title: Solubilization of ovine growth hormone with 2-hydroxypropyl-beta-cyclodextrin.

abstract: Studies were undertaken to determine the effects of 2-hydroxypropyl-β-cyclodextrin (HPCD) on the solubility of ovine-Growth Hormone (o-GH) in aqueous solution. HPCD, which is believed to form inclusion complexes with hydrophobic amino acid residues of peptides, markedly increased o-GH solubility at all o-GH and all HPCD concentrations tested. In HPCD solutions, o-GH was completely solubilized between pH 7.5 and 8.5, while in water, o-GH was not solubilized until a pH of 11.5 was reached. Temperature and repeated freeze-thaw cycles had little effect on o-GH solubility in HPCD. Vigorous shaking of solution of o-GH in HPCD did not result in precipitation of the peptide. These studies document that HPCD enhances o-GH solubility in aqueous solutions. Preparations of GH in HPCD may eliminate solubility problems observed in current human GH preparations intended for parenteral use.


title: The Euclidean algorithm generates traditional musical rhythms

abstract: The Euclidean algorithm (which comes down to us from Euclid’s Elements) computes the greatest common divisor of two given integers. It is shown here that the structure of the Euclidean algorithm may be used to generate, very efficiently, a large family of rhythms used as timelines (ostinatos), in sub-Saharan African music in particular, and world music in general. These rhythms, here dubbed Euclidean rhythms, have the property that their onset patterns are distributed as evenly as possible. Euclidean rhythms also find application in nuclear physics accelerators and in computer science, and are closely related to several families of words and sequences of interest in the study of the combinatorics of words, such as Euclidean strings, to which the Euclidean rhythms are compared.


title: Robots and Jobs: Evidence from US Labor Markets

abstract: As robots and other computer-assisted technologies take over tasks previously performed by labor, there is increasing concern about the future of jobs and wages. We analyze the effect of the increase in industrial robot usage between 1990 and 2007 on US local labor markets. Using a model in which robots compete against human labor in the production of different tasks, we show that robots may reduce employment and wages, and that the local labor market effects of robots can be estimated by regressing the change in employment and wages on the exposure to robots in each local labor marketâ€”defined from the national penetration of robots into each industry and the local distribution of employment across industries. Using this approach, we estimate large and robust negative effects of robots on employment and wages across commuting zones. We bolster this evidence by showing that the commuting zones most exposed to robots in the post-1990 era do not exhibit any differential trends before 1990. The impact of robots is distinct from the impact of imports from China and Mexico, the decline of routine jobs, offshoring, other types of IT capital, and the total capital stock (in fact, exposure to robots is only weakly correlated with these other variables). According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 percentage points and wages by 0.25-0.5 percent.


MINING AND THE DISCOURSE ON DISCRIMINATION


title: Graphene Based Electrochemical Sensors and Biosensors: A Review

abstract: Graphene, emerging as a true 2-dimensional material, has received increasing attention due to its unique physicochemical properties (high surface area, excellent conductivity, high mechanical strength, and ease of functionalization and mass production). This article selectively reviews recent advances in graphene-based electrochemical sensors and biosensors. In particular, graphene for direct electrochemistry of enzyme, its electrocatalytic activity toward small biomolecules (hydrogen peroxide, NADH, dopamine, etc.), and graphenebased enzyme biosensors have been summarized in more detail; Graphene-based DNA sensing and environmental analysis have been discussed. Future perspectives in this rapidly developing field are also discussed.


title: IoT-Based User-Driven Service Modeling Environment for a Smart Space Management System

abstract: The existing Internet environment has been extended to the Internet of Things (IoT) as an emerging new paradigm. The IoT connects various physical entities. These entities have communication capability and deploy the observed information to various service areas such as building management, energy-saving systems, surveillance services, and smart homes. These services are designed and developed by professional service providers. Moreover, users' needs have become more complicated and personalized with the spread of user-participation services such as social media and blogging. Therefore, some active users want to create their own services to satisfy their needs, but the existing IoT service-creation environment is difficult for the non-technical user because it requires a programming capability to create a service. To solve this problem, we propose the IoT-based user-driven service modeling environment to provide an easy way to create IoT services. Also, the proposed environment deploys the defined service to another user. Through the personalization and customization of the defined service, the value and dissemination of the service is increased. This environment also provides the ontology-based context-information processing that produces and describes the context information for the IoT-based user-driven service.


title: Assigning Trust to Wikipedia Content ∗

abstract: The Wikipedia is a collaborative encyclopedia: anyone can contribute to its articles simply by clicking on an "edit" button. The open nature of the Wikipedia has been key to its success, but has also created a challenge: how can readers develop an informed opinion on its reliability? We propose a system that computes quantitative values of trust for the text in Wikipedia articles; these trust values provide an indication of text reliability. The system uses as input the revision history of each article, as well as information about the reputation of the contributing authors, as provided by a reputation system. The trust of a word in an article is computed on the basis of the reputation of the original author of the word, as well as the reputation of all authors who edited text near the word. The algorithm computes word trust values that vary smoothly across the text; the trust values can be visualized using varying text-background colors. The algorithm ensures that all changes to an article's text are reflected in the trust values, preventing surreptitious content changes. We have implemented the proposed system, and we have used it to compute and display the trust of the text of thousands of articles of the English Wikipedia. To validate our trust-computation algorithms, we show that text labeled as low-trust has a significantly higher probability of being edited in the future than text labeled as high-trust.


title: Admixture of Poisson MRFs: A Topic Model with Word Dependencies

abstract: This paper introduces a new topic model based on an admixture of Poisson Markov Random Fields (APM), which can model dependencies between words as opposed to previous independent topic models such as PLSA (Hofmann, 1999), LDA (Blei et al., 2003) or SAM (Reisinger et al., 2010). We propose a class of admixture models that generalizes previous topic models and show an equivalence between the conditional distribution of LDA and independent Poissons--suggesting that APM subsumes the modeling power of LDA. We present a tractable method for estimating the parameters of an APM based on the pseudo log-likelihood and demonstrate the benefits of APM over previous models by preliminary qualitative and quantitative experiments.


title: Design of a novel DC-67-GHz 0.18-μm SiGe BiCMOS power divider

abstract: A low-loss millimeter-wave Wilkinson power divider has been developed on a 0.18-μm SiGe BiCMOS process. By employing a capacitive loading and slow-wave CPW structure, the power divider can be realized with more compact size and lower insertion loss compared to the conventional Wilkinson power divider. The developed power divider has extremely wideband performance from DC to 67 GHz with less than 1-dB insertion loss up to 61 GHz, amplitude and phase mismatches less than 0.5 dB and 2 degrees up to 67 GHz, respectively, and isolation greater than 15 dB across 37–67 GHz. Specifically at 60 GHz, 0.9 dB of insertion loss and better than 25 dB of isolation are obtained. The core chip size of the power divider is 150 μm × 525 μm.


title: A High-Efficiency Broadband Rectenna for Ambient Wireless Energy Harvesting

abstract: This paper presents a novel broadband rectenna for ambient wireless energy harvesting over the frequency band from 1.8 to 2.5 GHz. First of all, the characteristics of the ambient radio-frequency energy are studied. The results are then used to aid the design of a new rectenna. A novel two-branch impedance matching circuit is introduced to enhance the performance and efficiency of the rectenna at a relatively low ambient input power level. A novel broadband dual-polarized cross-dipole antenna is proposed which has embedded harmonic rejection property and can reject the second and third harmonics to further improve the rectenna efficiency. The measured power sensitivity of this design is down to $- {\bf 35}\;{\bf dBm}$ and the conversion efficiency reaches 55% when the input power to the rectifier is $- {\bf 10}\;{\bf dBm}$ . It is demonstrated that the output power from the proposed rectenna is higher than the other published designs with a similar antenna size under the same ambient condition. The proposed broadband rectenna could be used to power many low-power electronic devices and sensors and found a range of potential applications.


title: Unsupervised Learning of a Reduced Dimensional Controller for a Tendon Driven Robot Platform

abstract: In this paper we present a developmental framework to carry out goal-oriented learning in a low-dimensional space. The framework uses two stages of learning: one to synthesise a set of motor synergies and reduce the dimensionality of the control space in an unsupervised manner, and another to carry out supervised learning in the reduced control space. We test our framework in a reaching task carried out on a (real) tendon-driven robot actuated by four artificial muscles. Our results show that the robot is capable of learning to reach using a reduced control space using no prior information about its body apart from that inherent to the unsupervised and supervised learning rules.


title: Factors affecting the Z-Width of a haptic display

abstract: This paper addresses the performance of force-reflecting interfaces ("haptic displays"). The authors suggest that an important measure of performance is the dynamic range of achievable impedances-"Z-Width"-and that an impedance is achievable if it satisfies a robustness property such as passivity. Several factors affecting Z-Width-sample-and-hold, inherent interface dynamics, displacement sensor quantization, and velocity filtering-are discussed. A set of experiments designed to evaluate these factors is described and experimental results are presented. A striking result is that inherent interface damping exerts an overwhelming influence on Z-Width. >


title: Nonlinear Wavelet Image Processing : Variational Problems , Compression , and Noise Removal through Wavelet Shrinkage

abstract: This paper examines the relationship between wavelet-based image processing algorithms and variational problems. Algorithms are derived as exact or approximate minimizers of variational problems; in particular, we show that wavelet shrinkage can be considered the exact minimizer of the following problem. Given an image F defined on a square I, minimize over all g in the Besov space B11(L1(I)) the functional |F-g|L2(I)2+λ|g|(B11(L1(I))). We use the theory of nonlinear wavelet image compression in L2(I) to derive accurate error bounds for noise removal through wavelet shrinkage applied to images corrupted with i.i.d., mean zero, Gaussian noise. A new signal-to-noise ratio (SNR), which we claim more accurately reflects the visual perception of noise in images, arises in this derivation. We present extensive computations that support the hypothesis that near-optimal shrinkage parameters can be derived if one knows (or can estimate) only two parameters about an image F: the largest α for which F∈Bqα(Lq(I)),1/q=α/2+1/2, and the norm |F|Bqα(Lq(I)). Both theoretical and experimental results indicate that our choice of shrinkage parameters yields uniformly better results than Donoho and Johnstone's VisuShrink procedure; an example suggests, however, that Donoho and Johnstone's (1994, 1995, 1996) SureShrink method, which uses a different shrinkage parameter for each dyadic level, achieves a lower error than our procedure.


Nugget Detection Task : UMBC systems


"Gold Mine in Southeast Asia:" Russel Wright, Vietnamese Handicraft, and Transnational Consumption


A Prospective Multicenter Registry of Patients Undergoing Hysteroscopic Morcellation of Uterine Polyps and Myomas


title: Interaction+: Interaction enhancement for web-based visualizations

abstract: In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection, aggregation, arrangement, comparison, filtering, and annotation. Without accessing the underlying data or process how the visualization is constructed, Interaction+ is application-independent and can be employed in various visualizations on the web. We demonstrate its usage in two scenarios and evaluate its effectiveness with a qualitative user study.


title: Prioritizing Legal Requirements

abstract: Requirements prioritization is used in the early phases of software development to determine the order in which requirements should be implemented. Requirements are not all equally important to the final software system because time constraints, expense, and design can each raise the urgency of implementing some requirements before others. Laws and regulations can make requirements prioritization particularly challenging due to the high costs of noncompliance and the substantial amount of domain knowledge needed to make prioritization decisions. In the context of legal requirements, implementation order ideally should be influenced by the laws and regulations governing a given software system. In this paper, we present a prioritization technique for legal requirements. We apply our technique on a set of 63 functional requirements for an open-source electronic health records system that must comply with the U.S. Health Insurance Portability and Accountability Act.


title: Analyzing news media coverage to acquire and structure tourism knowledge.

abstract: Destination image significantly influences a tourist’s decision-making process. The impact of newsmedia coverage on destination image has attracted research attention and became particularly evi-dent after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series oflethal tsunamis. Building upon previous research, this article analyzes the prevalence of tourismdestinations among 162 international media sites. Term frequency captures the attention a destina-tion receives—from a general and, after contextual filtering, from a tourism perspective. Calculat-ing sentiment estimates positive and negative media influences on destination image at a givenpoint in time. Identifying semantic associations with the names of countries and major cities, theresults of co-occurrence analysis reveal the public profiles of destinations, and the impact of cur-rent events on media coverage. These results allow national tourism organizations to assess howtheir destination is covered by news media in general, and in a specific tourism context. To guideanalysts and marketers in this assessment, an iterative analysis of semantic associations extractstourism knowledge automatically, and represents this knowledge as ontological structures.Key words: Knowledge acquisition; News media; Destination coverage; Sentiment analysis;Tourism ontology


title: Voltage and power balance control for a cascaded multilevel solid state transformer

abstract: In this paper, a 20kVA Solid State Transformer (SST) based on 6.5kV IGBT is proposed for interface with 7.2kV distribution system voltage. The proposed SST consists of a cascaded multilevel AC/DC rectifier stage, a Dual Active Bridge (DAB) converter stage with high frequency transformers and a DC/AC inverter stage. Based on the single phase d-q vector control, a novel control strategy is proposed to balance the rectifier capacitor voltages and the real power through the DAB parallel modules. Furthermore, the power constraints of the voltage balance control are analyzed. The SST switching model simulation demonstrates the effectiveness of the proposed voltage and power balance controller. A 3kW SST scale-down prototype is implemented. The experiment results verify the single phase d-q vector controller for the SST cascaded multilevel rectifier.


title: SIEM implementation for global and distributed environments

abstract: Today's computer networks produce a huge amount of security log data. Handling this data is impossible without using Security Information and Event Management Systems (SIEM) to centralize the log management and increase the level of information security and data protection in the organization. SIEM collect and aggregate log data from various devices and applications through software called agents or connectors, filter uninteresting data and normalize to a proprietary format, analyses through correlation using contextual information and alert administrators in case of attack. SIEM provide proactive threat detection and real-time analysis of system activity. Handling these issues will be very hard without relying on consolidated, big data-powered SIEM. However, even having the most expensive SIEM solution, the organization should not expect the product to work great out of the box. The best SIEM solution does not guarantee success. The organization should focus on building various use cases to make their SIEM solution a success. In this paper, we propose a new model and architecture for SIEM implementation that is using multiple hierarchical SIEM Managers. The model is called “Hierarchical Managers Model”. We demonstrated how this model and architecture could be created and enabled in the leading SIEM system - ArcSight ESM [7]. We also provide examples of possible use cases that we have created and tested in our testing environment. These are meant to provide a good base starting point and should not be considered comprehensive for all situations. The use cases shown in this paper are created using the security event correlation framework from Hewlett-Packard - ArcSight ESM [7].


title: Reading and Interpreting Ethnography

abstract: Ethnography is an approach to understanding cultural life that is founded on the researcher’s participation, with the goal of understanding not simply what people are doing, but how they experience what they do. The researcher participates in the life of the target people, both to serve as a stimulus (asking questions) that generates reactions and insights and to fully engage in the evolution and understanding of what is happening.


title: Framework Fabrication with Rib Cartilage in Partial and Total Nasal Reconstruction

abstract: The framework reconstruction of the nose is a significant and complex component of its partial or total reconstruction. On the one hand, the design of the individual framework parts is based on the anatomic nature of available rib or ear cartilage, which must on the other hand be adapted to the anatomic characteristics of the defect. The framework parts must be anchored not only to each other but also stably to the facial skeleton. The symmetry of the framework reconstruction is an essential component of the aesthetics of the reconstructed nose. If these points are already considered in planning, the reconstruction of the nasal framework can be standardized insofar as the same principles for the basic design of the individual parts as well as stable solutions for the anchoring points can be chosen. With reproducible techniques, functionally and aesthetically good to very good results can be achieved, including in the long term. The surgeon must possess special skills in the field of nasal reconstruction to correctly choose, apply, and combine the various techniques of nasal framework reconstruction.


title: 3D imaging spectroscopy for measuring hyperspectral patterns on solid objects

abstract: Sophisticated methods for true spectral rendering have been developed in computer graphics to produce highly accurate images. In addition to traditional applications in visualizing appearance, such methods have potential applications in many areas of scientific study. In particular, we are motivated by the application of studying avian vision and appearance. An obstacle to using graphics in this application is the lack of reliable input data. We introduce an end-to-end measurement system for capturing spectral data on 3D objects. We present the modification of a recently developed hyperspectral imager to make it suitable for acquiring such data in a wide spectral range at high spectral and spatial resolution. We capture four megapixel images, with data at each pixel from the near-ultraviolet (359 nm) to near-infrared (1,003 nm) at 12 nm spectral resolution. We fully characterize the imaging system, and document its accuracy. This imager is integrated into a 3D scanning system to enable the measurement of the diffuse spectral reflectance and fluorescence of specimens. We demonstrate the use of this measurement system in the study of the interplay between the visual capabilities and appearance of birds. We show further the use of the system in gaining insight into artifacts from geology and cultural heritage.


title: Challenges and Factors Affecting the Implementation of E-Government in Jordan

abstract: Electronic government (e-Government) in its simplest form can mean using information and communication technology (ICT) tools to provide services to citizens. Still with the huge benefits and synergies that e-Government grants to governments and societies, it faces many obstacles and challenges. Therefore, there are always a number of critical success factors and risks associated with e-Government. This paper highlights some of the key ones; it critically assesses key factors that influence e-Government services adoption and diffusion. Thus, the aim of this study is to examine and identify the factors that influence and affect the utilization of e-Government in the developing countries, specifically in Jordan. Furthermore, this article investigates the challenges and barriers that must be overcome in order to successfully implement e-Government in Jordan. Semi-structured interviews were conducted and used in this study to collect the data. The results of this study show that the most significant challenges and factors influencing the implementation of e-Government services in Jordan are related to budgeting and financial costs, human expertise, social influence, technological issues, lack of awareness, resistance of public employees, data privacy and security, the legal framework, the needed technology, administrative obstacles, and trust or believing in e-Government. Conclusions, recommendations and future work are stated at the end of the paper.


title: Bayesian non-negative matrix factorization

abstract: There is provided an aroma composition containing 1,5,8,8-tetramethylbicyclo[8.1.0]undec-5-en-2-one. This sesquiterpene ketone has an aroma reminiscent of a variety of odors based on the strong powdery, woody note and furthermore has high diffusivity and retentivity and accords well with a number of perfume, fragrance and flavor materials.


Recommendation Engine Using Topic Modeling and Semantic Similarity


title: Underpowered samples, false negatives, and unconscious learning

abstract: The scientific community has witnessed growing concern about the high rate of false positives and unreliable results within the psychological literature, but the harmful impact of false negatives has been largely ignored. False negatives are particularly concerning in research areas where demonstrating the absence of an effect is crucial, such as studies of unconscious or implicit processing. Research on implicit processes seeks evidence of above-chance performance on some implicit behavioral measure at the same time as chance-level performance (that is, a null result) on an explicit measure of awareness. A systematic review of 73 studies of contextual cuing, a popular implicit learning paradigm, involving 181 statistical analyses of awareness tests, reveals how underpowered studies can lead to failure to reject a false null hypothesis. Among the studies that reported sufficient information, the meta-analytic effect size across awareness tests was dz = 0.31 (95 % CI 0.24–0.37), showing that participants’ learning in these experiments was conscious. The unusually large number of positive results in this literature cannot be explained by selective publication. Instead, our analyses demonstrate that these tests are typically insensitive and underpowered to detect medium to small, but true, effects in awareness tests. These findings challenge a widespread and theoretically important claim about the extent of unconscious human cognition.


title: Applying data mining techniques in job recommender system for considering candidate job preferences

abstract: Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that uses different strategies , here efforts have been put to make the job recommendations on the basis of candidate’s profile matching as well as preserving candidate’s job behavior or preferences. Firstly, rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidate’s own applied jobs history. Through this technique a significant level of accuracy has been achieved over other basic methods of job recommendations. Keywords— Data mining, Decision Tree, Classification Rules, Content Based similarity, Job recommendations.


1 Separation of Voiced and Unvoiced using Zero crossing rate and Energy of the Speech Signal


title: An asymmetric stagewise least square loss function for imbalanced classification

abstract: In this paper, we present an asymmetric stagewise least square (ASLS) loss function for imbalanced classification. While keeping all the advantages of the stagewise least square (SLS) loss function, such as, better robustness, computational efficiency and sparseness, the ASLS loss extends the SLS loss by adding another two parameters, namely, ramp coefficient and margin coefficient. Therefore, asymmetric ramps and margins can be formed which makes the ASLS loss be more flexible and appropriate for processing class imbalance problems. A reduced kernel classifier of the ASLS loss is also developed which only uses a small part of the dataset to generate an efficient nonlinear classifier. Experimental results confirm the effectiveness of the ASLS loss in imbalanced classification.


title: Wyrm: A Brain-Computer Interface Toolbox in Python

abstract: In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm (https://github.com/bbci/wyrm), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm’s software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.


title: Convolutional Monte Carlo Rollouts in Go

abstract: In this work, we present a MCTS-based Go-playing program which uses convolutional networks in all parts. Our method performs MCTS in batches, explores the Monte Carlo search tree using Thompson sampling and a convolutional network, and evaluates convnet-based rollouts on the GPU. We achieve strong win rates against open source Go programs and attain competitive results against state of the art convolutional net-based Go-playing programs.


title: Generation and storage of electrical energy from piezoelectric materials

abstract: The electrical energy generation and storage from piezoelectric materials are focused and discussed in this paper. This kind of materials is able to directly convert mechanical energy into electrical one, which can be later stored by utilizing energy harvesting technique/circuit. The energy conversion from ambient vibration is indeed nowadays fascinating research area. Due to the compatibility to integrate with electronics and microsystem with high voltage and supporting standalone circuit, it is the most popular electromechanical transducer materials for low power consumption applications, such as wireless and sensors. This paper focuses how to extract energy from piezoelectric materials to be stored in the energy storage device such as battery, in order to later supply electronic/electrical device/equipment. The simulation in MATLAB Simulink is presented and experimental results are also carried out in order to confirm the effectiveness of energy extraction. Consequently, this work may be practical for energy supplying of low power devices.


title: Operating Systems: Design and Implementation

abstract: Table of Contents CHAPTER 1 INTRODUCTION 1.1 WHAT IS AN OPERATING SYSTEM? 1.2 HISTORY OF OPERATING SYSTEMS 1.3 OPERATING SYSTEM CONCEPTS 1.4 SYSTEM CALLS 1.5 OPERATING SYSTEM STRUCTURE 1.6 OUTLINE OF THE REST OF THIS BOOK 1.7 SUMMARY CHAPTER 2 PROCESSES 2.1 INTRODUCTION TO PROCESSES 2.2 INTERPROCESS COMMUNICATION 2.3 CLASSICAL IPC PROBLEMS 2.4 SCHEDULING 2.5 OVERVIEW OF PROCESSES IN MINIX 3 2.6 IMPLEMENTATION OF PROCESSES IN MINIX 3 2.7 THE SYSTEM TASK IN MINIX 3 2.8 THE CLOCK TASK IN MINIX 3 2.9 SUMMARY CHAPTER 3 INPUT/OUTPUT 3.1 PRINCIPLES OF I/O HARDWARE 3.2 PRINCIPLES OF I/O SOFTWARE 3.3 DEADLOCKS 3.4 OVERVIEW OF I/O IN MINIX 3 3.5 BLOCK DEVICES IN MINIX 3 3.6 RAM DISKS 3.7 DISKS 3.8 TERMINALS 3.9 SUMMARY CHAPTER 4 MEMORY MANAGEMENT 4.1 BASIC MEMORY MANAGEMENT 4.2 SWAPPING 4.3 VIRTUAL MEMORY 4.4 PAGE REPLACEMENT ALGORITHMS 4.5 DESIGN ISSUES FOR PAGING SYSTEMS 4.6 SEGMENTATION 4.7 OVERVIEW OF THE MINIX 3 PROCESS MANAGER 4.8 IMPLEMENTATION OF THE MINIX 3 PROCESS MANAGER 4.9 SUMMARY CHAPTER 5 FILE SYSTEMS 5.1 FILES 5.2 DIRECTORIES 5.3 FILE SYSTEM IMPLEMENTATION 5.4 SECURITY 5.5 PROTECTION MECHANISMS 5.6 OVERVIEW OF THE MINIX 3 FILE SYSTEM 5.7 IMPLEMENTATION OF THE MINIX 3 FILE SYSTEM 5.8 SUMMARY CHAPTER 6 READING LIST AND BIBLIOGRAPHY 6.1 SUGGESTIONS FOR FURTHER READING 6.2 ALPHABETICAL BIBLIOGRAPHY APPENDIX A - INSTALLING MINIX 3 APPENDIX B - MINIX 3 SOURCE CODE LISTING APPENDIX C - INDEX TO FILES INDEX


title: Differentially private transit data publication: a case study on the montreal transportation system

abstract: With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequential data, collected every day. Yet, improper publishing and use of transit data could jeopardize passengers' privacy. In this paper, we present our solution to transit data publication under the rigorous differential privacy model for the Societe de transport de Montreal (STM). We propose an efficient data-dependent yet differentially private transit data sanitization approach based on a hybrid-granularity prefix tree structure. Moreover, as a post-processing step, we make use of the inherent consistency constraints of a prefix tree to conduct constrained inferences, which lead to better utility. Our solution not only applies to general sequential data, but also can be seamlessly extended to trajectory data. To our best knowledge, this is the first paper to introduce a practical solution for publishing large volume of sequential data under differential privacy. We examine data utility in terms of two popular data analysis tasks conducted at the STM, namely count queries and frequent sequential pattern mining. Extensive experiments on real-life STM datasets confirm that our approach maintains high utility and is scalable to large datasets.


title: Learning Multi-domain Convolutional Neural Networks for Visual Tracking

abstract: We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a large set of videos with tracking groundtruths to obtain a generic target representation. Our network is composed of shared layers and multiple branches of domain-specific layers, where domains correspond to individual training sequences and each branch is responsible for binary classification to identify target in each domain. We train each domain in the network iteratively to obtain generic target representations in the shared layers. When tracking a target in a new sequence, we construct a new network by combining the shared layers in the pretrained CNN with a new binary classification layer, which is updated online. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state. The proposed algorithm illustrates outstanding performance in existing tracking benchmarks.


title: Adaptive sampling for SGD by exploiting side information

abstract: This paper proposes a new mechanism for sampling training instances for stochastic gradient descent (SGD) methods by exploiting any side-information associated with the instances (for e.g. class-labels) to improve convergence. Previous methods have either relied on sampling from a distribution defined over training instances or from a static distribution that fixed before training. This results in two problems a) any distribution that is set apriori is independent of how the optimization progresses and b) maintaining a distribution over individual instances could be infeasible in large-scale scenarios. In this paper, we exploit the side information associated with the instances to tackle both problems. More specifically, we maintain a distribution over classes (instead of individual instances) that is adaptively estimated during the course of optimization to give the maximum reduction in the variance of the gradient. Intuitively, we sample more from those regions in space that have a larger gradient contribution. Our experiments on highly multiclass datasets show that our proposal converge significantly faster than existing techniques.


title: Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions

abstract: We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and possibly-truncated Gaussian or exponential variables allows us to sample from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters. We illustrate the advantages of these algorithms in several examples in which they outperform the Metropolis or Gibbs samplers.


title: Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco

abstract: This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents.


title: Classifying and sorting cluttered piles of unknown objects with robots: A learning approach

abstract: We consider the problem of sorting a densely cluttered pile of unknown objects using a robot. This yet unsolved problem is relevant in the robotic waste sorting business. By extending previous active learning approaches to grasping, we show a system that learns the task autonomously. Instead of predicting just whether a grasp succeeds, we predict the classes of the objects that end up being picked and thrown onto the target conveyor. Segmenting and identifying objects from the uncluttered target conveyor, as opposed to the working area, is easier due to the added structure since the thrown objects will be the only ones present. Instead of trying to segment or otherwise understand the cluttered working area in any way, we simply allow the controller to learn a mapping from an RGBD image in the neighborhood of the grasp to a predicted result---all segmentation etc. in the working area is implicit in the learned function. The grasp selection operates in two stages: The first stage is hardcoded and outputs a distribution of possible grasps that sometimes succeed. The second stage uses a purely learned criterion to choose the grasp to make from the proposal distribution created by the first stage. In an experiment, the system quickly learned to make good pickups and predict correctly, in advance, which class of object it was going to pick up and was able to sort the objects from a densely cluttered pile by color.


title: A methodology for extracting performance parameters in solid state disks (SSDs)

abstract: Solid state disks (SSDs) consisting of NAND flash memory are being widely used in laptops, desktops, and even enterprise servers. SSDs have many advantages over hard disk drives (HDDs) in terms of reliability, performance, durability, and power efficiency. Typically, the internal hardware and software organization varies significantly from SSD to SSD and thus each SSD exhibits different parameters which influence the overall performance. In this paper, we propose a methodology which can extract several essential parameters affecting the performance of SSDs. The target parameters of SSDs considered in this paper are (1) the size of read/write unit, (2) the size of erase unit, (3) the type of NAND flash memory used, (4) the size of read buffer, and (5) the size of write buffer. Obtaining these parameters will allow us to understand the internal architecture of the target SSD better and to get the most performance out of SSD by performing SSD-specific optimizations.


title: ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object

abstract: We present Autonomous Rssi based RElative poSitioning and Tracking (ARREST), a new robotic sensing system for tracking and following a moving, RF-emitting object, which we refer to as the Leader, solely based on signal strength information. This kind of system can expand the horizon of autonomous mobile tracking and distributed robotics into many scenarios with limited visibility such as nighttime, dense forests, and cluttered environments. Our proposed tracking agent, which we refer to as the TrackBot, uses a single rotating, off-the-shelf, directional antenna, novel angle and relative speed estimation algorithms, and Kalman filtering to continually estimate the relative position of the Leader with decimeter level accuracy (which is comparable to a state-of-the-art multiple access point based RF-localization system) and the relative speed of the Leader with accuracy on the order of 1 m/s. The TrackBot feeds the relative position and speed estimates into a Linear Quadratic Gaussian (LQG) controller to generate a set of control outputs to control the orientation and the movement of the TrackBot. We perform an extensive set of real world experiments with a full-fledged prototype to demonstrate that the TrackBot is able to stay within 5m of the Leader with: (1) more than $99\%$ probability in line of sight scenarios, and (2) more than $70\%$ probability in no line of sight scenarios, when it moves 1.8X faster than the Leader. For ground truth estimation in real world experiments, we also developed an integrated TDoA based distance and angle estimation system with centimeter level localization accuracy in line of sight scenarios. While providing a first proof of concept, our work opens the door to future research aimed at further improvements of autonomous RF-based tracking.


title: To buy or not to buy: mining airfare data to minimize ticket purchase price

abstract: As product prices become increasingly available on the World Wide Web, consumers attempt to understand how corporations vary these prices over time. However, corporations change prices based on proprietary algorithms and hidden variables (e.g., the number of unsold seats on a flight). Is it possible to develop data mining techniques that will enable consumers to predict price changes under these conditions?This paper reports on a pilot study in the domain of airline ticket prices where we recorded over 12,000 price observations over a 41 day period. When trained on this data, Hamlet --- our multi-strategy data mining algorithm --- generated a predictive model that saved 341 simulated passengers $198,074 by advising them when to buy and when to postpone ticket purchases. Remarkably, a clairvoyant algorithm with complete knowledge of future prices could save at most $320,572 in our simulation, thus H AMLET 's savings were 61.8% of optimal. The algorithm's savings of $198,074 represents an average savings of 23.8% for the 341 passengers for whom savings are possible. Overall, HAMLET saved 4.4% of the ticket price averaged over the entire set of 4,488 simulated passengers. Our pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum.


title: Problem solving in government project management.

abstract: Massachusetts Institute of Technology. Alfred P. Sloan School of Management. Thesis. 1966. M.S.


title: Character-Based Text Classification using Top Down Semantic Model for Sentence Representation

abstract: Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other character-based and word-based convolutional neural network models by \cite{zhang15} across seven different datasets with only 1\% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.


title: Time and the Observer

abstract: Two models of consciousness are contrasted with regard to their treatment of subjective timing. The standard Cartesian Theater model postulates a place in the brain where "it all comes together": where the discriminations in all modalities are somehow put into registration and "presented" for subjective judgment. In particular, the Cartesian Theater model implies that the temporal properties of the content-bearing events occurring within this privileged representational medium determine subjective order. The alternative, Multiple Drafts model holds that whereas the brain events that discriminate various perceptual contents are distributed in both space and time in the brain, and whereas the temporal properties of these various events are determinate, none of these temporal properties determine subjective order, since there is no single, constitutive "stream of consciousness" but rather a parallel stream of conflicting and continuously revised contents. Four puzzling phenomena that resist explanation by the standard model are analyzed: two results claimed by Libet, an apparent motion phenomenon involving color change (Kolers and von Grunau), and the "cutaneous rabbit" (Geldard and Sherrick) an illusion of evenly spaced series of "hops" produced by two or more widely spaced series of taps delivered to the skin. The unexamined assumptions that have always made the Cartesian Theater model so attractive are exposed and dismantled. The Multiple Drafts model provides a better account of the puzzling phenomena, avoiding the scientific and metaphysical extravagances of the Cartesian Theater.


title: Learning Markov Clustering Networks for Scene Text Detection

abstract: A novel framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph. Our method can detect text objects with arbitrary size and orientation without prior knowledge of object size. The stochastic flow graph encode objects' local correlation and semantic information. An object is modeled as strongly connected nodes, which allows flexible bottom-up detection for scale-varying and rotated objects. MCN generates bounding boxes without using Non-Maximum Suppression, and it can be fully parallelized on GPUs. The evaluation on public benchmarks shows that our method outperforms the existing methods by a large margin in detecting multi-oriented text objects. MCN achieves new state-of-art performance on challenging MSRA-TD500 dataset with precision of 0.88, recall of 0.79 and F-score of 0.83. Also, MCN achieves realtime inference with frame rate of 34 FPS, which is 1.5 A— speedup when compared with the fastest scene text detection algorithm.


title: Cooperation and security isolation of library OSes for multi-process applications

abstract: Library OSes are a promising approach for applications to efficiently obtain the benefits of virtual machines, including security isolation, host platform compatibility, and migration. Library OSes refactor a traditional OS kernel into an application library, avoiding overheads incurred by duplicate functionality. When compared to running a single application on an OS kernel in a VM, recent library OSes reduce the memory footprint by an order-of-magnitude. Previous library OS (libOS) research has focused on single-process applications, yet many Unix applications, such as network servers and shell scripts, span multiple processes. Key design challenges for a multi-process libOS include management of shared state and minimal expansion of the security isolation boundary. This paper presents Graphene, a library OS that seamlessly and efficiently executes both single and multi-process applications, generally with low memory and performance overheads. Graphene broadens the libOS paradigm to support secure, multi-process APIs, such as copy-on-write fork, signals, and System V IPC. Multiple libOS instances coordinate over pipe-like byte streams to implement a consistent, distributed POSIX abstraction. These coordination streams provide a simple vantage point to enforce security isolation.


title: Min(e)d your tags: Analysis of Question response time in StackOverflow

abstract: Given a newly posted question on a Question and Answer (Q&A) site, how long will it take until an answer is received? Does response time relate to factors about how the question asker composes their question? If so, what are those factors? With advances in social media and the Web, Q&A sites have become a major source of information for Internet users. Response time of a question is an important aspect in these sites as it is associated with the users' satisfaction and engagement, and thus the lifespan of these online communities. In this paper we study and estimate response time for questions in StackOverflow, a popular online Q&A forum where software developers post and answer questions related to programming. We analyze a long list of factors in the data and identify those that have clear relation with response time. Our key finding is that tag-related factors, such as their "popularity" (how often the tag is used) and the number of their "subscribers" (how many users can answer questions containing the tag), provide much stronger evidence than factors not related to tags. Finally, we learn models using the identified evidential features for predicting the response time of questions, which also demonstrate the significance of tags chosen by the question asker.


title: Autonomous Vehicle Safety: An Interdisciplinary Challenge

abstract: Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory approaches. Significant open technical challenges include validating inductive learning in the face of novel environmental inputs and achieving the very high levels of dependability required for full-scale fleet deployment. However, the biggest challenge may be in creating an end-to-end design and deployment process that integrates the safety concerns of a myriad of technical specialties into a unified approach.


title: Divergence-free smoothed particle hydrodynamics

abstract: In this paper we introduce an efficient and stable implicit SPH method for the physically-based simulation of incompressible fluids. In the area of computer graphics the most efficient SPH approaches focus solely on the correction of the density error to prevent volume compression. However, the continuity equation for incompressible flow also demands a divergence-free velocity field which is neglected by most methods. Although a few methods consider velocity divergence, they are either slow or have a perceivable density fluctuation. Our novel method uses an efficient combination of two pressure solvers which enforce low volume compression (below 0.01%) and a divergence-free velocity field. This can be seen as enforcing incompressibility both on position level and velocity level. The first part is essential for realistic physical behavior while the divergence-free state increases the stability significantly and reduces the number of solver iterations. Moreover, it allows larger time steps which yields a considerable performance gain since particle neighborhoods have to be updated less frequently. Therefore, our divergence-free SPH (DFSPH) approach is significantly faster and more stable than current state-of-the-art SPH methods for incompressible fluids. We demonstrate this in simulations with millions of fast moving particles.


title: Subgoal Discovery for Hierarchical Dialogue Policy Learning

abstract: Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.


title: Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions

abstract: There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which are important in deciphering information about decision-making, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contributions of this paper are both the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results, and the results themselves.


title: Efficient FPGA Implementations of Point Multiplication on Binary Edwards and Generalized Hessian Curves Using Gaussian Normal Basis

abstract: Efficient implementation of point multiplication is crucial for elliptic curve cryptographic systems. This paper presents the implementation results of an elliptic curve crypto-processor over binary fields GF(2m) on binary Edwards and generalized Hessian curves using Gaussian normal basis (GNB). We demonstrate how parallelization in higher levels can be performed by full resource utilization of computing point addition and point-doubling formulas for both binary Edwards and generalized Hessian curves. Then, we employ the ω-coordinate differential formulations for computing point multiplication. Using a lookup-table (LUT)-based pipelined and efficient digit-level GNB multiplier, we evaluate the LUT complexity and time-area tradeoffs of the proposed crypto-processor on an FPGA. We also compare the implementation results of point multiplication on these curves with the ones on the traditional binary generic curve. To the best of the authors' knowledge, this is the first FPGA implementation of point multiplication on binary Edwards and generalized Hessian curves represented by ω-coordinates.


title: Can we beat the prefix filtering?: an adaptive framework for similarity join and search

abstract: As two important operations in data cleaning, similarity join and similarity search have attracted much attention recently. Existing methods to support similarity join usually adopt a prefix-filtering-based framework. They select a prefix of each object and prune object pairs whose prefixes have no overlap. We have an observation that prefix lengths have significant effect on the performance. Different prefix lengths lead to significantly different performance, and prefix filtering does not always achieve high performance. To address this problem, in this paper we propose an adaptive framework to support similarity join. We propose a cost model to judiciously select an appropriate prefix for each object. To efficiently select prefixes, we devise effective indexes. We extend our method to support similarity search. Experimental results show that our framework beats the prefix-filtering-based framework and achieves high efficiency.


title: Wideband Compact 4-Port Dual Polarized Self-Grounded Bowtie Antenna

abstract: We present a dual-polarized self-grounded bowtie antenna. The antenna is mechanically simple and wideband. One of the characteristics is its flexibility: it can be used either as a 4-port antenna in multiple-input-multiple-output (MIMO) systems or as a directional 2-port dual-polarized antenna in radar or sensor systems. The latter application requires a balun. The 4-petal geometry of the antenna has been optimized for the best matching to 50 coax over 1.5-3 GHz. Both simulated and measured results are presented to verify the design. The antenna radiates over parts of the bandwidth similar to four Huygens source, one from each petal, each having directivity close to 4.8 dBi.


title: Stock Prediction by Searching for Similarities in Candlestick Charts

abstract: The aim of stock prediction is to effectively predict future stock market trends (or stock prices), which can lead to increased profit. One major stock analysis method is the use of candlestick charts. However, candlestick chart analysis has usually been based on the utilization of numerical formulas. There has been no work taking advantage of an image processing technique to directly analyze the visual content of the candlestick charts for stock prediction. Therefore, in this study we apply the concept of image retrieval to extract seven different wavelet-based texture features from candlestick charts. Then, similar historical candlestick charts are retrieved based on different texture features related to the query chart, and the “future” stock movements of the retrieved charts are used for stock prediction. To assess the applicability of this approach to stock prediction, two datasets are used, containing 5-year and 10-year training and testing sets, collected from the Dow Jones Industrial Average Index (INDU) for the period between 1990 and 2009. Moreover, two datasets (2010 and 2011) are used to further validate the proposed approach. The experimental results show that visual content extraction and similarity matching of candlestick charts is a new and useful analytical method for stock prediction. More specifically, we found that the extracted feature vectors of 30, 90, and 120, the number of textual features extracted from the candlestick charts in the BMP format, are more suitable for predicting stock movements, while the 90 feature vector offers the best performance for predicting short- and medium-term stock movements. That is, using the 90 feature vector provides the lowest MAPE (3.031p) and Theil’s U (1.988p) rates in the twenty-year dataset, and the best MAPE (2.625p, 2.945p) and Theil’s U (1.622p, 1.972p) rates in the two validation datasets (2010 and 2011).


title: Swapout: Learning an ensemble of deep architectures

abstract: We describe Swapout, a new stochastic training method, that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFAR-100. Swapout samples from a rich set of architectures including dropout, stochastic depth and residual architectures as special cases. When viewed as a regularization method swapout not only inhibits co-adaptation of units in a layer, similar to dropout, but also across network layers. We conjecture that swapout achieves strong regularization by implicitly tying the parameters across layers. When viewed as an ensemble training method, it samples a much richer set of architectures than existing methods such as dropout or stochastic depth. We propose a parameterization that reveals connections to exiting architectures and suggests a much richer set of architectures to be explored. We show that our formulation suggests an efficient training method and validate our conclusions on CIFAR-10 and CIFAR-100 matching state of the art accuracy. Remarkably, our 32 layer wider model performs similar to a 1001 layer ResNet model.


IEEE 802 . 11 p ahead of LTE-V 2 V for safety applications


title: Sparse seemingly unrelated regression modelling: Applications in finance and econometrics

abstract: A sparse seemingly unrelated regression (SSUR) model is proposed to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated regression (SUR) model parameters. The SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR model to dynamic models embed general structure constraints and model uncertainty in dynamic models. The models represent specific varieties of models recently developed in the growing high-dimensional sparse modelling literature. Two simulated examples illustrate the model and highlight questions regarding model uncertainty, searching, and comparison. The model is then applied to two real-world examples in macroeconomics and finance, according to which its identified structures have practical significance.


Business Value of the IoT : A Project Portfolio Selection Approach


title: Twitter turing test

abstract: Many machine-controlled Twitter accounts (also called "Sybils") are created each day to provide services, flood out messages for astroturf political campaigns, write fake product reviews, or produce an underground marketplace for purchasing Twitter followers, retweets, or URL advertisements. In addition, fake identities and user accounts in online communities are resources used by adversaries to spread malware, spam, and harmful links over social networks. In social networks, Sybil detectors rely on the assumption that Sybils will find it harder to befriend real users; thus, Sybils that are connected to each other form strongly connected subgraphs, which can be detected using the graph theory. However, a majority of Sybils have actually successfully integrated themselves into real social media user communities (such as Twitter and Facebook). In this study, we compared the current methods used for detecting Sybil accounts. We also explored the detection features of various types of Twitter Sybil accounts in order to build an effective and practical classifier. To evaluate our classifier, we collected and manually labeled a dataset of Twitter accounts, including human users, bots, and hybrids (i.e., tweets posted by both human and bots). We consider that this Twitter Sybils corpus will help researchers to conduct high-quality measurement studies. We also developed a browser plug-in, which utilizes our classifier and warns the user about possible Sybil accounts before accessing or following them after clicking on a Twitter account.


title: Aerial image segmentation for flood risk analysis

abstract: This paper presents a technique for image segmentation. We demonstrate its efficacy for classsifying high-resolution aerial images. The application is peak water flow estimation in a river catchment in the city of Zurich and the data covers a large rural and urban setting. The output of the segmentation process is used as input to a hydrological model. We introduce a combined, probabilistic, segmentation approach based on colour (the LAB colour space is used), texture (using entropy) and image features (gradients). Classification rates for natural land surfaces and man-made structures are up to 90% and 85% respectively. When the automatic segmentation result is compared to the official land use data and reclassified for use in GIS we achieve an overall classification accuracy of 70%. This new classification is tested on the WetSpa hydrological model and the resulting flow estimate compares favourably with that computed from hand-classified land use data.


title: Hardware in the loop radar environment simulation on wideband DRFM platforms

abstract: This paper describes the development and testing of a digital radio frequency memory (DRFM) kernel, as well as some of the functionality which has been added to the system to make it a fully-fledged hardware in the loop (HIL) radar environment simulator (RES) system for radar testing and evaluation. (5 pages)


title: Gender reassignment surgery: an overview

abstract: Gender dysphoria—a condition in which an individual has a severe discontent with the gender they were born with—can be treated with gender reassignment surgery. In this article, Selvaggi and Bellringer draw on their considerable experience to provide an overview of gender reassignment surgery. Both male-to-female and female-to-male surgeries and their associated complications will be discussed as well as the etiopathogenesis of gender dysphoria.


title: Multi-objective workflow grid scheduling using $$\varepsilon $$ ε -fuzzy dominance sort based discrete particle swarm optimization

abstract: With the rapid development of networking technology, grid computing has emerged as a source for satisfying the increasing demand of the computing power of scientific computing community. Mostly, the user applications in scientific and enterprise domains are constructed in the form of workflows in which precedence constraints between tasks are defined. Scheduling of workflow applications belongs to the class of NP-hard problems, so meta-heuristic approaches are preferred options. In this paper, $$\varepsilon $$ ? -fuzzy dominance sort based discrete particle swarm optimization ( $$\varepsilon $$ ? -FDPSO) approach is used to solve the workflow scheduling problem in the grid. The $$\varepsilon $$ ? -FDPSO approach has never been used earlier in grid scheduling. The metric, fuzzy dominance which quantifies the relative fitness of solutions in multi-objective domain is used to generate the Pareto optimal solutions. In addition, the scheme also incorporates a fuzzy based mechanism to determine the best compromised solution. For the workflow applications two scheduling problems are solved. In one of the scheduling problems, we addressed two major conflicting objectives, i.e. makespan (execution time) and cost, under constraints (deadline and budget). While, in other, we optimized makespan, cost and reliability objectives simultaneously in order to incorporate the dynamic characteristics of grid resources. The performance of the approach has been compared with other acknowledged meta-heuristics like non-dominated sort genetic algorithm and multi-objective particle swarm optimization. The simulation analysis substantiates that the solutions obtained with $$\varepsilon $$ ? -FDPSO deliver better convergence and uniform spacing among the solutions keeping the computation overhead limited.


title: Teaching computational thinking through bioinformatics to biology students

abstract: Modern biology has transformed from an insular entity into an interdisciplinary science, which in turn demands interdisciplinary and cross-disciplinary training for future work force in biology and life sciences. Computational thinking is a way of thinking that uses concepts and methodologies of computing to address questions in a broad range of subjects, and as such, computational thinking offers an important skill set in modern sciences. Despite its importance, the concept of computational thinking has generally been side-stepped in undergraduate biology education. Many students in life sciences are often weak in quantitative/computing skills and tend to avoid computing-orient courses. To address these issues, we incorporated computational thinking into a bioinformatics course for undergraduate life science majors. We developed comprehensive computer laboratory exercises that offer hands-on learning experience for the targeted student pool, and employed peer-assisted collaborative learning environment. Preliminary results of these explorative efforts will be helpful for others to teach computational thinking to biology students.


title: Information extraction and text mining of Ancient Vattezhuthu characters in historical documents using image zoning

abstract: The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient characters conversion still poses a big challenge. Because image recognition technology has reached near-perfection when it comes to scanning English and other language text. But optical character recognition (OCR) software capable of digitizing printed Tamil text with high levels of accuracy is still elusive. Only a few people are familiar with the ancient characters and make attempts to convert them into written documents manually. The proposed system overcomes such a situation by converting all the ancient historical documents from inscriptions and palm manuscripts into Tamil digital text format. It converts the digital text format using Tamil unicode. Our algorithm comprises different stages: i) image preprocessing, ii) feature extraction, iii) character recognition and iv) digital text conversion. The first phase conversion accuracy of the Brahmi script rate of our algorithm is 91.57% using the neural network and image zoning method. The second phase of the Vattezhuthu character set is to be implemented. Conversion accuracy of Vattezhuthu is 89.75%.


title: Recursive symmetries for geometrically complex and materially heterogeneous additive manufacturing

abstract: Abstract We present a generative method for the creation of geometrically complex and materially heterogeneous objects. By combining generative design and additive manufacturing, we demonstrate a unique form-finding approach and method for multi-material 3D printing. The method offers a fast, automated and controllable way to explore an expressive set of symmetrical, complex and colored objects, which makes it a useful tool for design exploration and prototyping. We describe a recursive grammar for the generation of solid boundary surface models suitable for a variety of design domains. We demonstrate the generation and digital fabrication of watertight 2-manifold polygonal meshes, with feature-aligned topology that can be produced on a wide variety of 3D printers, as well as post-processed with traditional 3D modeling tools. To date, objects with intricate spatial patterns and complex heterogeneous material compositions generated by this method can only be produced through 3D printing.


title: UC-EBG on LTCC for 60-GHz Frequency Band Antenna Applications

abstract: The relatively high dielectric constant of the low-temperature cofired ceramic (LTCC) substrate materials can reduce the antenna gain and increase the mutual coupling in antenna arrays due to the increased surface-wave power. We present a design of a uniplanar-compact electromagnetic band-gap (UC-EBG) structure on the Ferro A6-S LTCC tape system operating in the 60-GHz frequency band. The UC-EBG design is used with an aperture-coupled microstrip-line-fed patch antenna (ACMPA) and with a 16-element array to increase the antenna gain. The UC-EBG is also used to reduce the mutual coupling between antennas in the E- and H-planes. The performance of the antennas with and without UC-EBG are evaluated with the probe-station and radiation-pattern measurements. In addition, the reflection from the UC-EBG surface is measured using a WR-15 waveguide excitation. Good agreement is achieved between the simulations and the measurements. An increase of up to 4.5 and 2.3 dBi is achieved in the gain for a single element and for a 16-element array, respectively. A reduction of up to 11.8 dB is observed in the E-plane coupling between two patches. The presented design is suitable for electromagnetic shielding, reduction of coupling in integrated LTCC modules, and providing high gain antennas for low-cost millimeter-wave applications.


title: Interactive light source position estimation for augmented reality with an RGB-D camera

abstract: This work is partially supported by the Fish4Knowledge project, which is funded by the European Union 7th Framework Programme [FP7/2007-2013], by the HiPEAC Network of Excellence, by the Valencian Government grant BEFPI/2012/056 and by EPSRC (EP/P504902/1, EP/H012338/1).


title: A 128$\,\times$ 128 1.5% Contrast Sensitivity 0.9% FPN 3 µs Latency 4 mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Preamplifiers

abstract: Dynamic Vision Sensors (DVS) have recently appeared as a new paradigm for vision sensing and processing. They feature unique characteristics such as contrast coding under wide illumination variation, micro-second latency response to fast stimuli, and low output data rates (which greatly improves the efficiency of post-processing stages). They can track extremely fast objects (e.g., time resolution is better than 100 kFrames/s video) without special lighting conditions. Their availability has triggered a new range of vision applications in the fields of surveillance, motion analyses, robotics, and microscopic dynamic observations. One key DVS feature is contrast sensitivity, which has so far been reported to be in the 10-15% range. In this paper, a novel pixel photo sensing and transimpedance pre-amplification stage makes it possible to improve by one order of magnitude contrast sensitivity (down to 1.5%) and power (down to 4 mW), reduce the best reported FPN (Fixed Pattern Noise) by a factor of 2 (down to 0.9%), while maintaining the shortest reported latency (3 μs) and good Dynamic Range (120 dB), and further reducing overall area (down to 30 × 31 μm per pixel). The only penalty is the limitation of intrascene Dynamic Range to 3 decades. A 128 × 128 DVS test prototype has been fabricated in standard 0.35 μm CMOS and extensive experimental characterization results are provided.


title: Asymmetry of Inframammary Folds in Patients Undergoing Augmentation Mammaplasty

abstract: BACKGROUND ::: Variation in the anatomical position of the inframammary fold (IMF) in women remains poorly studied. ::: ::: ::: OBJECTIVES ::: The purpose of this study was to evaluate the incidence of asymmetry between IMF locations on the chest wall of women undergoing breast augmentation and to determine breast measurements associated with IMF asymmetry. ::: ::: ::: METHODS ::: Three-dimensional imaging analysis of the breasts was performed in 111 women with micromastia, using the Vectra Imaging System(TM). The following measurements were recorded: vertical distance between right and left IMF (inter-fold distance), vertical distance between nipples (inter-nipple distance), and difference between projection of right and left breasts in anterior-posterior direction. ::: ::: ::: RESULTS ::: Asymmetry between the right and left IMF positions was found in the majority of patients (95.4%), with symmetry only found in 5 patients (4.6%). In the majority of patients (60.3%), the right IMF was located inferior to the left IMF with median inter-fold distance 0.4 cm (range, 0.1, 2.1 cm). In 39 patients (35.1%), the left IMF was located inferior to the right with median inter-fold distance 0.4 cm (range, 0.1, 1.7 cm). There was strong correlation between the degree of asymmetry of IMF and asymmetry of nipple areola complex (NAC) positions (r = 0.687, P < .01). ::: ::: ::: CONCLUSIONS ::: The majority of women with micromastia demonstrate asymmetry of the IMF, which correlates with asymmetry of NAC location. The authors propose a classification system based on most commonly observed IMF locations as types I (right IMF inferior to left), type II (left IMF inferior to right) and type III (both IMF located on the same level). LEVEL OF EVIDENCE 4: Diagnostic.


title: PREVENTION OF WHITE SPOT LESION IN ORTHODONTIC PATIENTS USING CASEIN PHOSPHOPEPTIDE-STABILIZED AMORPHOUS CALCIUM PHOSPHATE -A SYSTEMATIC REVIEW

abstract: Early demineralization around orthodontic brackets causes white or brown marks during fixed orthodontic treatment. There is an increased risk of accumulation of dental plaque associated with increased risk of rapid demineralization of the enamel of teeth. However , demineralization is an early, but reversible, stage in the development of tooth. Therefore, remineralising agents were used to treat white lesions in postorthodontic patients. Review question was Prevention of white sot lesions using CPP-ACP . :An electronic search was done in Pubmed, Cochrane and Science Direct. One randomized control trial was identified for the reiew. It is seen that more evidence is required before the application of CPP-ACP to prevent white spot lesions in orthodontic patients.


Fact-Checking via Structured Discussions in Virtual Communities


title: Revisiting 2D vs 3D Implications on Spatial Memory

abstract: Prior research has shown that the efficient use of graphical user interfaces strongly depends on human capabilities for spatial cognition. Although it is tempting to believe that moving from two- to three-dimensional user interfaces will enhance user performance through natural support for spatial memory, it remains unclear whether 3D displays provide these benefits. An experiment by Tavanti and Lind, reported at InfoVis 2001, provides the most compelling result in favour of 3D---their participants recalled the location of letters of the alphabet more effectively when using a 3D interface than when using a 2D one. The experiment reported in this paper is based on Tavanti and Lind's, but it controls some previously uncontrolled factors. The results strongly suggest that the effectiveness of spatial memory is unaffected by the presence or absence of three-dimensional perspective effects in monocular static displays.


title: Cognitive structures in comprehension and memory of narrative discourse

abstract: Abstract The effects of structure and content variables on memory and comprehension of prose passages were studied in two experiments. The experimental passages exemplify a class of simple narrative stories that is described by a generative grammar of plot structures. A comprehension model is proposed that assumes a hierarchical organizational framework of stories in memory, determined by the grammar, representing the abstract structural components of the plot. The quality and characteristics of subjects' memory for stories were tested on a variety of experimental tasks in which story organization was manipulated. Comprehensibility and recall were found to be a function of the amount of inherent plot structure in the story, independent of passage content. Recall probability of individual facts from passages depended on the structural centrality of the facts: Subjects tended to recall facts corresponding to high-level organizational story elements rather than lower-level details. In addition, story summarizations from memory tended to emphasize general structural characteristics rather than specific content. For successively presented stories, both structure and content manipulations influenced recall. Furthermore, repeating story structure across two passages produced facilitation in recall of the second passage, while repeating story content produced proactive interference. The implications for a model of memory for narrative discourse are discussed.


title: OrdRec: an ordinal model for predicting personalized item rating distributions

abstract: We propose a collaborative filtering (CF) recommendation framework, which is based on viewing user feedback on products as ordinal, rather than the more common numerical view. This way, we do not need to interpret each user feedback value as a number, but only rely on the more relaxed assumption of having an order among the different feedback ratings. Such an ordinal view frequently provides a more natural reflection of the user intention when providing qualitative ratings, allowing users to have different internal scoring scales. Moreover, we can address scenarios where assigning numerical scores to different types of user feedback would not be easy. Our approach is based on a pointwise ordinal model, which allows it to linearly scale with data size. The framework can wrap most collaborative filtering algorithms, upgrading those algorithms designed to handle numerical values into being able to handle ordinal values. In particular, we demonstrate our framework with wrapping a leading matrix factorization CF method. A cornerstone of our method is its ability to predict a full probability distribution of the expected item ratings, rather than only a single score for an item. One of the advantages this brings is a novel approach to estimating the confidence level in each individual prediction. Compared to previous approaches to confidence estimation, ours is more principled and empirically superior in its accuracy. We demonstrate the efficacy of the approach on some of the largest publicly available datasets, the Netflix data, and the Yahoo! Music data.


title: Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

abstract: Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.


title: Questioning naturalism in 3D user interfaces

abstract: 3D UIs are uniquely able to achieve superior interaction fidelity, and this naturalism can be a huge advantage.


title: Periodic solutions for nonlinear evolution equations at resonance

abstract: Abstract We are concerned with periodic problems for nonlinear evolution equations at resonance of the form u ˙ ( t ) = − A u ( t ) + F ( t , u ( t ) ) , where a densely defined linear operator A : D ( A ) → X on a Banach space X is such that −A generates a compact C 0 semigroup and F : [ 0 , + ∞ ) × X → X is a nonlinear perturbation. Imposing appropriate Landesman–Lazer type conditions on the nonlinear term F, we prove a formula expressing the fixed point index of the associated translation along trajectories operator, in the terms of a time averaging of F restricted to Ker A. By the formula, we show that the translation operator has a nonzero fixed point index and, in consequence, we conclude that the equation admits a periodic solution.


title: Evidence-based diabetes nutrition therapy recommendations are effective: the key is individualization

abstract: Current nutrition therapy recommendations for the prevention and treatment of diabetes are based on a systematic review of evidence and answer important nutrition care questions. First, is diabetes nutrition therapy effective? Clinical trials as well as systematic and Cochrane reviews report a ~1%–2% lowering of hemoglobin A1c values as well as other beneficial outcomes from nutrition therapy interventions, depending on the type and duration of diabetes and level of glycemic control. Clinical trials also provide evidence for the effectiveness of nutrition therapy in the prevention of diabetes. Second, are weight loss interventions important and when are they beneficial? Modest weight loss is important for the prevention of type 2 diabetes and early in the disease process. However, as diabetes progresses, weight loss may or may not result in beneficial glycemic and cardiovascular outcomes. Third, are there ideal percentages of macronutrients and eating patterns that apply to all persons with diabetes? There is no ideal percentage of macronutrients and a variety of eating patterns has been shown to be effective for persons with diabetes. Treatment goals, personal preferences (eg, tradition, culture, religion, health beliefs, economics), and the individual’s ability and willingness to make lifestyle changes must all be considered by clinicians and/or educators when counseling and educating individuals with diabetes. A healthy eating pattern emphasizing nutrient-dense foods in appropriate portion sizes, regular physical activity, and support are priorities for all individuals with diabetes. Reduced energy intake for persons with prediabetes or type 2 diabetes as well as matching insulin to planned carbohydrate intake are intervention to be considered. Fourth, is the question of how to implement nutrition therapy interventions in clinical practice. This requires nutrition care strategies.


title: Denoising Adversarial Autoencoders

abstract: Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularisation during training to shape the distribution of the encoded data in latent space. We suggest denoising adversarial autoencoders, which combine denoising and regularisation, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of adversarial autoencoders. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance, and can synthesise samples that are more consistent with the input data than those trained without a corruption process.


title: Design of Winding Changeable BLDC Motor Considering Demagnetization in Winding Change Section

abstract: In this paper, a changeable winding brushless DC (BLDC) motor for the expansion of the speed region is described. The changeable winding BLDC motor is driven by a large number of phase turns at low speeds and by a reduced number of turns at high speeds. For this reason, the section where the winding changes is very important. Ideally, the time at which the windings are to be converted should be same as the time at which the voltage changes. However, if this timing is not exactly synchronized, a large current is generated in the motor, and the demagnetization of the permanent magnet occurs. In addition, a large torque ripple is produced. In this paper, we describe the demagnetization of the permanent magnet in a fault situation when the windings change, and we suggest a design process to solve this problem.


title: Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data

abstract: With the increasing availability of various sensor technologies, we now have access to large amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be jointly analyzed to explore their latent connections. Various component analysis methods have played an increasingly important role for the analysis of such coupled data. In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications. Then, we discuss their important extensions and generalization to multiblock multiway (tensor) data. We show how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data. Special emphasis is given to the flexible common and individual feature analysis of multiblock data with the aim to simultaneously extract common and individual latent components with desired properties and types of diversity. Illustrative examples are given to demonstrate their effectiveness for biomedical data analysis.


title: An Overview on Peer-to-Peer Information Systems

abstract: The limitations of client/server systems become evident in an Internetscale distributed environment. P2P systems offer an alternative to traditional client/server systems: Every node acts both as a client and a server and “pays” its participation by providing access to its computing resources. Systems such as Napster and Gnutella have proven their practical applicability. In this article we briefly introduce the key concepts and properties of the P2P paradigm and overview commercial systems and research approaches.


title: GridMonitoring: Secured Sovereign Blockchain Based Monitoring on Smart Grid

abstract: Electricity is the commonest commodity for most businesses in our world today. The use of electricity has been a breakthrough for the discovery of new technologies and has become the main driving force behind several innovations. With the introduction of smart grid systems, there have been improvements in how utility companies interact with their customers with regards to electricity use. However, since the readings are done via the Internet, there is the tendency for the data to be compromised when it gets into the hands of the wrong people. Moreover, customers mostly do not know why they pay huge amounts and which appliances use more electricity, since they are not privy to the readings. The sovereign blockchain technology, which provides transparency and provenance, is utilized in this paper to mitigate these above mentioned problems. A smart contract, which executes laid down procedures to provide a trust-based system between participants on the network is also implemented. Our system proves very efficient as the user can monitor how the electricity is used, and it also provides a platform where there is no manipulation from either party.


title: [Subjective glance in ethics and attitudes in psychiatric nursing].

abstract: Patients with psychiatric problems have the right to receive qualified and humane psychiatric nursing. To meet these requirements nurses should reflect on their daily practice and whether they support clients in respect of autonomy, empowerment and recovery or only meet the requirements of the institution and well-worn routines. The Code of Ethics for Nurses (International Council of Nurses [ICN] and the four principles of Beauchamp and Childress [respect of autonomy, nonmaleficence, beneficence and justice]) help nurses to decide in their daily work on the narrow line between autonomy and treating the patient like a child. Emphasis is laid on the nurses' duty to influence the political development in health services.


title: News Feed: What's in it for Me?

abstract: Over a billion people use social networking sites like Facebook to maintain awareness of their friends. Facebook's News Feed is the primary mechanism by which people are shown updates about their friends' daily activities on the site in the form of an algorithmically curated list of stories. This paper examines how people browse the News Feed, their perceptions and satisfaction while using it, and the interactions they make with their personal social network. We conducted a qualitative study involving think-aloud semi-structured interviews as the participants casually browsed their own feeds. We observed a wide variation in the use of the News Feed ranging from careful consideration of social conventions, judgment of people, and annoyance and frustration towards certain friends. Our findings suggest that people do not deliberately curate their own News Feed either due to lack of awareness or perceived social repercussions.


title: How Effective is Test-Driven Development?

abstract: Test-Driven Development (TDD) [Beck 2002] is one of the most referenced, yet least used agile practices in industry. Its neglect is due mostly to our lack of understanding of its effects on people, processes, and products. Although most people agree that writing a test case before code promotes more robust implementation and a better design, the unknown costs associated with TDD’s effects and the inversion of the ubiquitous programmer “code-then-test” paradigm has impeded TDD’s adoption.


title: Multilingual sentiment analysis: from formal to informal and scarce resource languages

abstract: The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.


title: User participation in information systems security risk management

abstract: This paper examines user participation in information systems security risk management and its influence in the context of regulatory compliance via a multi-method study at the organizational level. First, eleven informants across five organizations were interviewed to gain an understanding of the types of activities and security controls in which users participated as part of Sarbanes-Oxley compliance, along with associated outcomes. A research model was developed based on the findings of the qualitative study and extant user participation theories in the systems development literature. Analysis of the data collected in a questionnaire survey of 228 members of ISACA, a professional association specialized in information technology governance, audit, and security, supported the research model. The findings of the two studies converged and indicated that user participation contributed to improved security control performance through greater awareness, greater alignment between IS security risk management and the business environment, and improved control development. While the IS security literature often portrays users as the weak link in security, the current study suggests that users may be an important resource to IS security by providing needed business knowledge that contributes to more effective security measures. User participation is also a means to engage users in protecting sensitive information in their business processes.


title: Leveraging Internet of Things within the military network environment — Challenges and solutions

abstract: The widespread adoption of IoT technologies will significantly affect many aspects of military operations. A growing number of battlefield assets will soon become networked entities, thanks to capillary and high density personal and environment sensors systems. The accurate and fine-grained information gathered could significantly benefit military intelligence, surveillance, and reconnaissance operations, facilitate automated supply chain logistics, and facilitate urban operations in mega-city environments. To achieve these goals, research has to address several issues, such as reconciling the differences between commercial IoT architectural patterns and military network architectures, interoperability between different IoT systems, data processing and information management, and realization of resource-efficient IoT middleware solutions. The resource constrained tactical networking environment makes this research agenda particularly challenging but also pressing in terms of the need for novel middleware solutions.