title: Using a Deep Reinforcement Learning Agent for Traffic Signal Control

abstract: Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic microsimulator SUMO. We propose a new state space, the discrete traffic state encoding, which is information dense. The discrete traffic state encoding is used as input to a deep convolutional neural network, trained using Q-learning with experience replay. Our agent was compared against a one hidden layer neural network traffic signal control agent and reduces average cumulative delay by 82%, average queue length by 66% and average travel time by 20%.


title: Turntable-based 3D object reconstruction

abstract: In this paper, we present a system that can acquire graphical models from real objects. Given an image sequence of a complex shape object placed on a turntable, the presented algorithm generates automatically the 3D model. In contrast to previous approaches, the technique described here is only based on conies properties and uses the spatiotemporal aspect of the sequence of images. From the projective properties of the conies and using the camera calibration parameters the Euclidean 3D coordinates of a point are obtained from the geometric locus of the image points trajectories. An algorithm has been implemented to compute the 3D reconstruction automatically. Examples on both synthetic and real image sequences are presented


title: Generic Schema Matching with Cupid

abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems.


title: The Power of Convex Relaxation: Near-Optimal Matrix Completion

abstract: This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering. In general, accurate recovery of a matrix from a small number of entries is impossible; but the knowledge that the unknown matrix has low rank radically changes this premise, making the search for solutions meaningful. ::: This paper presents optimality results quantifying the minimum number of entries needed to recover a matrix of rank r exactly by any method whatsoever (the information theoretic limit). More importantly, the paper shows that, under certain incoherence assumptions on the singular vectors of the matrix, recovery is possible by solving a convenient convex program as soon as the number of entries is on the order of the information theoretic limit (up to logarithmic factors). This convex program simply finds, among all matrices consistent with the observed entries, that with minimum nuclear norm. As an example, we show that on the order of nr log(n) samples are needed to recover a random n x n matrix of rank r by any method, and to be sure, nuclear norm minimization succeeds as soon as the number of entries is of the form nr polylog(n).


title: DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

abstract: The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.


title: Vehicle Communication Strategies for Simulated Highway Driving

abstract: Interest in emergent communication has recently surged in Machine Learning. The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve problems that already have a viable solution. Here, we consider self-driving cars coordinating with each other and focus on how communication influences the agents' collective behavior. Our main result is that communication helps (most) with adverse conditions.


title: Inhalation provocation tests in chronic bird fancier's lung.

abstract: BACKGROUND ::: Patients with chronic bird fancier's lung (BFL) can be classified into two types. One group of patients develops chronic disease with fluctuating acute episodes, including low-grade fever, mild exertional dyspnea, and cough (fluctuating chronic BFL; formerly termed recurrent and relapsing chronic BFL). The other group of patients shows no history of acute episodes (insidious chronic BFL). The diagnosis of chronic BFL is difficult, since the onset of chronic BFL may be insidious, with few if any symptoms during the early stages of the disease process. ::: ::: ::: STUDY OBJECTIVE ::: To attempt to diagnose the conditions of these patients more precisely, inhalation provocation tests were conducted using avian dropping extracts. ::: ::: ::: DESIGN ::: Retrospective chart review. ::: ::: ::: SETTING ::: The Tokyo Medical and Dental University Hospital in Japan. ::: ::: ::: PATIENTS ::: Eleven patients with chronic BFL (6 with fluctuating chronic BFL and 5 with insidious chronic BFL) and 6 control subjects (4 asymptomatic bird owners and 2 idiopathic pulmonary fibrosis patients) were evaluated. ::: ::: ::: MEASUREMENTS AND RESULTS ::: Inhalation provocation tests using avian dropping extracts were conducted. All BFL patients were evaluated as positive or probable by inhalation challenge, whereas control subjects were evaluated as negative. A peripheral leukocytosis, an increase of alveolar-arterial oxygen pressure difference, an increase of body temperature, and the development of respiratory symptoms including cough and dyspnea were more frequently observed in chronic BFL patients than in control subjects. All the BFL patients had an increase in neutrophils in BAL fluids following inhalation challenge. ::: ::: ::: CONCLUSIONS ::: We validated the utility of inhalation challenge for the diagnosis of chronic BFL, including fluctuating and insidious BFL. We also demonstrated that neutrophilia in BAL fluids following inhalation challenge could be added to the diagnostic criteria for chronic BFL.


title: Social media engagement: What motivates user participation and consumption on YouTube?

abstract: Abstract This study unearths the motives for YouTube user engagement that has been conceptualized as active participation and passive content consumption. In light of the Uses and Gratifications framework, a sample of 1143 registered YouTube users completed online surveys that helped gauge user behavior. Results showed that for participation on YouTube, the strongest predictor for liking and disliking videos was the relaxing entertainment motive; commenting and uploading being strongly predicted by social interaction motive; sharing being strongly predicted by information giving motive. Passive content consumption in the form of video viewing was most strongly predicted by relaxing entertainment motive, and reading comments predicted by information seeking motive. Greater YouTube experience negatively predicted liking, and anonymity played a role in sharing and uploading videos. Males were more likely to dislike YouTube videos in comparison with females.


title: Student pass rates prediction using optimized support vector machine and decision tree

abstract: Since student performance and pass rates in school reflect teaching level of the school and even all education system, it is critical to improve student pass rates and reduce dropout rates. Decision Tree (DT) algorithm and Support Vector Machine (SVM) algorithm in data mining, have been used by researchers to find important student features and predict the student pass rates, however they did not consider the coefficient of initialization, and whether there is a dependency between student features. Therefore, in this study, we propose a new concept: features dependencies, and use the grid search algorithm to optimize DT and SVM, in order to improve the accuracy of the algorithm. Furthermore, we added 10-fold cross-validation to DT and SVM algorithm. The results show the experiment can achieve better results in this work. The purpose of this study is providing assistance to students who have greater difficulties in their studies, and students who are at risk of graduating through data mining techniques.


title: Movie recommendation system using clustering and pattern recognition network

abstract: As the world is going global, people have a lot to choose from when it comes to movies. There are different genres, cultures and languages to choose from in the world of movies. This arises the issue of recommending movies to users in automated systems. So far, a decent number of works has been done in this field. But there is always room for renovation. This paper proposed a machine learning approach to recommend movies to users using K-means clustering algorithm to separate similar users and creating a neural network for each cluster.


title: Information Extraction in Illicit Domains

abstract: Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.


title: CP-ABE With Constant-Size Keys for Lightweight Devices

abstract: Lightweight devices, such as radio frequency identification tags, have a limited storage capacity, which has become a bottleneck for many applications, especially for security applications. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic tool, where the encryptor can decide the access structure that will be used to protect the sensitive data. However, current CP-ABE schemes suffer from the issue of having long decryption keys, in which the size is linear to and dependent on the number of attributes. This drawback prevents the use of lightweight devices in practice as a storage of the decryption keys of the CP-ABE for users. In this paper, we provide an affirmative answer to the above long standing issue, which will make the CP-ABE very practical. We propose a novel CP-ABE scheme with constant-size decryption keys independent of the number of attributes. We found that the size can be as small as 672 bits. In comparison with other schemes in the literature, the proposed scheme is the only CP-ABE with expressive access structures, which is suitable for CP-ABE key storage in lightweight devices.


title: MTJ variation monitor-assisted adaptive MRAM write

abstract: Spin-transfer torque random access memory (STT-RAM) and magnetoelectric random access memory (MeRAM) are promising non-volatile memory technologies. But STT-RAM and Me RAM both suffer from high write error rate due to thermal fluctuation of magnetization. Temperature and wafer-level process variation significantly exacerbate these problems. In this paper, we propose a design that adaptively selects optimized write pulse for STT-RAM and MeRAM to overcome ambient process and temperature variation. To enable the adaptive write, we design specific MTJ-based variation monitor, which precisely senses process and temperature variation. The monitor is over 10X faster, 5X more energy-efficient, and 20X smaller compared with conventional thermal monitors of similar accuracy. With adaptive write, the write latency of STT-RAM and MeRAM cache are reduced by up to 17% and 59% respectively, and application run time is improved by up to 41%.


Critiquing the Knowledge Management Strategies of Nonprofit Organizations


Business Ecosystem: A conceptual model of an organisation population from the perspectives of complexity and evolution


title: Data mining with genetic algorithms on binary trees

abstract: Abstract This paper focuses on the automatic interaction detection (AID)-technique, which belongs to the class of decision tree data mining techniques. The AID-technique explains the variance of a dependent variable through an exhaustive and repeated search of all possible relations between the (binary) predictor variables and the dependent variable. This search results in a tree in which non-terminal nodes represent the binary predictor variables, edges represent the possible values of these predictor variables and terminal nodes or leafs correspond to classes of subjects. Despite of being self-evident, the AID-technique has its weaknesses. To overcome these drawbacks a technique is developed that uses a genetic algorithm to find a set of diverse classification trees, all having a large explanatory power. From this set of trees, the data analyst is able to choose the tree that fulfils his requirements and does not suffer from the weaknesses of the AID-technique. The technique developed in this paper uses some specialised genetic operators that are devised to preserve the structure of the trees and to preserve high fitness from being destroyed. An implementation of the algorithm exists and is freely available. Some experiments were performed which show that the algorithm uses an intensification stage to find high-fitness trees. After that, a diversification stage recombines high-fitness building blocks to find a set of diverse solutions.


title: Linearly-Recurrent Autoencoder Networks for Learning Dynamics

abstract: This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural network approximations of the underlying Koopman operator. Extended Dynamic...


title: CALCULATION AND UPDATE OF A 3D BUILDING MODEL OF BAVARIA USING LIDAR, IMAGE MATCHING AND CATASTRE INFORMATION

abstract: Abstract. The Bavarian State Office for Surveying and Geoinformation has launched a statewide 3D Building Model with standardized roof shapes without textures for all 8.1 million buildings in Bavaria. For acquisition of the 3D Building Model LiDAR-data are used as data basis as well as the building ground plans of the official cadastral map and a list of standardized roof shapes. The data management of the 3D Building Model is carried out by a central database with the usage of a nationwide standardized data model and the data exchange interface CityGML. On the one hand the update of the 3D Building Model for new buildings is done by terrestrial building measurements within the maintenance process of the cadastre. On the other hand the roofs of buildings which were built after the LiDAR flight and which were not measured terrestrially yet, are captured by means of picture-based digital surface-models derived from image-matching of oriented aerial photographs (DSM from image matching).


Iterative learning control of Hamiltonian systems based on self-adjoint structure


Representation using Rich Semantic Graph : A Case Study


title: Food image recognition with deep convolutional features

abstract: In this paper, we report the feature obtained from the Deep Convolutional Neural Network boosts food recognition accuracy greatly by integrating it with conventional hand-crafted image features, Fisher Vectors with HoG and Color patches. In the experiments, we have achieved 72.26% as the top-1 accuracy and 92.00% as the top-5 accuracy for the 100-class food dataset, UEC-FOOD100, which outperforms the best classification accuracy of this dataset reported so far, 59.6%, greatly.


Social Media: The Good, the Bad, and the Ugly


title: Conveying shape and features with image-based relighting

abstract: Hand-crafted illustrations are often more effective than photographs for conveying the shape and important features of an object, but they require expertise and time to produce. We describe an image compositing system and user interface that allow an artist to quickly and easily create technical illustrations from a set of photographs of an object taken from the same point of view under variable lighting conditions. Our system uses a novel compositing process in which images are combined using spatially-varying light mattes, enabling the final lighting in each area of the composite to be manipulated independently. We describe an interface that provides for the painting of local lighting effects (e.g. shadows, highlights, and tangential lighting to reveal texture) directly onto the composite. We survey some of the techniques used in illustration and lighting design to convey the shape and features of objects and describe how our system can be used to apply these techniques.


title: Signaling in Equity Crowdfunding

abstract: This paper presents an initial empirical examination of which start-up signals will induce small investors to commit financial resources in an equity crowdfunding context. We examine the impact of firms’ financial roadmaps (e.g., preplanned exit strategies such as IPOs or acquisitions), external certification (awards, government grants and patents), internal governance (such as board structure), and risk factors (such as amount of equity offered and the presence of disclaimers) on fundraising success. Our data highlight the importance of financial roadmaps and risk factors, as well as internal governance, for successful equity crowdfunding. External certification, by contrast, has little or no impact on success. We also discuss the implications for successful policy design.


title: Learning object affordances by leveraging the combination of human-guidance and self-exploration

abstract: Our work focuses on robots to be deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to efficiently learn the affordances of objects in their environment. This approach is promising because people naturally focus on showing salient aspects of the objects [1]. We replicate prior results and build on them to create a combination of self and supervised learning. We present experimental results with a robot learning 5 affordances on 4 objects using 1219 interactions. We compare three conditions: (1) learning through self-exploration, (2) learning from supervised examples provided by 10 naive users, and (3) self-exploration biased by the user input. Our results characterize the benefits of self and supervised affordance learning and show that a combined approach is the most efficient and successful.


title: A blueprint for introducing disruptive technology into the Internet

abstract: This paper argues that a new class of geographically distributed network services is emerging, and that the most effective way to design, evaluate, and deploy these services is by using an overlay-based testbed. Unlike conventional network testbeds, however, we advocate an approach that supports both researchers that want to develop new services, and clients that want to use them. This dual use, in turn, suggests four design principles that are not widely supported in existing testbeds: services should be able to run continuously and access a slice of the overlay's resources, control over resources should be distributed, overlay management services should be unbundled and run in their own slices, and APIs should be designed to promote application development. We believe a testbed that supports these design principles will facilitate the emergence of a new service-oriented network architecture. Towards this end, the paper also briefly describes PlanetLab, an overlay network being designed with these four principles in mind.


title: 350 mV, 5 GHz Class-D Enhanced Swing Differential and Quadrature VCOs in 65 nm CMOS

abstract: A new enhanced swing class-D VCO which operates from a supply voltage as low as 300 mV is presented. The architectural advantages are described along with an analysis for the oscillation frequency. Prototype differential and quadrature variants of the proposed VCO have been implemented in a 65 nm RF CMOS process with a 5 GHz VCO oscillation frequency. At a 350 mV supply, the measured phase noise performance for the quadrature VCO with a 5% tuning range is $-$ 137.1 dBc/Hz at 3 MHz offset with a power dissipation of 2.1 mW from a 0.35 V supply. The highest resulting figure-of-merit (FoM) is 198.3 dBc/Hz.


title: Integrating ACT-R Cognitive Models with the Unity Game Engine

abstract: The main aim of the chapter is to describe how cognitive models, developed using the ACT-R cognitive architecture, can be integrated with the Unity game engine in order to support the intelligent control of virtual characters in both 2D and 3D virtual environments. ACT-R is a cognitive architecture that has been widely used to model various aspects of human cognition, such as learning, memory, problem-solving, reasoning and so on. Unity, on the other hand, is a very popular game engine that can be used to develop 2D and 3D environments for both game and non-game purposes. The ability to integrate ACT-R cognitive models with the Unity game engine thus supports the effort to create virtual characters that incorporate at least some of the capabilities and constraints of the human cognitive system.


title: Overcoming the obfuscation of Java programs by identifier renaming

abstract: Decompilation is the process of translating object code to source code and is usually the first step towards the reverse-engineering of an application. Many obfuscation techniques and tools have been developed, with the aim of modifying a program, such that its functionalities are preserved, while its understandability is compromised for a human reader or the decompilation is made unsuccessful. Some approaches rely on malicious identifiers renaming, i.e., on the modification of the program identifiers in order to introduce confusion and possibly prevent the decompilation of the code. In this work we introduce a new technique to overcome the obfuscation of Java programs by identifier renaming. Such a technique relies on the intelligent modification of identifiers in Java bytecode. We present a new software tool which implements our technique and allows the processing of an obfuscated program in order to rename the identifiers as required by our technique. Moreover, we show how to use the existing tools to provide a partial implementation of the technique we propose. Finally, we discuss the feasibility of our approach by showing how to contrast the obfuscation techniques based on malicious identifier renaming recently presented in literature.


title: On the realization of constraints in nonholonomic mechanics

abstract: Abstract The possibility of realizing nonholonomic constraints using large nonconservative forces is considered. Mechanical characteristics of some geometric objects investigated in /1/ are presented. This makes it possible to consider in a natural way the transition from the principle for systems without constraints to that of systems with constraints. Basic formulations are given in invariant form. An example is presented.


Bridging GeoMQTT and REST


title: DDoS attack detection method using cluster analysis

abstract: Distributed Denial of Service (DDoS) attacks generate enormous packets by a large number of agents and can easily exhaust the computing and communication resources of a victim within a short period of time. In this paper, we propose a method for proactive detection of DDoS attack by exploiting its architecture which consists of the selection of handlers and agents, the communication and compromise, and attack. We look into the procedures of DDoS attack and then select variables based on these features. After that, we perform cluster analysis for proactive detection of the attack. We experiment with 2000 DARPA Intrusion Detection Scenario Specific Data Set in order to evaluate our method. The results show that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself.


title: On-chip spectrum analyzer for analog built-in self test

abstract: This paper presents the design of an on-chip spectrum analyzer. A novel architecture is used to mitigate the problems encountered in trying to implement architectures employed in conventional stand-alone instruments on a chip. Specifically, it makes use of a very-low IF architecture, which leads to a highly compact design, that can be used for measuring the frequency content of high frequency on-chip signals. The architecture and design considerations along with an implementation in a 0.18 /spl mu/ CMOS process is described. The design takes up an area of approximately 0.384 mm/sup 2/ with a simulated frequency range of 33 MHz to 3 GHz and a dynamic range of 60 dB.


title: Optimal Server Selection and Pricing in Receiver-Driven P2P Streaming Systems

abstract: In receiver-driven P2P streaming architectures, a client peer may get resources from one or more server peers. The optimal peer selection problem is to minimize the total streaming cost subject to the bandwidth requirement. However, P2P peers are usually selfish identities who seek to maximize their own benefits. In this paper, we analyze the optimal peer selection problem in a game theoretic approach and use the well-known VCG procedure to schedule the pricing policy. We inspirit the true revelation of server peers' cost value in order to minimize the total streaming cost. Our contributions also include a study of practical implementation of VCG and a discussion of the pricing mechanism in a repeated-game view


Modeling and Simulating Apache Spark Streaming Applications


title: Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

abstract: Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English–German language pair.


title: The village base station

abstract: Cellular communications, including handsets and base stations, have become ubiquitous technologies throughout the developing and developed world. Roughly three billion users spend large portions of their income on these basic communications [1]. However, the remaining half of the world currently has limited access, in large part due to lack of network coverage. Some areas do not have a high enough population density to support a traditional cellular deployment. Other areas are too far from established infrastructure to make a deployment economically feasible. This leads to many rural areas where there is no network coverage at all.


title: A Question Answering System on Holy Quran Translation Based on Question Expansion Technique and Neural Network Classification

abstract: In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.


title: Rational Protocol Design: Cryptography against Incentive-Driven Adversaries

abstract: Existing work on "rational cryptographic protocols" treats each party (or coalition of parties) running the protocol as a selfish agent trying to maximize its utility. In this work we propose a fundamentally different approach that is better suited to modeling a protocol under attack from an external entity. Specifically, we consider a two-party game between an protocol designer and an external attacker. The goal of the attacker is to break security properties such as correctness or privacy, possibly by corrupting protocol participants; the goal of the protocol designer is to prevent the attacker from succeeding. We lay the theoretical groundwork for a study of cryptographic protocol design in this setting by providing a methodology for defining the problem within the traditional simulation paradigm. Our framework provides ways of reasoning about important cryptographic concepts (e.g., adaptive corruptions or attacks on communication resources) not handled by previous game-theoretic treatments of cryptography. We also prove composition theorems that-for the first time-provide a sound way to design rational protocols assuming "ideal communication resources" (such as broadcast or authenticated channels) and then instantiate these resources using standard cryptographic tools. Finally, we investigate the problem of secure function evaluation in our framework, where the attacker has to pay for each party it corrupts. Our results demonstrate how knowledge of the attacker's incentives can be used to circumvent known impossibility results in this setting.


title: Keyword Search With Access Control Over Encrypted Cloud Data

abstract: In this paper, we study the problem of keyword search with access control (KSAC) over encrypted data in cloud computing. We first propose a scalable framework where user can use his attribute values and a search query to locally derive a search capability, and a file can be retrieved only when its keywords match the query and the user’s attribute values can pass the policy check. Using this framework, we propose a novel scheme called KSAC, which enables keyword search with access control over encrypted data. KSAC utilizes a recent cryptographic primitive called hierarchical predicate encryption to enforce fine-grained access control and perform multi-field query search. Meanwhile, it also supports the search capability deviation, and achieves efficient access policy update as well as keyword update without compromising data privacy. To enhance the privacy, KSAC also plants noises in the query to hide users’ access privileges. Intensive evaluations on real-world dataset are conducted to validate the applicability of the proposed scheme and demonstrate its protection for user’s access privilege.


title: Hierarchical Object Detection with Deep Reinforcement Learning

abstract: We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. ::: ::: We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. ::: ::: Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.


title: An N-path bandpass filter with a tuning range of 0.1–12 GHz and stopband rejection > 20 dB in 32 nm SOI CMOS

abstract: This paper presents an N-path bandpass filter with a tuning range of 0.1–12 GHz, a constant 3-dB bandwidth of 150 MHz (1-dB bandwidth of 80 MHz), and a stopband rejection greater than 20 dB. The filter includes two sets of 4-path mixers to remove the dependence of the rejection on the switch resistance, and also a clock (LO) divider which operates at 0.2–24 GHz. The passband insertion loss is 3–7.4 dB from 0.1–12 GHz with a power consumption of 20–130 mW. The measured in-band IIP3 is 21–6 dBm and the measured out-of-band IIP3 is 29–15 dBm at 120 MHz offset for 0.1–12 GHz, respectively. To the best of the authors' knowledge, this is the widest tuning range demonstrated for an N-path bandpass filter of any kind.


title: Generative Adversarial Positive-Unlabelled Learning

abstract: In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting strategy for U data, so that a decent decision boundary can be found. However, given limited P data, the conventional PU models tend to suffer from overfitting when adapted to very flexible deep neural networks. In contrast, we are the first to innovate a totally new paradigm to attack the binary PU task, from perspective of generative learning by leveraging the powerful generative adversarial networks (GAN). Our generative positive-unlabeled (GenPU) framework incorporates an array of discriminators and generators that are endowed with different roles in simultaneously producing positive and negative realistic samples. We provide theoretical analysis to justify that, at equilibrium, GenPU is capable of recovering both positive and negative data distributions. Moreover, we show GenPU is generalizable and closely related to the semi-supervised classification. Given rather limited P data, experiments on both synthetic and real-world dataset demonstrate the effectiveness of our proposed framework. With infinite realistic and diverse sample streams generated from GenPU, a very flexible classifier can then be trained using deep neural networks.


title: Myths and realities: the performance impact of garbage collection

abstract: This paper explores and quantifies garbage collection behavior for three whole heap collectors and generational counterparts: copying semi-space, mark-sweep, and reference counting, the canonical algorithms from which essentially all other collection algorithms are derived. Efficient implementations in MMTk, a Java memory management toolkit, in IBM's Jikes RVM share all common mechanisms to provide a clean experimental platform. Instrumentation separates collector and program behavior, and performance counters measure timing and memory behavior on three architectures.Our experimental design reveals key algorithmic features and how they match program characteristics to explain the direct and indirect costs of garbage collection as a function of heap size on the SPEC JVM benchmarks. For example, we find that the contiguous allocation of copying collectors attains significant locality benefits over free-list allocators. The reduced collection costs of the generational algorithms together with the locality benefit of contiguous allocation motivates a copying nursery for newly allocated objects. These benefits dominate the overheads of generational collectors compared with non-generational and no collection, disputing the myth that "no garbage collection is good garbage collection." Performance is less sensitive to the mature space collection algorithm in our benchmarks. However the locality and pointer mutation characteristics for a given program occasionally prefer copying or mark-sweep. This study is unique in its breadth of garbage collection algorithms and its depth of analysis.


title: Online Learning for Neural Machine Translation Post-editing

abstract: Neural machine translation has meant a revolution of the field. Nevertheless, post-editing the outputs of the system is mandatory for tasks requiring high translation quality. Post-editing offers a unique opportunity for improving neural machine translation systems, using online learning techniques and treating the post-edited translations as new, fresh training data. We review classical learning methods and propose a new optimization algorithm. We thoroughly compare online learning algorithms in a post-editing scenario. Results show significant improvements in translation quality and effort reduction.


title: Control of robot arm using pneumatic artificial muscle with spherical joint

abstract: This paper describes the robot arm with a high affinity to a human arm, an increased demand for which is anticipated in welfare-related and elderly nursing fields. We used the spherical bearing and the pneumatic artificial muscle actuator for the arm that we developed. Moreover, to achieve the affinity, safety, and the controllability by a good balance, it examined the control law in consideration of the characteristic of the mechanism and the actuator. The pneumatic artificial muscle is lightweight and high power. In addition, it is possible for it to emit exhaust into the atmosphere because the transmission medium of its energy is air, and it needs neither a tank nor maintenance like hydraulic equipment. In addition, safety to a person and the environment is high because the base of the actuator is a soft polymeric material even if the damage to the artificial muscle is caused. On the other hand, because the device is composed of a thin film cylinder, and easily receives the influence of the outside power for the conversion of the volume change in rubber into physical contraction axially, and its passive character is strong, precise positional control is difficult. Therefore, we apply the H/sub /spl infin// control that is the method of frequency shaped, and construct a stubborn system to the unstructured uncertainty.


Analysis of Glottal Stop in Assam Sora Language.


title: A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

abstract: The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current intrusion detection systems (IDSs) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimized solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected.


Study of Optimal Replacement of Thyroxine in the Elderly (SORTED) – results from the feasibility randomised controlled trial


title: Aesthetic information collages: generating decorative displays that contain information

abstract: Normally, the primary purpose of an information display is to convey information. If information displays can be aesthetically interesting, that might be an added bonus. This paper considers an experiment in reversing this imperative. It describes the Kandinsky system which is designed to create displays which are first aesthetically interesting, and then as an added bonus, able to convey information. The Kandinsky system works on the basis of aesthetic properties specified by an artist (in a visual form). It then explores a space of collages composed from information bearing images, using an optimization technique to find compositions which best maintain the properties of the artist's aesthetic expression.


title: A survey of key management for secure group communication

abstract: Group communication can benefit from IP multicast to achieve scalable exchange of messages. However, there is a challenge of effectively controlling access to the transmitted data. IP multicast by itself does not provide any mechanisms for preventing nongroup members to have access to the group communication. Although encryption can be used to protect messages exchanged among group members, distributing the cryptographic keys becomes an issue. Researchers have proposed several different approaches to group key management. These approaches can be divided into three main classes: centralized group key management protocols, decentralized architectures and distributed key management protocols. The three classes are described here and an insight given to their features and goals. The area of group key management is then surveyed and proposed solutions are classified according to those characteristics.


title: Altered Reward Processing in Women Recovered From Anorexia Nervosa

abstract: Objective: Individuals with anorexia nervosa are known to be ascetic and able to sustain self-denial of food as well as most comforts and pleasures in life. Building on previous findings of altered striatal dopamine binding in anorexia nervosa, the authors sought to assess the response of the anterior ventral striatum to reward and loss in this disorder. Method: Striatal responses to a simple monetary reward task were investigated using event-related functional magnetic resonance imaging. To avoid the confounding effects of malnutrition, the authors compared 13 healthy comparison women and 13 women who had recovered from restricting-type anorexia nervosa and had 1 year of normal weight and regular menstrual cycles, without binge eating or purging. Results: Recovered women showed greater hemodynamic activation in the caudate than comparison women. Only the recovered women showed a significant positive relationship between trait anxiety and the percentage change in hemodynamic signal in the caudate during e...


title: Ordered Mesoporous Boron Carbide Based Materials via Precursor Nanocasting

abstract: Ordered mesoporous boron carbide materials with high specific surface areas up to 778 m2/g and hexagonal pore arrangement symmetries were obtained for the first time using a nanocasting strategy and the molecular bisdecaboranyl-hexane precursor for the infiltration into an ordered mesoporous silica template (SBA-15). Different preparation conditions were investigated, and it was found that by adjusting the precursor loading either nanorod structured replicas or tubular structured (CMK-5-like) materials were obtained. Changing the impregnation techniques, the solvents, and/or the pyrolysis temperatures allows tailoring of the properties of the hexagonal ordered mesoporous boron carbide replicas. The pore arrangement was altered to cubic ordered mesoporous boron carbide using KIT-6 silica matrix for nanocasting.


title: Distortion in perceived size and body-based scaling in virtual environments

abstract: In this paper, we report findings pertaining to the size perception of objects and hands in a Virtual Environment(VE). First, we found that size perception is distorted in a VE and the effect is different between objects and hands. We perceive our virtual hands as larger and objects as smaller in VEs than in real environments(REs). However, when hands interact with objects, our body is used as a metric to scale the apparent sizes of objects in the environment (body-based scaling; BBS). We also found that not only does the size of our hands influence the perceived size of the environment, but the size of familiar-sized objects influences the perceived size of our hands as well. In summary, in contrast to the independent traits of size perception of hands and objects in VEs, we tend to perceive the size based on what we see at first, either hands or objects, when we interact with the objects. These findings provide a benchmark for scale adjustment for interactive scale-sensitive virtual reality applications so as to create perceptually more precise representations of virtual objects and bodies.


title: The Elaboration Likelihood Model of Persuasion

abstract: Publisher Summary ::: This chapter outlines the two basic routes to persuasion. One route is based on the thoughtful consideration of arguments central to the issue, whereas the other is based on the affective associations or simple inferences tied to peripheral cues in the persuasion context. This chapter discusses a wide variety of variables that proved instrumental in affecting the elaboration likelihood, and thus the route to persuasion. One of the basic postulates of the Elaboration Likelihood Model—that variables may affect persuasion by increasing or decreasing scrutiny of message arguments—has been highly useful in accounting for the effects of a seemingly diverse list of variables. The reviewers of the attitude change literature have been disappointed with the many conflicting effects observed, even for ostensibly simple variables. The Elaboration Likelihood Model (ELM) attempts to place these many conflicting results and theories under one conceptual umbrella by specifying the major processes underlying persuasion and indicating the way many of the traditionally studied variables and theories relate to these basic processes. The ELM may prove useful in providing a guiding set of postulates from which to interpret previous work and in suggesting new hypotheses to be explored in future research.


title: 3D vasculature segmentation using localized hybrid level-set method

abstract: BACKGROUND ::: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. ::: ::: ::: METHODS ::: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. ::: ::: ::: RESULTS ::: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. ::: ::: ::: CONCLUSIONS ::: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.


title: Positioning a Mobile Subscriber in a Cellular Network System based on Signal Strength

abstract: In this paper the study has been carried out for finding the position of a mobile subscriber from a network independent brokers’ point of view. As each mobile device monitors signal strength, this helps in assisting the network to take timely handoff decisions. These signal strength measurements are referred to as NMR (Network Measurement Report). A detailed study was conducted to analyze the use of such parameters to locate a user under the coverage area of a BTS used in a cellular network. Use of these parameters implies that such a system work in a network independent manner. This paper discusses the issues in the implementation of such a system and includes the statistics collected using the simulation tool OPNET [1].


title: Secure Three-Factor User Authentication Scheme for Renewable-Energy-Based Smart Grid Environment

abstract: Smart grid (SG) technology has recently received significant attention due to its usage in maintaining demand response management in power transmission systems. In SG, charging of electric vehicles becomes one of the emerging applications. However, authentication between a vehicle user and a smart meter is required so that both of them can securely communicate for managing demand response during peak hours. To address the above mentioned issues, in this paper, we propose a new efficient three-factor user authentication scheme for a renewable energy-based smart grid environment (TUAS-RESG), which uses the lightweight cryptographic computations such as one-way hash functions, bitwise XOR operations, and elliptic curve cryptography. The detailed security analysis shows the robustness of TUAS-RESG against various well-known attacks. Moreover, TUAS-RESG provides superior security with additional features, such as dynamic smart meter addition, flexibility for password and biometric update, user and smart meter anonymity, and untraceability as compared to other related existing schemes. The practical demonstration of TUAS-RESG is also proved using the widely accepted NS2 simulation.


title: Boredom, sustained attention and the default mode network

abstract: Boredom is a ubiquitous human experience that can best be described as an inability to engage with one’s environment despite the motivation to do so. Boredom is perceived as a negative experience and demonstrates strong associations with other negatively valenced states including depression and aggression. Although boredom has been shown to be elevated in neurological and psychiatric illnesses, little is known about the neural underpinnings of the state. We scanned the brains of healthy participants under four separate conditions: a resting state scan, a sustained attention task and two video-based mood inductions, one known to produce boredom and another we validated to produce a state of interest or engagement. Using independent components analyses, results showed common regions of correlated activation in posterior regions of the so-called default mode network (DMN) of the brain across all four conditions. The sustained attention and boredom induction scans were differentiated from the resting state scan by the presence of anticorrelated activity—i.e. when DMN regions were active, this region was deactivated—in the anterior insula cortex. This same region demonstrated correlated activity with both the DMN and the regions associated with attentional control during the interest mood induction. We interpret these findings to suggest that boredom represents a failure to engage executive control networks when faced with a monotonous task—in other words, when the task demands some level of engagement (watch the movie, search for infrequent targets), but is so mundane that attempts to do so fail.


title: Considerations for future automotive radar in the frequency range above 100 GHz

abstract: Within the last years automotive radar sensors became more and more important. Started with comfort systems like adaptive cruise control (ACC) and parking aid, the safety aspect increasingly came to the fore. In the near future not only upper class cars will be equipped with various radar applications like pre crash, collision warning and collision avoidance to increase traffic safety. Therefore many high performance radar sensors have to be integrated in the car. This paper describes the demands on future radar sensors. It is discussed how the step to higher operating frequencies could be beneficial particularly for urban situations with high traffic density. Furthermore the paper will provide and discuss design considerations for future mmW-Radar sensors in automotive safety applications.


title: Factors Influencing the Surprising Instability of Word Embeddings

abstract: Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability. We show that even relatively high frequency words (100-200 occurrences) are often unstable. We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.


title: Evaluating mobile apps for breathing training: The effectiveness of visualization

abstract: Breathing training can be beneficial to individuals' health and wellness.Mobile apps are a new tool for breathing training but lack formal evaluation in the literature.We compare three different designs, representative of those employed in current apps.Our analysis includes users' physiological parameters as well as subjective perception.A way of visualizing breathing patterns improved traditional audio-only instructions. Deep and slow breathing exercises can be an effective adjunct in the treatment of stress, anxiety, post-traumatic stress disorder, chronic pain and depression. Breathing techniques are traditionally learned in courses with trainers and/or with materials such as audio CDs for home practice. Recently, mobile apps have been proposed as novel breathing training tools, but to the best of our knowledge no research has focused on their evaluation so far. In this paper, we study three different designs for breathing training apps. The first employs audio instructions as in traditional training based on audio CDs, while the other two include visualizations of the breathing process, representative of those employed in current breathing training apps. We carry out a thorough analysis, focusing on users' physiological parameters as well as subjective perception. One visualization produces better results both objectively (measured deepness of breath) and subjectively (users' preferences and perceived effectiveness) than the more traditional audio-only design. This indicates that a visualization can contribute to the effectiveness of breathing training apps. We discuss which features could have allowed one visualization (but not the other) to obtain better results than traditional audio-only instructions.


title: Mapping complex timing constraints to simple real-time scheduling parameters

abstract: In this paper, we propose a novel approach for flexible representation of complex timing constraints for scheduling real-time tasks under different scheduling paradigms. It allows different instantiations of the task attributes, depending on which underlying scheduling policy is used. Scheduling parameters are flexibly derived from original timing constrains, rather than using the same set of task attributes for all schedulers. For each real-time task in the system, temporal feasibility windows are identified, such that if the task executes and completes within its feasibility window, the original timing constraints will be met. Then, scheduler dependent parameters are derived to guarantee the tasks' execution and completion within their feasibility windows.


title: Applicability of the 5S management method for quality improvement in health-care facilities: a review

abstract: BACKGROUND ::: The 5S management method (where 5S stands for sort, set in order, shine, standardize, and sustain) was originally implemented by manufacturing enterprises in Japan. It was then introduced to the manufacturing sector in the West and eventually applied to the health sector for organizing and standardizing the workplace. 5S has recently received attention as a potential solution for improving government health-care services in low- and middle-income countries. We conducted a narrative literature review to explore its applicability to health-care facilities globally, with a focus on three aspects: (a) the context of its application, (b) its impacts, and (c) its adoption as part of government initiatives. ::: ::: ::: METHODS ::: To identify relevant research articles, we researched public health databases in English, including CINAHL, PubMed, ScienceDirect, and Web of Science. We found 15 of the 114 articles obtained from the search results to be relevant for full-text analysis of the context and impacts of the 5S application. To identify additional information particularly on its adoption as part of government initiatives, we also examined other types of resources including reference books, reports, didactic materials, government documents, and websites. ::: ::: ::: RESULTS ::: The 15 empirical studies highlighted its application in primary health-care facilities and a wide range of hospital areas in Brazil, India, Jordan, Senegal, Sri Lanka, Tanzania, the UK, and the USA. The review also found that 5S was considered to be the starting point for health-care quality improvement. Ten studies presented its impacts on quality improvements; the changes resulting from the 5S application were classified into the three dimensions of safety, efficiency, and patient-centeredness. Furthermore, 5S was adopted as part of government quality improvement strategies in India, Senegal, Sri Lanka, and Tanzania. ::: ::: ::: CONCLUSIONS ::: 5S could be applied to health-care facilities regardless of locations. It could be not only a tool for health workers and facility managers but also a strategic option for policymakers. They could consider 5S as the starting point of a government-led quality improvement initiative for improving safety, efficiency, or patient-centeredness aspects particularly in low- and middle-income countries. However, the evidence base, particularly in resource-poor settings, must be expanded.


title: Preserving brightness in histogram equalization based contrast enhancement techniques

abstract: Histogram equalization (HE) has been a simple yet effective image enhancement technique. However, it tends to change the brightness of an image significantly, causing annoying artifacts and unnatural contrast enhancement. Brightness preserving bi-histogram equalization (BBHE) and dualistic sub-image histogram equalization (DSIHE) have been proposed to overcome these problems but they may still fail under certain conditions. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE). MMBEBHE has the feature of minimizing the difference between input and output image's mean. Simulation results showed that MMBEBHE can preserve brightness better than BBHE and DSIHE. Furthermore, this paper also formulated an efficient, integer-based implementation of MMBEBHE. Nevertheless, MMBEBHE also has its limitation. Hence, this paper further proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE). RMSHE is featured with scalable brightness preservation. Simulation results showed that RMSHE is the best compared to HE, BBHE, DSIHE, and MMBEBHE.


title: A generalised labelled multi-Bernoulli filter for extended multi-target tracking

abstract: This paper addresses extended multi-target tracking in clutter, i.e. tracking targets that may produce more than one measurement on each scan. We propose a new algorithm for solving this problem, that is capable of initiating and maintaining labelled estimates of the target kinematics, measurement rates and extents. Our proposed technique is based on modelling the multi-target state as a generalised labelled multi-Bernoulli (GLMB), combined with the gamma Gaussian inverse Wishart (GGIW) distribution for a single extended target. Previously, probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters based on GGIW mixtures have been proposed to solve the extended target tracking problem. Although these are computationally cheaper, they involve significant approximations, as well as lacking the ability to maintain target tracks over time. Here, we compare our proposed GLMB-based approach to the extended target PHD/CPHD filters, and show that the GLMB has improved performance.


title: Single Image Super-Resolution via Non Sub-sample Contourlet Transform based Learning and a Gabor Prior

abstract: Enhancing the quality of image is a continuous process in image processing related research activities. For some applications it becomes essential to have best quality of image such as in forensic department, where in order to retrieve maximum possible information, image has to be enlarged in terms of size, with higher resolution and other features associated with it. Such obtained high quality images have also a concern in satellite imaging, medical science, High Definition Television (HDTV), etc. In this paper a novel approach of getting high resolution image from a single low resolution image is discussed. The Non Sub-sampled Contourlet Transform (NSCT) based learning is used to learn the NSCT coefficients at the finer scale of the unknown high-resolution image from a dataset of high resolution images. The cost function consisting of a data fitting term and a Gabor prior term is optimized using an Iterative Back Projection (IBP). By making use of directional decomposition property of the NSCT and the Gabor filter bank with various orientations, the proposed method is capable to reconstruct an image with less edge artifacts. The validity of the proposed approach is proven through simulation on several images. RMS measures, PSNR measures and illustrations show the success of the proposed method.


Singapore's Overseas Industrial Parks


Comparing Social Media and Traditional Surveys around the Boston Marathon Bombing


Towards Automated GIS-based Analysis of Scoring Attempt Patterns in Association Football


title: From Artistry to Automation: A Structured Methodology for Procedural Content Creation

abstract: Procedural techniques will soon automate many aspects of content creation for computer games. We describe an efficient, deterministic, methodology for procedurally generating 3D game content of arbitrary size and complexity. The technique progressively amplifies simple dynamically generated data structures into complex geometry. We use a procedural pipeline with a minimum set of controls at each stage to facilitate authoring. We show two examples from our research. Our terrain generator can synthesize massive 3D terrains in real-time while our level generator can be used to create indoor environments offline or in real-time.


title: A Robust Classifier for Passive TCP/IP

abstract: Using probabilistic learning, we develop a naive Bayesian classifier to passively infer a host’s operating system from packet headers. We analyze traffic captured from an Internet exchange point and compare our classifier to rule-based inference tools. While the host operating system distribution is heavily skewed, we find operating systems that constitute a small fraction of the host count contribute a majority of total traffic. Finally as an application of our classifier, we count the number of hosts masquerading behind NAT devices and evaluate our results against prior techniques. We find a host count inflation factor due to NAT of approximately 9% in our traces.


title: Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

abstract: Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary’s prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.


title: An enterprise architecture planning for higher education using the open group architecture framework (togaf): Case study University of Lampung

abstract: University is an organization that obliged to provide higher education, research, and community service (Tri Dharma), it has autonomy to manage their institutions. In order to realize Tri Dharma, University should made a planning of information technology (IT) infrastructure to support alignment process between organization business strategy and IT strategy. University of Lampung (Unila) as a government university in Lampung Province, already had IT infrastructure and managed by special unit. IT management on Unila nowadays more complicated, disintegrated of IT management has an effect that IT infrastructure was not adaptive to response the quick change of business needs. This research aimed to design an adaptive IT infrastructure based on enterprise architecture framework The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM) on Unila. The results of this research found that there are 3 core business activities, and 6 supporting activities identified on this campus. We also proposed 12 application to developed, all applications should be modular and integrated with other.


title: Neurocognitive Consequences of Sleep Deprivation

abstract: Deficits in daytime performance due to sleep loss are experienced universally and associated with a significant social, financial, and human cost. Microsleeps, sleep attacks, and lapses in cognition increase with sleep loss as a function of state instability. Sleep deprivation studies repeatedly show a variable (negative) impact on mood, cognitive performance, and motor function due to an increasing sleep propensity and destabilization of the wake state. Specific neurocognitive domains including executive attention, working memory, and divergent higher cognitive functions are particularly vulnerable to sleep loss. In humans, functional metabolic and neurophysiological studies demonstrate that neural systems involved in executive function (i.e., prefrontal cortex) are more susceptible to sleep deprivation in some individuals than others. Recent chronic partial sleep deprivation experiments, which more closely replicate sleep loss in society, demonstrate that profound neurocognitive deficits accumulate over time in the face of subjective adaptation to the sensation of sleepiness. Sleep deprivation associated with disease-related sleep fragmentation (i.e., sleep apnea and restless legs syndrome) also results in neurocognitive performance decrements similar to those seen in sleep restriction studies. Performance deficits associated with sleep disorders are often viewed as a simple function of disease severity; however, recent experiments suggest that individual vulnerability to sleep loss may play a more critical role than previously thought.


title: Functional role of the supplementary and pre-supplementary motor areas

abstract: The supplementary motor complex consists of the supplementary motor area, the supplementary eye field and the pre-supplementary motor area. In recent years, these areas have come under increasing scrutiny from cognitive neuroscientists, motor physiologists and clinicians because they seem to be crucial for linking cognition to action. However, theories regarding their function vary widely. This Review brings together the data regarding the supplementary motor regions, highlighting outstanding issues and providing new perspectives for understanding their functions.


title: Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs

abstract: One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement learning has been less prominent than value-based methods in addressing these challenges, recent progress has generated renewed interest in pursuing modelbased approaches: Theoretical work on the exploration/exploitation tradeoff has yielded provably sound model-based algorithms such as E 3 and Rmax, while work on factored MDP representations has yielded model-based algorithms that can scale up to large problems. Recently the benefits of both achievements have been combined in the Factored E3 algorithm of Kearns and Koller. In this paper, we address a significant shortcoming of Factored E3: namely that it requires an oracle planner that cannot be feasibly implemented. We propose an alternative approach that uses a practical approximate planner, approximate linear programming, that maintains desirable properties. Further, we develop an exploration strategy that is targeted toward improving the performance of the linear programming algorithm, rather than an oracle planner. This leads to a simple exploration strategy that visits states relevant to tightening the LP solution, and achieves sample efficiency logarithmic in the size of the problem description. Our experimental results show that the targeted approach performs better than using approximate planning for implementing either Factored E3 or Factored Rmax.


title: Power quality improvement in PFC Bridgeless-Luo converter fed BLDC motor drive

abstract: This paper presents a PFC (Power Factor Correction) based BL-Luo (Bridgeless-Luo) converter fed BLDC (Brushless DC) motor drive. A single voltage sensor is used for the speed control of BLDC motor and PFC at AC mains. The voltage follower control is used for a BL-Luo converter operating in DICM (Discontinuous Inductor Current Mode). The speed of the BLDC motor is controlled by an approach of variable DC link voltage, which allows a low frequency switching of VSI (Voltage Source Inverter) for electronic commutation of BLDC motor; thus offers reduced switching losses. The proposed BLDC motor drive is designed to operate over a wide range of speed control with improved power quality at AC mains. The power quality indices thus obtained are under the recommended limits of IEC 61000-3-2. The performance of the proposed drive is validated with test results obtained on a developed prototype of the drive.


title: IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type

abstract: Previous empirical studies examining the relationship between IT capability and accounting-based measures of firm performance reported mixed results. We argued that prior work has relied on aggregate overall measures of the firm's IT capability, ignoring the specific type and nature of IT capability; and also has not fully considered important environmental conditions that influence the relationship. Drawing on a resource-based view, we advanced a contingency perspective and proposed that IT capabilities' impact on firm resources was contingent on the ''fit'' between the type of IT capability/resource a firm possesses and the demands of the industry in which it competes. Specifically, using publicly available rankings as proxies for two types of IT capabilities (internally and externally focused), we empirically examined the degree to which three industry characteristics (dynamism, munificence, and complexity) influenced the impact of each type of IT capability on measures of financial performance. After controlling for prior performance, we found there was general support for the posited contingency model. The implications of these findings are also discussed.


title: Crime and housing in Malaysia: case study of Taman Melati terrace housing in Kuala Lumpur

abstract: Recently, Malaysia has adopted the “Safe city program”, which is focused towards creating violence and crime free cities. To achieve this, it is important to address crimes and disorder in residential areas. So far studies identified that changes in built environment and ::: modifications in space design can impact residents and offenders’ perceptions of criminality. Various CPTED measures are employed to create defensible space. This paper examines the effect of physical design on the occurrences of crime in Taman Melati residential area of ::: Kuala Lumpur City and tests the effects of the built environment on the possibility of crime reduction through physical planning measures.


title: Bandwidth and Latency Requirements for Smart Transmission Grid Applications

abstract: The rapid increase of phasor measurements on the high voltage power system has opened opportunities for new applications to enhance the operation of the grid. To take advantage of the high sampling rates of these measurement data, these applications will require a high-bandwidth, networked communication system. The specifications for this next generation communication system that will overlay the continental power grids are under intense discussion at this time by organizations like the North-American Synchro-Phasor Initiative (NASPI). In this paper we present a method to simulate, design and test the adequacy of a communication system for a particular transmission grid. The main difference from typical communication system studies is that we formulate the communication requirements from the power grid application requirements, that is, the communication design, simulation and testing is from the viewpoint of the anticipated power applications. The method is demonstrated on a WECC 225 bus and a Polish 2383 bus transmission system models.


title: Image classification by combining local and global features

abstract: Several techniques have recently been proposed to extract the features of an image. Feature extraction is one of the most important steps in various image processing and computer vision applications such as image retrieval, image classification, matching, object recognition. Relevant feature (global or local) contains discriminating information and is able to distinguish one object from others. Global features describe the entire image, whereas local features describe the image patches (small group of pixels). In this paper, we present a novel descriptor to extract the color-texture features via two information types. Our descriptor named concatenation of local and global color features is based on the fusion of global features using wavelet transform and a modified version of local ternary pattern, whereas, for the local features, speeded-up robust feature descriptor and bag of words model were used. All the features are extracted from the three color planes. To evaluate the effectiveness of our descriptor for image classification, we carried out experiments using the challenging datasets: New-BarkTex, Outex-TC13, Outex-TC14, MIT scene, UIUC sports event, Caltech 101 and MIT indoor scene. Experimental results showed that our descriptor outperforms the existing state-of-the-art methods.


title: G2RHA:Group-to-Route Handover Authentication Scheme for Mobile Relays in LTE-A High-Speed Rail Networks

abstract: The introduction of mobile relay node (MRN) in long-term evolution advanced high-speed rail networks is an attractive method to provide uninterrupted connectivity for the group of user equipments on board. However, MRNs still suffer from frequent handovers and several security threats due to several rounds of message exchange and the insecure air interface between MRNs and donor eNBs (DeNBs). In this paper, we propose a group-to-route handover authentication scheme based on trajectory prediction for mobile relays. By our scheme, all of the DeNBs deployed along the trajectory can be formed a route-DeNB group and all of the MRNs deployed in the same train can construct a MRN group. Compared with the current 3GPP standards and other related schemes, our scheme can accomplish the mutual authentication and key agreement between the MRN group and the target DeNB with ideal efficiency in terms of authentication signaling overhead, bandwidth consumption, and computational cost. Security analysis by using the BAN logic and formal verification tool Scyther show that our scheme can work correctly and withstand several protocol attacks.


title: Neurophysiological evaluation of pain

abstract: Neurophysiological techniques for the evaluation of pain in humans have made important advances in the last decade. A number of features of neuroanatomy and physiology of nociception qualifies pain as a multidimensional phenomenon which is rather unique among the sensory systems and which poses a number of technical and procedural requirements for its appropriate diagnostic assessment. Various stimulation techniques to induce defined pain in humans and used in combination with the methodology of evoked electrical brain potentials and magnetic fields are presented. Most recent knowledge gathered from scalp topography and dipole source analysis of pain-relevant evoked potentials and fields is discussed. Particular emphasis is put upon laser-evoked potentials and their application for diagnosis, pathophysiological description and monitoring of patients with neurological disorders and abnormal pain states. Future perspectives in this growing field of research are discussed briefly. © 1998 Elsevier Science Ireland Ltd. All rights reserved


title: Linked Hypernyms : Enriching DBpedia with Targeted Hypernym Discovery

abstract: The Linked Hypernyms Dataset (LHD) provides entities described by Dutch, English and German Wikipedia articles with types in the DBpedia namespace. The types are extracted from the first sentences of Wikipedia articles using Hearst pattern matching over part-of-speech annotated text and disambiguated to DBpedia concepts. The dataset covers 1.3 million RDF type triples from English Wikipedia, out of which 1 million RDF type triples were found not to overlap with DBpedia, and 0.4 million with YAGO2s. There are about 770 thousand German and 650 thousand Dutch Wikipedia entities assigned a novel type, which exceeds the number of entities in the localized DBpedia for the respective language. RDF type triples from the German dataset have been incorporated to the German DBpedia. Quality assessment was performed altogether based on 16.500 human ratings and annotations. For the English dataset, the average accuracy is 0.86, for German 0.77 and for Dutch 0.88. The accuracy of raw plain text hypernyms exceeds 0.90 for all languages. The LHD release described and evaluated in this article targets DBpedia 3.8, LHD version for the DBpedia 3.9 containing approximately 4.5 million RDF type triples is also available.


title: Schooling behaviour of arctic cod, Boreogadus saida, in relation to drifting pack ice

abstract: Concentrations of arctic cod were detected with a hydroacoustic system in Resolute Bay, NWT during 2 weeks in August of 1986. Fish biomass within the bay was about 30 t. The fish were feeding primarily on amphipods, which were abundant. When the daily location of the schools was examined in relation to the extent and position of drifting pack ice, a pattern emerged suggesting that the distribution of the fish was influenced by the amount and location of ice cover. If the bay was relatively ice-free, the density of schooling cod was high and the size of the schools, as 2-dimensional surface area, was generally small. When ice covered the bay, density within the schools was lower and they occupied more area. Arctic cod were most dispersed after the bay had been filled with pack ice for several days. It is postulated that this behaviour is a response to potential predation by seabirds and marine mammals.


title: Code and tell: assessing young children's learning of computational thinking using peer video interviews with ScratchJr

abstract: In this paper, we present a novel technique for assessing the learning of computational thinking in the early childhood classroom. Students in three second grade classrooms learned foundational computational thinking concepts using ScratchJr and applied what they learned to creating animated collages, stories, and games. They then conducted artifact-based video interviews with each other in pairs using their iPad cameras. As discussed in the results, this technique can show a broad range of what young children learn about computational thinking in classroom interventions using ScratchJr than more traditional assessment techniques. It simultaneously provides a developmentally appropriate educational activity (i.e. peer interviews) for early childhood classrooms.


title: The autism spectrum quotient (AQ): evidence from Asperger syndrome/high functioning autism, males and females, scientists and mathematicians

abstract: Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0–50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 327plus; on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test—retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored.


title: Implementation of Sliding Mode Controller for Twin Rotor Multi Input Multi Output System without state observer

abstract: This paper elucidates the scope of design and implementation of Sliding Mode Controller (SMC) for a Twin Rotor MIMO System (TRMS). SMC provides robust control for non-linear system. The performance parameters improved by SMC includes decoupling design procedure, disturbance rejection, insensitivity to parameter variation and simple mathematical steps in designing of SMC. The system dynamics are decoupled into horizontal and vertical subsystem and two separate SMC's are designed for each subsystem. The design objectives of the controller are to stabilize the TRMS in significant cross coupling, reach a desired position and accurately track a specified trajectory. The proposed method is applied numerically and is validated by simulation results. The results are compared with conventional Proportional-Integral-Derivative (PID) controller to show effectiveness of SMC with respect to error and control indices.


title: A survey of honeypot research: Trends and opportunities

abstract: The number of devices connected to computer networks is increasing daily, and so is the number of network-based attacks. A honeypot is a system trap that is set to act against unauthorised use of information systems. The objective of this study was to survey the emergent trends in extant honeypot research with the aims of contributing to the knowledge gaps in the honeypot environment. The relevant literature was identified from a myriad of sources, such as books, journal articles, reports, et cetera. The findings suggest that honeypots are attracting the interest of researchers as a valuable security technique that can be implemented to mitigate network attacks and provides an opportunity to learn more about the nature of these attacks. Consequently a honeypot can be used as a research tool to gather data about network attacks.


title: Equity Portfolio Diversification

abstract: In this paper we examine the portfolios of more than 40,000 equity investment accounts from a large discount brokerage during a six year period (1991-96) in recent U.S. capital market history. Using the historical performance for the equities in these accounts, we find that a vast majority of investors in our sample are under-diversified. A cross-sectional examination of diversification reveals that young and active investors, and investors in low income and non-professional categories hold the least diversified portfolios. Over time, the average degree of diversification has improved but these improvements result primarily from changes in the correlation structure of the US equity market. Nonetheless, improved portfolio diversification has a considerable impact on the composition and performance of investor portfolios.


title: Social Capital and Value Creation: The Role of Intrafirm Networks

abstract: Using data collected from multiple respondents in all the business units of a large multinational electronics company, we examined the relationships both among the structural, relational, and cogni...


title: Cyber Security in the Energy World

abstract: The advancement of technology reshapes our energy landscape over the past few decades. More sensors are deployed; more processes are being automated and more information is being exchanged between systems. Technology advancement brings enormous opportunities; For instance, optimizing operation, reducing outage time, turning raw data into useful business information etc. However, as we move deeper into digitalization, cyber threats become imminent. Cyber-attack has evolved from petty crime to state-sponsored cyber terrorism. From Stuxnet that attacked the Iranian Nuclear Power Plant in 2010 to the Black Energy 3 that caused massive blackout in Ukraine in 2015, cyber-attacks on critical infrastructure has become a national security issue. Cyber-attack on critical infrastructure requires a combination of hacking and engineering knowledge to inflict maximum damages. Thus, protecting critical infrastructure requires both information technology (IT) and operational technology (OT) expertise. Cyber security approach must be holistic.


title: Design of parallel-jaw gripper tip surfaces for robust grasping

abstract: Parallel-jaw robot grippers can grasp almost any object and are ubiquitous in industry. Although the shape, texture, and compliance of gripper jaw surfaces affect grasp robustness, almost all commercially available grippers provide a pair of rectangular, planar, rigid jaw surfaces. Practitioners often modify these surfaces with a variety of ad-hoc methods such as adding rubber caps and/or wrapping with textured tape. This paper explores data-driven optimization of gripper jaw surfaces over a design space based on shape, texture, and compliance using rapid prototyping. In total, 37 jaw surface design variations were created using 3D printed casting molds and silicon rubber. The designs were evaluated with 1377 physical grasp experiments using a 4-axis robot (with automated reset). These tests evaluate grasp robustness as the probability that the jaws will acquire, lift, and hold a training set of objects at nominal grasp configurations computed by Dex-Net 1.0. Hill-climbing in parameter space yielded a grid pattern of 0.03 inch void depth and 0.0375 inch void width on a silicone polymer with durometer of A30. We then evaluated performance of this design using an ABB YuMi robot grasping a set of eight difficult-to-grasp 3D printed objects in 80 grasps with four gripper surfaces. The factory-provided gripper tips succeeded in 28.7% of the 80 trials, increasing to 68.7% when the tips were wrapped with tape. Gripper tips with gecko-inspired surfaces succeeded in 80.0% of trials, and gripper tips with the designed silicone surfaces succeeded in 93.7% of trials.


Successful treatment of severe keratosis pilaris rubra with a 595-nm pulsed dye laser.


title: Twitter financial community modeling using agent based simulation

abstract: With the empirical evidence that Twitter influences the financial market, there is a need for a bottom-up approach focusing on individual Twitter users and their message propagation among a selected Twitter community with regard to the financial market. This paper presents an agent-based simulation framework to model the Twitter network growth and message propagation mechanism in the Twitter financial community. Using the data collected through the Twitter API, the model generates a dynamic community network with message propagation rates by different agent types. The model successfully validates against the empirical characteristics of the Twitter financial community in terms of network demographics and aggregated message propagation pattern. Simulation of the 2013 Associated Press hoax incident demonstrates that removing critical nodes of the network (users with top centrality) dampens the message propagation process linearly and critical node of the highest betweenness centrality has the optimal effect in reducing the spread of the malicious message to lesser ratio of the community.


title: Tracking the Trackers: A Large-Scale Analysis of Embedded Web Trackers

abstract: We perform a large-scale analysis of third-party trackers on the World Wide Web. We extract third-party embeddings from more than 3.5 billion web pages of the CommonCrawl 2012 corpus, and aggregate those to a dataset representing more than 41 million domains. With that, we study global online tracking on two levels: (1) On a global level, we give a precise figure for the extent of tracking, and analyse which trackers (and subsequently, which companies) are used by how many websites. (2) On a country-specific level, we analyse which trackers are used by websites in different countries, and identify the countries in which websites choose significantly different trackers than in the rest of the world. We find that trackers are widespread (as expected), and that very few trackers dominate the web (Google, Facebook and Twitter), except for a few countries such as China and Russia.


title: Knowledge, Attitude and Practice of Skin Whitening among Female University Students in Northeastern Nigeria

abstract: Introduction: Skin whitening is the act of brightening the skin complexion by using substances that are applied on the skin or administered into the body by other means. The use of skin whitening agents has its drawback in terms of causing harmful effects such as skin disorders like depigmentation, rashes, pimples, discolorations, kidney damaged, cancers, neurological and psychiatric disorders depending on how the agents for skin whitening are used. Objective: The paper examines the knowledge, attitude and practice of skin whitening by university female students in north eastern Nigeria, where some dark skinned women in particular tend to bleach their skin. Method: A cross sectional descriptive study. Results: The awareness level of the students about skin whitening agents and their harmful effects was found to be high among the students and little below, half of them are already users of such agents mainly for beautification. Few of them think the use of skin whitening agents can be addictive and lead to social stigma but they don’t believe that the use of such agents should be restricted to medical only. Conclusion: Majority of the female students in the Universities in north-eastern Nigeria bleach their skin with dangerous chemical agents meant for other reasons mainly to attract the opposite sex despite having high knowledge of the harmful effects. Government is highly recommended to take the necessary steps to curtail such menace.


title: Assessment Methodology in Smart Cities Based on Public Value

abstract: Smart city is an approach to managing and coping with urban challenges in search for innovative solutions leading to better quality of life and sustainability in cities. Several initiatives have been undertaken, with a significant worldwide growth trend in the coming years. Such initiatives, however, may require non-trivial public investment, and failures resulting from them can have important consequences such as monetary loss, loss of reputation, reduced confidence and lack of public value. This article aims at setting a methodology in smart cities composed of a reference model and an assessment method from the Public Value perspective. It follows Design Science as its epistemological paradigm and Design Science Research as its method, uniting theoretical and methodological rigor as well as practical utility for society.