title: A hybrid CAD-based construction site layout planning system using genetic algorithms

abstract: Abstract The efficient layout planning of a construction site is a fundamental task to any project undertaking. In an attempt to enhance the general practice of layout planning of construction sites, the paper introduces a novel approach for producing the sought layouts. This approach integrates the highly sophisticated graphical capabilities of computer-aided design (CAD) platforms with the robust search and optimization capabilities of genetic algorithms (GAs). In this context, GAs are utilized from within the CAD environment to optimize the location of temporary facilities on site. The functional interaction between GAs and CAD and the details of the GA-based layout optimization procedure are presented. A fully automated computer system is further developed to demonstrate the practicality of the chosen approach. In order to evaluate the system's performance, a local construction project with a 24,000m2 site is used. The automated system produced highly satisfactory results and showed notable flexibility through its CAD-based input/output media.


title: Storing and querying ordered XML using a relational database system

abstract: XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.


title: OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs

abstract: The rise of Uber as the global alternative taxi operator has attracted a lot of interest recently. Aside from the media headlines which discuss the new phenomenon, e.g. on how it has disrupted the traditional transportation industry, policy makers, economists, citizens and scientists have engaged in a discussion that is centred around the means to integrate the new generation of the sharing economy services in urban ecosystems. In this work, we aim to shed new light on the discussion, by taking advantage of a publicly available longitudinal dataset that describes the mobility of yellow taxis in New York City. In addition to movement, this data contains information on the fares paid by the taxi customers for each trip. As a result we are given the opportunity to provide a first head to head comparison between the iconic yellow taxi and its modern competitor, Uber, in one of the world's largest metropolitan centres. We identify situations when Uber X, the cheapest version of the Uber taxi service, tends to be more expensive than yellow taxis for the same journey. We also demonstrate how Uber's economic model effectively takes advantage of well known patterns in human movement. Finally, we take our analysis a step further by proposing a new mobile application that compares taxi prices in the city to facilitate traveller's taxi choices, hoping to ultimately to lead to a reduction of commuter costs. Our study provides a case on how big datasets that become public can improve urban services for consumers by offering the opportunity for transparency in economic sectors that lack up to date regulations.


Anesthesia Workspace Cleanliness and Safety: Implementation of a Novel Syringe Bracket Using 3D Printing Techniques


title: Chucky: exposing missing checks in source code for vulnerability discovery

abstract: Uncovering security vulnerabilities in software is a key for operating secure systems. Unfortunately, only some security flaws can be detected automatically and the vast majority of vulnerabilities is still identified by tedious auditing of source code. In this paper, we strive to improve this situation by accelerating the process of manual auditing. We introduce Chucky, a method to expose missing checks in source code. Many vulnerabilities result from insufficient input validation and thus omitted or false checks provide valuable clues for finding security flaws. Our method proceeds by statically tainting source code and identifying anomalous or missing conditions linked to security-critical objects.In an empirical evaluation with five popular open-source projects, Chucky is able to accurately identify artificial and real missing checks, which ultimately enables us to uncover 12 previously unknown vulnerabilities in two of the projects (Pidgin and LibTIFF).


title: 32 × 32 CMOS SPAD Imager for Gated Imaging, Photon Timing, and Photon Coincidence

abstract: We present the design and simulations of a single-photon sensitive imager based on single photon avalanche diodes (SPADs) with an innovative pixel architecture that includes four separate SPADs with independent active time-gating and quenching circuit, a shared time-to-digital converter (TDC) with 50-ps resolution, four independent photon counters, and multiple operation modes. The TDC is driven by smart arbitration logic, which preserves spatial information among the four detectors; furthermore, an alternative operation mode exploits photon-coincidence on multiple detectors to reduce the effect of high background levels, e.g., in light detection and ranging applications with strong ambient light. Key features are the ability to operate in simultaneous photon counting and timing modes for capturing 2-D and 3-D images of the scene in a single shot (frame), the option of a counting-only mode, reducing power consumption, and increasing achievable frame-rate when timing information is not needed, and the ability to individually shut down noisy detectors or to enable just some regions of interests.


title: Minimally invasive porcelain veneers: indications for a conservative esthetic dentistry treatment modality.

abstract: Patients have many restorative options for changing the appearance of their teeth. The most conservative restorative treatments for changing the appearance of teeth include tooth bleaching, direct composite resin veneers, and porcelain veneers. Patients seeking esthetic treatment should undergo a comprehensive clinical examination that includes an esthetic evaluation. When selecting a conservative treatment modality, the use of minimally invasive or no-preparation porcelain veneers should be considered. As with any treatment decision, the indications and contraindications must be considered before a definitive treatment plan is made. Long-term research has demonstrated a 94% survival rate for minimally invasive porcelain veneers. While conservation of tooth structure is important, so is selecting the right treatment modality for each patient based on clinical findings.


title: Text Categorization as a Graph Classification Problem

abstract: In this paper, we consider the task of text categorization as a graph classification problem. By representing textual documents as graph-of-words instead of historical n-gram bag-of-words, we extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept of k-core, we reduce the graph representation to its densest part – its main core – speeding up the feature extraction step for little to no cost in prediction performances. Experiments on four standard text classification datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baseline approaches.


title: Measuring semantic similarity between Gene Ontology terms

abstract: Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. This paper adds two new contributions. First, a study of the correlation between Gene Ontology (GO) terms and family similarity demonstrates that protein families constitute an appropriate baseline for validating GO similarity. Secondly, we introduce GraSM, a novel method that uses all the information in the graph structure of the Gene Ontology, instead of considering it as a hierarchical tree. GraSM gives a consistently higher family similarity correlation on all aspects of GO than the original semantic similarity measures.


title: Text to 3D Scene Generation with Rich Lexical Grounding

abstract: The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them. We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects. Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods. We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments. To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments.


title: Real-time, continuous level of detail rendering of height fields

abstract: We present an algorithm for real-time level of detail reduction and display of high-complexity polygonal surface data. The algorithm uses a compact and efficient regular grid representation, and employs a variable screen-space threshold to bound the maximum error of the projected image. A coarse level of simplification is performed to select discrete levels of detail for blocks of the surface mesh, followed by further simplification through repolygonalization in which individual mesh vertices are considered for removal. These steps compute and generate the appropriate level of detail dynamically in real-time, minimizing the number of rendered polygons and allowing for smooth changes in resolution across areas of the surface. The algorithm has been implemented for approximating and rendering digital terrain models and other height fields, and consistently performs at interactive frame rates with high image quality.


title: Weakly Supervised Action Learning with RNN Based Fine-to-Coarse Modeling

abstract: We present an approach for weakly supervised learning of human actions. Given a set of videos and an ordered list of the occurring actions, the goal is to infer start and end frames of the related action classes within the video and to train the respective action classifiers without any need for hand labeled frame boundaries. To address this task, we propose a combination of a discriminative representation of subactions, modeled by a recurrent neural network, and a coarse probabilistic model to allow for a temporal alignment and inference over long sequences. While this system alone already generates good results, we show that the performance can be further improved by approximating the number of subactions to the characteristics of the different action classes. To this end, we adapt the number of subaction classes by iterating realignment and reestimation during training. The proposed system is evaluated on two benchmark datasets, the Breakfast and the Hollywood extended dataset, showing a competitive performance on various weak learning tasks such as temporal action segmentation and action alignment.


title: A self-healing 2.4GHz LNA with on-chip S11/S21 measurement/calibration for in-situ PVT compensation

abstract: This paper presents a 2.4GHz, reconfigurable RF LNA using on-chip peak detection and calibration to measure and optimize its input impedance (S 11 ) and gain (S 21 ) in-situ, compensating for the unpredictable effects of process, voltage and temperature (PVT) variations. Measurement results show that the calibration of the LNA across PVT corners improves the S 11 by 5.1dB, S 21 by 3dB, while not significantly degrading the Noise Figure (0.22dB degradation) and linearity (1.7dBm degradation).


title: Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

abstract: Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods.


title: An optimal randomized algorithm for maximum Tukey depth

abstract: We present the first optimal algorithm to compute the maximum Tukey depth (also known as location or halfspace depth) for a non-degenerate point set in the plane. The algorithm is randomized and requires O(n log n) expected time for n data points. In a higher fiexed dimension d ≥ 3, the expected time bound is O(nd-1), which is probably optimal as well. The result is obtained using an interesting variant of the author's randomized optimization technique, capable of solving "implicit" linear-programming-type problems; some other applications of this technique are briefly mentioned.


title: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification

abstract: Highlights? A novel HONs for classification task of data mining. ? A novel improved PSO for training FLANN (ISO-FLANN). ? A new complex medical domain dataset is introduced for validating the method. Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN=ISO-FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).


title: Leveraging stereo matching with learning-based confidence measures

abstract: We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then train regression forests again to predict the correctness (confidence) of a match by using selected confidence measures. In addition, we present a confidence-based matching cost modulation scheme based on the predicted correctness for improving the robustness and accuracy of various stereo matching algorithms. We apply the proposed scheme to the semi-global matching algorithm to make it robust under unexpected difficulties that can occur in outdoor environments. We verify the proposed confidence measure selection and cost modulation methods through extensive experimentation with various aspects using KITTI and challenging outdoor datasets.


title: Cyber Security Resource Allocation: A Markov Decision Process Approach

abstract: An effective defense-in-depth in cyber security applies multiple layers of defense throughout a system. The goalis to defend a system against cyber-attack using severalindependent methods. Therefore, a cyber-attack that is able to penetrate one layer of defense may be unsuccessful in other layers. Common layers of cyber defense include: attack avoidance, prevention, detection, survivability and recovery. It follows that in security-conscious organizations, the cyber security investment portfolio is divided into different layers of defense. For instance, a two-way division is agility and recovery. Cyber agility pursues attack avoidance techniques such that cyber-attacks are rendered as ineffective, whereas cyber recovery seeks to fight-through successful attacks. We show that even when the primary focus is on the agility of a system, recovery should be an essential point during implementation because the frequency of attacks will degrade the system and a quick and fast recovery is necessary. However, there is not yet an optimum mechanism to allocate limited cyber security resourcesinto the different layers. We propose an approach using theMarkov Decision Process (MDP) framework for resourcesallocation between the two end layers: agility and recovery.


title: Process Mining Put into Context

abstract: Process mining techniques help organizations discover and analyze business processes based on raw event data. The recently released "Process Mining Manifesto" presents guiding principles and challenges for process mining. Here, the authors summarize the manifesto's main points and argue that analysts should take into account the context in which events occur when analyzing processes.


title: Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

abstract: Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm's way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly when exposed to real-world data. We explore the reality gap in the context of 6-DoF pose estimation of known objects from a single RGB image. We show that for this problem the reality gap can be successfully spanned by a simple combination of domain randomized and photorealistic data. Using synthetic data generated in this manner, we introduce a one-shot deep neural network that is able to perform competitively against a state-of-the-art network trained on a combination of real and synthetic data. To our knowledge, this is the first deep network trained only on synthetic data that is able to achieve state-of-the-art performance on 6-DoF object pose estimation. Our network also generalizes better to novel environments including extreme lighting conditions, for which we show qualitative results. Using this network we demonstrate a real-time system estimating object poses with sufficient accuracy for real-world semantic grasping of known household objects in clutter by a real robot.


title: A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks

abstract: Distributed Denial of Service (DDoS) flooding attacks are one of the biggest concerns for security professionals. DDoS flooding attacks are typically explicit attempts to disrupt legitimate users' access to services. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies (i.e., Botnets). Once an attack army has been set up, an attacker can invoke a coordinated, large-scale attack against one or more targets. Developing a comprehensive defense mechanism against identified and anticipated DDoS flooding attacks is a desired goal of the intrusion detection and prevention research community. However, the development of such a mechanism requires a comprehensive understanding of the problem and the techniques that have been used thus far in preventing, detecting, and responding to various DDoS flooding attacks. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. We categorize the DDoS flooding attacks and classify existing countermeasures based on where and when they prevent, detect, and respond to the DDoS flooding attacks. Moreover, we highlight the need for a comprehensive distributed and collaborative defense approach. Our primary intention for this work is to stimulate the research community into developing creative, effective, efficient, and comprehensive prevention, detection, and response mechanisms that address the DDoS flooding problem before, during and after an actual attack.


Económico Simple Market Timing with Moving Averages


title: The Role of Social Learning Networks in Mobile Assisted Language Learning: Edmodo as a Case Study

abstract: In this study the effect of the Edmodo social learning environment on mobile assisted language learning (MALL) was examined by seeking the opinions of students. Using a quantitative experimental approach, this study was conducted by conducting a questionnaire before and after using the social learning network Edmodo. Students attended lessons with their mobile devices. The course materials were shared in the network via Edmodo group sharing tools. The students exchanged idea and developed projects, and felt as though they were in a real classroom setting. The students were also able to access various multimedia content. The results of the study indicate that Edmodo improves students' foreign language learning, increases their success, strengthens communication among students, and serves as an entertaining learning environment for them. The educationally suitable sharing structure and the positive user opinions described in this study indicate that Edmodo is also usable in other lessons. Edmodo can be used on various mobile devices, including smartphones, in addition to the web. This advantageous feature contributes to the usefulness of Edmodo as a scaffold for education.


title: Distortion-Aware Scalable Video Streaming to Multinetwork Clients

abstract: We consider the problem of scalable video streaming from a server to multinetwork clients over heterogeneous access networks, with the goal of minimizing the distortion of the received videos. This problem has numerous applications including: 1) mobile devices connecting to multiple licensed and ISM bands, and 2) cognitive multiradio devices employing spectrum bonding. In this paper, we ascertain how to optimally determine which video packets to transmit over each access network. We present models to capture the network conditions and video characteristics and develop an integer program for deterministic packet scheduling. Solving the integer program exactly is typically not computationally tractable, so we develop heuristic algorithms for deterministic packet scheduling, as well as convex optimization problems for randomized packet scheduling. We carry out a thorough study of the tradeoff between performance and computational complexity and propose a convex programming-based algorithm that yields good performance while being suitable for real-time applications. We conduct extensive trace-driven simulations to evaluate the proposed algorithms using real network conditions and scalable video streams. The simulation results show that the proposed convex programming-based algorithm: 1) outperforms the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about 10-15 dB higher video quality; 2) reduces average delivery delay by over 90% compared to DCCP; 3) results in higher average video quality of 4.47 and 1.92 dB than the two developed heuristics; 4) runs efficiently, up to six times faster than the best-performing heuristic; and 5) does indeed provide service differentiation among users.


title: M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments

abstract: In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.


title: Mining search engine query logs for query recommendation

abstract: This paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial strength search engine. In order to get a more comprehensive solution, we combine two methods together. On the one hand, we study and model search engine users' sequential search behavior, and interpret this consecutive search behavior as client-side query refinement, that should form the basis for the search engine's own query refinement process. On the other hand, we combine this method with a traditional content based similarity method to compensate for the high sparsity of real query log data, and more specifically, the shortness of most query sessions. To evaluate our method, we use one hundred day worth query logs from SINA' search engine to do off-line mining. Then we analyze three independent editors evaluations on a query test set. Based on their judgement, our method was found to be effective for finding related queries, despite its simplicity. In addition to the subjective editors' rating, we also perform tests based on actual anonymous user search sessions.


title: Customer lifetime value modeling and its use for customer retention planning

abstract: We present and discuss the important business problem of estimating the effect of retention efforts on the Lifetime Value of a customer in the Telecommunications industry. We discuss the components of this problem, in particular customer value and length of service (or tenure) modeling, and present a novel segment-based approach, motivated by the segment-level view marketing analysts usually employ. We then describe how we build on this approach to estimate the effects of retention on Lifetime Value. Our solution has been successfully implemented in Amdocs' Business Insight (BI) platform, and we illustrate its usefulness in real-world scenarios.


title: Automated Decision Support for Human Tasks in a Collaborative System: The Case of Deletion in Wikipedia

abstract: Wikipedia's low barriers to participation have the unintended effect of attracting a large number of articles whose topics do not meet Wikipedia's inclusion standards. Many are quickly deleted, often causing their creators to stop contributing to the site. We collect and make available several datasets of deleted articles, heretofore inaccessible, and use them to create a model that can predict with high precision whether or not an article will be deleted. We report precision of 98.6% and recall of 97.5% in the best case and high precision with lower, but still useful, recall, in the most difficult case. We propose to deploy a system utilizing this model on Wikipedia as a set of decision-support tools to help article creators evaluate and improve their articles before posting, and new article patrollers make more informed decisions about which articles to delete and which to improve.


title: Hierarchical Macro Strategy Model for MOBA Game AI

abstract: The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated crossagent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.


title: Measuring the crowd: a preliminary taxonomy of crowdsourcing metrics

abstract: Crowdsourcing initiatives benefit from tapping into diversity. A vast plethora of disparate individuals, organizations, frameworks and skillsets can all play a role in sourcing solutions to a challenge. Nevertheless, while crowdsourcing has become a pervasive phenomenon, there is a paucity of research that addresses how the crowdsourcing process is measured. Whereas research has advanced various taxonomies of crowdsourcing none to date have specifically addressed the issue of measuring either specific stages of the crowdsourcing process or the process as a whole. As a first step towards achieving this goal, this research-in-progress paper examines crowdsourcing at the operational level with a view towards (i) identifying the parts of the process (ii) identifying what can be measured and (iii) categorising operational metrics to facilitate deployment in practice. The taxonomy advanced is overarching in nature and can be deployed across disciplines. Furthermore, the preliminary taxonomy presented will offer practitioners a comprehensive list of metrics that will enable them to facilitate comparison across various crowdsourcing initiatives.


title: Annexing Reality: Enabling Opportunistic Use of Everyday Objects as Tangible Proxies in Augmented Reality

abstract: Advances in display and tracking technologies hold the promise of increasingly immersive augmented-reality experiences. Unfortunately, the on-demand generation of haptic experiences is lagging behind these advances in other feedback channels. We present Annexing Reality; a system that opportunistically annexes physical objects from a user's current physical environment to provide the best-available haptic sensation for virtual objects. It allows content creators to a priori specify haptic experiences that adapt to the user's current setting. The system continuously scans user's surrounding, selects physical objects that are similar to given virtual objects, and overlays the virtual models on to selected physical ones reducing the visual-haptic mismatch. We describe the developer's experience with the Annexing Reality system and the techniques utilized in realizing it. We also present results of a developer study that validates the usability and utility of our method of defining haptic experiences.


title: Lightning protection of PV systems

abstract: Lightning strikes can affect photovoltaic (PV) generators and their installations, involving also the inverter's electronics. It is therefore necessary to evaluate the risk connected to lightning strikes in order to adopt the correct protective measures for the system. The Standard IEC (EN) 62305-2 reports the procedures for the risk calculation and for the choice of proper lightning protection systems. Usually the technical guidelines suggest protecting with SPDs (surge protective devices) both DC and AC sides of the PV installation. The paper estimates overvoltages due to lightning discharges and evaluates the actual need of lightning protection measures on the basis of the results of the risk analysis and of the protection costs. The paper in the first part presents the procedure for the evaluation of the risk connected to lightning strikes according to the Standard IEC EN 62305-2; then it applies the procedure to typical PV installations, analyzing risks and risk components which have to be kept into account. In the second part the paper studies the surge overcurrents to be expected on LV systems, induced voltages caused by direct flashes and by flashes near the PV installation. Approximated equations for the calculation of induced voltages and currents are given for different types of LPS (lightning protection systems) and lightning flashes. In the last part of the paper the methodology is applied as an example to a practical case and some conclusions are given.


title: Estimating Word Alignment Quality for SMT Reordering Tasks

abstract: Previous studies of the effect of word alignment on translation quality in SMT generally explore link level metrics only and mostly do not show any clear connections between alignment and SMT quali ...


title: The role of Information and communication technologies in healthcare: Taxonomies, perspectives, and challenges

abstract: Abstract Progress in Information and Communication Technologies (ICTs) is shaping more and more the healthcare domain. ICTs adoption provides new opportunities, as well as discloses novel and unforeseen application scenarios. As a result, the overall health sector is potentially benefited, as the quality of medical services is expected to be enhanced and healthcare costs are reduced, in spite of the increasing demand due to the aging population. Notwithstanding the above, the scientific literature appears to be still quite scattered and fragmented, also due to the interaction of scientific communities with different background, skills, and approaches. A number of specific terms have become of widespread use (e.g., regarding ICTs-based healthcare paradigms as well as at health-related data formats), but without commonly-agreed definitions. While scientific surveys and reviews have also been proposed, none of them aims at providing a holistic view of how today ICTs are able to support healthcare. This is the more and more an issue, as the integrated application of most if not all the main ICTs pillars is the most agreed upon trend, according to the Industry 4.0 paradigm about ongoing and future industrial revolution. In this paper we aim at shedding light on how ICTs and healthcare are related, identifying the most popular ICTs-based healthcare paradigms, together with the main ICTs backing them. Studying more than 300 papers, we survey outcomes of literature analyses and results from research activities carried out in this field. We characterize the main ICTs-based healthcare paradigms stemmed out in recent years fostered by the evolution of ICTs. Dissecting the scientific literature, we also identify the technological pillars underpinning the novel applications fueled by these technological advancements. Guided by the scientific literature, we review a number of application scenarios gaining momentum thanks to the beneficial impact of ICTs. As the evolution of ICTs enables to gather huge and invaluable data from numerous and highly varied sources in easier ways, here we also focus on the shapes that this healthcare-related data may take. This survey provides an up-to-date picture of the novel healthcare applications enabled by the ICTs advancements, with a focus on their specific hottest research challenges. It helps the interested readership (from both technological and medical fields) not to lose orientation in the complex landscapes possibly generated when advanced ICTs are adopted in application scenarios dictated by the critical healthcare domain.


title: On 3D face reconstruction via cascaded regression in shape space

abstract: Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance. This paper investigates thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of the number of 3D vertices; (iii) the way of using standalone automated landmark detection methods; and (iv) the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised, which can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. Moreover, an effective training method is proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been done with comparison to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of proposed method.


Controlling Politeness in Neural Machine Translation via Side Constraints


title: An executable formal semantics of C with applications

abstract: This paper describes an executable formal semantics of C. Being executable, the semantics has been thoroughly tested against the GCC torture test suite and successfully passes 770 of 776 test programs. It is the most complete and thoroughly tested formal definition of C to date. The semantics yields an interpreter, debugger, state space search tool, and model checker “for free”. The semantics is shown capable of automatically finding program errors, both statically and at runtime. It is also used to enumerate nondeterministic behavior.


title: Cornering control method for front-and-rear-wheel-independent-drive-type electric vehicle (FRID EV) on roads with low friction coefficients

abstract: Front-and-rear-wheel-independent-drive-type electric vehicles (FRID EVs) have been proposed as next-generation advanced vehicles that can solve the problems of the propulsion-power generating systems of conventional EVs in terms of safety and running performance. The outstanding running performances of FRID EVs were clarified previously through various evaluations. This study focused on the cornering performance of FRID EVs on roads with low friction coefficients (μ). FRID EVs have structural features that can independently control the longitudinal forces of the front and rear wheels; moreover, they can dependently control the lateral forces of the left and right wheels on the front and rear ends. In this paper, a cornering control method that is effective for stable operations on low-μ roads is proposed. The proposed control method guarantees the lateral force required for stable revolutions according to the principle of a friction circle. The effectiveness of the proposed cornering control method is demonstrated and verified by experiments on low-μ roads.


title: Transfer in Reinforcement Learning: a Framework and a Survey

abstract: Transfer in reinforcement learning is a novel research area that focuses on the development of methods to transfer knowledge from a set of source tasks to a target task. Whenever the tasks are similar, the transferred knowledge can be used by a learning algorithm to solve the target task and significantly improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formalization of the general transfer problem, we identify the main settings which have been investigated so far, and we review the most important approaches to transfer in reinforcement learning.


Germline alterations in a consecutive series of acute myeloid leukemia


title: Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

abstract: An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.


title: Neural 3D Mesh Renderer

abstract: For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.


title: Quality Dimensions of the DeLone-McLean Model to Measure User Satisfaction: An Empirical Test on the Information Security Context

abstract: User satisfaction is the most widely measure used to assess information system success in Delone-McLean model. Using quality dimensions as antecedents to user satisfaction, this model is largely accepted in the information systems field. However, there are some contexts and relationships among variables that still unexplored in this model. In this sense, we proposed empirically measure user satisfaction using the quality dimensions of the Delone-McLean model on the information security context, and test the relationship among quality variables and user satisfaction variable. To do that, we performed a survey with 176 information system users about their satisfaction with information security practices. The results demonstrated that the information quality was positive associated with user satisfaction, the system quality was negative associated with user satisfaction, and the service quality was not associated with user satisfaction. The quality variables relationship presented positive relationship among them, unless when information quality pointed to service quality.


title: Race, gender, and self-presentation in teen profile photographs

abstract: This study analyzes how teens represent themselves through their profile photographs on a popular nonymous chat site. Using visual content analysis methods, we analyzed 400 profile photographs, controlling for the self-reported gender and the apparent race of the photographic subject. The analysis finds significant differences in gaze, posture, dress, and distance from the camera according to gender and race, although racial differences are stronger for boys than for girls. To a surprising extent, the findings mirror previous findings of gender and race differences in face-to-face interaction, suggesting that the teens construe their profile images as invitations to interact with others online. At the same time, their photo choices reproduce culturally dominant ideologies of gender and race as reinforced by mass media images.


BEING “IN THE GAME”


title: Topics in Multi-User Information Theory

abstract: This survey reviews fundamental concepts of multi-user information theory. Starting with typical sequences, the survey builds up knowledge on random coding, binning, superposition coding, and capacity converses by introducing progressively more sophisticated tools for a selection of source and channel models. The problems addressed include: Source Coding; Rate-Distortion and Multiple Descriptions; Capacity-Cost; The Slepian–Wolf Problem; The Wyner-Ziv Problem; The Gelfand-Pinsker Problem; The Broadcast Channel; The Multiaccess Channel; The Relay Channel; The Multiple Relay Channel; and The Multiaccess Channel with Generalized Feedback. The survey also includes a review of basic probability and information theory.


title: Mirroring in Dance/Movement Therapy: Potential mechanisms behind empathy enhancement

abstract: Abstract Mirroring, an exercise practiced in Dance/Movement Therapy (DMT), is considered by practitioners and patients to enhance emotional understanding and empathy for others. Mirroring involves imitation by the therapist of movements, emotions, or intentions implied by a client's movement, and is commonly practiced in order to enhance empathy of the therapist for the client. Despite enthusiastic claims for its effectiveness, a clear theoretical framework that would explain the effects of mirroring on empathy has not yet been presented, and empirical research on the topic is generally lacking. In this review, we propose that mirroring in DMT enhances understanding of others’ emotional intentions through enhanced use of mirror neuron circuitry. Research on the mirror neuron system (MNS) suggests that the brain areas involved in perception and production of movement overlap, and that these brain areas are also involved in the understanding of movement intention ( Rizzolatti & Craighero, 2004 ). One important route to emotion recognition involves a neural simulation of another person's emotional actions in order to infer the intentions behind those actions, and empathize with them. Future research is proposed in order to systematically explore the effectiveness of mirroring in dance therapy, the neural mechanisms behind it, and its applicability to patient populations who have problems with empathy.


title: A wearable device for physical and emotional health monitoring

abstract: Personal health monitoring systems are emerging as promising solutions to develop ultra-small, portable devices that can continuously monitor and process several vital body parameters. In this work, we present a wearable device for physical and emotional health monitoring. The device obtains user's key physiological signals: ECG, respiration, Impedance Cardiogram (ICG), blood pressure and skin conductance and derives the user's emotion states as well. We have developed embedded algorithms that process the bio-signals in real-time to detect any abnormalities (cardiac arrhythmias and morphology changes) in the ECG and to detect key parameters (such as the Pre- Ejection Period and fluid status level) from the ICG. We present a novel method to detect continuous beat-by-beat blood pressure from the ECG and ICG signals, as well as a real-time embedded emotion classifier that computes the emotion levels of the user. Emotions are classified according to their attractiveness (positive valence) or their averseness (negative valence) in the horizontal valence dimension. The excitement level induced by the emotions is represented by high to low positions in the vertical arousal dimension of the valence-arousal space. The signals are measured either intermittently by touching the metal electrodes on the device (for point-of-care testing) or continuously, using a chest strap for long term monitoring. The processed data from device is sent to a mobile phone using a Bluetooth Low Energy protocol. Our results show that the device can monitor the signals continuously, providing accurate detection of the motion state, for over 72 hours on a single battery charge.


title: A Perspective on Sentiment Analysis

abstract: Sentiment Analysis (SA) is indeed a fascinating area of research which has stolen the attention of researchers as it has many facets and more importantly it promises economic stakes in the corporate and governance sector. SA has been stemmed out of text analytics and established itself as a separate identity and a domain of research. The wide ranging results of SA have proved to influence the way some critical decisions are taken. Hence, it has become relevant in thorough understanding of the different dimensions of the input, output and the processes and approaches of SA.


title: 6.6-kW Onboard Charger Design Using DCM PFC Converter With Harmonic Modulation Technique and Two-Stage DC/DC Converter

abstract: This paper suggests another candidate for high-power onboard charger for electric vehicles. It is based on a discontinuous conduction mode (DCM) power factor correction (PFC) converter with harmonic modulation technique that improves the power factor in DCM PFC operation and a two-stage dc/dc converter composed of a resonant converter and a DCM buck converter. Separating the functions of the dc/dc stage into control and isolation helps to reduce the transformer by using high-frequency resonance. The feasibility of the proposed charger has been verified with a 6.6-kW prototype.


title: Personality Traits and Group-Based Information Behaviour: An Exploratory Study.

abstract: Introduction. The relationship between hypothesised behaviour resulting from a personality test and actual information behaviour resulting from a group-based assignment process is addressed in this paper. ::: Methods. Three voluntary groups of ten librarianship and information science students were followed during a project assignment. The long version of the commonly-used NEO-PI-R test instrument was employed to describe and compare each group member�s personality traits at a more detailed level. Data were also collected through a process survey, a diary and an interview. ::: Analysis. The calculation of data from the personality test resulted in various T-scores on personality factors and facets for each group member. Data from the demographic survey and the process surveys were calculated in Excel, while data from diaries and interviews were coded in the analytical data software Atlas.ti. ::: Results. Information behaviour associated with personality traits was identified, but the presence of personality effects tended to vary with the perceived presence of the social context. ::: Conclusions. Some matches were identified between group members� personality traits and their actual information behaviour but there were also deviations, which were found that seemed to be related to the group-work context. The importance of studying personality traits in context has further been confirmed.


The computational origin of representation and conceptual change


title: Dermal exposure, handwashing, and hand dermatitis in the rubber manufacturing industry.

abstract: Skin diseases, such as hand dermatitis, are thought to be a common problem in the rubber manufacturing industry, as workers are exposed to a wide range of chemicals with known irritant and sensitizing potential. We conducted a cross-sectional survey of a representative sample of rubber manufacturing workers (N = 202), selected from nine different rubber companies. Prevalence of hand dermatitis ("major" and "minor" dermatitis) and skin injuries was assessed on the basis of a diagnosis by a dermatologist. We investigated the possible relations between actual skin exposure, handwashing practices, and hand dermatitis. Prevalence of major hand dermatitis (7%) was comparable with that in the general population; however, minor signs of dermatitis were more common among the surveyed population (28%), as were traumata of the skin (17%). Dermal exposure to cyclohexane-soluble agents at work was related to the occurrence of major hand dermatitis, but not to the occurrence of minor hand dermatitis. Moderate and frequent handwashing especially with industrial surfactants containing scrubbing particles were found to be strongly associated with the occurrence of minor dermatitis [odds ratio = 4.27 (95% confidence interval = 0.90-20.27) and odds ratio = 6.38 (95% confidence interval = 1.33-30.17, respectively)].


title: MAPE-K Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems

abstract: Designing software systems that have to deal with dynamic operating conditions, such as changing availability of resources and faults that are difficult to predict, is complex. A promising approach to handle such dynamics is self-adaptation that can be realized by a MAPE-K feedback loop (Monitor-Analyze-Plan-Execute plus Knowledge). To provide evidence that the system goals are satisfied, given the changing conditions, the state of the art advocates the use of formal methods. However, little research has been done on consolidating design knowledge of self-adaptive systems. To support designers, this paper contributes with a set of formally specified MAPE-K templates that encode design expertise for a family of self-adaptive systems. The templates comprise: (1) behavior specification templates for modeling the different components of a MAPE-K feedback loop (based on networks of timed automata), and (2) property specification templates that support verification of the correctness of the adaptation behaviors (based on timed computation tree logic). To demonstrate the reusability of the formal templates, we performed four case studies in which final-year Masters students used the templates to design different self-adaptive systems.


title: Stop propagating the learning styles myth

abstract: Abstract We all differ from each other in a multitude of ways, and as such we also prefer many different things whether it is music, food or learning. Because of this, many students, parents, teachers, administrators and even researchers feel that it is intuitively correct to say that since different people prefer to learn visually, auditively, kinesthetically or whatever other way one can think of, we should also tailor teaching, learning situations and learning materials to those preferences. Is this a problem? The answer is a resounding: Yes! Broadly speaking, there are a number of major problems with the notion of learning styles. First, there is quite a difference between the way that someone prefers to learn and that which actually leads to effective and efficient learning. Second, a preference for how one studies is not a learning style. Most so-called learning styles are based on types; they classify people into distinct groups. The assumption that people cluster into distinct groups, however, receives very little support from objective studies. Finally, nearly all studies that report evidence for learning styles fail to satisfy just about all of the key criteria for scientific validity. This article delivers an evidence-informed plea to teachers, administrators and researchers to stop propagating the learning styles myth.


The synergistic effect of concatenation in phylogenomics: the case in Pantoea


title: Facial profile markers in second- and third-trimester fetuses with trisomy 18

abstract: OBJECTIVES ::: To evaluate nasal bone length (NBL), maxilla-nasion-mandible (MNM) angle, fetal profile (FP) line, prenasal thickness (PT), prenasal thickness to nasal bone length (PT:NBL) ratio and prefrontal space ratio (PFSR) as markers of trisomy 18 in the second and third trimesters of pregnancy. ::: ::: ::: METHODS ::: The NBL, MNM angle, FP line, PT, PT:NBL ratio and PFSR were measured retrospectively from stored two-dimensional images or three-dimensional volumes of trisomy-18 fetuses, and were compared with our previously reported normal ranges for euploid fetuses. Additional ultrasound findings were noted at initial routine second-trimester scan and at subsequent advanced ultrasound examination performed after referral for karyotyping. ::: ::: ::: RESULTS ::: A total of 43 trisomy-18 fetuses were included in the analysis. At initial examination, median gestational age was 21 + 2 weeks. NBL and PT were correlated with gestational age (P < 0.001), but the other markers were not. Mean NBL, MNM angle, PT, PT:NBL ratio and PFSR were 3.76 mm, 16.67°, 4.25 mm, 1.39 and 0.87, respectively. The FP line was zero (normal) in 53.7% of cases and negative (abnormal) in 46.3%. All markers were significantly associated with trisomy 18, with the PT:NBL ratio yielding the highest detection rate (88.4%) followed by NBL (83.7%), MNM angle (56.4%), FP line (46.3%), PT (27.9%) and the PFSR (20.5%) (for a 5% false-positive rate for the continuous variables). Various combinations of the four best markers (NBL, FP line, MNM angle and PT:NBL ratio) yielded detection rates of between 72% and 95%. Structural anomalies were not detected in 22% of fetuses at the initial scan and in 2% at the advanced scan. ::: ::: ::: CONCLUSIONS ::: The PT:NBL ratio and NBL are robust second- and third-trimester markers for trisomy 18. A negative FP line has a 0% false-positive rate and the potential to differentiate between trisomy 18 and Down syndrome, as in the latter the FP line is often positive. No major anomaly was observed at the initial scan in about a quarter of trisomy-18 fetuses, underlining the role of second-trimester facial marker evaluation.


title: Future E-Enabled Aircraft Communications and Security: The Next 20 Years and Beyond

abstract: Aircraft data communications and networking are key enablers for civilian air transportation systems to meet projected aviation demands of the next 20 years and beyond. In this paper, we show how the envisioned e-enabled aircraft plays a central role in streamlining system modernization efforts. We show why performance targets such as safety, security, capacity, efficiency, environmental benefit, travel comfort, and convenience will heavily depend on communica- tions, networking and cyber-physical security capabilities of the e-enabled aircraft. The paper provides a comprehensive overview of the state-of-the-art research and standardization efforts. We highlight unique challenges, recent advances, and open problems in enhancing operations as well as certification of the future e-enabled aircraft.


title: Tetris game design based on the FPGA

abstract: Tetris game is a classic game of logic control. This article gives a programming design of Tetris game based on the FPGA using VHDL. Game players can move and rotate blocks with the PS/2 interface keyboard, and the game video is showed in a VGA monitor. The game realized the function of the movement and rotation of blocks, randomly generating next blocks, eliminating rows, getting scores and speeding up. It also contained the normal mode corresponding to 7 types of blocks and the expert mode corresponding to 11 types of blocks. The successful transplant of Tetris game provides a template for the development of other visual control systems in the FPGA.


title: CAp 2017 challenge: Twitter Named Entity Recognition

abstract: The paper describes the CAp 2017 challenge. The challenge concerns the problem of Named Entity Recognition (NER) for tweets written in French. We first present the data preparation steps we followed for constructing the dataset released in the framework of the challenge. We begin by demonstrating why NER for tweets is a challenging problem especially when the number of entities increases. We detail the annotation process and the necessary decisions we made. We provide statistics on the inter-annotator agreement, and we conclude the data description part with examples and statistics for the data. We, then, describe the participation in the challenge, where 8 teams participated, with a focus on the methods employed by the challenge participants and the scores achieved in terms of F$_1$ measure. Importantly, the constructed dataset comprising $\sim$6,000 tweets annotated for 13 types of entities, which to the best of our knowledge is the first such dataset in French, is publicly available at \url{this http URL} .


title: Theoretical and practical analysis of a current sensing principle that exploits the resistance of the copper trace

abstract: The output current of contemporary AC-DC front-end converters is steadily increasing due to higher power density requirements. The use of shunt resistors to sense the output current is becoming unacceptable due to high power losses and new techniques for current sensing need to be investigated. In this paper, we investigate the possible use of the voltage drop across the trace resistance to sense the output current. This approach promises very low cost since no dedicated shunt resistor is required, no additional power losses occur and no extra space on the printed-circuit-board (PCB) is necessary. To overcome the problems associated with the temperature drift of the copper, and variability in the trace resistance of the copper track, a digital controller can be used to calibrate the trace resistance and implement a temperature drift compensation. This form of microcontroller is readily available on todaypsilas AC-DC front-end converters. However, theoretical and practical investigations revealed that the parasitic inductance and skin effect may limit the bandwidth of this measurement principle down to several hundred Hz. To overcome this limitation, we proposed a compensation technique that has the potential to increase the bandwidth beyond ten kilohertz. Experiments with output currents of up to 240 A demonstrated that the measurement uncertainty for DC currents is less than plusmn1 A for temperatures between 25 to 60 degrees, and that the effective bandwidth can be enhanced using a compensation technique.


title: Light YOLO for High-Speed Gesture Recognition

abstract: This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.


title: Heated-Up Softmax Embedding

abstract: Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples. The problem is generally solved by learning an embedding for each sample such that the embeddings of samples of the same category are compact while the embeddings of samples of different categories are spread-out in the feature space. We study the features extracted from the second last layer of a deep neural network based classifier trained with the cross entropy loss on top of the softmax layer. We show that training classifiers with different temperature values of softmax function leads to features with different levels of compactness. Leveraging these insights, we propose a "heating-up" strategy to train a classifier with increasing temperatures, leading the corresponding embeddings to achieve state-of-the-art performance on a variety of metric learning benchmarks.


title: A PYNQ-Based Framework for Rapid CNN Prototyping

abstract: This work presents a self-contained and modifiable framework for fast and easy convolutional neural network prototyping on the Xilinx PYNQ platform. With a Python-based programming interface, the framework combines the convenience of high-level abstraction with the speed of optimised FPGA implementation. Our work is freely available on GitHub for the community to use and build upon.


title: Yoga as an Intervention for Patients with Autism Spectrum Disorder: A Review of the Evidence and Future Directions

abstract: Background: The increase in prevalence of Autism Spectrum Disorder (ASD) has led to a corresponding surge in demand for novel, effective, and safe clinical interventions. Evidence-based treatment options for ASD are limited, resulting in a high utilization rate of complementary and alternative medicine (CAM) treatments in the ASD population. Yoga is a CAM practiced by over 20 million people in the United States, and multiple studies have investigated yoga as a possible effective treatment intervention for patients with ASD. Patients with ASD could potentially benefit from yoga either directly, through the targeting of core ASD symptoms, or indirectly through the improvement of commonly occurring co-morbid psychiatric conditions. ::: ::: Objective: To review the evidence for yoga as an effective treatment option for patients with ASD. Method: Using a variety of online databases, including PubMed, PsychINFO, Scopus, CINAHL, and Google Scholar, published studies meeting the authors' selection criteria were reviewed for evidence of improvement in core ASD and related symptoms in patients with ASD. ::: ::: Results: There is a paucity of studies published in peer-reviewed journals that met the search criteria. While each of the studies investigated the use of yoga with ASD subjects, they targeted a broad range of symptom clusters, and varied greatly in overall quality, methods, outcome measures, and results. ::: ::: Conclusion: Based on published studies, there is little current evidence that yoga improves core autism symptoms and co-morbid psychiatric symptoms in patients with ASD. However, there is a clear need for additional, larger randomized trials targeting both core ASD symptoms and co-morbid psychiatric conditions. Future clinical trials should utilize more uniform intervention methods, a standardized set of outcome measures, and blinded raters in order to determine whether yoga is indeed an evidence-based treatment option for patients with ASD.


title: Grammar Based Music Composition

abstract: L-Systems have traditionally been used as a popular method for the modelling of spacefilling curves, biological systems and morphogenesis. In this paper, we adapt string rewriting grammars based on L-Systems into a system for music composition. Representation of pitch, duration and timbre are encoded as grammar symbols, upon which a series of re-writing rules are applied. Parametric extensions to the grammar allow the specification of continuous data for the purposes of modulation and control. Such continuous data is also under control of the grammar. Using non-deterministic grammars with context sensitivity allows the simulation of Nth-order Markov models with a more economical representation than transition matrices and greater flexibility than previous composition models based on finite state automata or Petri nets. Using symbols in the grammar to represent relationships between notes, (rather than absolute notes) in combination with a hierarchical grammar representation, permits the emergence of complex music compositions from a relatively simple grammars.


Integrated methodologies and technologies for the reconstructive study of Dur-Sharrukin (Iraq). In: AntiCIPAting the future of the cultural past


title: Interaction of MRI field gradients with the human body

abstract: In this review, the effects of low-frequency electromagnetic fields encountered specifically during magnetic resonance imaging (MRI) are examined. The primary biological effect at frequencies of between 100 and 5000 Hz (typical of MRI magnetic field gradient switching) is peripheral nerve stimulation, the result of which can be a mild tingling and muscle twitching to a sensation of pain. The models for nerve stimulation and how they are related to the rate of change of magnetic field are examined. The experimental measurements, and analytic and computational modelling work in this area are reviewed. The review concludes with a discussion of current regulation in this area and current practice as both are applied to MRI.


Joint Concept Learning and Semantic Parsing from Natural Language Explanations


title: Incremental on-line learning: A review and comparison of state of the art algorithms

abstract: Abstract Recently, incremental and on-line learning gained more attention especially in the context of big data and learning from data streams, conflicting with the traditional assumption of complete data availability. Even though a variety of different methods are available, it often remains unclear which of them is suitable for a specific task and how they perform in comparison to each other. We analyze the key properties of eight popular incremental methods representing different algorithm classes. Thereby, we evaluate them with regards to their on-line classification error as well as to their behavior in the limit. Further, we discuss the often neglected issue of hyperparameter optimization specifically for each method and test how robustly it can be done based on a small set of examples. Our extensive evaluation on data sets with different characteristics gives an overview of the performance with respect to accuracy, convergence speed as well as model complexity, facilitating the choice of the best method for a given application.


A fast algorithm for computing large Fibonacci numbers


title: Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications

abstract: This paper provides a study of the smart grid projects realised in Europe and presents their technological solutions with a focus on smart metering Low Voltage (LV) applications. Special attention is given to the telecommunications technologies used. For this purpose, we present the telecommunication technologies chosen by several European utilities for the accomplishment of their smart meter national roll-outs. Further on, a study is performed based on the European Smart Grid Projects, highlighting their technological options. The range of the projects analysed covers the ones including smart metering implementation as well as those in which smart metering applications play a significant role in the overall project success. The survey reveals that various topics are directly or indirectly linked to smart metering applications, like smart home/building, energy management, grid monitoring and integration of Renewable Energy Sources (RES). Therefore, the technological options that lie behind such projects are pointed out. For reasons of completeness, we also present the main characteristics of the telecommunication technologies that are found to be used in practice for the LV grid.


Ontogenesis of Agency in Machines : A Multidisciplinary Review


title: UAV for 3D mapping applications: a review

abstract: Unmanned aerial vehicle (UAV) platforms are nowadays a valuable source of data for inspection, surveillance, mapping, and 3D modeling issues. As UAVs can be considered as a low-cost alternative to the classical manned aerial photogrammetry, new applications in the short- and close-range domain are introduced. Rotary or fixed-wing UAVs, capable of performing the photogrammetric data acquisition with amateur or SLR digital cameras, can fly in manual, semiautomated, and autonomous modes. Following a typical photogrammetric workflow, 3D results like digital surface or terrain models, contours, textured 3D models, vector information, etc. can be produced, even on large areas. The paper reports the state of the art of UAV for geomatics applications, giving an overview of different UAV platforms, applications, and case studies, showing also the latest developments of UAV image processing. New perspectives are also addressed.


title: COMAN: a web server for comprehensive metatranscriptomics analysis

abstract: Microbiota-oriented studies based on metagenomic or metatranscriptomic sequencing have revolutionised our understanding on microbial ecology and the roles of both clinical and environmental microbes. The analysis of massive metatranscriptomic data requires extensive computational resources, a collection of bioinformatics tools and expertise in programming. We developed COMAN (Comprehensive Metatranscriptomics Analysis), a web-based tool dedicated to automatically and comprehensively analysing metatranscriptomic data. COMAN pipeline includes quality control of raw reads, removal of reads derived from non-coding RNA, followed by functional annotation, comparative statistical analysis, pathway enrichment analysis, co-expression network analysis and high-quality visualisation. The essential data generated by COMAN are also provided in tabular format for additional analysis and integration with other software. The web server has an easy-to-use interface and detailed instructions, and is freely available at http://sbb.hku.hk/COMAN/ ::: COMAN is an integrated web server dedicated to comprehensive functional analysis of metatranscriptomic data, translating massive amount of reads to data tables and high-standard figures. It is expected to facilitate the researchers with less expertise in bioinformatics in answering microbiota-related biological questions and to increase the accessibility and interpretation of microbiota RNA-Seq data.


title: A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains

abstract: Product recovery operations in reverse supply chains face continually and rapidly changing product demand characterized by an ever increasing number of product offerings with reduced lifecycles due to both technological advancements and environmental concerns. Capacity planning is a strategic issue of increased complexity importance for the profitability of reverse supply chains due to their highly variable return flows. In this work we tackle the development of efficient capacity planning policies for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues, such as the take-back obligation imposed by legislation and the “green image” effect on customer demand. The behavior of the generic system under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model provides an experimental tool, which can be used to evaluate alternative long-term capacity planning policies (“what-if” analysis) using total supply chain profit as measure of policy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights. 2005 Elsevier Ltd. All rights reserved.


title: Texture analysis for classification of thyroid ultrasound images

abstract: Ultrasonography (USG) is one of the best imaging modalities for early detection of malignancy in the thyroid gland. Computer Aided Diagnosis (CAD) plays an important role in classifying thyroid nodules. CAD is used as an objective consideration, this is due to the high subjectivity in physician's interpretation of ultrasound images that can lead to difference scanning results. This research proposes a classification of thyroid ultrasound images by using some texture features into two classes. The dataset consists of 39 ultrasound images which grouped into 25 cystic cases and 14 solid cases. An initial step of image pre-processing is conducted to enhance the detection capability. Afterwards, followed by some methods of morphological operation, that is active contours without Edges (ACWE) and histogram equalization. The feature extraction is developed based on texture analysis by using Gray Level Co-occurrence Matrix (GLCM), Histogram and Gray Level Run Length Matrix (GLRLM). Finally, Multilayer Perceptron (MLP) is used to classify cystic nodule from solid nodule. The result shows that the proposed method achieves the accuracy of 89.74%, sensitivity of 88.89%, specificity of 91.67%, positive predictive value (PPV) of 96.00% and negative predictive value (NPV) of 78.57%. This indicates that the proposed method is excellent in classifying thyroid ultrasound images.


Job Satisfaction, Organizational Commitment, and Turnover Intention: A Case Study on Employees of a Retail Company in Malaysia


title: Restriction fragment length polymorphism analysis of the chicken B-F and B-L genes and their association with serologically defined B haplotypes

abstract: Seven serologically defined chicken haplotypes have been analysed by restriction fragment length polymorphism (RFLP) with chicken cDNA probes specific for MHC class I and II. The results demonstrate an excellent correlation between the observed RFLP banding patterns in the investigated haplotypes and the serological B-typing. In future, RFLP analysis in addition to serological B-typing may sharpen the tools in the search for recombinant chromosomes separating B-F and B-L.


title: RecPlay: a fully integrated practical record/replay system

abstract: This article presents a practical solution for the cyclic debugging of nondeterministic parallel programs. The solution consists of a combination of record/replay with automatic on-the-fly data race detection. This combination enables us to limit the record phase to the more efficient recording of the synchronization operations, while deferring the time-consuming data race detection to the replay phase. As the record phase is highly efficient, there is no need to switch it off, hereby eliminating the possibility of Heisenbugs because tracing can be left on all the time. This article describes an implementation of the tools needed to support RecPlay.


title: Design and Analysis of a New Loadless 4T SRAM Cell in Deep Submicron CMOS Technologies

abstract: The goal of this paper is to reduce the power and area of the Static Random Access Memory (SRAM) array while maintaining the competitive performance. Here the various configuration of SRAM array is designed using both the six-transistor (6T) SRAM cell and a new loadless fourtransistor (4T) SRAM cell in deep submicron (130nm, 90nm and 65nm) CMOS technologies. Then it is simulated using HSPICE to check for its functionality, Static Noise Margin (SNM), power dissipation, area occupancy and access time. Except the precharge circuits and the basic storage cells, remaining part of the circuitry is same for both 6T SRAM array and New Loadless 4T SRAM array. Compared to the conventional 6T SRAM array, the new loadless 4T SRAM array consumes less power with less area in deep submicron CMOS technologies. Also the SNM of the new loadless 4T SRAM cell is as good as that of the 6T SRAM cell for higher values of Cell Ratio (CR).


title: COLLABORATION CAPABILITY – A FOCAL CONCEPT IN KNOWLEDGE CREATION AND COLLABORATIVE INNOVATION IN NETWORKS

abstract: Sustainable competitive advantage is not possible in knowledge-based competition without continuous innovation. Innovations, by nature, emerge in social interaction in which diverse actors share complementary knowledge. Collaboration capability is considered a prerequisite for actors if they wish to leverage such knowledge. The concept is analysed through a state-of-the-art review of earlier conceptual and empirical research on network collaboration, and as a result we propose that collaboration capability integrates the key elements in many closely related but semantically diverse conceptualisations. As a concept, it underlines the relational perspective and enables knowledge creation in a risky and uncertain environment. It can be defined as 'the actor's capability to build and manage network relationships based on mutual trust, communication and commitment'. Furthermore, it could be considered an integrative and cross-level concept explaining much of the knowledge creation and innovation in networks.


title: Theorising on risk homeostasis in the context of information security behaviour

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to discuss and theorise on the appropriateness and potential impact of risk homeostasis in the context of information security. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The discussion is mainly based on a literature survey backed up by illustrative empirical examples. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Risk homeostasis in the context of information security is an under-explored topic. The principles, assumptions and methodology of a risk homeostasis framework offer new insights and knowledge to explain and predict contradictory human behaviour in information security. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The paper shows that explanations for contradictory human behaviour (e.g. the privacy paradox) would gain from considering risk homeostasis as an information security risk management model. The ideas discussed open up the prospect to theorise on risk homeostasis as a framework in information security and should form a basis for further research and practical implementations. On a more practical level, it offers decision makers useful information and new insights that could be advantageous in a strategic security planning process. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This is the first systematic comprehensive review of risk homeostasis in the context of information security behaviour and readers of the paper will find new theories, guidelines and insights on risk homeostasis.


title: Tangible programming elements for young children

abstract: Tangible programming elements offer the dynamic and programmable properties of a computer without the complexity introduced by the keyboard, mouse and screen. This paper explores the extent to which programming skills are used by children during interactions with a set of tangible programming elements: the Electronic Blocks. An evaluation of the Electronic Blocks indicates that children become heavily engaged with the blocks, and learn simple programming with a minimum of adult support.


title: Applications of Trajectory Data in Transportation: Literature Review and Maryland Case Study

abstract: This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.


title: Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines

abstract: Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral–spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral–spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral–spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral–spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral–spatial classification methods are applied on real-world hyperspectral data in our experiments.


title: "Stop. I See a Conflict Happening.": A Robot Mediator for Young Children's Interpersonal Conflict Resolution

abstract: The ability to constructively resolve interpersonal conflicts is a crucial set of social skills people need to effectively work and live well together. Is it possible to design social robots to support the early development of children's interpersonal conflict resolution skills? To investigate this question, 64 (32 pairs of) children ages 3-6 years engaged in a 50-minute play session consisting of 5 activities facilitated by the robot Keepon. Children were randomly assigned to 1 of 2 conditions. In the mediation condition, Keepon directed the play session flow by indicating when and which activity to switch to, and whenever possible, signaled the onset of object possession conflicts that occurred between the pair and offered prompts for constructive conflict resolution. In the control condition, Keepon only facilitated and directed the play session and did not intervene during children's conflicts. Results show that children were more likely to resolve conflicts constructively in the mediation condition than in the control condition, and that a key function for a robot mediator within the conflict process is to successfully flag the conflict onset. Drawing from these findings, we discuss design recommendation for a robot mediator.


title: Generalizing Hamiltonian Monte Carlo with Neural Networks

abstract: We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demonstrate large empirical gains on a collection of simple but challenging distributions, for instance achieving a 106x improvement in effective sample size in one case, and mixing when standard HMC makes no measurable progress in a second. Finally, we show quantitative and qualitative gains on a real-world task: latent-variable generative modeling. We release an open source TensorFlow implementation of the algorithm.


title: Combining Over-Sampling and Under-Sampling Techniques for Imbalance Dataset

abstract: An important problem in medical data analysis is imbalance dataset. This problem is a cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails in diagnostic of patient who have disease that means he cannot treat patient in timely. However, the problem can be easily solved by adding or removing the data to closely balance for performance of diagnostic in medically. This paper proposed a solution to adjust imbalance dataset by combining Neighbor Cleaning Rule (NCL) and Synthetic Minority Over-Sampling Technique (SMOTE) techniques. The process of work is using NCL technique for removing sample data that are outliers in majority class and SMOTE technique is used for increasing sample data in minority class to closely balance dataset. After that, the balanced medical dataset is classified by Naive Bayes, SMO and KNN algorithm. The experimental results show that the recall rate can be improved from the models that were created from balanced dataset.


title: Angora: Efficient Fuzzing by Principled Search

abstract: Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration. On the LAVA-M data set, Angora found almost all the injected bugs, found more bugs than any other fuzzer that we compared with, and found eight times as many bugs as the second-best fuzzer in the program who. Angora also found 103 bugs that the LAVA authors injected but could not trigger. We also tested Angora on eight popular, mature open source programs. Angora found 6, 52, 29, 40 and 48 new bugs in file, jhead, nm, objdump and size, respectively. We measured the coverage of Angora and evaluated how its key techniques contribute to its impressive performance.


Modular Architecture for Recommender Systems Applied in a Brazilian e-Commerce


A New Algorithm of Encryption and Decryption of Images Using Chaotic Mapping


title: Weka : Practical Machine Learning Tools and Techniques with Java Implementations

abstract: The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries can be run on any computer with a Web browsing capability; this allows users to apply machine learning techniques to their own data regardless of computer platform.


title: Noncespaces: Using randomization to defeat cross-site scripting attacks

abstract: Cross-site scripting (XSS) vulnerabilities are among the most common and serious web application vulnerabilities. It is challenging to eliminate XSS vulnerabilities because it is difficult for web applications to sanitize all user input appropriately. We present Noncespaces, a technique that enables web clients to distinguish between trusted and untrusted content to prevent exploitation of XSS vulnerabilities. Using Noncespaces, a web application randomizes the the (X)HTML tags and attributes in each document before delivering it to the client. As long as the attacker is unable to guess the random mapping, the client can distinguish between trusted content created by the web application and untrusted content provided by an attacker. To implement Noncespaces with minimal changes to web applications, we leverage a popular web application architecture to automatically apply Noncespaces to static content processed through a popular PHP template engine. We design a policy language for Noncespaces, implement a training mode to assist policy development, and conduct extensive security testing of a generated policy for two large web applications to show the effectiveness of our technique.


title: 3D Simulation for Robot Arm Control with Deep Q-Learning

abstract: Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline. However, the high dimensionality of the state space often means that it is impractical to generate sufficient training data with real-world experiments. As an alternative solution, we propose to learn a robot controller in simulation, with the potential of then transferring this to a real robot. Building upon the recent success of deep Q-networks, we present an approach which uses 3D simulations to train a 7-DOF robotic arm in a control task without any prior knowledge. The controller accepts images of the environment as its only input, and outputs motor actions for the task of locating and grasping a cube, over a range of initial configurations. To encourage efficient learning, a structured reward function is designed with intermediate rewards. We also present preliminary results in direct transfer of policies over to a real robot, without any further training.


title: Smart Helmet: Wearable Multichannel ECG and EEG

abstract: Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.


Baclofen and back pain: A paradoxical phenomena


title: Spectral clustering and the high-dimensional stochastic blockmodel

abstract: Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people who communicate with each other, are one example. Communities or clusters of highly connected actors form an essential feature in the structure of several empirical networks. Spectral clustering is a popular and computationally feasible method to discover these communities. The stochastic blockmodel [Social Networks 5 (1983) 109--137] is a social network model with well-defined communities; each node is a member of one community. For a network generated from the Stochastic Blockmodel, we bound the number of nodes"misclustered"by spectral clustering. The asymptotic results in this paper are the first clustering results that allow the number of clusters in the model to grow with the number of nodes, hence the name high-dimensional. In order to study spectral clustering under the stochastic blockmodel, we first show that under the more general latent space model, the eigenvectors of the normalized graph Laplacian asymptotically converge to the eigenvectors of a"population"normalized graph Laplacian. Aside from the implication for spectral clustering, this provides insight into a graph visualization technique. Our method of studying the eigenvectors of random matrices is original.