title: Techniques for Software Maintenance

abstract: Software maintenance constitutes a major phase of the software life cycle. Studies indicate that software maintenance is responsible for a significant percentage of a system’s overall cost and effort. The software engineering community has identified four major types of software maintenance, namely, corrective, perfective, adaptive, and preventive maintenance. Software maintenance can be seen from two major points of view. First, the classic view where software maintenance provides the necessary theories, techniques, methodologies, and tools for keeping software systems operational once they have been deployed to their operational environment. Most legacy systems subscribe to this view of software maintenance. The second view is a more modern emerging view, where maintenance is an integral part of the software development process and it should be applied from the early stages in the software life cycle. Regardless of the view by which we consider software maintenance, the fact is that it is the driving force behind software evolution, a very important aspect of a software system. This entry provides an in-depth discussion of software maintenance techniques, methodologies, tools, and emerging trends. Q1


title: Ethics of the health-related internet of things: a narrative review

abstract: The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT.


title: A dual-space approach to tracking and sensor management in wireless sensor networks

abstract: Wireless ad hoc sensor networks have the advantage of spanning a large geographical region and being able to collaboratively detect and track non-local spatio-temporal events. This paper presents a dual-space approach to event tracking and sensor resource management in sensor networks. The dual-space transformation maps a non-local phenomenon, e.g., the edge of a half-plane shadow, to a single point in the dual space, and maps locations of distributed sensor nodes to a set of lines that partitions the dual space. The detection problem becomes finding and tracking the cell that contains the point in the arrangement defined by these lines. This mechanism can be effectively used for power management of the sensor network - nodes that will not be immediately visited by an event can be turned off to save energy required for sensing, processing, and communication. The approach has been successfully demonstrated on a laboratory testbed built using the UC Berkeley motes sensors. An implemented application of detecting and tracking light shadow edges moving over a sensor field is described.


title: Using Vital Sensors in Mobile Healthcare Business Applications - Challenges, Examples, Lessons Learned

abstract: Today, sensors are increasingly used for data collection. In the medical domain, for example, vital signs (e.g., pulse or oxygen saturation) of patients can be measured with sensors and used for further processing. In this paper, different types of applications will be discussed whether sensors might be used in the context of these applications and their suitability for applying external sensors to them. Furthermore, a system architecture for adding sensor technology to respective applications is presented. For this purpose, a real-world business application scenario in the field of well-being and fitness is presented. In particular, we integrated two different sensors in our fitness application. We report on the lessons learned from the implementation and use of this application, e.g., in respect to connection and data structure. They mainly deal with problems relating to the connection and communication between the smart mobile device and the external sensors, as well as the selection of the appropriate type of application. Finally, a robust sensor framework, arising from this fitness application is presented. This framework provides basic features for connecting sensors. Particularly, in the medical domain, it is crucial to provide an easy to use toolset to relieve medical staff.


title: Neural Program Meta-Induction

abstract: Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a $k$-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.


title: First insights from a mobile honeypot

abstract: Computer systems are commonly attacked by malicious transport contacts. We present a comparative study that analyzes to what extent those attacks depend on the network access, in particular if an adversary targets specifically on mobile or non-mobile devices. Based on a mobile honeypot that extracts first statistical results, our findings indicate that a few topological domains of the Internet have started to place particular focus on attacking mobile networks.


title: I Would Like To..., I Shouldn't..., I Wish I...: Exploring Behavior-Change Goals for Social Networking Sites

abstract: Despite benefits and uses of social networking sites (SNSs) users are not always satisfied with their behaviors on the sites. These desires for behavior change both provide insight into users' perceptions of how SNSs impact their lives (positively or negatively) and can inform tools for helping users achieve desired behavior changes. We use a 604-participant online survey to explore SNS users' behavior-change goals for Facebook, Instagram, and Twitter. While some participants want to reduce site use, others want to improve their use or increase a range of behaviors. These desired changes differ by SNS, and, for Twitter, by participants' levels of site use. Participants also expect a range of benefits from these goals, including increased time, contact with others, intrinsic benefits, better security/privacy, and improved self presentation. Based on these results we provide insights both into how participants perceive different SNSs, as well as potential designs for behavior-change mechanisms to target SNS behaviors.


title: Learning Two-Branch Neural Networks for Image-Text Matching Tasks

abstract: Image-language matching tasks have recently attracted a lot of attention in the computer vision field. These tasks include image-sentence matching, i.e., given an image query, retrieving relevant sentences and vice versa, and region-phrase matching or visual grounding, i.e., matching a phrase to relevant regions. This paper investigates two-branch neural networks for learning the similarity between these two data modalities. We propose two network structures that produce different output representations. The first one, referred to as an embedding network , learns an explicit shared latent embedding space with a maximum-margin ranking loss and novel neighborhood constraints. Compared to standard triplet sampling, we perform improved neighborhood sampling that takes neighborhood information into consideration while constructing mini-batches. The second network structure, referred to as a similarity network , fuses the two branches via element-wise product and is trained with regression loss to directly predict a similarity score. Extensive experiments show that our networks achieve high accuracies for phrase localization on the Flickr30K Entities dataset and for bi-directional image-sentence retrieval on Flickr30K and MSCOCO datasets.


title: Cinematographic User Models for Automated Realtime Camera Control in Dynamic 3D Environments

abstract: Advances in 3D graphics technology have accelerated the construction of dynamic 3D environments. Despite their promise for scientific and educational applications, much of this potential has gone unrealized because runtime camera control software lacks user-sensitivity. Current environments rely on sequences of viewpoints that directly require the user’s control or are based primarily on actions and geometry of the scene. Because of the complexity of rapidly changing environments, users typically cannot manipulate objects in environments while simultaneously issuing camera control commands. To address these issues, we have developed UCam, a realtime camera planner that employs cinematographic user models to render customized visualizations of dynamic 3D environments. After interviewing users to determine their preferred directorial style and pacing, UCam examines the resulting cinematographic user model to plan camera sequences whose shot vantage points and cutting rates are tailored to the user in realtime. Evaluations of UCam in a dynamic 3D testbed are encouraging.


title: Whanau: A Sybil-proof Distributed Hash Table

abstract: Whanau is a novel routing protocol for distributed hash tables (DHTs) that is efficient and strongly resistant to the Sybil attack. Whanau uses the social connections between users to build routing tables that enable Sybil-resistant lookups. The number of Sybils in the social network does not affect the protocol's performance, but links between honest users and Sybils do.When there are n well-connected honest nodes, Whanau can tolerate up to O(n/ log n) such "attack edges". This means that an adversary must convince a large fraction of the honest users to make a social connection with the adversary's Sybils before any lookups will fail. ::: ::: Whanau uses ideas from structured DHTs to build routing tables that contain O(√n log n) entries per node. It introduces the idea of layered identifiers to counter clustering attacks, a class of Sybil attacks challenging for previous DHTs to handle. Using the constructed tables, lookups provably take constant time. Simulation results, using social network graphs from LiveJournal, Flickr, YouTube, and DBLP, confirm the analytic results. Experimental results on PlanetLab confirmthat the protocol can handle modest churn.


title: Riemann hypothesis and Quantum Mechanics

abstract: In their 1995 paper, Jean-Benoit Bost and Alain Connes (BC) constructed a quantum dynamical system whose partition function is the Riemann zeta function ζ(β), where β is an inverse temperature. We formulate Riemann hypothesis (RH) as a property of the low-temperature Kubo–Martin–Schwinger (KMS) states of this theory. More precisely, the expectation value of the BC phase operator can be written as where Nq = ∏qk = 1pk is the primorial number of order q and ψb is a generalized Dedekind ψ function depending on one real parameter b as Fix a large inverse temperature β > 2. The RH is then shown to be equivalent to the inequality for q large enough. Under RH, extra formulas for high-temperature KMS states (1.5 < β < 2) are derived.'Number theory is not pure Mathematics. It is the Physics of the world of Numbers.'Alf van der Poorten


Quantifying Stuck Pipe Risk in Gulf of Mexico Oil and Gas Drilling


title: A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation

abstract: Recent theoretical results describing the sum-capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum-rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the energy of the transmitted signal. The paper is comprised of two parts. In this second part, we show that, after the regularization of the channel inverse introduced in the first part, a certain perturbation of the data using a "sphere encoder" can be chosen to further reduce the energy of the transmitted signal. The performance difference with and without this perturbation is shown to be dramatic. With the perturbation, we achieve excellent performance at all signal-to-noise ratios. The results of both uncoded and turbo-coded simulations are presented.


Deeper Conversations with Interactive Art: Or Why Artists Must Program


title: Emergent Logical Structure in Vector Representations of Neural Readers

abstract: Reading comprehension is a question answering task where the answer is to be found in a given passage about entities and events not mentioned in general knowledge sources. A significant number of neural architectures for this task (neural readers) have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the existence of logical structure in the hidden state vectors of "aggregation readers" such as the Attentive Reader and Stanford Reader. The logical structure of aggregation readers reflects the architecture of "explicit reference readers" such as the Attention-Sum Reader, the Gated Attention Reader and the Attention-over-Attention Reader. This relationship between aggregation readers and explicit reference readers presents a case study in emergent logical structure. In an independent contribution, we show that the addition of linguistics features to the input to existing neural readers significantly boosts performance yielding the best results to date on the Who-did-What datasets.


title: Study and Mitigation of Origin Stripping Vulnerabilities in Hybrid-postMessage Enabled Mobile Applications

abstract: postMessage is popular in HTML5 based web apps to allow the communication between different origins. With the increasing popularity of the embedded browser (i.e., WebView) in mobile apps (i.e., hybrid apps), postMessage has found utility in these apps. However, different from web apps, hybrid apps have a unique requirement that their native code (e.g., Java for Android) also needs to exchange messages with web code loaded in WebView. To bridge the gap, developers typically extend postMessage by treating the native context as a new frame, and allowing the communication between the new frame and the web frames. We term such extended postMessage "hybrid postMessage" in this paper. We find that hybrid postMessage introduces new critical security flaws: all origin information of a message is not respected or even lost during the message delivery in hybrid postMessage. If adversaries inject malicious code into WebView, the malicious code may leverage the flaws to passively monitor messages that may contain sensitive information, or actively send messages to arbitrary message receivers and access their internal functionalities and data. We term the novel security issue caused by hybrid postMessage "Origin Stripping Vulnerability" (OSV). In this paper, our contributions are fourfold. First, we conduct the first systematic study on OSV. Second, we propose a lightweight detection tool against OSV, called OSV-Hunter. Third, we evaluate OSV-Hunter using a set of popular apps. We found that 74 apps implemented hybrid postMessage, and all these apps suffered from OSV, which might be exploited by adversaries to perform remote real-time microphone monitoring, data race, internal data manipulation, denial of service (DoS) attacks and so on. Several popular development frameworks, libraries (such as the Facebook React Native framework, and the Google cloud print library) and apps (such as Adobe Reader and WPS office) are impacted. Lastly, to mitigate OSV from the root, we design and implement three new postMessage APIs, called OSV-Free. Our evaluation shows that OSV-Free is secure and fast, and it is generic and resilient to the notorious Android fragmentation problem. We also demonstrate that OSV-Free is easy to use, by applying OSV-Free to harden the complex "Facebook React Native" framework. OSV-Free is open source, and its source code and more implementation and evaluation details are available online.


title: Loss of ift122, a Retrograde Intraflagellar Transport (IFT) Complex Component, Leads to Slow, Progressive Photoreceptor Degeneration Due to Inefficient Opsin Transport*

abstract: In the retina, aberrant opsin transport from cell bodies to outer segments leads to retinal degenerative diseases such as retinitis pigmentosa. Opsin transport is facilitated by the intraflagellar transport (IFT) system that mediates the bidirectional movement of proteins within cilia. In contrast to functions of the anterograde transport executed by IFT complex B (IFT-B), the precise functions of the retrograde transport mediated by IFT complex A (IFT-A) have not been well studied in photoreceptor cilia. Here, we analyzed developing zebrafish larvae carrying a null mutation in ift122 encoding a component of IFT-A. ift122 mutant larvae show unexpectedly mild phenotypes, compared with those of mutants defective in IFT-B. ift122 mutants exhibit a slow onset of progressive photoreceptor degeneration mainly after 7 days post-fertilization. ift122 mutant larvae also develop cystic kidney but not curly body, both of which are typically observed in various ciliary mutants. ift122 mutants display a loss of cilia in the inner ear hair cells and nasal pit epithelia. Loss of ift122 causes disorganization of outer segment discs. Ectopic accumulation of an IFT-B component, ift88, is observed in the ift122 mutant photoreceptor cilia. In addition, pulse-chase experiments using GFP-opsin fusion proteins revealed that ift122 is required for the efficient transport of opsin and the distal elongation of outer segments. These results show that IFT-A is essential for the efficient transport of outer segment proteins, including opsin, and for the survival of retinal photoreceptor cells, rendering the ift122 mutant a unique model for human retinal degenerative diseases.


title: An MRAS Observer for Sensorless DFIM Drives With Direct Estimation of the Torque and Flux Rotor Current Components

abstract: This paper proposes a model reference adjustable system observer for the sensorless control of doubly fed induction machines. The formulation is based on the direct calculation of the torque and flux rotor current components in the stator-flux-oriented coordinates by using the stator currents and voltages and the norm of the rotor current space vector. The tuning of the observer is discussed, and a theoretical sensitivity and stability analysis is performed in order to investigate the behavior of the observer under mismatched parameters. The paper is completed by the experimental validation of the observer performances.


title: Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition

abstract: DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors.


title: Vaginoplasty using autologous in vitro cultured vaginal tissue in a patient with Mayer-von-Rokitansky-Kuster-Hauser syndrome

abstract: Mayer-von-Rokitansky-Küster-Hauser syndrome (MRKHS) is characterized by vaginal agenesis with variable Müllerian duct abnormalities. The Abbè-McIndoe technique is considered a valid treatment option for vaginoplasty but no consensus has been reached on what material should be used for the neovagina canal wall lining. We report the first case of autologous vaginal tissue transplantation in a 28-year-old women with MRKHS. The patient was subjected to a 1 cm2 full-thickness mucosal biopsy from the vaginal vestibule. Following enzymatic dissociation, cells were inoculated onto collagen IV-coated plates and cultured for 2 weeks. The patient was subjected to a vaginoplasty with a modified Abbè-McIndoe vaginoplasty with 314 cm2 autologous in vitro cultured vaginal tissue for the canal lining. At 1 month from surgery, the vagina appeared normal in length and depth and a vaginal biopsy revealed normal vaginal tissue. The use of autologous in vitro cultured vaginal tissue to create a neovagina appears as an easy, minimally invasive and useful method.


title: A review of modeling approaches for sustainable supply chain management

abstract: More than 300 papers have been published in the last 15years on the topic of green or sustainable (forward) supply chains. Looking at the research methodologies employed, only 36 papers apply quantitative models. This is in contrast to, for example, the neighboring field of reverse or closed-loop supply chains where several reviews on respective quantitative models have already been provided. The paper summarizes research on quantitative models for forward supply chains and thereby contributes to the further substantiation of the field. While different kinds of models are applied, it is evident that the social side of sustainability is not taken into account. On the environmental side, life-cycle assessment based approaches and impact criteria clearly dominate. On the modeling side there are three dominant approaches: equilibrium models, multi-criteria decision making and analytical hierarchy process. There has been only limited empirical research so far. The paper ends with suggestions for future research.


title: Capacitive coupling for power and data telemetry to implantable biomedical microsystems

abstract: This paper proposes a new method for power telemetry and wireless data transfer to biomedical implants. Confinement of the transferred energy within a predefined area, hence not influencing the neighboring sensitive circuitry on the microsystem, and also allowing for more than one telemetry link on the same platform and even operating at the same carrier frequency are among the advantages of the proposed method over the traditional approaches for power and data telemetry.


title: Emoticons in mind: An event-related potential study

abstract: It is now common practice, in digital communication, to use the character combination “:-)”, known as an emoticon, to indicate a smiling face. Although emoticons are readily interpreted as smiling faces, it is unclear whether emoticons trigger face-specific mechanisms or whether separate systems are utilized. A hallmark of face perception is the utilization of regions in the occipitotemporal cortex, which are sensitive to configural processing. We recorded the N170 event-related potential to investigate the way in which emoticons are perceived. Inverting faces produces a larger and later N170 while inverting objects which are perceived featurally rather than configurally reduces the amplitude of the N170. We presented 20 participants with images of upright and inverted faces, emoticons and meaningless strings of characters. Emoticons showed a large amplitude N170 when upright and a decrease in amplitude when inverted, the opposite pattern to that shown by faces. This indicates that when upright, emoticons...


title: Commenting on Writing Typology and Perceived Helpfulness of Comments from Novice Peer Reviewers and Subject Matter Experts

abstract: How do comments on student writing from peers compare to those from subject-matter experts? This study examined the types of comments that reviewers produce as well as their perceived helpfulness. Comments on classmates’ papers were collected from two undergraduate and one graduate-level psychology course. The undergraduate papers in one of the courses were also commented on by an independent psychology instructor experienced in providing feedback to students on similar writing tasks. The comments produced by students at both levels were shorter than the instructor’s. The instructor’s comments were predominantly directive and rarely summative. The undergraduate peers’ comments were more mixed in type; directive and praise comments were the most frequent. Consistently, undergraduate peers found directive and praise comments helpful. The helpfulness of the directive comments was also endorsed by a writing expert.


title: Sleep, dreams, and memory consolidation: The role of the stress hormone cortisol

abstract: We discuss the relationship between sleep, dreams, and memory, proposing that the content of dreams reflects aspects of memory consolidation taking place during the different stages of sleep. Although we acknowledge the likely involvement of various neuromodulators in these phenomena, we focus on the hormone cortisol, which is known to exert influence on many of the brain systems involved in memory. The concentration of cortisol escalates over the course of the night's sleep, in ways that we propose can help explain the changing nature of dreams across the sleep cycle.


title: Effectiveness of virtual reality exposure in the treatment of arachnophobia using 3D games.

abstract: Buying or creating a virtual reality (VR) software is very costly. A less expensive alternative could be to modify already existing 3D computer games. The goal of this study is to assess the effectiveness of in virtuo exposure in the treatment of arachnophobia using modified 3D games. Participants were 10 women and 1 man. Virtual worlds were created using the game editor of a 3D computer game (Half-Life), modified to offer graduals hierarchies of fearful stimuli (spiders). Analyses revealed significant improvement between pre and post results on the behavioral avoidance test, the Spider Beliefs Questionnaire, and perceived self-efficacy. These promising results suggest that therapy using virtual reality exposure via a modified computer game is useful in the treatment of arachnophobia.


title: Application of software-defined storage

abstract: Software Defined Storage (SDS) can provide services for external storage applications by abstracting and pooling all types of storage resources in order to achieve the required resources to use. Based on different interpretation or description of SDS from various authoritative advisory body, and other major manufacturers, this paper refines the common characteristics and advantages of the SDS. Since the SDS has a self-service, virtualization, hardware loosely coupled, rich data interface and other features, mainstream manufacturers are put into the SDS application areas. The emphasis and technical advantages of each individual is slightly different. By analyzing two typical SDS software, testing and building the appropriate physical environment, this paper analyzes the key indicators and thereby abstract application scenarios SDS to provide some reference for service providers SDS selection.


title: Security and Privacy for Cloud-Based IoT: Challenges

abstract: The Internet of Things is increasingly becoming a ubiquitous computing service, requiring huge volumes of data storage and processing. Unfortunately, due to the unique characteristics of resource constraints, self-organization, and shortrange communication in IoT, it always resorts to the cloud for outsourced storage and computation, which has brought about a series of new challenging security and privacy threats. In this article, we introduce the architecture and unique security and privacy requirements for the next generation mobile technologies on cloud-based IoT, identify the inappropriateness of most existing work, and address the challenging issues of secure packet forwarding and efficient privacy preserving authentication by proposing new efficient privacy preserving data aggregation without public key homomorphic encryption. Finally, several interesting open problems are suggested with promising ideas to trigger more research efforts in this emerging area.


title: FPGA based signal processing module design and implementation for FMCW vehicle radar systems

abstract: In this paper, we design the signal processing module based on FPGA for vehicle FMCW radar systems. We employ the detection algorithms based on two-step FFT (Fast Fourier Transform) using the several fast ramps in order to resolve radar range-velocity ambiguities. We implement the detection algorithms including the first FFT, DBF (Digital Beam Forming), the second FFT, and CFAR (Constant False Alarm) onto FPGA. In particular, the designed architecture supports multiple-receive channels to estimate target angles. The architecture also digitally generates a transmit wave using a DDS (Digital Direct Synthesizer) device. All algorithms and the controller are implemented into a Xilinx Virtex-5 platform and verified under the test platform.


Graph evolution: Densification and shrinking diameters


Comments on The Consultation Paper on Options for the protection of the public posed by the inappropriate use of psychological testing issued by the Psychology Board of Australia May 2010 11


Augmented Reality: Virtual fitting room using Kinect


title: AdaGAN: Boosting Generative Models

abstract: Generative Adversarial Networks (GAN) are an effective method for training generative models of complex data such as natural images. However, they are notoriously hard to train and can suffer from the problem of missing modes where the model is not able to produce examples in certain regions of the space. We propose an iterative procedure, called AdaGAN, where at every step we add a new component into a mixture model by running a GAN algorithm on a re-weighted sample. This is inspired by boosting algorithms, where many potentially weak individual predictors are greedily aggregated to form a strong composite predictor. We prove analytically that such an incremental procedure leads to convergence to the true distribution in a finite number of steps if each step is optimal, and convergence at an exponential rate otherwise. We also illustrate experimentally that this procedure addresses the problem of missing modes.


title: Semantic interoperability in global information systems

abstract: Internet, Web and distributed computing infrastructures continue to gain in popularity as a means of communication for organizations, groups and individuals alike. In such an environment, characterized by large distributed, autonomous, diverse, and dynamic information sources, access to relevant and accurate information is becoming increasingly complex. This complexity is exacerbated by the evolving system, semantic and structural heterogeneity of these potentially global, cross-disciplinary, multicultural and rich-media technologies. Clearly, solutions to these challenges require addressing directly a variety of interoperability issues.


title: Machine Learning for the New York City Power Grid

abstract: Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid.


title: Patterns of contact and communication in scientific research collaboration

abstract: In this paper, we describe the influence of physical proximity on the development of collaborative relationships between scientific researchers and on the execution of their work. Our evidence is drawn from our own studies of scientific collaborators, as well as from observations of research and development activities collected by other investigators. These descriptions provide the foundation for a discussion of the actual and potential role of communications technology in professional work, especially for collaborations carried out at a distance.


title: Learning to dress: synthesizing human dressing motion via deep reinforcement learning

abstract: Creating animation of a character putting on clothing is challenging due to the complex interactions between the character and the simulated garment. We take a model-free deep reinforcement learning (deepRL) approach to automatically discovering robust dressing control policies represented by neural networks. While deepRL has demonstrated several successes in learning complex motor skills, the data-demanding nature of the learning algorithms is at odds with the computationally costly cloth simulation required by the dressing task. This paper is the first to demonstrate that, with an appropriately designed input state space and a reward function, it is possible to incorporate cloth simulation in the deepRL framework to learn a robust dressing control policy. We introduce a salient representation of haptic information to guide the dressing process and utilize it in the reward function to provide learning signals during training. In order to learn a prolonged sequence of motion involving a diverse set of manipulation skills, such as grasping the edge of the shirt or pulling on a sleeve, we find it necessary to separate the dressing task into several subtasks and learn a control policy for each subtask. We introduce a policy sequencing algorithm that matches the distribution of output states from one task to the input distribution for the next task in the sequence. We have used this approach to produce character controllers for several dressing tasks: putting on a t-shirt, putting on a jacket, and robot-assisted dressing of a sleeve.


title: Mining positive and negative association rules

abstract: Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very useful for constructing associative classifiers. In this paper, we propose an algorithm that mines positive and negative association rules without adding any additional measure and extra database scans.


Lazarsfeld'S Map of Media Effects


title: An Extended Grid-based Clustering Algorithm with Referential Value of Parameters

abstract: GRPC algorithm (a grid-based clustering algorithm with referential parameters) had put forward by authors of this paper, and the algorithm provided user with a feasible technology for reducing the blindness of parameter assignment. The work of this paper is to develop GRPC algorithm in the aspects of scalability and high-dimensionality. We process large-scale data set by means of random sampling technique and transform the clustering of high-dimensional data into the clustering of two-dimensional data. Experimental results confirmed that this algorithm (EGRPC) could effectively cluster vast data set whose data is three-dimensional or other high-dimensional.


title: A brief introduction to distributed systems

abstract: Distributed systems are by now commonplace, yet remain an often difficult area of research. This is partly explained by the many facets of such systems and the inherent difficulty to isolate these facets from each other. In this paper we provide a brief overview of distributed systems: what they are, their general design goals, and some of the most common types.


title: An Implementation of Intrusion Detection System Using Genetic Algorithm

abstract: Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.


title: Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

abstract: Since human observers are the ultimate receivers of digital images, image quality metrics should be designed from a human-oriented perspective. Conventionally, a number of full-reference image quality assessment (FR-IQA) methods adopted various computational models of the human visual system (HVS) from psychological vision science research. In this paper, we propose a novel convolutional neural networks (CNN) based FR-IQA model, named Deep Image Quality Assessment (DeepQA), where the behavior of the HVS is learned from the underlying data distribution of IQA databases. Different from previous studies, our model seeks the optimal visual weight based on understanding of database information itself without any prior knowledge of the HVS. Through the experiments, we show that the predicted visual sensitivity maps agree with the human subjective opinions. In addition, DeepQA achieves the state-of-the-art prediction accuracy among FR-IQA models.


title: The Beck Cognitive Insight Scale (BCIS): translation and validation of the Taiwanese version

abstract: BackgroundOver the last few decades, research concerning the insight of patients with schizophrenia and its relationships with other clinical variables has been given much attention in the clinical setting. Since that time, a series of instruments assessing insight have been developed. The purpose of this study was to examine the reliability and validity of the Taiwanese version of the Beck Cognitive Insight Scale (BCIS). The BCIS is a self-administered instrument designed to evaluate cognitive processes that involves reevaluating patients' anomalous experiences and specific misinterpretations.MethodsThe English language version of the BCIS was translated into Taiwanese for use in this study. A total of 180 subjects with and without psychosis completed the Taiwanese version of the BCIS and additional evaluations to assess researcher-rated insight scales and psychopathology. Psychometric properties (factor structures and various types of reliability and validity) were assessed for this translated questionnaire.ResultsOverall, the Taiwanese version of the BCIS showed good reliability and stability over time. This translated scale comprised a two-factor solution corresponding to reflective attitude and certain attitude subscales. Following the validation of the internal structure of the scale, we obtained an R-C (reflective attitude minus certain attitude) index of the translated BCIS, representing the measurement of cognitive insight by subtracting the score of the certain attitude subscale from that of the reflective attitude subscale. As predicted, the differences in mean reflective attitude, certain attitude and R-C index between subjects with and without psychosis were significant. Our data also demonstrated that psychotic patients were significantly less reflective, more confident in their beliefs, and had less cognitive insight compared with nonpsychotic control groups.ConclusionsIn light of these findings, we believe that the Taiwanese version of BCIS is a valid and reliable instrument for the assessment of cognitive insight in psychotic patients.


title: An attachment-based model of complicated grief including the role of avoidance

abstract: Introduction ::: Complicated grief is a prolonged grief disorder with elements of a stress response syndrome. We have previously proposed a biobehavioral model showing the pathway to complicated grief. Avoidance is a component that can be difficult to assess and pivotal to treatment. Therefore we developed an avoidance questionnaire to characterize avoidance among patients with CG.


title: Personality, Culture, and System Factors - Impact on Affective Response to Multimedia

abstract: Whilst affective responses to various forms and genres of multimedia content have been well researched, precious few studies have investigated the combined impact that multimedia system parameters and human factors have on affect. Consequently, in this paper we explore the role that two primordial dimensions of human factors - personality and culture - in conjunction with system factors - frame rate, resolution, and bit rate - have on user affect and enjoyment of multimedia presentations. To this end, a two-site, cross-cultural study was undertaken, the results of which produced three predictve models. Personality and Culture traits were shown statistically to represent 5.6% of the variance in positive affect, 13.6% in negative affect and 9.3% in enjoyment. The correlation between affect and enjoyment, was significant. Predictive modeling incorporating human factors showed about 8%, 7% and 9% improvement in predicting positive affect, negative affect and enjoyment respectively when compared to models trained only on system factors. Results and analysis indicate the significant role played by human factors in influencing affect that users experience while watching multimedia.


title: Document-to-Sentence Level Technique for Novelty Detection

abstract: Novelty identification is accustomed to distinguishing novel data from an approaching stream of documents. In this study, we proposed a novel methodology for document-level novelty identification by utilizing document-to-sentence-level strategy. This work first splits a document into sentences, decides the novelty of every sentence, then registers the record-level novelty score in view of an altered limit. Exploratory results on an arrangement of document demonstrate that our methodology beats standard document-level novelty discovery as far as repetition exactness and excess review. This work applies on the document-level information from an arrangement of documents. It is valuable in identifying novel data in information with a high rate of new documents. It has been effectively incorporated in a true novelty identification framework in the zone of information retrieval.


title: Impulse Commutated Zero-Current Switching Current-Fed Push–Pull Converter: Analysis, Design, and Experimental Results

abstract: Impulse commutated current-fed push-pull (ICCPP) converter has been analyzed and designed. The topology maintains zero-current switching (ZCS) of the semiconductor devices. Variable frequency modulation has been adopted to regulate load voltage and achieve ZCS over variable input voltage. At fixed input voltage, ZCS and load voltage are load independent. Natural clamping of devices is achieved via impulse commutation. Push-pull converters have only two devices with common ground with source requiring simple gate drive circuitry. A comprehensive study of the proposed converter, including steady-state analysis and design, has been reported. A 500-W prototype was developed in the laboratory to evaluate and demonstrate the performance of the converter.


title: Intersecting Gender and Race in Globalization: Beyond the Evolution from Cultural Imperialism to Cultural Hybridity

abstract: Contemporary trans-cultural flow negates the dominant-subordinate binary scheme suggested by early cultural imperialism. Indeed, it is a complicated, ambiguous, and multilateral process. This essay explores how the theories of global trans-cultural influence have evolved in the realm of communication research since the 1960s. It first examines how the discourse of globalization has historically moved from cultural imperialism to cultural hybridity. It then attempts to intersect such evolution with issues of gender and race. This essay is theoretically grounded in the intersection of cultural hybridity and postcolonial feminism. Further, it owes its empirical approach to feminist ethnographers who try to encompass the diversity of women all over the world. Such scholarly frameworks can be intertwined in terms of their overarching concern, i.e., cultural hybridity, feminism, and ethnography strive to empower the powerless, such as women and the Third World, while criticizing the unequal distribution of power. I seek to grasp a “backward” global flow, i.e., subversive engagement of indigenous people with global media empowered by cultural hybridity and postcolonial feminism. More specifically, as an Asian feminist who is studying in the United States, I desire to de-Westernize the discourse on subaltern women and let them speak.


title: On Interpretation of Network Embedding via Taxonomy Induction

abstract: Network embedding has been increasingly used in many network analytics applications to generate low-dimensional vector representations, so that many off-the-shelf models can be applied to solve a wide variety of data mining tasks. However, similar to many other machine learning methods, network embedding results remain hard to be understood by users. Each dimension in the embedding space usually does not have any specific meaning, thus it is difficult to comprehend how the embedding instances are distributed in the reconstructed space. In addition, heterogeneous content information may be incorporated into network embedding, so it is challenging to specify which source of information is effective in generating the embedding results. In this paper, we investigate the interpretation of network embedding, aiming to understand how instances are distributed in embedding space, as well as explore the factors that lead to the embedding results. We resort to the post-hoc interpretation scheme, so that our approach can be applied to different types of embedding methods. Specifically, the interpretation of network embedding is presented in the form of a taxonomy. Effective objectives and corresponding algorithms are developed towards building the taxonomy. We also design several metrics to evaluate interpretation results. Experiments on real-world datasets from different domains demonstrate that, by comparing with the state-of-the-art alternatives, our approach produces effective and meaningful interpretation to embedding results.


Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis


title: Coronary artery disease detection using photoplethysmography

abstract: This paper presents a technique for coronary artery disease (CAD) detection through photoplethysmography (PPG). This work is aimed at developing a non-invasive, inexpensive screening technique suitable for home monitoring. Time domain analysis of PPG signal and its second derivative has been carried out to extract distinguishing features. Support Vector Machine based classifier has been used to classify CAD patients. ICU patient data from MIMIC-II dataset has been used for performance evaluation. Sensitivity of 85% and specificity of 78% has been achieved for the analysed data.


title: Discriminant Learning Analysis

abstract: Linear discriminant analysis (LDA) as a dimension reduction method is widely used in classification such as face recognition. However, it suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size, as in images where features are high dimensional and correlated. In this paper, we propose to address the SSS problem in the framework of statistical learning theory. We compute linear discriminants by regularized least squares regression, where the singularity problem is resolved. The resulting discriminants are complete in that they include both regular and irregular information. We show that our proposal and its nonlinear extension belong to the same framework where powerful classifiers such as support vector machines are formulated. In addition, our approach allows us to establish an error bound for LDA. Finally, our experiments validate our theoretical analysis results.


title: Strategic games on defense trees ⋆

abstract: In this paper we use defense trees, an extension of attack trees with countermeasures, to represent attack scenarios and game theory to detect the most promising actions attacker and defender. On one side the attacker wants to break the system (with as little efforts as possible), on the opposite side the defender want to protect it (sustaining the minimum cost). ::: ::: As utility function for the attacker and for the defender we consider economic indexes (like the Return on Investment (ROI) and the Return on Attack (ROA)). We show how our approach can be used to evaluate effectiveness and economic profitability of countermeasures as well as their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.


title: The evolution of human skin colouration and its relevance to health in the modern world

abstract: Functionally naked skin which comes in a range of colours is unique to the human species. This review summarises current evidence pertaining to the evolution of these attributes. The biggest changes in the integument occurred during the course of human evolution in equatorial Africa, under regimes of high daytime temperatures and high ultraviolet radiation (UVR). Loss of most functional body hair was accompanied by the evolution of an epidermis with a specialised stratum corneum and permanent, protective, eumelanin pigmentation. The main reason for the evolution of dark pigmentation was to protect against folate deficiency caused by elevated demands for folate in cell division, DNA repair, and melanogenesis stimulated by UVR. Dispersal out of tropical Africa created new challenges for human physiology especially because of lower and more seasonal levels of UVR and ultraviolet B (UVB) outside of the tropics. In these environments, the challenge of producing a vitamin D precursor in the skin from available UVB was met by natural selection acting on mutations capable of producing varying degrees of depigmentation. The range of pigmentation observed in modern humans today is, thus, the product of two opposing clines, one favoring photoprotection near the equator, the other favoring vitamin D photosynthesis nearer the poles. Recent migrations and changes in lifestyle in the last 500 years have brought many humans into UVR regimes different from those experienced by their ancestors and, accordingly, exposed them to new disease risks, including skin cancer and vitamin D deficiency.


title: Sharing Personal Content Online: Exploring Channel Choice and Multi-Channel Behaviors

abstract: People share personal content online with varied audiences, as part of tasks ranging from conversational-style content sharing to collaborative activities. We use an interview- and diary-based study to explore: 1) what factors impact channel choice for sharing with particular audiences; and 2) what behavioral patterns emerge from the ability to combine or switch between channels. We find that in the context of different tasks, participants match channel features to selective-sharing and other task-based needs, shaped by recipient attributes and communication dynamics. Participants also combine multiple channels to create composite sharing features or reach broader audiences when one channel is insufficient. We discuss design implications of these channel dynamics.


title: Patterned orocutaneous therapy improves sucking and oral feeding in preterm infants

abstract: Aim: To determine whether NTrainer patterned orocutaneous therapy affects preterm infants' non-nutritive suck and/or oral feeding success. ::: ::: Subjects: Thirty-one preterm infants (mean gestational age 29.3 weeks) who demonstrated minimal non-nutritive suck output and delayed transition to oral feeds at 34 weeks post-menstrual age. ::: ::: Intervention: NTrainer treatment was provided to 21 infants. The NTrainer promotes non-nutritive suck output by providing patterned orocutaneous stimulation through a silicone pacifier that mimics the temporal organization of suck. ::: ::: Method: Infants' non-nutritive suck pressure signals were digitized in the NICU before and after NTrainer therapy and compared to matched controls. Non-nutritive suck motor pattern stability was calculated based on infants' time- and amplitude-normalized digital suck pressure signals, producing a single value termed the Non-Nutritive Suck Spatiotemporal Index. Percent oral feeding was the other outcome of interest, and revealed the NTrainer's ability to advance the infant from gavage to oral feeding. ::: ::: Results: Multilevel regression analyses revealed that treated infants manifest a disproportionate increase in suck pattern stability and percent oral feeding, beyond that attributed to maturational effects alone. ::: ::: Conclusion: The NTrainer patterned orocutaneous therapy effectively accelerates non-nutritive suck development and oral feeding success in preterm infants who are at risk for oromotor dysfunction.


title: Analysis of Obstacle-Climbing Capability of Planetary Exploration Rover with Rocker-Bogie Structure

abstract: Wheeled mobile robots are increasingly being utilized in unknown and dangerous situations such as planetary surface exploration. Based on force analysis of the differential joints and force analysis between the wheels and the ground, this paper established the quasi-static mathematical model of the 6-wheel mobile system of planetary exploration rover with rocker-bogie structure. Considering the constraint conditions, with the method of finding the wheels’friction force solution space feasible region, obstacle-climbing capability of the mobile mechanism was analyzed. Given the same obstacle heights and contact angles of wheel-ground, the single side forward obstacle-climbing of the wheels was simulated respectively, and the results show that the rear wheel has the best obstacle-climbing capability, the middle wheel is the worst, and the front wheel is moderate.


title: Punjabi Poetry Classification: The Test of 10 Machine Learning Algorithms

abstract: Literature of country represents the prosperity of that country. India, being a multilingual country, is having a rich heritage and literature. In order to retrieve literature pieces easily, it must be classified. In this research work, vocabulary-content based classification of Punjabi poetry is done. 4 different poetry categories are populated with 240 poetries (with 60 poems in each category). These 240 poetry documents are passed through typical NLP text classification phases like Sentence Splitting, Tokenization and Bag-of-Words (BOW) representation, finally yielding to their Vector Space Model (VSM) representation. Total 9867 unique words extracted from last step are used for building the different machine learning models. For the first time in research community, 10 different machine learning algorithms are trained and tested for any Indian language, using weka, with an aim to find the most suitable algorithm. Results for Punjabi poetry classification revealed that 4 machine learning algorithms namely, Hyperpipes (HP), K- nearest neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) with an accuracy of 50.63 %, 52.92 %, 52.75 % and 58.79 % respectively, outperformed all other machine learning algorithms under the test.


Introduction to the special section on continuous space and related methods in natural language processing


title: Quantifying the Isolation Characteristics in Container Environments

abstract: In recent years, container technologies have attracted intensive attention due to the features of light-weight and easy-portability. The performance isolation between containers is becoming a significant challenge, especially in terms of network throughput and disk I/O. In traditional VM environments, the performance isolation is often calculated based on performance loss ratio. In container environments, the performance loss of well-behaved containers may be incurred not only by misbehaving containers but also by container orchestration and management. Therefore, the measurement models that only take performance loss into consideration will be not accurate enough. In this paper, we proposed a novel performance isolation measurement model that combines the performance loss and resource shrinkage of containers. Experimental results validate the effectiveness of our proposed model. Our results highlight the performance isolation between containers is different with the issue in VM environments.


title: The Meaning of Types - From Intrinsic to Extrinsic Semantics

abstract: A definition of a typed language is said to be "intrinsic" if it assigns meanings to typings rather than arbitrary phrases, so that ill-typed phrases are meaningless. In contrast, a definition is said to be "extrinsic" if all phrases have meanings that are independent of their typings, while typings represent properties of these meanings. For a simply typed lambda calculus, extended with recursion, subtypes, and named products, we give an intrinsic denotational semantics and a denotational semantics of the underlying untyped language. We then establish a logical relations theorem between these two semantics, and show that the logical relations can be "bracketed" by retractions between the domains of the two semantics. From these results, we derive an extrinsic semantics that uses partial equivalence relations.


title: Fall Detection Based on Body Part Tracking Using a Depth Camera

abstract: The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.


title: Circulating tumor cells (CTC) detection: clinical impact and future directions.

abstract: Recent molecular and clinical studies have shown that invasion may occur very early in tumor development, thus emphasizing the potential importance of specific and sensitive detection of circulating tumor cells (CTC) and circulating tumor microemboli (CTM). The technical challenge in this field consists of finding "rare" tumor cells (just a few CTCs mixed with the approximately 10 million leukocytes and 5 billion erythrocytes in 1ml of blood) and being able to distinguish them from epithelial non-tumor cells and leukocytes. Many recent studies have discussed the clinical impact of detecting CTC/CTM. Although conflicting results have been obtained, these studies suggest the vast potential of CTC/CTM detection in cancer prognosis and follow up. However, the variable technical approaches which were used, as well as the number of millilitres of blood analyzed, the quality of sensitivity and specificity tests, the number of patients versus controls and the data interpretation make it very difficult to draw firm conclusions. A particularly important recent finding is that invasive tumor cells tend to loose their epithelial antigens by the epithelial to mesenchymal transition (EMT) process. Furthermore, it is known that non-tumor epithelial cells can also be present in blood. Thus, it appears that a reliable diagnostic identification of CTC and CTM cannot be based on the expression of epithelial-specific transcripts or antigens. Cytopathological examination of CTC/CTM, sensitively enriched from blood, represents a potentially useful alternative and can now be employed in routine analyses as a specific diagnostic assay, and be tested in large, blind, multicenter clinical trials. This basic approach can be complemented by immunological and molecular studies for further characterization of CTC/CTM and of their malignant potential. This review is aimed at helping oncologists critically evaluate past and future research work in this field. The interest in development and assessment of this noninvasive marker should lead to more effective and better tailored anticancer treatments for individual patients, thus resulting in their improved life expectancy.


title: Singularity: Scientific containers for mobility of compute

abstract: Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.


title: Three-Dimensional Face Recognition Using Shapes of Facial Curves

abstract: We study shapes of facial surfaces for the purpose of face recognition. The main idea is to 1) represent surfaces by unions of level curves, called facial curves, of the depth function and 2) compare shapes of surfaces implicitly using shapes of facial curves. The latter is performed using a differential geometric approach that computes geodesic lengths between closed curves on a shape manifold. These ideas are demonstrated using a nearest-neighbor classifier on two 3D face databases: Florida State University and Notre Dame, highlighting a good recognition performance


title: Weighted Elastic Net Penalized Mean-Variance Portfolio Design and Computation

abstract: It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this paper we address the problem by regularizing the mean-variance objective function with a weighted elastic net penalty. We show that the use of this penalty can be motivated by a robust reformulation of the mean-variance criterion that directly accounts for parameter uncertainty. With this interpretation of the weighted elastic net penalty we derive data driven techniques for calibrating the weighting parameters based on the level of uncertainty in the parameter estimates. We test our proposed technique on US stock return data and our results show that the calibrated weighted elastic net penalized portfolio outperforms both the unpenalized portfolio and uniformly weighted elastic net penalized portfolio. ::: This paper also introduces a novel Adaptive Support Split-Bregman approach which leverages the sparse nature of $\ell_{1}$ penalized portfolios to efficiently compute a solution of our proposed portfolio criterion. Numerical results show that this modification to the Split-Bregman algorithm results in significant improvements in computational speed compared with other techniques.


title: Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

abstract: This column discusses the authors' efforts to project the winner of the 2013 Pakistan and the 2014 Indian prime minister election using social network analysis and methods to create a diffusion estimation model.


title: Torque ripple and misalignment torque compensation for the built-in torque sensor of harmonic drive systems

abstract: A harmonic drive is a compact, lightweight, and high-ratio torque transmission device which is used in many electrically actuated robot manipulators. In this paper a built-in torque sensor for harmonic-drive systems is examined in detail. The method proposed by Hashimoto, in which strain gauges are directly mounted on the flexspline, is employed and improved in this paper. To minimize sensing inaccuracy, four Rosette strain gauges are used employing an accurate positioning method. To cancel the torque ripples, the oscillation observed on the measured torque and caused mainly by gear teeth meshing, Kalman filter estimation is used. A simple fourth-order harmonic oscillator proved to accurately model the torque ripples. Moreover, the error model is extended to incorporate any misalignment torque. By on-line implementation of the Kalman filter, it has been shown that this method is a fast and accurate way to filter torque ripples and misalignment torque. Hence, the intelligent built-in torque sensor is a viable and economical way to measure the harmonic-drive transmitted torque and to employ that for torque feedback strategies.


title: Advanced illumination techniques for GPU volume raycasting

abstract: Volume raycasting techniques are important for both visual arts and visualization. They allow an efficient generation of visual effects and the visualization of scientific data obtained by tomography or numerical simulation. Thanks to their flexibility, experts agree that GPU-based raycasting is the state-of-the art technique for interactive volume rendering. It will most likely replace existing slice-based techniques in the near future. Volume rendering techniques are also effective for the direct rendering of implicit surfaces used for soft body animation and constructive solid geometry. The lecture starts off with an in-depth introduction to the concepts behind GPU-based ray-casting to provide a common base for the following parts. The focus of this course is on advanced illumination techniques which approximate the physically-based light transport more convincingly. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion and simple Monte-Carlo based approaches to global illumination including translucency and scattering. With the proposed techniques, users are able to interactively create convincing images from volumetric data whose visual quality goes far beyond traditional approaches. The optical properties in participating media are defined using the phase function. Many approximations to the physically based light transport applied for rendering natural phenomena such as clouds or smoke assume a rather homogenous phase function model. For rendering volumetric scans on the other hand different phase function models are required to account for both surface-like structures and fuzzy boundaries in the data. Using volume rendering techniques, artists who create medical visualization for science magazines may now work on tomographic scans directly, without the necessity to fall back to creating polygonal models of anatomical structures.


title: Control-flow integrity principles, implementations, and applications

abstract: Current software attacks often build on exploits that subvert machine-code execution. The enforcement of a basic safety property, control-flow integrity (CFI), can prevent such attacks from arbitrarily controlling program behavior. CFI enforcement is simple and its guarantees can be established formally, even with respect to powerful adversaries. Moreover, CFI enforcement is practical: It is compatible with existing software and can be done efficiently using software rewriting in commodity systems. Finally, CFI provides a useful foundation for enforcing further security policies, as we demonstrate with efficient software implementations of a protected shadow call stack and of access control for memory regions.


title: Fully Distributed Scrum: Linear Scalability of Production between San Francisco and India

abstract: The Scrum software development framework was designed for the hyperproductive state where productivity increases by 5-10 times over waterfall teams and many co-located teams have achieved this effect. In 2006, Xebia (The Netherlands) started localized projects with half Dutch and half Indian team members. After establishing a localized velocity of five times their waterfall competitors on the same project, they moved the Indian members of the team to India and showed stable velocity with fully distributed teams. The ability to achieve hyperproductivity with distributed, outsourced teams was shown to be a repeatable process and a fully distributed model is now the recommended standard when organizations have disciplined Scrum teams with full implementation of XP engineering practices inside the Scrum. Previous studies used overlapping time zones to ease communication and create a single distributed team. The goal of this report is to go one step further and show the same results with team members separated by the 12.5 hour time difference between India and San Francisco. If Scrum works without overlapping time zones then applying it to the mainstream offshoring practice in North America will be possible. In 2008, Xebia India started engagements with partners like TBD.com, a social networking site in San Francisco. TBD has an existing core team of developers doing Scrum with an established local velocity. Adding Xebia India developers to the San Francisco team with a Fully Distributed Scrum model achieved linear scalability with a globally distributed outsourced team.


title: Churn Intent Detection in Multilingual Chatbot Conversations and Social Media

abstract: We propose a new method to detect when users express the intent to leave a service, also known as churn. While previous work focuses solely on social media, we show that this intent can be detected in chatbot conversations. As companies increasingly rely on chatbots they need an overview of potentially churny users. To this end, we crowdsource and publish a dataset of churn intent expressions in chatbot interactions in German and English. We show that classifiers trained on social media data can detect the same intent in the context of chatbots. ::: We introduce a classification architecture that outperforms existing work on churn intent detection in social media. Moreover, we show that, using bilingual word embeddings, a system trained on combined English and German data outperforms monolingual approaches. As the only existing dataset is in English, we crowdsource and publish a novel dataset of German tweets. We thus underline the universal aspect of the problem, as examples of churn intent in English help us identify churn in German tweets and chatbot conversations.


title: Generating SQL queries using natural language syntactic dependencies and metadata

abstract: This research concerns with translating natural language questions into SQL queries by exploiting the MySQL framework for both hypothesis construction and thesis verification in the task of question answering. We use linguistic dependencies and metadata to build sets of possible SELECT and WHERE clauses. Then we exploit again the metadata to build FROM clauses enriched with meaningful joins. Finally, we combine all the clauses to get the set of all possible SQL queries, producing an answer to the question. Our algorithm can be recursively applied to deal with complex questions, requiring nested SELECT instructions. Additionally, it proposes a weighting scheme to order all the generated queries in terms of probability of correctness. ::: ::: Our preliminary results are encouraging as they show that our system generates the right SQL query among the first five in the 92% of the cases. This result can be greatly improved by re-ranking the queries with a machine learning methods.


Paradigm Whose Time Has Come by


title: Plastic Surgery Trends Parallel Playboy Magazine: The Pudenda Preoccupation

abstract: Background: Aesthetic vulvovaginal procedures are being performed with increased frequency. Many experts have suggested that the growing demand for these procedures relates to the availability and revealing nature of nude images on the Internet and in other media. ::: ::: Objectives: The authors examined chronologically organized nude photographs from a popular magazine and objectively measured the position of the vulva relative to the center of focus to observe trends for the past 6 decades. ::: ::: Methods: Playboy magazine centerfold photographs from 1954 to 2013 were analyzed and categorized. The positions of the vaginal area (V-line) and the breast area (N-line) were measured in relation to the horizontal midline of the photograph. Images also were assessed for degree of grooming and exposure of the breast and pubic areas, as well as visibility of the pudendal cleft, labia majora, and labia minora. ::: ::: Results: Four hundred ninety images met inclusion criteria for the analysis. Full exposure of the V-line increased from 0 instances in the 1950s to 78.6% of images from 2010 through 2013 ( P < .001). Moreover, the V-line position became 41.0% closer to the photographic midline ( P = .045). ::: ::: Conclusions: The results demonstrate a trend toward increased visibility and prominence of the female genitalia as the focal point of popular nude photographs.


SVM Based Effective Malware Detection System


Uplift modeling for clinical trial data


title: High-efficiency design of multiphase synchronous mode soft-switching converter for wide input and load range

abstract: A four-phase interleaved bidirectional dc-dc converter with coupled inductor is adopted in both boost- and buck-mode operations. The circuit allows synchronous mode operation to achieve zero-voltage switching. Together with the use of low R ds-on power MOSFET as the switching device, ultra high efficiency can be obtained. This paper shows a high-voltage example system operating completely in synchronous mode with 99.2% efficiency at the full load, but only 93% at 10% load. A low-voltage system example is then proposed to operate in synchronous mode under light-load condition and in continuous conducting mode under heavy load condition to achieve high efficiency over a wide load range. The peak efficiency of the second example system reaches 99.5% in between 10 and 20% load and maintains above 98% throughout the rest of load range.


title: Comparison of LQR and PD controller for stabilizing Double Inverted Pendulum System

abstract: this paper presented comparison of the time specification performance between two type of controller for a Double Inverted Pendulum system. Double Inverted Pendulum is a non-linear ,unstable and fast reaction system. DIP is stable when its two pendulums allocated in vertically position and have no oscillation and movement and also inserting force should be zero. The objective is to determine the control strategy that to delivers better performance with respect to pendulum angle's and cart position. In this paper simple multi PD controller designed on the theory of pole placement and its performance is compared with Linear Quadratic Regulator controller using MATLAB and Simulink.


title: Cootes. Boosted regression active shape models. BMVC

abstract: We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.


title: Childhood IQ and Adult Mental Disorders: A Test of the Cognitive Reserve Hypothesis

abstract: Objective: Cognitive reserve has been proposed as important in the etiology of neuropsychiatric disorders. However, tests of the association between premorbid IQ and adult mental disorders other than schizophrenia have been limited and inconclusive. The authors tested the hypothesis that low childhood IQ is associated with increased risk and severity of adult mental disorders. Method: Participants were members of a representative 1972–1973 birth cohort of 1,037 males and females in Dunedin, New Zealand, who were followed up to age 32 with 96% retention. WISC-R IQ was assessed at ages 7, 9, and 11. Research diagnoses of DSM mental disorders were made at ages 18, 21, 26, and 32. Results: Lower childhood IQ was associated with increased risk of developing schizophrenia spectrum disorder, adult depression, and adult anxiety. Lower childhood IQ was also associated with greater comorbidity and with persistence of depression; the association with persistence of generalized anxiety disorder was nearly significant...


title: Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems

abstract: Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry. Such work can help develop rich insights into various phenomena of interest that cannot be fully understood using only a quantitative or a qualitative method. Notwithstanding the benefits and repeated calls for such work, there is a dearth of mixed methods research in information systems. Building on the literature on recent methodological advances in mixed methods research, we develop a set of guidelines for conducting mixed methods research in IS. We particularly elaborate on three important aspects of conducting mixed methods research: (1) appropriateness of a mixed methods approach; (2) development of meta-inferences (i.e., substantive theory) from mixed methods research; and (3) assessment of the quality of meta-inferences (i.e., validation of mixed methods research). The applicability of these guidelines is illustrated using two published IS papers that used mixed methods.


title: Machine learning for interactive systems and robots: a brief introduction

abstract: Research on interactive systems and robots, i.e. interactive machines that perceive, act and communicate, has applied a multitude of different machine learning frameworks in recent years, many of which are based on a form of reinforcement learning (RL). In this paper, we will provide a brief introduction to the application of machine learning techniques in interactive learning systems. We identify several dimensions along which interactive learning systems can be analyzed. We argue that while many applications of interactive machines seem different at first sight, sufficient commonalities exist in terms of the challenges faced. By identifying these commonalities between (learning) approaches, and by taking interdisciplinary approaches towards the challenges, we anticipate more effective design and development of sophisticated machines that perceive, act and communicate in complex, dynamic and uncertain environments.


title: Harmonic Load Mitigation Using the Optimal Double Tuned Passive Filter Technique

abstract: Harmonic is one of the power quality disturbances customarily imminent in an unbalanced electrical system. Harmonic represents as the multiple integral of fundamental frequency of voltage and current inflicting towards the shifting in system frequency causing to a disruptive operation of electrical devices. This paper investigates on the performance of passive filter intrinsically by utilizing the inductor and capacitor electrical components to mitigate harmonic problem emanating from an unbalanced electrical system. In particular, explication in this paper will focus on the optimal parameters specification for the double tuned passive filter that used to overcome the phenomenon of harmonic issue. The two case studies constituting with different number of harmonic orders injected in a system were introduced to distinguish effectiveness of double tuned passive filter in solving the aforesaid problems. The parameters configuration of the passive filter are automatically tuned by the MATLAB® software to reduce the total harmonic distortion incurred in a system designed under the Simulink® software.


title: Neural Message Passing for Quantum Chemistry

abstract: Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.


title: Vehicles Capable of Dynamic Vision

abstract: Survey of artificial intelligence applications to vehicular computer vision including aircraft and ground vehicles


title: Microbial Growth and Quorum Sensing Antagonist Activities of Herbal Plants Extracts

abstract: Antimicrobial and antiquorum sensing (AQS) activities of fourteen ethanolic extracts of different parts of eight plants were screened against four Gram-positive, five Gram-negative bacteria and four fungi. Depending on the plant part extract used and the test microorganism, variable activities were recorded at 3 mg per disc. Among the Grampositive bacteria tested, for example, activities of Laurus nobilis bark extract ranged between a 9.5 mm inhibition zone against Bacillus subtilis up to a 25 mm one against methicillin resistant Staphylococcus aureus. Staphylococcus aureus and Aspergillus fumigatus were the most susceptible among bacteria and fungi tested towards other plant parts. Of interest is the tangible antifungal activity of a Tecoma capensis flower extract, which is reported for the first time. However, minimum inhibitory concentrations (MIC's) for both bacteria and fungi were relatively high (0.5-3.0 mg). As for antiquorum sensing activity against Chromobacterium violaceum, superior activity (>17 mm QS inhibition) was associated with Sonchus oleraceus and Laurus nobilis extracts and weak to good activity (8-17 mm) was recorded for other plants. In conclusion, results indicate the potential of these plant extracts in treating microbial infections through cell growth inhibition or quorum sensing antagonism, which is reported for the first time, thus validating their medicinal use.


title: Affective Computing and Sentiment Analysis

abstract: Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.


title: Analysis and Design of Reduced-Size Marchand Rat-Race Hybrid for Millimeter-Wave Compact Balanced Mixers in 130-nm CMOS Process

abstract: The analysis and design flow for reduced-size Marchand rat-race hybrids are presented in this paper. A simplified single-to-differential mode is used to analyze the Marchand balun, and the methodology to reduce the size of Marchand balun is developed. The 60-GHz CMOS singly balanced gate mixer and diode mixer using the reduced-size Marchand rat-race hybrid are implemented to verify the design methodology. The monolithic microwave integrated circuit mixers achieve comparable performance with a compact chip size among the reported 60-GHz CMOS mixers.


title: Specific electrophysiological components disentangle affective sharing and empathic concern in psychopathy

abstract: Empathic impairment is one of the hallmarks of psychopathy, a personality dimension associated with poverty in affective reactions, lack of attachment to others, and a callous disregard for the feelings, rights, and welfare of others. Neuroscience research on the relation between empathy and psychopathy has predominately focused on the affective sharing and cognitive components of empathy in forensic populations, and much less on empathic concern. The current study used high-density electroencephalography in a community sample to examine the spatiotemporal neurodynamic responses when viewing people in physical distress under two subjective contexts: one evoking affective sharing, the other, empathic concern. Results indicate that early automatic (175–275 ms) and later controlled responses (LPP 400–1,000 ms) were differentially modulated by engagement in affective sharing or empathic concern. Importantly, the late event-related potentials (ERP) component was significantly impacted by dispositional empathy and psychopathy, but the early component was not. Individual differences in dispositional empathic concern directly predicted gamma coherence (25–40 Hz), whereas psychopathy was inversely modulatory. Interestingly, significant suppression in the mu/alpha band (8–13 Hz) when perceiving others in distress was positively associated with higher trait psychopathy, which argues against the assumption that sensorimotor resonance underpins empathy. Greater scores on trait psychopathy were inversely related to subjective ratings of both empathic concern and affective sharing. Overall, the study demonstrates that neural markers of affective sharing and empathic concern to the same cues of another's distress can be distinguished at an electrophysiological level, and that psychopathy alters later time-locked differentiations and spectral coherence associated with empathic concern.


title: Trust-E: A Trusted Embedded Operating System Based on the ARM Trustzone

abstract: Security is an emerging topic in the field of embedded devices. ARM Trust Zone is a technology used to increase security of embedded systems using the ARM processor, the most common processor for embedded consumer devices. Trust zone is a hardware isolation mechanism that improves software security without the need for extra hardware chips. It separates critical applications from the normal (rich) OS, by supporting unsecure and secure worlds running in independent and isolated execution environments. In order to activate the Trust Zone, a full Trusted Execution Environment (TEE) ecosystem must be implemented. This paper presents a trusted embedded operating system architecture, Trust-E, based on the ARM Trust Zone processor that implements the TEE. Our proposed framework's design and implementation is not only for ARM Trust Zone Processors, but also for other processors that have similar features with spatial isolation functions. We also designed and implemented this framework, including the implementation of the trusted kernel, T-OS, running in the TEE, to support secure services and communication mechanism. Finally we have developed a demo of a mobile payment application to demonstrate the correctness and effectiveness of these approaches, using Android as the rich OS and the SMDK210 development board. The amount of experiment results show these approaches we proposed can effectively meet user's security requirement.


title: An Adaptive Canny Edge Detector using Histogram Concavity Analysis

abstract: The traditional Canny edge detector has some drawbacks. Gaussian filter can’t remove the impulsive noise. Moreover, it is difficult to automatically select the dual-threshold. Especially when the noise intensity increases, the dual-threshold selection method of traditional Canny detector is invalid. In this paper, we present an adaptive Canny edge detector using histogram concavity analysis. The improved detector uses switching median filter to remove the impulsive noise and then uses Gaussian filter to smooth other types of noise. It determines the dual-threshold through histogram concavity analysis. Experimental results show that the proposed detector is better than the LOG detector and traditional Canny detector in strong noise environment. The improved Canny detector can automatically select the dual-threshold.


title: Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

abstract: A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, inverse reinforcement learning (IRL) module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-depth sensor. The inverse reinforcement learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with different state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a three-layer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from IRL module, and a low-level system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.


title: Rumor detection on Twitter using extracted patterns from conversational tree

abstract: These days, Twitter social network is one of the main platforms for getting news, among people over the world. This is because of the high volume of data generated by this social media, which makes Twitter up to date with news and information. Nevertheless, the existence of invalid information over the social network makes the users unhappy and also arises some problems in the real world, particularly in the crisis. To overcome these problems and other possible issues, automatic detection of rumor on Twitter must be taken into account. Despite such issues, in this paper, rumor detection in Twitter is studied. In this paper, the rumor is validated by considering the user's feedback, as the source data for rumor study. In our proposed method, pattern recognition and its analysis of the user conversational tree in Twitter is studied. These recognized patterns feed into as features for training a classifier for rumor detection. The model for training a classifier is an Extreme Learning Machine and its extension. The dataset for experiments of our method is the standard dataset of SemEval-2017 Task 8. Experiments of our proposed method with respect to competitor methods in rumor detection show that our method outperforms the state of the art methods.


title: A Design Space of Visualization Tasks

abstract: Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it.


title: Editorial - Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research

abstract: We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems IS community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and significance.


Protein and lipid species in seminal plasma of fertile Holstein-Friesian bulls