakness. The most common form of SMA, accounting for 95% of cases, is autosomal recessive proximal SMA associated with mutations in the survival of motor neurons (SMN1) gene. Relentless progress during the past 15 years in the understanding of the molecular genetics and pathophysiology of SMA has resulted in a unique opportunity for rational, effective therapeutic trials. The goal of SMA therapy is to increase the expression levels of the SMN protein in the correct cells at the right time. With this target in sight, investigators can now effectively screen potential therapies in vitro, test them in accurate, reliable animal models, move promising agents forward to clinical trials, and accurately diagnose patients at an early or presymptomatic stage of disease. A major challenge for the SMA community will be to prioritize and develop the most promising therapies in an efficient, timely, and safe manner with the guidance of the appropriate regulatory agencies. This review will take a historical perspective to highlight important milestones on the road to developing effective therapies for SMA.


title: Cognitive neuroscience 2.0: building a cumulative science of human brain function

abstract: Cognitive neuroscientists increasingly recognize that continued progress in understanding human brain function will require not only the acquisition of new data, but also the synthesis and integration of data across studies and laboratories. Here we review ongoing efforts to develop a more cumulative science of human brain function. We discuss the rationale for an increased focus on formal synthesis of the cognitive neuroscience literature, provide an overview of recently developed tools and platforms designed to facilitate the sharing and integration of neuroimaging data, and conclude with a discussion of several emerging developments that hold even greater promise in advancing the study of human brain function.


title: Cyberbullying among Turkish Adolescents

abstract: ABSTRACT Cyberbullying, harassment through the use of information and communication technology such as cell phones and the Internet, is an emerging phenomenon all around the world. Extensive research on aggression and bullying is guiding educators' understanding of cyberbullying. Yet the gap between the advancement in technology and the dearth of study on cyberbullying suggests that more research is needed to understand the scope of this form of bullying. In order to fill the gap in literature, 269 secondary school Turkish students were surveyed on their engagement in and coping strategies for cyberbullying. The results show that 35.7% of the students displayed bully behaviors, and 23.8% of the students displayed bully-victim behaviors. Only 5.9% of the students were victims. More boys displayed bully, victim, and bully-victim behaviors than girls. When faced with cyberbullying, 25% of the students reported telling their peers and parents about the cyberbullying incident, and 30.6% of the students reporte...


title: Closing loops without places

abstract: This paper proposes a new topo-metric representation of the world based on co-visibility that simplifies data association and improves the performance of appearance-based recognition. We introduce the concept of dynamic bagof-words, which is a novel form of query expansion based on finding cliques in the landmark co-visibility graph. The proposed approach avoids the - often arbitrary - discretisation of space from the robot's trajectory that is common to most image-based loop closure algorithms. Instead we show that reasoning on sets of co-visible landmarks leads to a simple model that out-performs pose-based or view-based approaches. Using real and simulated imagery, we demonstrate that dynamic bag-of-words query expansion can improve precision and recall for appearance-based localisation.


title: EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface

abstract: A major challenge in EEG-based brain-computer interfaces BCIs is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation EEG-DSA, to linearly transform the EEG data from the target space evaluation session, such that the distribution difference to the source space training session is minimized. Using the Kullback-Leibler KL divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm PMean.


title: Predictors of Computer Use in Community-Dwelling, Ethnically Diverse Older Adults

abstract: Objective: In this study, we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders to investigate the relationship computer use has with demographics, well-being, and other key psychosocial variables in older adults.Background: Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than do others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders.Method: With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are indepe...


Improving Reinforcement Learning Performance Using Continuous Action Spaces Applied to Dynamic Environments


Robust Discriminative Localization Maps


title: Semantic-Driven Generation of Hyperlapse from 360 Degree Video

abstract: We present a system for converting a fully panoramic (360 degree) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input 360 degree video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a large-scale user study.


title: Local and Overall Buckling in Thin‐Walled Beams and Columns

abstract: The design of steel beams and columns must take into account overall instability (Euler, torsional, lateral‐torsional buckling, etc.), and also, if the section is made up of thin plate elements, the instability of the component plates in the form of local plate buckling. The possible interaction of these two modes of buckling, and its effect on member stability and strength, is therefore of interest. A tool has been developed for this type of analysis by adapting a finite element for a thin‐walled beam‐column of arbitrary cross section to include “local” degrees of freedom, allowing cross‐sectional distortion. The formulation involves geometric nonlinearities due to large displacements and rotations, but material behavior is limited to the elastic case.


title: Soft-Switching Current-Fed Push–Pull Converter for 250-W AC Module Applications

abstract: In this paper, a soft-switching single-inductor push-pull converter is proposed. A push-pull converter is suitable for low-voltage photovoltaic ac module systems, because the step-up ratio of the high-frequency transformer is high, and the number of primary-side switches is relatively small. However, the conventional push-pull converter does not have high efficiency because of high-switching losses due to hard switching and transformer losses (copper and iron losses) as a result of the high turn ratio of the transformer. In the proposed converter, primary-side switches are turned ON at the zero-voltage switching condition and turned OFF at the zero-current switching condition through parallel resonance between the secondary leakage inductance of the transformer and a resonant capacitor. The proposed push-pull converter decreases the switching loss using soft switching of the primary switches. In addition, the turn ratio of the transformer can be reduced by half using a voltage-doubler of secondary side. The theoretical analysis of the proposed converter is verified by simulation and experimental results.


title: A new approach to select solvents and operating conditions for supercritical antisolvent precipitation processes by using solubility parameter and group contribution methods

abstract: Abstract A new approach is proposed to select operating temperature and pressure for supercritical antisolvent particle precipitation based on solubility parameter calculated by group contribution methods and using only the critical properties of the solvent. Solubility parameters are also used to choose the most suitable organic solvent for a given application. Supercritical antisolvent precipitation operating conditions of 36 systems are investigated including 8 organic solvents (methanol, ethanol, acetone, DMSO, DCM, chloroform, NMP and acetic acid) and 6 solid solutes (atenolol, tartaric acid, flunisolide, paracetamol, amoxicillin and cholesterol) in the temperature and pressure ranges of 25⿿85 °C and 50⿿250 bar. The results show a good agreement between the experimental and calculated data for these systems. Although particle precipitation depends on several parameters such as mass-transfer rates and hydrodynamics, the focus of this work is on the role of thermodynamics to indicate the preliminary conditions for a successful antisolvent precipitation process. Validation and results of this new approach suggest that it can be a useful tool for a qualitative and completely predictive evaluation of supercritical antisolvent particle precipitation in a cheaper way than carrying out experimental runs.


A fast and accurate algorithm for eye opening or closing detection based on local maximum vertical derivative pattern


title: An Investigation of the Decision-Making Process in Agile Teams

abstract: This paper first explores the decision-making process in agile teams using scrum practices and second identifies factors that influence the decision-making process during the Sprint Planning and Daily Scrum Meetings. We conducted 34 semi-structured interviews and 18 observations across four agile teams. Our findings show that a rational decision-making process is sometimes followed in the Sprint Planning and Daily Scrum Meetings and that three factors can influence the rational decision-making process: sprint duration, experience and resource availability. Additionally, decisions are not always made in a collaborative manner by team members. This research contributes to the decision-making literature and project management literature by highlighting difficulties pertinent to decision making in agile teams.


title: DOCUMENT IMAGE REGISTRATION FOR IMPOSED LAYER EXTRACTION

abstract: Extraction of filled-in information from document images in the presence of template poses challenges due to geometrical distortion. Filled-in document image consists of null background, general information foreground and vital information imposed layer. Template document image consists of null background and general information foreground layer. In this paper a novel document image registration technique has been proposed to extract imposed layer from input document image. A convex polygon is constructed around the content of the input and the template image using convex hull. The vertices of the convex polygons of input and template are paired based on minimum Euclidean distance. Each vertex of the input convex polygon is subjected to transformation for the permutable combinations of rotation and scaling. Translation is handled by tight crop. For every transformation of the input vertices, Minimum Hausdorff distance (MHD) is computed. Minimum Hausdorff distance identifies the rotation and scaling values by which the input image should be transformed to align it to the template. Since transformation is an estimation process, the components in the input image do not overlay exactly on the components in the template, therefore connected component technique is applied to extract contour boxes at word level to identify partially overlapping components. Geometrical features such as density, area and degree of overlapping are extracted and compared between partially overlapping components to identify and eliminate components common to input image and template image. The residue constitutes imposed layer. Experimental results indicate the efficacy of the proposed model with computational complexity. Experiment has been conducted on variety of filled-in forms, applications and bank cheques. Data sets have been generated as test sets for comparative analysis.


A Modified Functionally Generated Path Technique ( FGP ) for Making Posterior Unit Metal-Ceramic


title: Condition Based Maintenance on Rail Vehicles – Possibilities for a more effective maintenance strategy

abstract: Today, the Swedish industry is budgeting billions in maintenance related costs on a yearly basis. The high costs come from productivity losses due to low availability. With condition based maintenance the maintenance intervals will be a lot more dynamic and no actions will be done unnecessarily. Sensor technology makes it possible to monitor an assets most important components. Different Artificial Intelligence techniques make it possible to analyze the measured data. With process knowledge of the monitored asset the analysis will show whether maintenance actions need to be performed or not. The condition based maintenance approach has been tried successfully on different engineering industries; now, the time has come to rail vehicles.


title: System Level Evaluation of LTE-V2V Mode 4 Communications and Its Distributed Scheduling

abstract: The 3GPP has recently published the first version of the Release 14 standard that includes support for V2V communications using LTE sidelink communications (referred to as LTE-V, LTE-V2X, LTE-V2V or Cellular V2X). The standard includes a mode (mode 4) where vehicles autonomously select and manage the radio resources without any cellular infrastructure support. This is highly relevant since V2V safety applications cannot depend on the availability of infrastructure-based cellular coverage, and transforms LTE-V into a possible (or complimentary) alternative to 802.11p. The performance of LTE-V in mode 4 is highly dependent on its distributed scheduling protocol (sensing-based Semi-Persistent Scheduling) that is used by vehicles to reserve resources for their transmissions. This paper presents the first evaluation of the performance and operation of this protocol under realistic traffic conditions in urban scenarios. The evaluation demonstrates that further enhancements should be investigated to reduce packet collisions.


title: Stopping Criterion for Active Learning with Model Stability

abstract: Active learning selectively labels the most informative instances, aiming to reduce the cost of data annotation. While much effort has been devoted to active sampling functions, relatively limited attention has been paid to when the learning process should stop. In this article, we focus on the stopping criterion of active learning and propose a model stability--based criterion, that is, when a model does not change with inclusion of additional training instances. The challenge lies in how to measure the model change without labeling additional instances and training new models. Inspired by the stochastic gradient update rule, we use the gradient of the loss function at each candidate example to measure its effect on model change. We propose to stop active learning when the model change brought by any of the remaining unlabeled examples is lower than a given threshold. We apply the proposed stopping criterion to two popular classifiers: logistic regression (LR) and support vector machines (SVMs). In addition, we theoretically analyze the stability and generalization ability of the model obtained by our stopping criterion. Substantial experiments on various UCI benchmark datasets and ImageNet datasets have demonstrated that the proposed approach is highly effective.


title: Design and feedback control of a biologically-inspired miniature quadruped

abstract: Insect-scale legged robots have the potential to locomote on rough terrain, crawl through confined spaces, and scale vertical and inverted surfaces. However, small scale implies that such robots are unable to carry large payloads. Limited payload capacity forces miniature robots to utilize simple control methods that can be implemented on a simple onboard microprocessor. In this study, the design of a new version of the biologically-inspired Harvard Ambulatory MicroRobot (HAMR) is presented. In order to find the most suitable control inputs for HAMR, maneuverability experiments are conducted for several drive parameters. Ideal input candidates for orientation and lateral velocity control are identified as a result of the maneuverability experiments. Using these control inputs, two simple feedback controllers are implemented to control the orientation and the lateral velocity of the robot. The controllers are used to force the robot to track trajectories with a minimum turning radius of 55 mm and a maximum lateral to normal velocity ratio of 0.8. Due to their simplicity, the controllers presented in this work are ideal for implementation with on-board computation for future HAMR prototypes.


title: Training Artificial Neural Network using Particle Swarm Optimization Algorithm

abstract: In this paper, the adaptation of network weights using Particle Swarm Optimization (PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in classification of IRIS dataset. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.


title: E-Government Research: A Review via the Lens of Structuration Theory

abstract: E-government has become a burgeoning field for research on the nature of relationships among government, citizens and technology. In most cases, studies on the subject adopt viewpoints that reflect ideas taken from the technological imperative and/or strategic choice views. These two perspectives may paint an incomplete picture of the effect digital government will have on governments and their relationships with citizens and technology, as they both fail to take into account the fact that social and organizational structures undergo constant change, with information technology itself an integral element of those structures. Viewing e-government through the lens of structuration theory offers researchers interesting possibilities in exploring these relationships and insights into the complex interactions that shape the relationships among government, citizens and technology.


title: Narrative Review of Dance-based Exercise and Its Specific Impact on Depressive Symptoms in Older Adults

abstract: Background: Depression is a chronic condition that results in considerable disability, and particularly in later life, severely impacts the life quality of the individual with this condition. The first aim of this review article was to summarize, synthesize, and evaluate the research base concerning the use of dance-based exercises on health status, in general, and secondly, specifically for reducing depressive symptoms, in older adults. A third was to provide directives for professionals who work or are likely to work with this population in the future. Methods: All English language peer reviewed publications detailing the efficacy of dance therapy as an intervention strategy for older people in general, and specifically for minimizing depression and dependence among the elderly were analyzed. Key words: dance therapy and depression were included. Databases used were Academic Search Complete, Cinahl, PubMed, Scopus, PsycINFO, and Web of Science. Results: Collectively, this data reveal dance therapy may be useful as a rehabilitation strategy for older adults, in general, as well as for elders with varying degrees of depression, regardless of strategy employed. Conclusions: Although more research is needed, older individuals with or without chronic depression or depressive symptoms can benefit emotionally from dance based exercise participation. Geriatric clinicians can expect this form of exercise will also heighten the life quality of the older individual with depression or subclinical depression.


title: Half mode substrate integrated waveguide (HMSIW) multi-way power divider

abstract: With merits of low loss and highly integration, substrate integrated waveguide (SIW) has been widely used in the designs of modern microwave and millimeter circuits. However it suffers from the big size in low microwave band. Recently, a novel half mode substrate integrated waveguide (HMSIW) has been proposed, which keeps the good performance of SIW with a nearly 50% reduction in size. So far, no HMSIW multi-way power divider is reported, mainly because of the unsymmetry of itself. In this paper, a new HMSIW T-junction is proposed, with which the design of HMSIW multi-way power divider become feasible, and two classes of multi-way power dividers. The agreement between measured data and simulated results shows that HMSIW can be used to design effective feeds for planar array antenna.


title: SMASH: a distributed sensing and processing garment for the classification of upper body postures

abstract: This paper introduces a smart textile for posture classification. A distributed sensing and processing architecture is implemented into a loose fitting long sleeve shirt. Standardized interfaces to remote periphery support the variable placement of different sensor modalities at any location of the textile. ::: ::: The shirt is equipped with acceleration sensors in order to determine the postural resolution and the systems feasibility for applications in movement rehabilitation. For the garment characterization an arm posture measurement method is proposed and applied in a study with 5 users. ::: ::: The classification performance is analyzed on data from overall 8 users, conducting 12 posture types, relevant for shoulder and elbow joint rehabilitation. We present results for different user-modes, with classification rates of 89% for a user-independent evaluation. Moreover, the relation of body dimensions on the posture classification performance are analyzed.


title: GeNeDA: An Open-Source Workflow for Design Automation of Gene Regulatory Networks Inspired from Microelectronics.

abstract: Abstract The topic of this article is the development of an open-source automated design framework for synthetic biology, specifically for the design of artificial gene regulatory networks based on a digital approach. In opposition to other tools, GeNeDA is an open-source online software based on existing tools used in microelectronics that have proven their efficiency over the last 30 years. The complete framework is composed of a computation core directly adapted from an Electronic Design Automation tool, input and output interfaces, a library of elementary parts that can be achieved with gene regulatory networks, and an interface with an electrical circuit simulator. Each of these modules is an extension of microelectronics tools and concepts: ODIN II, ABC, the Verilog language, SPICE simulator, and SystemC-AMS. GeNeDA is first validated on a benchmark of several combinatorial circuits. The results highlight the importance of the part library. Then, this framework is used for the design of a sequential...


Automatic Steering Methods for Autonomous Automobile Path Tracking


title: The 5G candidate waveform race: a comparison of complexity and performance

abstract: 5G will have to cope with a high degree of heterogeneity in terms of services and requirements. Among these latter, the flexible and efficient use of non-contiguous unused spectrum for different network deployment scenarios is considered a key challenge for 5G systems. To maximize spectrum efficiency, the 5G air interface technology will also need to be flexible and capable of mapping various services to the best suitable combinations of frequency and radio resources. In this work, we propose a comparison of several 5G waveform candidates (OFDM, UFMC, FBMC and GFDM) under a common framework. We assess spectral efficiency, power spectral density, peak-to-average power ratio and robustness to asynchronous multi-user uplink transmission. Moreover, we evaluate and compare the complexity of the different waveforms. In addition to the complexity analysis, in this work, we also demonstrate the suitability of FBMC for specific 5G use cases via two experimental implementations. The benefits of these new waveforms for the foreseen 5G use cases are clearly highlighted on representative criteria and experiments.


title: Power-Combining Transformer Techniques for Fully-Integrated CMOS Power Amplifiers

abstract: Fully integrated CMOS power amplifiers (PAs) with parallel power-combining transformer are presented. For the high power CMOS PA design, two types of transformers, series-combining and parallel-combining, are fully analyzed and compared in detail to show the parasitic resistance and the turn ratio as the limiting factor of power combining. Based on the analysis, two kinds of parallel-combining transformers, a two-primary with a 1:2 turn ratio and a three-primary with a 1:2 turn ratio, are incorporated into the design of fully-integrated CMOS PAs in a standard 0.18-mum CMOS process. The PA with a two-primary transformer delivers 31.2 dBm of output power with 41% of power-added efficiency (PAE), and the PA with a three-primary transformer achieves 32 dBm of output power with 30% of PAE at 1.8 GHz with a 3.3-V power supply.


title: Beyond Heuristics: Learning Visualization Design

abstract: In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available.


title: Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations

abstract: The main contribution of this work is the comparison of three user modeling strategies based on job titles, educational fields and skills in LinkedIn profiles, for personalized MOOC recommendations in a cold start situation. Results show that the skill-based user modeling strategy performs best, followed by the job- and edu-based strategies.


title: Solving Rubik's Cube Using SAT Solvers

abstract: Rubik's Cube is an easily-understood puzzle, which is originally called the "magic cube". It is a well-known planning problem, which has been studied for a long time. Yet many simple properties remain unknown. This paper studies whether modern SAT solvers are applicable to this puzzle. To our best knowledge, we are the first to translate Rubik's Cube to a SAT problem. To reduce the number of variables and clauses needed for the encoding, we replace a naive approach of 6 Boolean variables to represent each color on each facelet with a new approach of 3 or 2 Boolean variables. In order to be able to solve quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer encoding of 18-subtype turns based on 6-type turns. To speed up the solving further, we encode some properties of two-phase algorithm as an additional constraint, and restrict some move sequences by adding some constraint clauses. Using only efficient encoding cannot solve this puzzle. For this reason, we improve the existing SAT solvers, and develop a new SAT solver based on PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver replaces the lookahead solving strategy with an ALO (\emph{at-least-one}) solving strategy, and decomposes the original problem into sub-problems. Each sub-problem is solved by PrecoSAT. The empirical results demonstrate both our SAT translation and new solving technique are efficient. Without the efficient SAT encoding and the new solving technique, Rubik's Cube will not be able to be solved still by any SAT solver. Using the improved SAT solver, we can find always a solution of length 20 in a reasonable time. Although our solver is slower than Kociemba's algorithm using lookup tables, but does not require a huge lookup table.


title: R2O, an Extensible and Semantically based Database-to-Ontology Mapping Language

abstract: We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models.


title: Lung cancer staging essentials: the new TNM staging system and potential imaging pitfalls.

abstract: Lung cancer is the leading cause of cancer-related deaths worldwide, with a dismal 5-year survival rate of 15%. The TNM (tumor-node-metastasis) classification system for lung cancer is a vital guide for determining treatment and prognosis. Despite the importance of accuracy in lung cancer staging, however, correct staging remains a challenging task for many radiologists. The new 7th edition of the TNM classification system features a number of revisions, including subdivision of tumor categories on the basis of size, differentiation between local intrathoracic and distant metastatic disease, recategorization of malignant pleural or pericardial disease from stage III to stage IV, reclassification of separate tumor nodules in the same lung and lobe as the primary tumor from T4 to T3, and reclassification of separate tumor nodules in the same lung but not the same lobe as the primary tumor from M1 to T4. Radiologists must understand the details set forth in the TNM classification system and be familiar with the changes in the 7th edition, which attempts to better correlate disease with prognostic value and treatment strategy. By recognizing the relevant radiologic appearances of lung cancer, understanding the appropriateness of staging disease with the TNM classification system, and being familiar with potential imaging pitfalls, radiologists can make a significant contribution to treatment and outcome in patients with lung cancer.


title: Crowdsourcing the creation of image segmentation algorithms for connectomics

abstract: To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with ground truth from human experts. The winning team had no prior experience with EM images, and employed a convolutional network. This ``deep learning'' approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.


title: Examination of the Correlation Between Internet Addiction and Social Phobia in Adolescents

abstract: This was a descriptive and cross-sectional study conducted with adolescents to examine the correlation between Internet addiction and social phobia. The population of the study consisted of 24,260 students aged between 11 and 15 years. Sampling method was used from population with known number, and 1,450 students were calculated as sample of the study. In this study, 13.7% of the adolescents had an Internet addiction, and 4.2% spent more than 5 hr on the computer every day. There was a positive correlation between Internet addiction and social phobia. The form of time spent on Internet was examined in terms of addiction and social phobia; although Internet addiction was related to games, dating sites, and web surfing, social phobia was related to homework, games, and web surfing. It was hypothesized that adolescents with social phobia were Internet addicts, and the participants used the Internet to spend time rather than socialize.


title: A 0.5ps 1.4mW 50MS/s Nyquist bandwidth time amplifier based two-step flash-ΔΣ time-to-digital converter

abstract: We propose a 50-MS/s two-step flash-ΔΣ time-to-digital converter (TDC) using stable time amplifiers (TAs). The TDC demonstrates low-levels of shaped quantization noise. The system is simulated in 40-nm CMOS and consumes 1.3 mA from a 1.1 V supply. The bandwidth is broadened to Nyquist rate. At frequencies below 25 MHz, the integrated TDC error is as low as 143 fsrms, which is equal to an equivalent TDC resolution of 0.5 ps.


title: IDReAM: intrusion detection and response executed with agent mobility architecture and implementation

abstract: This paper deals with a new approach to build a completely distributed and decentralized Intrusion Detection and Response System (IDRS) in computer networks. This approach is called Intrusion Detection and Response executed with Agent Mobility or IDReAM for short. Conceptually, IDReAM combines Mobile Agents (MAs) with self-organizing paradigms inspired by natural life systems. The Intrusion Detection System (IDS) borrows mechanisms from the immune system that protect the human body against external aggressions. The Intrusion Response System (IRS) borrows mechanisms from the stigmergic paradigm of a colony of ants. The two natural systems exhibit a social life by the organization of their entities (immune cells and ants) which is not possible without the functionality of mobility. Thus, in a natural way, MAs are good candidates to provide this property of mobility. After having presented IDReAM's conceptual model in a previous paper, the present paper concretely describes IDReAM's architecture and the corresponding implementation based on the conceptual model. The implementation is carried out with J-Seal2, a pure Java MA platform. This paper also provides IDReAM's assessment in term of resource consumption and intrusion detection and intrusion response efficiency.


title: Digital expression profiling of the compartmentalized translatome of Purkinje neurons

abstract: Underlying the complexity of the mammalian brain is its network of neuronal connections, but also the molecular networks of signaling pathways, protein interactions, and regulated gene expression within each individual neuron. The diversity and complexity of the spatially intermingled neurons pose a serious challenge to the identification and quantification of single neuron components. To address this challenge, we present a novel approach for the study of the ribosome-associated transcriptome-the translatome-from selected subcellular domains of specific neurons, and apply it to the Purkinje cells (PCs) in the rat cerebellum. We combined microdissection, translating ribosome affinity purification (TRAP) in nontransgenic animals, and quantitative nanoCAGE sequencing to obtain a snapshot of RNAs bound to cytoplasmic or rough endoplasmic reticulum (rER)-associated ribosomes in the PC and its dendrites. This allowed us to discover novel markers of PCs, to determine structural aspects of genes, to find hitherto uncharacterized transcripts, and to quantify biophysically relevant genes of membrane proteins controlling ion homeostasis and neuronal electrical activities.


title: Automatic Text Analysis by Artificial Intelligence

abstract: Text is one of the traditional ways of communication between people. With the growing availability of text data in electronic form, handling and analysis of text by means of computers gained popularity. Handling text data with machine learning methods brought interesting challenges to the area that got further extended by incorporation of some natural language specifics. As the methods were capable of addressing more complex problems related to text data, the expectations become bigger calling for more sophisticated methods, in particular a combination of methods from different research areas including information retrieval, machine learning, statistical data analysis, data mining, natural language processing, semantic technologies. Automatic text analysis become an integral part of many systems, pushing boundaries of research capabilities towards what one can refer to as an artificial intelligence dream never ending learning from text aiming at mimicking ways of human learning. The paper presents development of text analysis research in Slovenian that we have been personally involved in, pointing out interesting research problems that have been and are still addressed by the research, example tasks that have been addressed and some challenges on the way.


title: Story Understanding Through Multi-Representation Model Construction

abstract: We present an implemented model of story understanding and apply it to the understanding of a children's story. We argue that understanding a story consists of building multi-representation models of the story and that story models are efficiently constructed using a satisfiability solver. We present a computer program that contains multiple representations of commonsense knowledge, takes a narrative as input, transforms the narrative and representations of commonsense knowledge into a satisfiability problem, runs a satisfiability solver, and produces models of the story as output. The narrative, models, and representations are expressed in the language of Shanahan's event calculus.


title: Anchoring Revisited: The Role of the Comparative Question

abstract: When people estimate a numeric value after judging whether it is larger or smaller than a high or low anchor value (comparative question), estimates are biased in the direction of the anchor. One explanation for this anchoring effect is that people selectively access knowledge consistent with the anchor value as part of a positive test strategy. Two studies (total N = 184) supported the alternative explanation that people access knowledge consistent with their own answer to the comparative question. Specifically, anchoring effects emerged when the answer to the comparative question was unexpected (lower than the low anchor or higher than the high anchor). For expected answers (lower than the high anchor or higher than the low anchor), however, anchoring effects were attenuated or reversed. The anchor value itself was almost never reported as an absolute estimate.


title: Modeling cocaine relapse in rodents: Behavioral considerations and circuit mechanisms

abstract: Abstract Addiction is a chronic relapsing disorder, in that most addicted individuals who choose to quit taking drugs fail to maintain abstinence in the long-term. Relapse is especially likely when recovering addicts encounter risk factors like small “priming” doses of drug, stress, or drug-associated cues and locations. In rodents, these same factors reinstate cocaine seeking after a period of abstinence, and extensive preclinical work has used priming, stress, or cue reinstatement models to uncover brain circuits underlying cocaine reinstatement. Here, we review common rat models of cocaine relapse, and discuss how specific features of each model influence the neural circuits recruited during reinstated drug seeking. To illustrate this point, we highlight the surprisingly specific roles played by ventral pallidum subcircuits in cocaine seeking reinstated by either cocaine-associated cues, or cocaine itself. One goal of such studies is to identify, and eventually to reverse the specific circuit activity that underlies the inability of some humans to control their drug use. Based on preclinical findings, we posit that circuit activity in humans also differs based on the triggers that precipitate craving and relapse, and that associated neural responses could help predict the triggers most likely to elicit relapse in a given person. If so, examining circuit activity could facilitate diagnosis of subgroups of addicted people, allowing individualized treatment based on the most problematic risk factors.


eIF2alpha kinases provide a new solution to the puzzle of substrate specificity.


title: CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH

abstract: Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA’s machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.


title: Issues in sports forecasting

abstract: A large amount of effort is spent on forecasting the outcomes of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Instead, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive a considerable amount of information about the forecasts and the forecasting process from studies that have tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team will beat the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) compared. The paper also examines the evidence with regard to the existence of biases in the forecasts, and concludes by discussing the applicability of these results to forecasting in general.


title: Mobile 3D graphics and virtual reality interaction

abstract: Mobile devices such as smartphones and tablets offer great new possibilities for the creation of 3D games and virtual reality environments. However, interaction with objects in these virtual worlds is often difficult -- for example due to the devices' small form factor. In this paper, we define different 3D visualization concepts and evaluate related interactions such as navigation and selection of objects. Detailed experiments with a smartphone and a tablet illustrate the advantages and disadvantages of the various 3D visualization concepts. Our results provide new insight with respect to interaction and highlight important aspects for the design of interactive virtual environments on mobile devices and related applications -- especially for mobile 3D gaming.


title: Dynamic Memory Networks for Visual and Textual Question Answering

abstract: Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the \babi-10k text question-answering dataset without supporting fact supervision.


title: A GENERIC AND CUSTOMIZABLE FRAMEWORK FOR THE DESIGN OF ETL SCENARIOS

abstract: Extraction-transformation-loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we delve into the logical design of ETL scenarios and provide a generic and customizable framework in order to support the DW designer in his task. First, we present a metamodel particularly customized for the definition of ETL activities. We follow a workflow-like approach, where the output of a certain activity can either be stored persistently or passed to a subsequent activity. Also, we employ a declarative database programming language, LDL, to define the semantics of each activity. The metamodel is generic enough to capture any possible ETL activity. Nevertheless, in the pursuit of higher reusability and flexibility, we specialize the set of our generic metamodel constructs with a palette of frequently used ETL activities, which we call templates. Moreover, in order to achieve a uniform extensibility mechanism for this library of built-ins, we have to deal with specific language issues. Therefore, we also discuss the mechanics of template instantiation to concrete activities. The design concepts that we introduce have been implemented in a tool, ARKTOS II, which is also presented.


title: Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

abstract: In speech emotion recognition, training and test data used for system development usually tend to fit each other perfectly, but further 'similar' data may be available. Transfer learning helps to exploit such similar data for training despite the inherent dissimilarities in order to boost a recogniser's performance. In this context, this paper presents a sparse auto encoder method for feature transfer learning for speech emotion recognition. In our proposed method, a common emotion-specific mapping rule is learnt from a small set of labelled data in a target domain. Then, newly reconstructed data are obtained by applying this rule on the emotion-specific data in a different domain. The experimental results evaluated on six standard databases show that our approach significantly improves the performance relative to learning each source domain independently.


title: Security attacks against the availability of LTE mobility networks: Overview and research directions

abstract: Modern LTE (Long Term Evolution) cellular networks provide advanced services for billions of users that go beyond traditional voice and short messaging traffic. The recent trend of Distributed Denial of Service (DDoS) attacks impacting the availability of communication systems illustrate the importance of strengthening the resiliency of mobility networks against Denial of Service (DoS) and DDoS threats, ensuring this way full LTE network availability against security attacks. In parallel, the advent of the Advanced Persistent Threat (APT) has capsized the common assumptions about attackers and threats. When it comes to very well planned and funded cyber-attacks, the scale of the threat is not the key element anymore. Instead, scenarios such as a local DoS attack, for example, against the cell service around a large corporation's headquarters or the Stock Exchange become very relevant. Therefore, traditionally overlooked low range threats, such as radio jamming, should not be de-prioritized in security studies. In this paper we present an overview of the current threat landscape against the availability of LTE mobility networks. We identify a set of areas of focus that should be considered in mobility security in order to guarantee availability against security attacks. Finally, we introduce potential research directions, including a new attack detection layer, to tackle these problems. The final goal is to rethink the architecture of a mobility network within the current security context and threat landscape and considering the current evolution towards a near future scenario where nearly every electronic device will be connected through Machine-to-Machine (M2M) systems.


title: A Tensor-Based Multiple Clustering Approach With Its Applications in Automation Systems

abstract: Multiple clustering analysis has the clear advantages to discover latent data pattern in big data from different views, so it has tremendous practical values in automation industries. However, most of current algorithms are difficult to group heterogeneous data to multiple clusterings according to the requirements of different applications. This paper presents a flexible multiple clustering analytic and service framework, and a novel tensor-based multiple clusterings (TMC) approach. Heterogeneous data objects in cyber-physical-social systems are first represented as low-order tensors and a weight tensor construction approach is proposed to measure the importance of attributes combinations in heterogeneous feature spaces. Then, a selective weighted tensor distance is explored to cluster tensorized data objects for different applications. This paper, through a real-world smart bike maintenance system, illustrates TMC and evaluates its clustering performance. Experimental results reveal TMC can obtain higher quality clustering results but with lower redundancies to meet different requirements of applications in automation systems.


MITTAL et al.: HAND DETECTION USING MULTIPLE PROPOSALS 1 Hand detection using multiple proposals


title: 3D measurements from imaging laser radars: how good are they?

abstract: Abstract In this paper we analyse a class of imaging range finders —amplitude-modulated continuous-wave laser radars in the context of computer vision and robotics. The analysis develops measurement models from the fundamental principles of laser radar operation and identifies the nature and cause of key problems that affect measurements from this class of sensors. We classify the problems as fundamental (e.g. related to the signal-tonoise ratio), as architectural (e.g. limited by encoding distance by angles in [0, 2π]) and as artifacts of particular hardware implementations (e.g. insufficient temperature compensation). Experimental results from two different devices — scanning laser rangefinders designed for autonomous navigation — illustrate and support the analysis.


title: Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

abstract: This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their effectiveness. Hence, an informed and integrated examination of emotion theories from multiple areas will need to become part of computing practice if truly effective real-world systems are to be achieved. This survey discusses theoretical perspectives that view emotions as expressions, embodiments, outcomes of cognitive appraisal, social constructs, products of neural circuitry, and psychological interpretations of basic feelings. It provides meta-analyses on existing reviews of affect detection systems that focus on traditional affect detection modalities like physiology, face, and voice, and also reviews emerging research on more novel channels such as text, body language, and complex multimodal systems. This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. In this way, models and methods can be compared, and emerging insights from various disciplines can be more expertly integrated.


title: Chromatic framework for vision in bad weather

abstract: Conventional vision systems are designed to perform in clear weather. However, any outdoor vision system is incomplete without mechanisms that guarantee satisfactory performance under poor weather conditions. It is known that the atmosphere can significantly alter light energy reaching an observer. Therefore, atmospheric scattering models must be used to make vision systems robust in bad weather. In this paper, we develop a geometric framework for analyzing the chromatic effects of atmospheric scattering. First, we study a simple color model for atmospheric scattering and verify it for fog and haze. Then, based on the physics of scattering, we derive several geometric constraints on scene color changes, caused by varying atmospheric conditions. Finally, using these constraints we develop algorithms for computing fog or haze color depth segmentation, extracting three dimensional structure, and recovering "true" scene colors, from two or more images taken under different but unknown weather conditions.


title: The effect of methotrexate on cardiovascular disease in patients with rheumatoid arthritis: a systematic literature review

abstract: OBJECTIVES ::: Patients with RA have an increased prevalence of cardiovascular disease (CVD). This is due to traditional risk factors and the effects of chronic inflammation. MTX is the first-choice DMARD in RA. We performed a systematic literature review to determine whether MTX affects the risk of CVD in patients with RA. ::: ::: ::: METHODS ::: We searched Medline, Embase, Cochrane database, database of abstracts of reviews of effects, health technology assessment and Science Citation Index from 1980 to 2008. Conference proceedings (British Society of Rheumatology, ACR and EULAR) were searched from 2005 to 2008. Papers were included if they assessed the relationship between MTX use and CVD in patients with RA. Two reviewers independently assessed each title and abstract for relevance and quality. ::: ::: ::: RESULTS ::: A total of 2420 abstracts were identified, of which 18 fulfilled the inclusion criteria. Two studies assessed the relationship between MTX use and CVD mortality, one demonstrated a significant reduction in CVD mortality and the second a trend towards reduction. Five studies considered all-cause CVD morbidity. Four demonstrated a significant reduction in CVD morbidity and the fifth a trend towards reduction. MTX use in the year prior to the development of RA decreased the risk of CVD for 3-4 years. Four studies considered myocardial infarction, one demonstrated a decreased risk and three a trend towards decreased risk with MTX use. ::: ::: ::: CONCLUSION ::: The current evidence suggests that MTX use is associated with a reduced risk of CVD events in patients with RA. This suggests that reducing the inflammation in RA using MTX not only improves disease-specific outcomes but may also reduce collateral damage such as atherosclerosis.


title: Leaky-Wave Antennas Based on Noncutoff Substrate Integrated Waveguide Supporting Beam Scanning From Backward to Forward

abstract: In this paper, we propose an approach to realize substrate integrated waveguide (SIW)-based leaky-wave antennas (LWAs) supporting continuous beam scanning from backward to forward above the cutoff frequency. First, through phase delay analysis, it was found that SIWs with straight transverse slots support backward and forward radiation of the $-1$ -order mode with an open-stopband (OSB) in between. Subsequently, by introducing additional longitudinal slots as parallel components, the OSB can be suppressed, leading to continuous beam scanning at least from $-40^\circ$ through broadside to 35°. The proposed method only requires a planar structure and obtains less dispersive beam scanning compared with a composite right/left-handed (CRLH) LWA. Both simulations and measurements verify the intended beam scanning operation while verifying the underlying theory.


title: Discriminative and Affective Touch: Sensing and Feeling

abstract: The multimodal properties of the human somatosensory system continue to be unravelled. There is mounting evidence that one of these submodalities—touch—has another dimension, providing not only its well-recognized discriminative input to the brain, but also an affective input. It has long been recognized that touch plays an important role in many forms of social communication and a number of theories have been proposed to explain observations and beliefs about the "power of touch." Here, we propose that a class of low-threshold mechanosensitive C fibers that innervate the hairy skin represent the neurobiological substrate for the affective and rewarding properties of touch.