title: A collaborative medium for the support of conversational props

abstract: Our work focuses on providing computational support for informal communication among people who are geographically separated. To better understand the use of artifacts in communication, we looked at the contents of office whiteboards after they had been used in conversations. Our analysis revealed that whiteboards are used to present and discuss various classes of objects with specific semantic properties. We call these objects “conversational props,” and we have come to think of a whiteboard as a conversational medium in which props are introduced and manipulated. This study motivated our design of the Conversation Board, an experimental prototype of a multi-user drawing tool which allows remote use of conversational props. We compare the Conversation Board to various other multi-user drawing tools along a number of dimensions.


title: Super-Resolution Image Reconstruction From Aliased FLIR Imagery

abstract: Abstract : This paper presents a super-resolution image reconstruction from a sequence of aliased imagery. The sub-pixel shifts (displacement) among the images are unknown due to uncontrolled natural jitter of the imager. A correlation method is utilized to estimate sub-pixel shifts between each low resolution aliased image with respect to a reference image. An error-energy reduction algorithm is derived to reconstruct the high-resolution alias-free output image by imposing the spatial domain constraint and the spatial frequency domain constraint in an iterative fashion to form the desired alias-free (super-resolved) image. The results of testing the proposed algorithm on the simulated low resolution FLIR (Forward-Looking Infrared) images and real world FLIR images are provided.


title: Qualitative Research Method: Grounded Theory

abstract: In this literature review paper, the researcher has done some efforts to identify and further to elaborate the basiccomponents of a research methods chapter that are integral part of any research paper while conductinggrounded theory approach. Grounded theory is one of the data collection approach in qualitative researchmethods which is totally based on data rather thantry to emerge theory from data. There are bulk of books andresearch papers written by world renowned researchers and authors but the aim of this paper is to help the earlystage researchers in conducting their projects in grounded theory approach. In this paper, the researcher has shedlight on history of grounded theory, how this theory rather approach works, target population, samplingtechnique, data collection methods and the role of a researcher in this whole research process, and anotheressential part of a qualitative research, ethics which play a crucial role while conducting and gathering aqualitative data, have also been discussed in this paper.


title: Smart destinations and tech-savvy millennial tourists: hype versus reality

abstract: This paper aims to contrast the expectations placed on the smart destination as a theoretical management approach with the reality of emergent tech-savvy tourism demand by examining the response of this segment to three critical dimensions of technology use in the context of smart destinations. Tech-savvy tourists are here represented by highly educated Spanish millennial tourists.,Data were obtained through an online survey and analysed through descriptive techniques and exploratory factor analysis using SPSS. Literature review was critical for setting the foundations of the research in this novel context.,Results suggest the existence of a gap between the smart destination theoretical expectations and created hype and the real response of the examined demand in the three main scopes for this tourist-smart destination technology-based relationship, namely, mobile technology use, data sharing for personalised experiences and smart technologies for enhanced experiences.,Convenience sampling was used, and the results of the study cannot be generalised to all millennial tourists. The research is a first approximation to the interrelationship between tourists and smart destinations.,Tourists’ role and experiences mediated by information and communication technologies (ICTs) are decisive in smart destinations. Destination Management Organisations (DMOs), through their policies and actions, ought to take into account the limited predisposition of tourists in using mobile devices, sharing data and using smart technologies for their experiences. Privacy concerns appear to have special relevance for tourists and, therefore, for the future of smart destinations.,The findings offer relevant insights for smart destinations from a neglected angle hitherto, as they introduce several interesting nuances which do not match entirely the fast track taken by institutions, media and academia. This is critical for better understanding tourists in the current panorama, for DMOs and for the theoretical foundations of smart tourism. Besides, the exploratory data analysis reveals potential dimensions of millennials’ behaviour, which can be useful for further investigations.


title: 3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training

abstract: In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. We also introduce back-projection, a simple and effective semi-supervised training method that leverages unlabeled video data. We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints. In the supervised setting, our fully-convolutional model outperforms the previous best result from the literature by 6 mm mean per-joint position error on Human3.6M, corresponding to an error reduction of 11%, and the model also shows significant improvements on HumanEva-I. Moreover, experiments with back-projection show that it comfortably outperforms previous state-of-the-art results in semi-supervised settings where labeled data is scarce. Code and models are available at https://github.com/facebookresearch/VideoPose3D


title: COCOTS: A COTS Software Integration Lifecycle Cost Model - Model Overview and Preliminary Data Collection Findings

abstract: As the use of commercial-of-the-shelf (COTS) components becomes ever more prevalent in the creation of large software systems, the need for the ability to reasonably predict the true lifetime cost of using such software components grows accordingly. In using COTS components, immediate short-term gains in direct development effort & schedule are possible, but usually as a trade-off for a more complicated long-term postdeployment maintenance environment. In addition, there are risks associated with COTS software separate from those of creating components from scratch. These unique risks can further complicate the development and postdeployment situations. This paper discusses a model being developed as an extension of the COCOMO II [1] cost model. COCOTS attempts to predict the lifecycle costs of using COTS components by capturing the more significant COTS risks in its modeling parameters. The current state of the model is presented, along with some preliminary findings suggested by an analysis of calibration data collected to date. The paper concludes with a discussion of the on-going effort to further refine the accuracy and scope of COCOTS.


FELICS - Fair Evaluation of Lightweight Cryptographic Systems


title: Design of a Scalable Event Notification Service: Interface and Architecture

abstract: Event-based distributed systems are programmed to operate in response to events. An event notication service is an application-independent infrastructure that supports the construction of event-based systems. While numerous technologies have been developed for supporting event-based interactions over local-area networks, these technologies do not scale well to wide-area networks such as the Internet. Wide-area networks pose new challenges that have to be attacked with solutions that specically address issues of scalability. This paper presents Siena, a scalable event notication service that is based on a distributed architecture of event servers. We rst present a formally dened interface that is based on an extension to the publish/subscribe protocol. We then describe and compare several different server topologies and routing algorithms. We conclude by briey discussing related work, our experience with an initial implementation of Siena, and a framework for evaluating the scalability of event notication services such as Siena.


title: Discourse structure and concurrent processes. Some evidence from pronominal anaphora interpretation

abstract: The aim of this paper is to take a look at discourse structure from the standpoint of pronominal anaphora processing and socalled ‘accessibility domains’. The core hypothesis of the paper is that attention-based anaphora interpretation models like Focus Theory or Centering Theory can be utilized in a more satisfying way if discourse is considered as a bundle of concurrent, interacting processes. Elaborating on this hypothesis, in the paper a central role is played by various notions borrowed from non-linear phonological frameworks.


title: A co-training method for identifying the same person across social networks

abstract: In order to fit the diverse scenes in life, more and more people choose to join different types of social networks simultaneously. These different networks often contain the information that people leave in a particular scene. Under the circumstances, identifying the same person across different social networks is a crucial way to help us understand the user from multiple aspects. The current solution to this problem focuses on using only profile matching or relational matching method. Some other methods take the two aspect of information into consideration, but they associate the profile similarity with relation similarity simply by a parameter. The matching results on two dimensions may have large difference, directly link them may reduce the overall similarity. Unlike the most of the previous work, we propose to utilize collaborative training method to tackle this problem. We run experiments on two real-world social network datasets, and the experimental results confirmed the effectiveness of our method.


title: Stability control in aerial manipulation

abstract: Aerial manipulation, grasping, and perching in small unmanned aerial vehicles (UAVs) require specific control systems to compensate for changing inertial properties. Grasped objects, external forces from terrain objects, or manipulator movements themselves may destabilize or otherwise alter the flight characteristics of small UAVs during operation resulting in undesirable outcomes. Traditional control methods that assume static mass and inertial properties must be modified to produce stable control of a quadrotor system. This paper presents work towards a control scheme to achieve dynamic stability of an aerial vehicle while under the influence of manipulators and grasped objects. A quadrotor with attached multi-degree of freedom manipulators is implemented in simulation and constructed for testing. Compensation of the inertial changes due to in-flight manipulator movements is investigated. A control scheme is developed and results are presented.


title: Strategic Instruction in Middle School Social Studies: Enhancing Academic and Literacy Outcomes for At-Risk Students.

abstract: This article describes a cognitive strategy designed to enhance middle school students' comprehension of social studies texts and reports its implementation in social studies classrooms. The PEP road map instructional framework is intended to help social studies teachers scaffold the reading and writing experiences of at-risk students as they interact with social studies textbooks. Implementation by middle school students indicated an academically significant increase in reading comprehension for all students, improved metacognitive ability, attention to information contained in texts, and confidence in ability to grapple with ideas in authentic texts. Students can be taught to activate and refine their reading comprehension skills as they engage in meaningful encounters with conceptual ideas about social studies. Content-area teacher responses to the implementation of the strategy in their classrooms were encouraging.


title: Changes in the Producer-Consumer Relationship - Towards Digital Transformation

abstract: The purpose of this paper is to analyze shifts in the producer- consumer relationship resulting from the increased use of digital technologies. In this study, we aim to understand how this relationship is fundamentally changing and the role of digital technologies in such a change. Therefore, we provide a state-of-the-art review of information systems and management litera- ture using analysis techniques borrowed from the method of grounded theory. The results of our study indicate that the constructs of digital density, digital in- terconnectedness, and consumer-centricity are key drivers of changes in the producer-consumer relationship. With the growing role of digital technologies in both society and organizations, our study contributes with implications for information technology and business managers, offering them insights on how to deal with this phenomenon. Finally, our study provides a useful framework for future interdisciplinary research in this field.


title: Co-operative downloading in vehicular ad-hoc wireless networks

abstract: Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.


title: How Instructional Feedback Has Been Employed in Instructional Units for Teaching Software Project Management Tools A Systematic Literature Review

abstract: The software industry has a growing demand for project managers which has leveraged the efforts to improve the teaching of project management competencies in higher education computer courses. Part of these efforts has been concentrated on improving instructional feedback when teaching these competencies. Instructional feedback is essential in order to help the students to learn based on an evaluation of their actions and decisions. However, so far there is few information available on how to provide this instructional feedback when teaching software project management. Therefore, we performed a systematic literature review that aims at providing an overview on existing feedback strategies, specifically when teaching the use of project management tools, such as MS Project or dotProject. As result we identified 8 relevant studies. Their results are systematically presented and a discussion is carried out, identifying the most adopted feedback strategies, how they were implemented, as well as their effects on student learning. These results may be useful for software engineering instructors to decide on the adoption some of the strategies as well as for instructional designers that may employ some of these feedback strategies when developing new instructional units. Keywords-Project Management; PMBOK; PM tool; Instructional feedback;Instructional Unit; SLR.


title: A Deep Reinforced Model for Abstractive Summarization

abstract: Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.


title: A new wavelet-based measure of image focus

abstract: We present a new measure of image focus. It is based on wavelet transform of the image and is defined as a ratio of high-pass band and low-pass band norms. We show this measure is monotonic with respect to the degree of defocusation and sufficiently robust. We experimentally illustrate its performance on simulated as well as real data and compare it with existing focus measures (gray-level variance and energy of Laplacian). Finally, an application of the new measure in astronomical imaging is shown.


title: RQ-RDF-3X: Going beyond triplestores

abstract: Efficient storage and querying of large repositories of RDF content is important due to the widespread growth of Semantic Web and Linked Open Data initiatives. Many novel database systems that store RDF in its native form or within traditional relational storage have demonstrated their ability to scale to large volumes of RDF content. However, it is increasingly becoming obvious that the simple dyadic relationship captured through traditional triples alone is not sufficient for modelling multi-entity relationships, provenance of facts, etc. Such richer models are supported in RDF through two techniques — first, called reification which retains the triple nature of RDF and the second, a non-standard extension called N-Quads. In this paper, we explore the challenges of supporting such richer semantic data by extending the state-of-the-art RDF-3X system. We describe our implementation of RQ-RDF-3X, a reification and quad enhanced RDF-3X, which involved a significant re-engineering ranging from the set of indexes and their compression schemes to the query processing pipeline for queries over reified content. Using large RDF repositories such as YAGO2S and DBpedia, and a set of SPARQL queries that utilize reification model, we demonstrate that RQ-RDF-3X is significantly faster than RDF-3X.


title: Gating Neural Network for Large Vocabulary Audiovisual Speech Recognition

abstract: Audio-based automatic speech recognition (A-ASR) systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition (AV-ASR) is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network (DNN) and hidden Markov models (HMMs). This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.


title: Novel highly integrated current measurement method for drive inverters

abstract: Measuring the load current of drive inverters is still inevitable for a precise current control. In common industrial and automotive inverters hall effect based current sensors are widely used. [1] The presented novel current measurement method is based on an inductive current sensor like a Rogowski coil. This can be fabricated much smaller and cheaper than hall based current sensors and can be integrated into drive inverter systems easily. Unlike using the standard evaluation electronic for Rogowski coils, AC and DC components of the load current can be measured using a special evaluation scheme. The arrangement of the sensors also allows them to be used for fast short-circuit detection and di/dt measurement, so that some synergies with the gate drive circuitry can be used to create a more compact and reliable system.


title: Towards More Confident Recommendations: Improving Recommender Systems Using Filtering Approach Based on Rating Variance

abstract: In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.


2052-8604 Applying social network analysis to security


Common Subspace Transfer for Reinforcement Learning Tasks


title: Radio Frequency Time-of-Flight Distance Measurement for Low-Cost Wireless Sensor Localization

abstract: Location-aware wireless sensor networks will enable a new class of applications, and accurate range estimation is critical for this task. Low-cost location determination capability is studied almost entirely using radio frequency received signal strength (RSS) measurements, resulting in poor accuracy. More accurate systems use wide bandwidths and/or complex time-synchronized infrastructure. Low-cost, accurate ranging has proven difficult because small timing errors result in large range errors. This paper addresses estimation of the distance between wireless nodes using a two-way ranging technique that approaches the Cramer-Rao Bound on ranging accuracy in white noise and achieves 1-3 m accuracy in real-world ranging and localization experiments. This work provides an alternative to inaccurate RSS and complex, wide-bandwidth methods. Measured results using a prototype wireless system confirm performance in the real world.


title: Porcine models of coronary atherosclerosis and vulnerable plaque for imaging and interventional research

abstract: Animal models facilitate our understanding of human disease by providing a controlled environment permitting testing of mechanisms of disease, diagnostic technologies and therapeutic interventions. The ideal animal model should display coronary lesions resembling those seen in human atherosclerosis. No suitable large animal model of high-risk (vulnerable) plaque exists. Lack of such a model has hampered studies designed to validate imaging technologies and to scrutinise the effects of therapeutic interventions in atherosclerotic arteries. Several porcine models of advanced human-like coronary atherosclerosis exist. In this review some of the most promising porcine models are discussed, focusing on their applicability in the development and validation of coronary imaging technologies and interventional devices. In the evolving era of technological development, the availability and use of such animal models of advanced human-like coronary atherosclerosis and vulnerable plaque will become critically important in the preclinical testing of emerging technologies in interventional cardiology.


title: In-Datacenter Performance Analysis of a Tensor Processing Unit

abstract: Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.


title: Cold-Start Reinforcement Learning with Softmax Policy Gradient

abstract: Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax value function that requires neither of these procedures. Our method combines the advantages of policy-gradient methods with the efficiency and simplicity of maximum-likelihood approaches. We apply this new cold-start reinforcement learning method in training sequence generation models for structured output prediction problems. Empirical evidence validates this method on automatic summarization and image captioning tasks.


title: Developing an explanatory model for the process of online radicalisation and terrorism

abstract: While the use of the internet and social media as a tool for extremists and terrorists has been well documented, understanding the mechanisms at work has been much more elusive. This paper begins with a grounded theory approach guided by a new theoretical approach to power that utilizes both terrorism cases and extremist social media groups to develop an explanatory model of radicalization. Preliminary hypotheses are developed, explored and refined in order to develop a comprehensive model which is then presented. This model utilizes and applies concepts from social theorist Michel Foucault, including the use of discourse and networked power relations in order to normalize and modify thoughts and behaviors. The internet is conceptualized as a type of institution in which this framework of power operates and seeks to recruit and radicalize. Overall, findings suggest that the explanatory model presented is a well suited, yet still incomplete in explaining the process of online radicalization.


title: Do GANs actually learn the distribution? An empirical study

abstract: Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of (Goodfellow et al 2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al (to appear at ICML 2017) raised doubts whether the same holds when discriminator has finite size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support ---in other words, the training objective is unable to prevent mode collapse. The current note reports experiments suggesting that such problems are not merely theoretical. It presents empirical evidence that well-known GANs approaches do learn distributions of fairly low support, and thus presumably are not learning the target distribution. The main technical contribution is a new proposed test, based upon the famous birthday paradox, for estimating the support size of the generated distribution.


title: Torque and power improvement for a variable flux permanent magnet synchronous machine

abstract: This paper presents a method to improve the variable flux synchronous machine's performance in the flux weakening region. A dq-model of the machine was used analytically to account for the magnet flux's variation with d-axis current, and the dq-axis inductance variation with dq-axis currents as well. The method was experimentally evaluated on a variable flux syncronous machine and showed a significant improvement of the overall machine's characteristics.


title: Reverse Curriculum Generation for Reinforcement Learning

abstract: Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These goal-oriented tasks present a considerable challenge for reinforcement learning, since their natural reward function is sparse and prohibitive amounts of exploration are required to reach the goal and receive some learning signal. Past approaches tackle these problems by exploiting expert demonstrations or by manually designing a task-specific reward shaping function to guide the learning agent. Instead, we propose a method to learn these tasks without requiring any prior knowledge other than obtaining a single state in which the task is achieved. The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal. Our method automatically generates a curriculum of start states that adapts to the agent's performance, leading to efficient training on goal-oriented tasks. We demonstrate our approach on difficult simulated navigation and fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.


title: PINwI: pedestrian indoor navigation without infrastructure

abstract: Navigation in larger unfamiliar buildings like town halls, airports, shopping malls or other public indoor locations is often difficult for humans. Due to the high amount of infrastructure needed for indoor positioning, just a few navigation services for indoor environments exist. Therefore in many of these buildings 'YOU-ARE-HERE' (YAH) maps are provided, often located at the entrance or other key places, to facilitate orientation and navigation within the building, but they have the disadvantages of being stationary. In this paper, we try to overcome these problems by presenting PINwI (Pedestrian Indoor Navigation without Infrastructure), an application that allows the user of a mobile camera device with integrated compass and accelerometer to utilize a photo of such an indoor YAH-map to navigate through the corresponding building. Using a dead reckoning approach, we enrich stationary analog YAH-maps with basic location functionality and turn them into a digital and dynamic medium that can help decision making while taking turns or estimating distances.


title: Implementing Medical Technological Equipment in the OR: Factors for Successful Implementations

abstract: from the Article: ::: "Operating rooms (ORs) more and more evolve into high-tech environments with increasing pressure on finances, logistics, and a not be neglected impact on patient safety. Safe and cost-effective implementation of technological equipment in ORs is notoriously difficult to manage, specifically as generic implementation activities omit as hospitals have implemented local policies for implementations of technological equipment. )e purpose of this study is to identify success factors for effective implementations of new technologies and technological equipment in ORs, based on a systematic literature review. We accessed ten databases and reviewed included articles. )e search resulted in 1592 titles for review, and finally 37 articles were included in this review. We distinguish influencing factors and resulting factors based on the outcomes of this research. Six main categories of influencing factors on successful implementations of medical equipment in ORs were identified: “processes and activities,” “staff,” “communication,” “project management,” “technology,” and “training.” We identified a seventh category “performance” referring to resulting factors during implementations. We argue that aligning the identified influencing factors during implementation impacts the success, adaptation, and safe use of new technological equipment in the OR and thus the outcome of an implementation. The identified categories in literature are considered to be a baseline, to identify factors as elements of a generic holistic implementation model or protocol for new technological equipment in ORs."


title: Conceptual framework and models for identifying and organizing usability impact factors of mobile phones

abstract: Usability has been regarded as a critical factor affecting the quality of mobile phones. Many studies have examined usability impact factors of mobile phones on the basis of software usability concepts. However, considering mobile phones as multi-media and information appliances, a new usability concept and associated factors should be developed. This paper proposes a conceptual framework which has five views to reflect different aspect of interactions between users and mobile phones, and from which various usability impact factor models can be derived. Five views include user view, product view, interaction view, dynamic view, and execution view. Furthermore, we developed a hierarchical model which organizes usability factors in terms of goal-means relations. Through two case studies, we could verify the usefulness of the framework and model. Lastly, we developed a set of checklists that are helpful to measure the usability of mobile phones, thereby increasing the practicality of the framework and model.


title: Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

abstract: Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field.


title: A predictive maintenance system based on regularization methods for ion-implantation

abstract: Ion Implantation is one of the most sensitive processes in Semiconductor Manufacturing. It consists in impacting accelerated ions with a material substrate and is performed by an Implanter tool. The major maintenance issue of such tool concerns the breaking of the tungsten filament contained within the ion source of the tool. This kind of fault can happen on a weekly basis, and the associated maintenance operations can last up to 3 hours. It is important to optimize the maintenance activities by synchronizing the Filament change operations with other minor maintenance interventions. In this paper, a Predictive Maintenance (PdM) system is proposed to tackle such issue; the filament lifetime is estimated on a statistical basis exploiting the knowledge of physical variables acting on the process. Given the high-dimensionality of the data, the statistical modeling has been based on Regularization Methods: Lasso, Ridge Regression and Elastic Nets. The predictive performances of the aforementioned regularization methods and of the proposed PdM module have been tested on actual productive semiconductor data.


title: 25th Anniversary Article: The Evolution of Electronic Skin (E‐Skin): A Brief History, Design Considerations, and Recent Progress

abstract: Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.


title: Intrinsically motivated reinforcement learning: A promising framework for procedural content generation

abstract: So far, Evolutionary Algorithms (EA) have been the dominant paradigm for Procedural Content Generation (PCG). While we believe the field has achieved a remarkable success, we claim that there is a wide window for improvement. The field of machine learning has an abundance of methods that promise solutions to some aspects of PCG that are still under-researched. In this paper, we advocate the use of Intrinsically motivated reinforcement learning for content generation. A class of methods that thrive for knowledge for its own sake rather than as a step towards finding a solution. We argue that this approach promises solutions to some of the well-known problems in PCG: (1) searching for novelty and diversity can be easily incorporated as an intrinsic reward, (2) improving models of player experience and generation of adapted content can be done simultaneously through combining extrinsic and intrinsic rewards, and (3) mix-initiative design tools can incorporate more knowledge about the designer and her preferences and ultimately provide better assistance. We demonstrate our arguments and discuss the challenges that face the proposed approach.


title: A Survey on Domain-Specific Languages in Robotics

abstract: The design, simulation and programming of robotics systems is challenging as expertise from multiple domains needs to be integrated conceptually and technically. Domain-specific modeling promises an efficient and flexible concept for developing robotics applications that copes with this challenge. It allows to raise the level of abstraction through the use of specific concepts that are closer to the respective domain concerns and easier to understand and validate. Furthermore, it focuses on increasing the level of automation, e.g. through code generation, to bridge the gap between the modeling and the implementation levels and to improve the efficiency and quality of the software development process. Within this contribution, we survey the literature available on domain-specific (modeling) languages in robotics required to realize a state-of-the-art real-world example from the [email protected] competition. We classify 41 publications in the field as reference for potential DSL users. Furthermore, we analyze these contributions from a DSL-engineering viewpoint and discuss quantitative and qualitative aspects such as the methods and tools used for DSL implementation as well as their documentation status and platform integration. Finally, we conclude with some recommendations for discussion in the robotics programming and simulation community based on the insights gained with this survey.


title: Attitudes towards science: a review of the literature and its implications

abstract: This article offers a review of the major literature about attitudes to science and its implications over the past 20 years. It argues that the continuing decline in numbers choosing to study science at the point of choice requires a research focus on students’ attitudes to science if the nature of the problem is to be understood and remediated. Starting from a consideration of what is meant by attitudes to science, it considers the problems inherent to their measurement, what is known about students’ attitudes towards science and the many factors of influence such as gender, teachers, curricula, cultural and other variables. The literature itself points to the crucial importance of gender and the quality of teaching. Given the importance of the latter we argue that there is a greater need for research to identify those aspects of science teaching that make school science engaging for pupils. In particular, a growing body of research on motivation offers important pointers to the kind of classroom environment and activities that might raise pupils’ interest in studying school science and a focus for future research.


title: Gamma Rays, Electrons, Hard X-Rays, and the Central Parsec of the Milky Way

abstract: The complex interplay of processes at the Galactic Center is at the heart of numerous past, present, and (likely) future mysteries. We aim at a more complete understanding of how spectra extending to>10 TeV result. We first construct a simplified model to account for the peculiar energy and angular dependence of the intense central parsec photon field. This allows for calculating anisotropic inverse Compton scattering and mapping gamma-ray extinction due to gamma gamma ->e^+ e^- attenuation. Coupling these with a method for evolving electron spectra, we examine several clear and present excesses, including the diffuse hard X-rays seen by NuSTAR and GeV gamma rays by Fermi. We address further applications to cosmic rays, dark matter, neutrinos, and gamma rays from the Center and beyond.


title: Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition

abstract: To address the high computational and memory cost in 3-D volumetric convolutional neural networks (CNNs), we propose an approach to train binary volumetric CNNs for 3-D object recognition. Our method is specifically designed for 3-D data, in which it transforms the inputs and weights in convolutional/fully connected layers to binary values, which can potentially accelerate the networks by efficient bitwise operations. Two loss calculation methods are designed to solve the accuracy decrease problem when the weights in the last layer are binarized. Four binary volumetric CNNs are obtained from their corresponding floating-point networks using our approach. Evaluations on three public datasets from different domains (Computer Aided Design (CAD), light detection and ranging (LiDAR), and RGB-D) show that our binary volumetric CNNs can achieve a comparable recognition performance as their floating-point counterparts but consume less computational and memory resources.


A Guide to Formal Analysis of Join Processing in Massively Parallel Systems


Graph Cut Based Automatic Lung Boundary Detection in Chest Radiographs


title: The State-of-the-Practice of Modern Structural Health Monitoring for Bridges: A Comprehensive Review

abstract: Since the National Bridge Inventory (NBI) was first conducted, structural health monitoring (SHM) of the United States bridge infrastructure has consisted largely of labor-intensive, subjective measures like chain dragging and the tap test. Recent developments in a variety of sensor technologies and an improvement of computing and networking capabilities have allowed for the installation of in-situ sensor networks responsible for monitoring—among other parameters—the strain and deformation of structural members and concrete deck cracking. These relatively new techniques are quite costly, however, and in many cases are infeasible for SHM because they require installation in hard-to-reach places or during construction, limiting their application to the small number of bridges being built today compared to the current population of in-service bridges. Stand-off SHM techniques such as radar, electro-optical, laser scanning and other remote sensing technologies may offer an innovative, cost-effective method of monitoring the dynamic conditions of U.S. bridges in real-time. This paper investigates the state of the practice of SHM and provides summaries of existing technologies, both in-situ sensors and networks and remote techniques, as well as case studies of instrumented bridges.


title: Too Cool at School - Understanding Cool Teenagers

abstract: Cool can be thought about on three levels; the having of cool things, the doing of cool stuff and the being of cool. Whilst there is some understanding of cool products, the concept, of being cool is much more elusive to designers and developers of systems. This study examines this space by using a set of pre-prepared teenage personas as probes with a set of teenagers with the aim of better understanding what is, and isn’t cool about teenage behaviours. The study confirmed that teenagers are able to rank personas in order of cool and that the process of using personas can provide valuable insights around the phenomenon of cool. The findings confirm that cool is indeed about having cool things but in terms of behaviours cool can be a little bit, but not too, naughty.


title: Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach

abstract: Ability to predict the risk of damaging events (e.g. wildfires) is crucial in helping emergency services in their decision making processes, to mitigate and reduce the impact of such events. Today, wildfire rating systems have been in operation extensively in many countries around the world to estimate the danger of wildfires. In this paper we propose a data-driven approach to predict wildfire risk using weather data. We show how we address the inherent challenge arising due to the temporal dynamicity of weather data. Weather observations naturally change in time, with finer-scale variation (e.g. stationary day or night) or large variations (nonstationary day or night), and this determines a temporal variation of the predicted wildfire danger. We show how our dynamic wildfire danger prediction model addresses the aforementioned challenge using context-based anomaly detection techniques. We call our predictive model a Context-Based Fire Risk (CBFR) model. The advantage of our model is that it maintains multiple historical models for different temporal variations (e.g. day versus night), and uses ensemble learning techniques to predict wildfire risk with high accuracy. In addition, it is completely unsupervised and does not rely on expert knowledge, which makes it flexible and easily applied to any region of interest. Our CBFR model is also scalable and can potentially be parallelised to speed up computation. We have considered multiple wildfire locations in the Blue Mountains, Australia as a case study, and compared the results of our system with the existing well-established Australian wildfire rating system. The experimental results show that our predictive model has a substantially higher accuracy in predicting wildfire risk, which makes it an effective model to supplement the operational Australian wildfire rating system.


title: Conformal Magnetic Composite RFID for Wearable RF and Bio-Monitoring Applications

abstract: This paper introduces for the first time a novel flexible magnetic composite material for RF identification (RFID) and wearable RF antennas. First, one conformal RFID tag working at 480 MHz is designed and fabricated as a benchmarking prototype and the miniaturization concept is verified. Then, the impact of the material is thoroughly investigated using a hybrid method involving electromagnetic and statistical tools. Two separate statistical experiments are performed, one for the analysis of the impact of the relative permittivity and permeability of the proposed material and the other for the evaluation of the impact of the dielectric and magnetic loss on the antenna performance. Finally, the effect of the bending of the antenna is investigated, both on the S-parameters and on the radiation pattern. The successful implementation of the flexible magnetic composite material enables the significant miniaturization of RF passives and antennas in UHF frequency bands, especially when conformal modules that can be easily fine-tuned are required in critical biomedical and pharmaceutical applications.


title: Alone in a crowd of sheep: Asymmetric perceptions of conformity and their roots in an introspection illusion.

abstract: The results of 5 studies showed that people see others as more conforming than themselves. This asymmetry was found to occur in domains ranging from consumer purchases to political views. Participants claimed to be less susceptible than their average peers to broad descriptions of social influences, and they also claimed to be less susceptible than specific peers to specific instances of conformity. These studies further demonstrated that this asymmetry is not simply the result of social desirability, but it is also rooted in people's attention to introspective versus behavioral information when making conformity assessments. The participants displayed an introspection illusion, placing more weight on introspective evidence of conformity (relative to behavioral evidence) when judging their own susceptibility to social influence as opposed to someone else's. Implications for self-other asymmetries, implicit social influence, and interpersonal conflict are discussed.


title: Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

abstract: Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.


title: Low Level Control in a Modular System Architecture for Realizing Precise Driving Maneuvers of the Autonomous Vehicle Caroline

abstract: Autonomous transportation on public roads has been demonstrated on German Highways since the early nineties. So far, there are few approaches to extend activities to urban scenarios. The 2007 DARPA Urban Challenge afforded the opportunity to demonstrate the abilities of the Technische Universitat Braunschweig to develop the autonomous vehicle Caroline and to show its capabilities in urban like environments. Among 11 teams from the initial 89, team CarOLO mastered the challenges to qualify for the final DARPA Urban Challenge event. This paper describes the overall concept implemented in the field of autonomous vehicle guidance of team CarOLO for the DARPA Urban Challenge 2007. Compared to autonomous driving on highways, an urban environment requires an efficient and flexible interface to realize precise driving maneuvers. The approaches taken to handle complex maneuvers as well as the control structure of the system is shown, which was developed at the Institute of Control Engineering.


title: A novel low-power, low-offset, and high-speed CMOS dynamic latched comparator

abstract: A novel dynamic latched comparator with offset voltage compensation is presented. The proposed comparator uses one phase clock signal for its operation and can drive a larger capacitive load with complementary version of the regenerative output latch stage. As it provides a larger voltage gain up to 22 V/V to the regenerative latch, the input-referred offset voltage of the latch is reduced and metastability is improved. The proposed comparator is designed using 90 nm PTM technology and 1 V power supply voltage. It demonstrates up to 24.6% less offset voltage and 30.0% less sensitivity of delay to decreasing input voltage difference (17 ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption. In addition, with a digitally controlled capacitive offset calibration technique, the offset voltage of the proposed comparator is further reduced from 6.03 to 1.10 mV at 1-sigma at the operating clock frequency of 3 GHz, and it consumes 54 μW/GHz after calibration.


title: An Analog Neural Network Processor with Programmable Topology

abstract: The architecture, implementation, and applications of a special-purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its data path is particularly suitable for the convolutional topologies that are typical in classification networks, but can also be configured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6 b accuracy for the weights and 3 b for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130000 connections and was evaluated in 1 ms. >


title: Effects of ambient conditions on partial discharges at very low frequency (VLF) sinusoidal voltage excitation

abstract: Effects of temperatures on corona discharges developed by needle/bowl electrode configuration at different frequencies are investigated. PD measurements are carried out under enclosed controlled air temperature range between 20°C and 40°C. The test chamber is airtight so the humidity is constant. Sinusoidal voltage waveforms in the frequency range of 0.05Hz to 50Hz are used to stress samples. There are evidences that PD behaviors at VLF and power frequency are dependent on ambient temperature in a similar manner. Nevertheless, there are still noticeable differences in PD patterns at VLF at various temperatures, especially in the phase position of PD events. These differences are discussed in details.


title: An Introduction to Temporal Graphs: An Algorithmic Perspective

abstract: A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence \(G_1,G_2\ldots ,G_l\) of static graphs over the same (static) set of nodes V. Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension in added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.


title: Variable Structure Control of Pendulum-driven Spherical Mobile Robots

abstract: In this paper, we propose a hierarchical sliding mode control approach for a fixed point regulation of pendulum-driven spherical mobile robots. A simplified dynamic model is established for their longitudinal motion and the controller is designed to have double layer structure because the system is divided into two subsystems. In the first layer, the sliding surfaces are hierarchically designed for each subsystem, and in the second layer, the whole sliding surface is designed as the linear combination of the two subsystem surfaces. The asymptotic stability of the whole system is verified by Lyapunov analysis, the stability of sliding surfaces and convergence of output variables are proved utilizing mathematical techniques. Finally, we carry out a simulation to illustrate the effectiveness of the proposed control scheme. Keywords-spherical robots; longitudinal motion; asymptotic stability; sliding mode control; underactuated systems Omni-directional rolling of spherical mobile robots can be realized by combinations of their longitudinal and lateral motions. Our research in this paper is confined to the longitudinal motion control problems. We propose a hierarchical sliding mode control approach for the pendulum-driven spherical mobile robot system, which is a single-input multi-output nonlinear underactuated system. In the proposed control system, the double layer structure is adopted to guarantee the stability of the whole system. In addition, the sliding surfaces are utilized to drive the output tracking errors to zero. Here, the sliding surfaces of the first layer are designed at each subsystem, while the whole sliding surface is the linear combination of the two subsystem sliding surfaces.


title: Design for variety: developing standardized and modularized product platform architectures

abstract: Developing a robust, product platform architecture brings an important competitive advantage to a company. The major benefits are reduced design effort and time-to-market for future generations of the product. This paper describes a step-by-step method that aids companies in developing such product platform architectures. Using the concept of specification "flows" within a product development project, the design for variety (DFV) method develops two indices to measure a product's architecture. The first index is the generational variety index (GVI), a measure for the amount of redesign effort required for future designs of the product. The second index is the coupling index (CI), a measure of the coupling among the product components. The design team uses these two indices to develop a decoupled architecture that requires less design effort for follow-on products. This paper describes the DFV method and uses a water cooler example to illustrate the method.


title: Broadband Integrated Lens for Illuminating Reflector Antenna With Constant Aperture Efficiency

abstract: A new integrated shaped lens antenna configuration is described with frequency stable radiation pattern and phase center position across a broad 1:3 frequency band, which can be used for focal plane reflector feeding in quasi-optical radio telescope systems. The lens is compatible with the integration of ultrawideband uniplanar printed feeds at its base and equally broadband mixing devices, like the Hot Electron Bolometer (HEB), although these are not used in the present work. Measurements on a scaled mm-wave lab prototype have confirmed stable performance versus frequency, with only dB directivity variation, and better than 94% Gaussicity, thanks to the possibility to impose a predefined output radiation pattern template. Simulations were performed to test the illumination of an off-set parabolic reflector by the lens radiation pattern, which confirmed reasonably constant aperture efficiency in the order of 78% across the 100% bandwidth.


Scalable Generative Models for Multi-label Learning with Missing Labels


title: A Social Media Based Index of Mental Well-Being in College Campuses

abstract: Psychological distress in the form of depression, anxiety and other mental health challenges among college students is a growing health concern. Dearth of accurate, continuous, and multi-campus data on mental well-being presents significant challenges to intervention and mitigation efforts in college campuses. We examine the potential of social media as a new "barometer" for quantifying the mental well-being of college populations. Utilizing student-contributed data in Reddit communities of over 100 universities, we first build and evaluate a transfer learning based classification approach that can detect mental health expressions with 97% accuracy. Thereafter, we propose a robust campus-specific Mental Well-being Index: MWI. We find that MWI is able to reveal meaningful temporal patterns of mental well-being in campuses, and to assess how their expressions relate to university attributes like size, academic prestige, and student demographics. We discuss the implications of our work for improving counselor efforts, and in the design of tools that can enable better assessment of the mental health climate of college campuses.


title: Image Noise Level Estimation by Principal Component Analysis

abstract: The problem of blind noise level estimation arises in many image processing applications, such as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation method on the basis of principal component analysis of image blocks. We show that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13 existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than other methods. Our method does not assume the existence of homogeneous areas in the input image and, hence, can successfully process images containing only textures.


Mortality and morbidity patterns in under-five children with severe acute malnutrition (SAM) in Zambia: a five-year retrospective review of hospital-based records (2009–2013)


title: Challenges in Urdu Text Tokenization and Sentence Boundary Disambiguation

abstract: Urdu is morphologically rich language with different nature of its characters. Urdu text tokenization and sentence boundary disambiguation is difficult as compared to the language like English. Major hurdle for tokenization is improper use of space between words, where as absence of case discrimination makes the sentence boundary detection a difficult task. In this paper some issues regarding both of these language processing tasks have been identified.


title: Conjugate directions for stochastic gradient descent

abstract: The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore ideas from conjugate gradient in the stochastic (online) setting, using fast Hessian-gradient products to set up low-dimensional Krylov subspaces within individual mini-batches. In our benchmark experiments the resulting online learning algorithms converge orders of magnitude faster than ordinary stochastic gradient descent.


title: A lightweight infrastructure for graph analytics

abstract: Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system. We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tackled by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.


title: Comparing memory systems for chip multiprocessors

abstract: There are two basic models for the on-chip memory in CMP systems:hardware-managed coherent caches and software-managed streaming memory. This paper performs a direct comparison of the two modelsunder the same set of assumptions about technology, area, and computational capabilities. The goal is to quantify how and when they differ in terms of performance, energy consumption, bandwidth requirements, and latency tolerance for general-purpose CMPs. We demonstrate that for data-parallel applications, the cache-based and streaming models perform and scale equally well. For certain applications with little data reuse, streaming scales better due to better bandwidth use and macroscopic software prefetching. However, the introduction of techniques such as hardware prefetching and non-allocating stores to the cache-based model eliminates the streaming advantage. Overall, our results indicate that there is not sufficient advantage in building streaming memory systems where all on-chip memory structures are explicitly managed. On the other hand, we show that streaming at the programming model level is particularly beneficial, even with the cache-based model, as it enhances locality and creates opportunities for bandwidth optimizations. Moreover, we observe that stream programming is actually easier with the cache-based model because the hardware guarantees correct, best-effort execution even when the programmer cannot fully regularize an application's code.


title: Spectral Characteristics Analysis and Water Content Detection of Potato Plants Leaves

abstract: Abstract The water content of potato leaves in different leaf positions was studied using hyperspectral technique in this research. According to leaf position, the leaves were divided into upper, middle and lower 3 groups. The experiment was conducted, in which the fresh leaves were collected and dried, and the reflectance spectra and water content were measured. The relationship between the spectral characteristics of different leaf positions and water content distribution of potato plants was analyzed. It was found that the reflectance and water content of fresh leaves in different positions were different. With the leaf position from top to bottom, the reflectance increased successively (R upper R middle >R lower) in 1403.6-1704.2 nm and the average value of water content increased in turn. After drying, with the leaf position from top to bottom, the spectral reflectance of leaves decreased successively (R upper


title: Person Transfer GAN to Bridge Domain Gap for Person Re-identification

abstract: Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT17 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.


title: Temporal structure of syntactic parsing: Early and late event-related brain potential effects.

abstract: Event-related brain potentials (ERPs) were recorded from participants listening to or reading sentences that were correct, contained a violation of the required syntactic category, or contained a syntactic-category ambiguity. When sentences were presented auditorily (Experiment 1), there was an early left anterior negativity for syntactic-category violations, but not for syntactic-category ambiguities. Both anomaly types elicited a late centroparietally distributed positivity. When sentences were presented visually word by word (Experiment 2), again an early left anterior negativity was found only for syntactic-category violations, and both types of anomalies elicited a late positivity. The combined data are taken to be consistent with a 2-stage model of parsing, including a 1st stage, during which an initial phrase structure is built and a 2nd stage, during which thematic role assignment and, if necessary, reanalysis takes place. Disruptions to the 1st stage of syntactic parsing appear to be correlated with an early left anterior negativity, whereas disruptions to the 2nd stage might be correlated with a late posterior distributed positivity.


title: WiFi-based through-the-wall presence detection of stationary and moving humans analyzing the doppler spectrum

abstract: Through-the-wall (TTW) sensing is relevant in several scenarios. In particular, a system able to detect the presence of a noncollaborative person behind a wall could be used by law enforcement for better planning actions in case of standoffs and hostage situations. For emergency situations, first responders could use such a system to detect the presence of people through rubble and collapsed structures. Traditionally, these types of systems have been designed using a radar approach. In particular, ultrawideband systems (2 GHz of bandwidth) have been proposed for detecting human presence by using radio frequency (RF) signals [1], [2]. These systems usually require a large power source and big antennas. Recently, to reduce the power and complexity of these devices, the use of opportunity signals, such as WiFi signals, has been considered [3]–[6].


title: Realtime Analysis of Information Diffusion in Social Media

abstract: The goal of this thesis is to investigate real-time analysis methods on social media with a focus on information diffusion. From a conceptual point of view, we are interested both in the structural, sociological and temporal aspects of information diffusion in social media with a twist on the real time factor of what is happening right now. From a technical side, the sheer size of current social media services (100's of millions of users) and the large amount of data produced by these users renders conventional approaches for these costly analyses impossible. For that, we need to go beyond the state-of-the-art infrastructure for data-intensive computation. Our high level goal is to investigate how information diffuses in real time on the underlying social network and the role of different users in the propagation process. We plan to implement these analyses with full and partially missing datasets and compare the cost and quality of both approaches.


title: Effects of fast food branding on young children's taste preferences.

abstract: OBJECTIVE ::: To examine the effects of cumulative, real-world marketing and brand exposures on young children by testing the influence of branding from a heavily marketed source on taste preferences. ::: ::: ::: DESIGN ::: Experimental study. Children tasted 5 pairs of identical foods and beverages in packaging from McDonald's and matched but unbranded packaging and were asked to indicate if they tasted the same or if one tasted better. ::: ::: ::: SETTING ::: Preschools for low-income children. ::: ::: ::: PARTICIPANTS ::: Sixty-three children (mean +/- SD age, 4.6 +/- 0.5 years; range, 3.5-5.4 years). ::: ::: ::: MAIN EXPOSURE ::: Branding of fast foods. ::: ::: ::: OUTCOME MEASURES ::: A summary total taste preference score (ranging from -1 for the unbranded samples to 0 for no preference and +1 for McDonald's branded samples) was used to test the null hypothesis that children would express no preference. ::: ::: ::: RESULTS ::: The mean +/- SD total taste preference score across all food comparisons was 0.37 +/- 0.45 (median, 0.20; interquartile range, 0.00-0.80) and significantly greater than zero (P<.001), indicating that children preferred the tastes of foods and drinks if they thought they were from McDonald's. Moderator analysis found significantly greater effects of branding among children with more television sets in their homes and children who ate food from McDonald's more often. ::: ::: ::: CONCLUSION ::: Branding of foods and beverages influences young children's taste perceptions. The findings are consistent with recommendations to regulate marketing to young children and also suggest that branding may be a useful strategy for improving young children's eating behaviors.


Deep Learning in Natural Language Processing


title: Integrated switch current sensor for shortcircuit protection and current control of 1.7-kV SiC MOSFET modules

abstract: This paper presents design and implementations of a switch current sensor based on Rogowski coils. The current sensor is designed to address the issue of using desaturation circuit to protect the SiC MOSFET during shortcircuit. Specifications are given to meet the application requirement for SiC MOSFETs. It is also designed for high accuracy and high bandwidth for converter current control. PCB-based winding and shielding layout is proposed to minimize the noises caused by the high dv/dt at switching. The coil on PCB are modeled by impedance measurement, thus the bandwidth of coil is calculated. At the end, various test results are demonstrated to validate the great performance of the switch current sensor.


title: Tools to support systematic reviews in software engineering: a feature analysis

abstract: Background The labour intensive and error prone nature of the systematic review process has led to the development and use of a range of tools to provide automated support. Aim The aim of this research is to evaluate a set of candidate tools that provide support for the overall systematic review process. Method A feature analysis is performed to compare and evaluate four candidate tools. Results Each of the candidates has some strengths and some weaknesses. SLuRp has the highest overall score and SLRTOOL has the lowest overall score. SLuRp scores well on process management features such as support for multiple users and document management and less well on ease of installation. Conclusions Although the tools do not yet support the whole systematic review process they provide a good basis for further development. We suggest a community effort to establish a set of features that can inform future tool development.


title: Differences in the Progression of Successive Flowering between Summer and Autumn Ecotype Cultivars in Common Buckwheat : (Fagopyrum esculentum Moench)

abstract: The common buckwheat cultivars "Shinanonatsusoba"(summer ecotype)and "Miyazakizairai"(autumn ecotype)were cultivated in pots with soil(sandy loam)in summer and autumn at Meijo University, Nagoya. The flowering position in the flower clusters on a plant and their flowering date were investigated. Shinanonatsusoba in summer and Miyazakizairai in autumn had 600 to 850 flowers per plant during the flowering period of about a month. Shinanonatsusoba in autumn and Miyazakizairai in summer yielded fewer seeds. In these cases, Miyazakizairai had remarkably many flowers(more than 2, 600)and a long flowering period of four months, but the seed-setting ratio was low. Shinanonatsusoba had few flowers(199)and a short flowering period of half a month, although the seed-setting ratio was high. Miyazakizairai in summer showed three peaks of flowering and the same end time with autumn cropping. Furthermore, (1)continuation of the occurrence of new flower clusters, (2)differentiation of new flower buds on flower clusters that had once ceased flowering, and(3)regrowth of dormant lateral buds were observed at the later stage. Within a flower cluster, a greater number of flowers and a longer flowering period were also evident. Moreover, It is important to note that they showed long intervals of flowering among flower clusters, subflower clusters, or flowers in a subflower cluster. In autumn, Shinanonatsusoba had the same intervals as in summer, but they had only few flower clusters, subflower clusters, and flowers in a subflower cluster.


title: Practical and secure nearest neighbor search on encrypted large-scale data

abstract: Nearest neighbor search (or k-nearest neighbor search in general) is one of the most fundamental queries on massive datasets, and it has extensive applications such as pattern recognition, statistical classification, graph algorithms, Location-Based Services and online recommendations. With the raising trend of outsourcing massive sensitive datasets to public clouds, it is urgent for companies and organizations to demand fast and secure nearest neighbor search solutions over their outsourced data, but without revealing privacy to untrusted clouds. However, existing solutions for secure nearest neighbor search still face significant limitations, which make them far from practice. In this paper, we propose a new searchable encryption scheme, which can efficiently and securely enable nearest neighbor search over encrypted data on untrusted clouds. Specifically, we modify the search algorithm of nearest neighbors with tree structures (e.g., R-trees), where the modified algorithm adapts to lightweight cryptographic primitives (e.g., Order-Preserving Encryption) without affecting the original faster-than-linear search complexity. As a result, we address all the limitations in the previous works while still maintaining correctness and security. Moreover, our design is general, which can be used for secure k-nearest neighbor search, and it is compatible with other similar tree structures. Our experimental results on Amazon EC2 show that our scheme is extremely practical over massive datasets.


title: 3.5-Gbit/s QPSK Signal Radio-Over-Fiber Transmission With 60-GHz Integrated Photonic Array-Antenna Beam Forming

abstract: This paper studies the performance of 3.5-Gbit/s quadrature phase shift keying (QPSK) signal radio-over-fiber (RoF) transmission with beam forming of array antennas. An “integrated photonic array- antenna (IPA)” is utilized in this experiment, where uni-travelling carrier photodiodes are fully integrated with 60-GHz-band 4 × 2 array-antenna elements into a single board. The beam forming operation for the IPA is experimentally demonstrated by using a QPSK signal assuming real mobile data traffic, and the QPSK signal quality during the beam forming operation for the IPA is quantitatively evaluated. The expandability for more advanced modulation formats, such as 16-quadratic-amplitude modulation, is also studied. In addition, the required system parameters for practical use are shown by investigating the induced transmission penalty. The obtained results enable us to design next generation radio access networks based on 60-GHz RoF transmission technique.


title: Application identification system for SDN QoS based on machine learning and DNS responses

abstract: In recent years, the demand for application-specific qualify of service (QoS) management has grown. To effectively do application-specific QoS, a system albe to do flow classification at the application level is required. This paper presents an application identification system that can be integrated with a QoS management system in a software defined network (SDN). This paper describes the method to obtain ground truth (label) of the flow from four mainstream operating systems (OS), and the method to classify flow based on supervised machine learning and DNS responses. In our experiment, average F-measure of all applications reached 93.48%. The testing data set contained 294 applications, given that each platform version or execution file of an application was one application. The testing data set included Skype, Facebook, and other popular applications. Results showed that this system can identify application traffic on different platforms with high accuracy.


title: Learning an Integrated Distance Metric for Comparing Structure of Complex Networks

abstract: Graph comparison plays a major role in many network applications. We often need a similarity metric for comparing networks according to their structural properties. Various network features - such as degree distribution and clustering coefficient - provide measurements for comparing networks from different points of view, but a global and integrated distance metric is still missing. In this paper, we employ distance metric learning algorithms in order to construct an integrated distance metric for comparing structural properties of complex networks. According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks. For evaluating our proposed method which is called NetDistance, we applied it as the distance metric in K-nearest-neighbors classification. Empirical results show that NetDistance outperforms previous methods, at least 20 percent, with respect to precision.


Enhancement of Images Using Histogram Processing Techniques


title: HEAD DETECTION IN STEREO DATA FOR PEOPLE COUNTING AND SEGMENTATION

abstract: In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.


title: Blade-type crawler vehicle bio-inspired by a wharf roach

abstract: Unmanned rescue, observation and/or research vehicles with high terrain adaptability, fast velocity and high reliability to reach difficult rough terrain locations are still demanded, but most vehicles increase rough terrain adapt ability at the expense of low velocity and/or complex mechanisms. We propose a novel vehicle consisting of a very simple blade-type crawler and active antennas with rollers, resembling a wharf roach. This configuration assures stable travelling over uneven terrain at high-speeds, with a very simple and reliable mechanism. This article presents the basic concept, mechanical design of the crawler and the antennas, and the basic motion control strategies. Moreover, we built and tested the first small mechanical prototype named KEIOS-I, and got successful results from experiments on uneven terrain at a high-speed traveling, and also lateral travelling and jumping motions. Future work will also implement roller skating mode using the active antennas equipped with rollers.


title: An enhanced CBIR using HSV quantization, discrete wavelet transform and edge histogram descriptor

abstract: Now a days, a great attention has received for content-based image retrieval by the researchers. It is very popular and interesting topic of computer vision. The basic requirement of content based image retrieval is to extract the appropriate information from the large image repository corresponding to query image on the basis of contents with better system performance. But, in the development of CBIR system with an appropriate fusion of low-level features is a big problem. This paper suggested an enhanced retrieval system by using the combination of HSV histogram, discrete wavelet transforms and edge histogram descriptor to extract the relevant information of an image. In this work, we used HSV color histogram with quantized non-uniform 72 bins to extract color information of image, discrete wavelet transform on each component (H, S and V) of HSV image to extract the complex texture pattern of image and global as well as local edge histogram descriptor on V component of HSV image to extract the geometry information of image. Here, Euclidean distance is used as similarity measurement to find out how similar the user image is with respect to the image in the database. For experimental analysis, 600 images of Wang image database are used and the results show that this approach gives good performance in term of precision and adaptableness while comparing with other's combining scheme.


title: A New, Innovative, Lengthening Surgical Procedure for Peyronie's Disease by Penile Prosthesis Implantation with Double Dorsal‐Ventral Patch Graft: The “Sliding Technique”

abstract: INTRODUCTION ::: Peyronie's disease is the result of the formation of fibrous plaques in the tunica albuginea of the penis; typical presentations of the disease are represented by pain during erection, erectile dysfunction, and penile deformities, such as curvature, narrowing, and penile shortening. The most complex treatment is related to penile shortening. ::: ::: ::: AIM ::: To find a safe procedure in penile shortening due to Peyronie's disease providing a satisfactory lengthening, allowing an early stabilization of the penis, and preventing axial tension on the neurovascular bundles during dilation. ::: ::: ::: METHODS ::: We describe a new lengthening surgical procedure based on a ventro-dorsal incision of the tunica albuginea, penile prosthesis implantation, and double dorsal-ventral patch grafting with porcine small intestinal submucosa. Three patients, affected by Peyronie's disease with penile shortening and erectile dysfunction, underwent this procedure with approval of our local ethical committee. We evaluated the penis lengthening, intraoperative and postoperative complications, patient's preoperative and postoperative sexual life satisfaction (International Index of Erectile Function [IIEF] questionnaire). ::: ::: ::: RESULTS ::: The average operative time was 2 hours and 50 minutes. No major intraoperative nor postoperative complications occurred. No significant bleedings were recorded. Patients were discharged after 48-72 hours. The average increase in length obtained was 3.2 cm. All patients resumed sexual intercourses with satisfaction; no significant loss of sensitivity or any sign of vascular distress of the glans was recorded. The follow-up is 13 months. The average IIEF score is 60. ::: ::: ::: CONCLUSIONS ::: The lengthening of the penis by a double dorsal-ventral patch graft is an innovative procedure that is based on current techniques of plaque incision and grafting, and that can easily resolve severe shortening of the penis due to Peyronie's disease. In the cases presented, this procedure resulted easily, effectively, and safely. Nevertheless, the technique proposed in this article shall be validated through prospective studies with larger samples.


title: Automatically Extracting Procedural Knowledge from Instructional Texts using Natural Language Processing

abstract: Procedural knowledge is the knowledge required to perform certain tasks, and forms an important part of expertise. A major source of procedural knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring procedural knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire procedural knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system.


title: Personalized peer-comparison feedback and its effect on emergency medicine resident ultrasound scan numbers

abstract: Clinician-performed ultrasound has become a widely utilized tool in emergency medicine and is a mandatory component of the residency curricula. We aimed to assess the effect of personalized peer-comparison feedback on the number of ultrasound scans performed by emergency medicine residents. A personalized peer-comparison feedback was performed by sending 44 emergency medicine residents a document including personally identified scan numbers and class averages. The number of ultrasound scans per clinical shift for a 3-month period before and after the feedback intervention was calculated. The average number of ultrasound exams per shift improved from 0.39 scans/shift before to 0.61 scans/shift after feedback (p = 0.04). Among the second year residents, the scans/shift ratio improved from 0.35 to 0.87 (p = 0.07); for third year residents, from 0.51 to 0.58 (p = 0.46); and from 0.33 to 0.41 (p = 0.21) for the fourth year residents before and after the intervention, respectively. A personalized peer-comparison feedback provided to emergency medicine residents resulted in increased ultrasound scan numbers per clinical shift. Incorporating this method of feedback may help encourage residents to scan more frequently.


title: Underwater laser communication with sloped pulse modulation in turbid water

abstract: In this article, we propose the demonstration of an underwater wireless laser communication link operating at data rates of up to 100 Mbps over a band-limited turbid water channel using a modified ...


Does gold offer a better protection against sovereign debt crisis than other metals ?


title: An Analysis of the Privacy and Security Risks of Android VPN Permission-enabled Apps

abstract: Millions of users worldwide resort to mobile VPN clients to either circumvent censorship or to access geo-blocked content, and more generally for privacy and security purposes. In practice, however, users have little if any guarantees about the corresponding security and privacy settings, and perhaps no practical knowledge about the entities accessing their mobile traffic. In this paper we provide a first comprehensive analysis of 283 Android apps that use the Android VPN permission, which we extracted from a corpus of more than 1.4 million apps on the Google Play store. We perform a number of passive and active measurements designed to investigate a wide range of security and privacy features and to study the behavior of each VPN-based app. Our analysis includes investigation of possible malware presence, third-party library embedding, and traffic manipulation, as well as gauging user perception of the security and privacy of such apps. Our experiments reveal several instances of VPN apps that expose users to serious privacy and security vulnerabilities, such as use of insecure VPN tunneling protocols, as well as IPv6 and DNS traffic leakage. We also report on a number of apps actively performing TLS interception. Of particular concern are instances of apps that inject JavaScript programs for tracking, advertising, and for redirecting e-commerce traffic to external partners.


title: K.R.: Performance Factors Analysis -- A New Alternative to Knowledge Tracing

abstract: Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used to compute latencies of actions. On the other hand, existing models used for educational data mining (e.g. Learning Factors Analysis (LFA)[2]) and model search do not tend to allow the creation of a “model overlay” that traces predictions for individual students with individual skills so as to allow the adaptive instruction to automatically remediate performance. Because these limitations make the transition from model search to model application in adaptive instruction more difficult, this paper describes our work to modify an existing data mining model so that it can also be used to select practice adaptively. We compare this new adaptive data mining model (PFA, Performance Factors Analysis) with two versions of LFA and then compare PFA with standard KT.


title: Maximal strength training: the impact of eccentric overload

abstract: This is the first study to apply evoked potential recordings to investigate effects on efferent neural drive following high-intensity strength training with and without eccentric overload in a func...


title: Distilled Binary Neural Network for Monaural Speech Separation

abstract: Monaural speech separation, aiming at solving the cocktail party problem, has many important application scenarios, most of which ask for the real-time response, high energy efficiency and efficient storage. However, the state-of-the-art Deep Neural Network based separation models usually require huge memory and computation for the 32-bit floating point multiply accumulations, hence most of them cannot meet those requirements. Recently, there are many methods proposed to solve the problem, and binary neural networks have drawn many attentions for they compress and speed up its counterparts at the cost of some performance. Hence, in this paper, we binarize Deep Neural Network based separation models, aiming to deploy them on embedded devices for real-time applications. Furthermore, we improve the separation performance by integrating knowledge distillation into the training phase of binary neural network based models, which is referred as Distilled Binary Neural Network (DBNN). To the best of our knowledge, DBNN is the first attempt to integrate two types of model compression. In the experiments, we demonstrate the effectiveness of our proposed method, which successfully binarizes the Deep Neural Network based separation models with a comparable performance.