title: A cascading mentoring pedagogy in a CS service learning course to broaden participation and perceptions

abstract: This study reports on the design, implementation, and evaluation of a service-learning course based on a "cascading mentoring" model linking together the faculty, administration, and undergraduates of an urban university's computer science department with local high school students. We present findings from surveys and post-interviews that illustrate undergraduates' and high school students' experiences in the program and how their perceptions of computing and mentoring changed based upon the outreach. In our discussion, we focus on the institutional and conceptual challenges of implementing the community service course within the university's computer science department, while also highlighting the learning opportunities for streamlining such a model for future iterations.


title: Mid-arm and epitrochlear lymphadenopathy: a clinico-radiological surprise

abstract: An 18-year-old man presented with multiple subcutaneous nodules over the anteromedial aspect of his right lower arm. Based on findings of sonography, histopathology, circulating filarial antigen test, and therapeutic response to diethylcarbazine, a diagnosis of filarial lymphadenopathy affecting mid-arm nodes and epitrochlear lymph nodes was made.


title: The category concept: an extension to the entity-relationship model

abstract: Abstract An enhanced version of the Entity-Relationship (ER) data model called the Entity-Category-Relationship (ECR) data model is presented. The principal extension is the introduction of the concept of a category. Categories permit the grouping of entities from different entity types according to the roles they play in a relationship, as well as the representation of ISA and generalization hierarchies. The structures of the ECR data model are defined, and a graphic representation technique for their display is presented. Language operations to define and use an ECR database are defined. Two realistic examples of the use of the ECR model for database design are demonstrated. The examples show how ECR structures can be directly mapped into relational and network structures. The definition of derived relationships on an ECR database gives the power to phrase higher order recursive queries in a first order query language.


title: Gender portrayals in food commercials: A content analysis of Spanish television advertisements

abstract: Content analysis of media messages from a gender perspective has a long tradition. Regarding advertising, most of the researches have been focused on generic samples of advertisements or on the advertising traditionally associated to one gender or the other. However, few authors have analyzed gender contents in food advertising, in spite of the close relationship between gender structures and the social processes of food purchase and preparation. Thus, gender representations in food advertising broadcast in Spanish television are analyzed in this article. The main results obtained show clear gender differences. This way, there is a clear predominance of the male gender in the voices-over and a more balanced situation regarding the protagonists. Besides, female protagonists are more frequently located indoors, portrayed as attractive women and connected to advertising claims related to beauty or to the healthy qualities of the product.


title: A resource-based perspective on information technology capability and firm performance: an empirical investigation

abstract: The resource-based view of the firm attributes superior financial performance to organizational resources and capabilities. This paper develops the concept of IT as an organizational capability and empirically examines the association between IT capability and firm performance. Firm specific IT resources are classified as IT infrastructure, human IT resources, and IT-enabled intangibles. A matched-sample comparison group methodology and publicly available ratings are used to assess IT capability and firm performance. Results indicate that firms with high IT capability tend to outperform a control sample of firms on a variety of profit and cost-based performance measures.


title: Exploiting social ties for cooperative D2D communications: a mobile social networking case

abstract: Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of mobile social networks, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device (D2D) communications. Specifically, as handheld devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game-theoretic framework to devise social-tie-based cooperation strategies for D2D communications. We also develop a network-assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, truthful, and computationally efficient. We evaluate the performance of the mechanism by using real social data traces. Simulation results corroborate that the proposed mechanism can achieve significant performance gain over the case without D2D cooperation.


title: Management of service quality – differences in values, practices and outcomes

abstract: This article concerns a study with the purpose of identifying differences in the management of quality between manufacturing, private service and public service organisations. A questionnaire was sent to members of the Swedish Association for Quality. Some interesting differences were identified. Customer orientation is highest in the private service sector and lowest in the public service sector. The manufacturing companies’ usage of ISO 9000 is extremely high and their usage of the Swedish Quality Award is fairly low whereas the public service organisations use both these two models equally. This corresponds with the finding that ISO 9000 produces better results in the manufacturing sector whereas The Swedish Quality Award produces better results in the service sector. Generally, the indication is that quality management is most successful in the manufacturing sector and least successful in the public service sector. There is also a difference in that improvements in the manufacturing sector are more often about the processes whereas the organisations within the public service sector more often report improvements regarding personnel.


title: Higher-order abstract syntax

abstract: We describe motivation, design, use, and implementation of higher-order abstract syntax as a central representation for programs, formulas, rules, and other syntactic objects in program manipulation and other formal systems where matching and substitution or unification are central operations. Higher-order abstract syntax incorporates name binding information in a uniform and language generic way. Thus it acts as a powerful link integrating diverse tools in such formal environments. We have implemented higher-order abstract syntax, a supporting matching and unification algorithm, and some clients in Common Lisp in the framework of the Ergo project at Carnegie Mellon University.


title: The cricket indoor location system

abstract: Indoor environments present opportunities for a rich set of location-aware applications such as navigation tools for humans and robots, interactive virtual games, resource discovery, asset tracking, location-aware sensor networking etc. Typical indoor applications require better accuracy than what current outdoor location systems provide. Outdoor location technologies such as GPS have poor indoor performance because of the harsh nature of indoor environments. Further, typical indoor applications require different types of location information such as physical space, position and orientation. ::: This dissertation describes the design and implementation of the Cricket indoor location system that provides accurate location in the form of user space, position and orientation to mobile and sensor network applications. ::: Cricket consists of location beacons that are attached to the ceiling of a building, and receivers, called listeners, attached to devices that need location. Each beacon periodically transmits its location information in an RF message. At the same time, the beacon also transmits an ultrasonic pulse. The listeners listen to beacon transmissions and measure distances to nearby beacons, and use these distances to compute their own locations. This active-beacon passive-listener architecture is scalable with respect to the number of users, and enables applications that preserve user privacy. ::: This dissertation describes how Cricket achieves accurate distance measurements between beacons and listeners. Once the beacons are deployed, the MAT and AFL algorithms, described in this dissertation, use measurements taken at a mobile listener to configure the beacons with a coordinate assignment that reflects the beacon layout. This dissertation presents beacon interference avoidance and detection algorithms, as well as outlier rejection algorithms to prevent and filter out outlier distance estimates caused by uncoordinated beacon transmissions. ::: The Cricket listeners can measure distances with an accuracy of 5 cm. The listeners can detect boundaries with an accuracy of 1 cm. Cricket has a position estimation accuracy of 10 cm and an orientation accuracy of 3 degrees. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: Exploring benchmark corporations in the semiconductor industry based on efficiency

abstract: Abstract The aim of this study is to explore benchmark corporations in the semiconductor industry based on efficiency. In this study, perspectives of intellectual capital and corporate governance are taken into account for the input data, whereas firm value is considered as the output data. Data envelope analysis (DEA), including CCR and BCC models, is utilized to ensure that the benchmark standards are precisely selected. Based on the result, suggestions for both semiconductor corporations and future research are provided at the end of this article.


title: Investments in information systems and technology in the healthcare: Project management mediation

abstract: Healthcare organisations must improve their business practices and internal procedures in order to answer the increasing demand of health professionals and the general public for more and better information. Hospitals invest massively in information systems and technology (IS/IT) in the hope that these investments will improve healthcare and meet patients’ demands. The main objective of our research is to study how organisational maturity, enhanced by investments in IS/IT, project management and best practices, leads to successful projects in public healthcare organisations. The rational of our model is that organisational maturity has a positive effect on IS/IT project success, and that this success is also positively enhanced by the use of project management practices. We emphasise that this combination of approaches can increase the effectiveness of projects. Furthermore, it can also improve the confidence that the results of investments will meet stakeholders’ expectations.


title: Fully Convolutional Regression Network for Accurate Detection of Measurement Points

abstract: Accurate automatic detection of measurement points in ultrasound video sequences is challenging due to noise, shadows, anatomical differences, and scan plane variation. This paper proposes to address these challenges by a Fully Convolutional Neural Network (FCN) trained to regress the point locations. The series of convolutional and pooling layers is followed by a collection of upsampling and convolutional layers with feature forwarding from the earlier layers. The final location estimates are produced by computing the center of mass of the regression maps in the last layer. The temporal consistency of the estimates is achieved by a Long Short-Term memory cells which processes several previous frames in order to refine the estimate in the current frame. The results on automatic measurement of left ventricle in parasternal long axis view of the heart show detection errors below 5% of the measurement line which is within inter-observer variability.


title: Pattern-based safety development methods: overview and comparison

abstract: Design patterns provide good solutions to re-occurring problems and several patterns and methods how to apply them have been documented for safety-critical systems. However, due to the large amount of safety-related patterns and methods, it is difficult to get an overview of their capabilities and shortcomings as there currently is no survey on safety patterns and their application methods available in literature. To give an overview of existing pattern-based safety development methods, this paper presents existing methods from literature and discusses and compares several aspects of these methods such as the patterns and tools they use, their integration into a safety development process, or their maturity.


title: Comparison of multivariate analysis techniques in plastic injection moulding process

abstract: In this paper, we present a comparison between several statistical discriminant analysis techniques applied to a plastic injection moulding process for monitoring quality of injected moulded parts. Comparison among different ways of training the system can provide useful conclusions about the behaviour of the different models in poor conditions. The goal of this paper is to establish a baseline for comparing the performance between different algorithms. A wide variety of research objectives throughout the literature makes it difficult to provide a feasible comparison between results. The evaluation is intended to provide detailed, empirical information on the effectiveness and impact of different model parameters on the performance of the different approaches. The pros and cons of the approaches used are discussed. In order to predict the quality of a plastic part, we extract a set of salient features that characterise an injection cycle and then match these features against a database of stored examples of predefined classes by using supervised classification. The database was created from 199 real plastic injections without any overlap between training and testing datasets.


Public-Private Partnerships and the 2030 Agenda for Sustainable Development : Fit for purpose ? 1


title: Attack graph — Based vulnerability assessment of rank property in RPL-6LOWPAN in IoT

abstract: A significant segment of the Internet of Things (IoT) is the resource constrained Low Power and Lossy Networks (LLNs). The communication protocol used in LLNs is 6LOWPAN (IPv6 over Low-power Wireless Personal Area Network) which makes use of RPL (IPv6 Routing Protocol over Low power and Lossy network) as its routing protocol. In recent times, several security breaches in IoT networks occurred by targeting routers to instigate various DDoS (Distributed Denial of Service) attacks. Hence, routing security has become an important problem in securing the IoT environment. Though RPL meets all the routing requirements of LLNs, it is important to perform a holistic security assessment of RPL as it is susceptible to many security attacks. An important attribute of RPL is its rank property. The rank property defines the placement of sensor nodes in the RPL DODAG (Destination Oriented Directed Acyclic Graphs) based on an Objective Function. Examples of Objective Functions include Expected Transmission Count, Packet Delivery Rate etc. Rank property assists in routing path optimization, reducing control overhead and maintaining a loop free topology through rank based data path validation. In this paper, we investigate the vulnerabilities of the rank property of RPL by constructing an Attack Graph. For the construction of the Attack Graph we analyzed all the possible threats associated with rank property. Through our investigation we found that violation of protocols related to rank property results in several RPL attacks causing topological sub-optimization, topological isolation, resource consumption and traffic disruption. Routing security essentially comprises mechanisms to ensure correct implementation of the routing protocol. In this paper, we also present some observations which can be used to devise mechanisms to prevent the exploitation of the vulnerabilities of the rank property.


title: Bayesian Point Cloud Reconstruction

abstract: In this paper, we propose a novel surface reconstruction technique based on Bayesian statistics: The measurement process as well as prior assumptions on the measured objects are modeled as probability distributions and Bayes’ rule is used to infer a reconstruction of maximum probability. The key idea of this paper is to define both measurements and reconstructions as point clouds and describe all statistical assumptions in terms of this finite dimensional representation. This yields a discretization of the problem that can be solved using numerical optimization techniques. The resulting algorithm reconstructs both topology and geometry in form of a well-sampled point cloud with noise removed. In a final step, this representation is then converted into a triangle mesh. The proposed approach is conceptually simple and easy to extend. We apply the approach to reconstruct piecewise-smooth surfaces with sharp features and examine the performance of the algorithm on different synthetic and real-world data sets.


title: The nesC language: A holistic approach to networked embedded systems

abstract: We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.


title: Manufacturing and finite element assessment of a novel pressure reducing insole for Diabetic Neuropathic patients

abstract: Diabetes is one of the metabolic diseases. Uncontrolled diabetes can lead to diabetic foot ulcers and if it was not treated would lead to amputation. Foot ulcers can be prevented by using suitable insoles which are made of appropriate material and geometrically designed by constituent layers. In this study, single-layer and three-layer insoles have been compared during static and dynamic loading. The selected materials were silicone gel (SG), plastazote foam (PLZ), polyfoam (PF) and ethyl vinyl acetate foam (EVA). Four single-layer and 18 combinations of three-layer insoles were selected. Materials behaviors were determined by using a uniaxial pressure test. The description of stress and strain is obtained by using the model of three dimensional nonlinear Finite Element Method (FEM). Then samples were tested by using commercially available plantar pressure measurement system. The FEM results showed that the SG and PLZ insoles are more appropriate compared to single-layer insoles. The combinations of PLZ, SG and EVA (from top to bottom) are recognized as the best between three-layer insoles. Also the best three-layer insole is more effective in promoting a favourable stress and strain distribution than single-layer insoles, especially in dynamic mode. According to simulation results, three-layer insole decreases stress concentration by 9 %. Also experimental tests showed that using three-layer insole decreases plantar pressure by 63 % compared to barefoot condition bare foot.


title: Extended abdominoperineal resection with gluteus maximus flap reconstruction of the pelvic floor for rectal cancer

abstract: Background: ::: Intraoperative tumour perforation, positive tumour margins, wound complications and local recurrence are frequent difficulties with conventional abdominoperineal resection (APR) for rectal cancer. An alternative technique is the extended posterior perineal approach with gluteus maximus flap reconstruction of the pelvic floor. The aim of this study was to report the technique and early experience of extended APR in a select cohort of patients. ::: ::: Methods: ::: The principles of operation are that the mesorectum is not dissected off the levator muscles, the perineal dissection is done in the prone position and the levator muscles are resected en bloc with the anus and lower rectum. The perineal defect is reconstructed with a gluteus maximus flap. Between 2001 and 2005, 28 patients with low rectal cancer were treated accordingly at the Karolinska Hospital. ::: ::: Results: ::: Two patients had ypT0 tumours, 20 ypT3 and six ypT4 tumours. Bowel perforation occurred in one, the circumferential resection margin (CRM) was positive in two, and four patients had local perineal wound complications. Two patients developed local recurrence after a median follow-up of 16 months. ::: ::: Conclusion: ::: The extended posterior perineal approach with gluteus maximus flap reconstruction in APR has a low risk of bowel perforation, CRM involvement and local perineal wound complications. The rate of local recurrence may be lower than with conventional APR. Copyright © 2006 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.


Choice reaction time: Basic concepts


title: Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research

abstract: This meta-analysis reviewed the literature on socioeconomic status (SES) and academic achievement in journal articles published between 1990 and 2000. The sample included 101,157 students, 6,871 schools, and 128 school districts gathered from 74 independent samples. The results showed a medium to strong SES–achievement relation. This relation, however, is moderated by the unit, the source, the range of SES variable, and the type of SES–achievement measure. The relation is also contingent upon school level, minority status, and school location. The author conducted a replica of White’s (1982) meta-analysis to see whether the SES–achievement correlation had changed since White’s initial review was published. The results showed a slight decrease in the average correlation. Practical implications for future research and policy are discussed.


Employment of Diode Laser in the Treatment of Epulis Fissuratum. Case Report


title: Statistical Timing Analysis: From Basic Principles to State of the Art

abstract: Static-timing analysis (STA) has been one of the most pervasive and successful analysis engines in the design of digital circuits for the last 20 years. However, in recent years, the increased loss of predictability in semiconductor devices has raised concern over the ability of STA to effectively model statistical variations. This has resulted in extensive research in the so-called statistical STA (SSTA), which marks a significant departure from the traditional STA framework. In this paper, we review the recent developments in SSTA. We first discuss its underlying models and assumptions, then survey the major approaches, and close by discussing its remaining key challenges.


J.K.: View invariant human action recognition using histograms of 3D joints


Brain Ischemic Stroke Segmentation : A Survey


title: Youth Computational Participation in the Wild: Understanding Experience and Equity in Participating and Programming in the Online Scratch Community

abstract: Most research in primary and secondary computing education has focused on understanding learners within formal classroom communities, leaving aside the growing number of promising informal online programming communities where young users contribute, comment, and collaborate on programs to facilitate learning. In this article, we examined trends in computational participation in Scratch, an online community with over 1 million registered youth designers. Drawing on a random sample of 5,004 youth programmers and their activities over 3 months in early 2012, we examined programming concepts used in projects in relation to level of participation, gender, and length of membership of Scratch programmers. Latent class analysis results identified the same four groups of programmers in each month based on the usage of different programming concepts and showed how membership in these groups shifted in different ways across time. Strikingly, the largest group of project creators (named Loops) used the simplest and fewest programming concepts. Further, this group was the most stable in membership and was disproportionately female. In contrast, the more complex programming groups (named Variables, Low Booleans, and High Booleans) showed much movement across time. Further, the Low Booleans and High Booleans groups, the only groups to use “and,” “or,” and “not” statements in their programs, were disproportionately male. In the discussion, we address the challenges of analyzing young learners’ programming in informal online communities and opportunities for designing more equitable computational participation.


title: The impact of the ‘open’ workspace on human collaboration

abstract: Organizations' pursuit of increased workplace collaboration has led managers to transform traditional office spaces into 'open', transparency-enhancing architectures with fewer walls, doors and other spatial boundaries, yet there is scant direct empirical research on how human interaction patterns change as a result of these architectural changes. In two intervention-based field studies of corporate headquarters transitioning to more open office spaces, we empirically examined-using digital data from advanced wearable devices and from electronic communication servers-the effect of open office architectures on employees' face-to-face, email and instant messaging (IM) interaction patterns. Contrary to common belief, the volume of face-to-face interaction decreased significantly (approx. 70%) in both cases, with an associated increase in electronic interaction. In short, rather than prompting increasingly vibrant face-to-face collaboration, open architecture appeared to trigger a natural human response to socially withdraw from officemates and interact instead over email and IM. This is the first study to empirically measure both face-to-face and electronic interaction before and after the adoption of open office architecture. The results inform our understanding of the impact on human behaviour of workspaces that trend towards fewer spatial boundaries.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.


On Teaching Narrative Infographics in an Information Visualization Course


title: Practical Stabilization of a Skid-steering Mobile Robot - A Kinematic-based Approach

abstract: This paper presents kinematic control problem of skid-steering mobile robot using practical smooth and time-varying stabilizer. The stability result is proved using Lyapunov analysis and takes into account both input signal saturation and uncertainty of kinematics. In order to ensure stable motion of the robot the condition of permissible velocities is formulated according to dynamic model and wheel-surface interaction. Theoretical considerations are illustrated by simulation results.


title: Rational randomness: the role of sampling in an algorithmic account of preschooler's causal learning.

abstract: Abstract Probabilistic models of cognitive development indicate the ideal solutions to computational problems that children face as they try to make sense of their environment. Under this approach, children's beliefs change as the result of a single process: observing new data and drawing the appropriate conclusions from those data via Bayesian inference. However, such models typically leave open the question of what cognitive mechanisms might allow the finite minds of human children to perform the complex computations required by Bayesian inference. In this chapter, we highlight one potential mechanism: sampling from probability distributions. We introduce the idea of approximating Bayesian inference via Monte Carlo methods, outline the key ideas behind such methods, and review the evidence that human children have the cognitive prerequisites for using these methods. As a result, we identify a second factor that should be taken into account in explaining human cognitive development—the nature of the mechanisms that are used in belief revision.


title: FP-Outlier: frequent pattern based outlier detection

abstract: An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.


title: A Credit Scoring Model Based on Collaborative Filtering

abstract: To ensure property safety, risk assessment plays an essential role in modern society. Credit scoring, which is a significant branch of exposure rating, becomes a hot topic. As a result, various kinds of credit scoring models are established to evaluate the customers' credit rank. In this paper, a simple credit scoring model, Collaborative Filtering based on Matrix Factorization with data whose continuous attributes are discretized considering Information Entropy (CF-MF-D-IE), is constructed to solve credit scoring issues. The proposed model is tested on two important credit data sets in UCI Repository of Machine Learning databases. Compared with Collaborative Filtering using non-discretized data and Support Vector Machines with discretized data, CF-MF-D-IE has better classification accuracy rate.


title: Disambiguating Noun Groupings With Respect To Wordnet Senses

abstract: Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automatic sense disambiguation of nouns appearing within sets of related nouns — the kind of data one finds in on-line thesauri, or as the output of distributional clustering algorithms. Disambiguation is performed with respect to WordNet senses, which are fairly fine-grained; however, the method also permits the assignment of higher-level WordNet categories rather than sense labels. The method is illustrated primarily by example, though results of a more rigorous evaluation are also presented.


title: SemSignWriting: A Proposed Semantic System for Arabic Text-to-SignWriting Translation

abstract: Arabic Sign Language (ArSL) is the native language for the Arab deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express the thought instead of sounds. In recent years, interest in translating sign language automatically for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we present a proposed system for semantically translating Arabic text to Arabic SignWriting in the jurisprudence of prayer domain. The system is designed to translate Arabic text by applying Arabic Sign Language (ArSL) grammatical rules as well as semantically looking up the words in domain ontology. The results of qualitatively evaluating the system based on a SignWriting expert judgment proved the correctness of the translation results.


title: Predicting protein structures with a multiplayer online game

abstract: Predicting the structure of a folded protein from first principles for any given amino-acid sequence remains a formidable computational challenge. To recruit human abilities to the task, these authors turned their Rosetta structure prediction algorithm into an online multiplayer game in which thousands of non-scientists competed and collaborated to produce new algorithms and search strategies for protein structure refinement. This shows that computationally complex problems can be effectively 'crowd-sourced' through interactive multiplayer games.


title: Cuneiform: a Functional Language for Large Scale Scientific Data Analysis

abstract: The need to analyze massive scientific data sets on the one hand and the availability of distributed compute resources with an increasing number of CPU cores on the other hand have promoted the development of a variety of languages and systems for parallel, distributed data analysis. Among them are data-parallel query languages such as Pig Latin or Spark as well as scientific workflow languages such as Swift or Pegasus DAX. While data-parallel query languages focus on the exploitation of data parallelism, scientific workflow languages focus on the integration of external tools and libraries. However, a language that combines easy integration of arbitrary tools, treated as black boxes, with the ability to fully exploit data parallelism does not exist yet. Here, we present Cuneiform, a novel language for large-scale scientific data analysis. We highlight its functionality with respect to a set of desirable features for such languages, introduce its syntax and semantics by example, and show its flexibility and conciseness with use cases, including a complex real-life workflow from the area of genome research. Cuneiform scripts are executed dynamically on the workflow execution platform Hi-WAY which is based on Hadoop YARN. The language Cuneiform, including tool support for programming, workflow visualization, debugging, logging, and provenance-tracing, and the parallel execution engine Hi-WAY are fully implemented.


title: Robotic grasp detection using deep convolutional neural networks

abstract: Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this paper, we present a novel robotic grasp detection system that predicts the best grasping pose of a parallel-plate robotic gripper for novel objects using the RGB-D image of the scene. The proposed model uses a deep convolutional neural network to extract features from the scene and then uses a shallow convolutional neural network to predict the grasp configuration for the object of interest. Our multi-modal model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and runs at real-time speeds. This redefines the state-of-the-art for robotic grasp detection.


title: Deep representation learning with target coding

abstract: We consider the problem of learning deep representation when target labels are available. In this paper, we show that there exists intrinsic relationship between target coding and feature representation learning in deep networks. Specifically, we found that distributed binary code with error correcting capability is more capable of encouraging discriminative features, in comparison to the 1-of-K coding that is typically used in supervised deep learning. This new finding reveals additional benefit of using error-correcting code for deep model learning, apart from its well-known error correcting property. Extensive experiments are conducted on popular visual benchmark datasets.


abstract: Conventional video segmentation methods often rely on temporal continuity to propagate masks. Such an assumption suffers from issues like drifting and inability to handle large displacement. To overcome these issues, we formulate an effective mechanism to prevent the target from being lost via adaptive object re-identification. Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module. The former module produces an initial probability map by flow warping while the latter module retrieves missing instances by adaptive matching. With these two modules iteratively applied, our VS-ReID records a global mean (Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017 DAVIS Challenge.


title: Client Attacks: Network infiltration with client-side attacks

abstract: This article is the first in a 2-part series on the vulnerabilities of networks to client-side attacks, and the risk to networks from 'inside-out' compromises. Internet and computer usage is exponentially increasing; more people are beginning to use the internet as part of their everyday lives, these activities ranging from instant messaging to ordering shopping online. Even most large corporate businesses provide their employees with some kind of access to the internet.


title: Clustered deferred and forward shading

abstract: This paper presents and investigates Clustered Shading for deferred and forward rendering. In Clustered Shading, view samples with similar properties (e.g. 3D-position and/or normal) are grouped into clusters. This is comparable to tiled shading, where view samples are grouped into tiles based on 2D-position only. We show that Clustered Shading creates a better mapping of light sources to view samples than tiled shading, resulting in a significant reduction of lighting computations during shading. Additionally, Clustered Shading enables using normal information to perform per-cluster back-face culling of lights, again reducing the number of lighting computations. We also show that Clustered Shading not only outperforms tiled shading in many scenes, but also exhibits better worst case behaviour under tricky conditions (e.g. when looking at high-frequency geometry with large discontinuities in depth). Additionally, Clustered Shading enables real-time scenes with two to three orders of magnitudes more lights than previously feasible (up to around one million light sources).


title: The instability of graded structure: implications for the nature of concepts

abstract: After a brief introduction to graded structure, this chapter reviews empirical findings showing that the graded structure of a category is unstable, varying widely across contexts. Implications of these findings for theories of categorization are discussed, and it is concluded that graded structures do not represent invariant structural characteristics of Categories. Instead it is proposed that in stability in graded structure occurs because different concept temporarily represent the same categoy in working memory on different occasions.Rather than being retrieved as static units from memory to represent categories, concepts originate in a highly flexible process that retrieves generic and episodic information from long-term memory to construct temporary concepts in Work-ing memorу. Because this concept construction process is highly constrained by goals, context, and jecent experience, the same concept is rarely if ever constructed for a category. A theory of concept construction is presented, and the relations of this theory to dreaming (Foulkes 1985), conceptual combination (Hampton in press a,b; Smith & Osherson 1984), exemplar theories (Brooks 1978; Jacoby & Brooks .1984; Media & Schaeffer 1978), norm theory (Kaimeman &: Miller, 1986), and parallel distributed processing (McClelland & Rumelhart 1986; Rumelhart & McClelland, 1986) are discussed.


title: Iterated learning and the evolution of language

abstract: Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins.


title: Use of Docker for deployment and testing of astronomy software

abstract: Abstract We describe preliminary investigations of using Docker for the deployment and testing of astronomy software. Docker is a relatively new containerization technology that is developing rapidly and being adopted across a range of domains. It is based upon virtualization at operating system level, which presents many advantages in comparison to the more traditional hardware virtualization that underpins most cloud computing infrastructure today. A particular strength of Docker is its simple format for describing and managing software containers, which has benefits for software developers, system administrators and end users. We report on our experiences from two projects – a simple activity to demonstrate how Docker works, and a more elaborate set of services that demonstrates more of its capabilities and what they can achieve within an astronomical context – and include an account of how we solved problems through interaction with Docker’s very active open source development community, which is currently the key to the most effective use of this rapidly-changing technology.


title: Principles of Smart Home Control

abstract: Seeking to be sensitive to users, smart home researchers have focused on the concept of control. They attempt to allow users to gain control over their lives by framing the problem as one of end-user programming. But families are not users as we typically conceive them, and a large body of ethnographic research shows how their activities and routines do not map well to programming tasks. End-user programming ultimately provides control of devices. But families want more control of their lives. In this paper, we explore this disconnect. Using grounded contextual fieldwork with dual-income families, we describe the control that families want, and suggest seven design principles that will help end-user programming systems deliver that control.


title: The Withdrawal Assessment Tool–1 (WAT–1): An assessment instrument for monitoring opioid and benzodiazepine withdrawal symptoms in pediatric patients*

abstract: Objective ::: To develop and test the validity and reliability of the Withdrawal Assessment Tool - Version 1 (WAT-1) for monitoring opioid and benzodiazepine withdrawal symptoms in pediatric patients.


title: Experiments in Single and Multi-Document Summarization Using MEAD

abstract: In this paper, we describe four experiments in text summarization. The first experiment involves the automatic creation of 120 multi-document summaries and 308 single-document summaries from a set of 30 clusters of related documents. We present official results from a multi-site manual evaluation of the quality of the summaries. The second experiment is about the identification by human subjects of cross-document structural relationships such as identity, paraphrase, elaboration, and fulfillment. The third experiment focuses on a particular cross-document structural relationship, namely subsumption. The last experiment asks human judges to determine which of the input articles in a given cluster were used to produce individual sentences of a manual summary. We present numerical evaluations of all four experiments. All automatic summaries have been produced by MEAD, a flexible summarization system under development at the University of Michigan.


Author Obfuscation using WordNet and Language Models


title: A 635pW battery voltage supervisory circuit for miniature sensor nodes

abstract: We propose a low power battery voltage supervisory circuit for micro-scale sensor systems that provides power-on reset, brown-out detection, and recovery detection to prevent malfunction and battery damage. Ultra-low power is achieved using a 57pA, fast stabilizing two-stage voltage reference and an 81pA leakage-based oscillator and clocked comparator. The supervisor was fabricated in 180nm CMOS and integrated with a complete 1 mm3 sensor system. It consumes 635pW at 3.6V supply voltage, which is an 850× reduction over the best prior work.


title: Automated Story Selection for Color Commentary in Sports

abstract: Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We present a system called the sports commentary recommendation system (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.


title: Discriminative learning for differing training and test distributions

abstract: We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.


title: A rational extension of Piegl's method for filling n-sided holes

abstract: N-sided hole filling plays an important role in vertex blending. To deal with the case that the corner is surrounded by rational surfaces (i.e. NURBS surfaces), an algorithm to fill n-sided holes with @e- G^1 continuous NURBS patches that interpolate the given boundary curves and approximate the given cross-boundary derivatives is presented based on Piegl's method. The NURBS surfaces joining along inner or boundary curves have normal vectors that do not deviate more than the user-specified angular tolerance @e. The boundaries as well as cross-boundary derivatives can all be NURBS curves. No restrictions are imposed on the number of boundary curves, and the cross-boundary derivatives can be specified independently.


title: Faster dynamic programming for Markov decision processes

abstract: Markov decision processes (MDPs) are a general framework used in artificial intelligence (AI) to model decision theoretic planning problems. Solving real world MDPs has been a major and challenging research topic in the AI literature, since classical dynamic programming algorithms converge slowly. We discuss two approaches in expediting dynamic programming. The first approach combines heuristic search strategies with dynamic programming to expedite the convergence process. The second makes use of graphical structures in MDPs to perform dynamic programming in a better order.


title: Community-based Data Validation Practices in Citizen Science

abstract: Technology-supported citizen science has created huge volumes of data with increasing potential to facilitate scientific progress, however, verifying data quality is still a substantial hurdle due to the limitations of existing data quality mechanisms. In this study, we adopted a mixed methods approach to investigate community-based data validation practices and the characteristics of records of wildlife species observations that affected the outcomes of collaborative data quality management in an online community where people record what they see in the nature. The findings describe the processes that both relied upon and added to information provenance through information stewardship behaviors, which led to improved reliability and informativity. The likelihood of community-based validation interactions were predicted by several factors, including the types of organisms observed and whether the data were submitted from a mobile device. We conclude with implications for technology design, citizen science practices, and research.


title: AmiGO: online access to ontology and annotation data

abstract: AmiGO is a web application that allows users to query, browse, and visualize ontologies and related gene product annotation (association) data. AmiGO can be used online at the Gene Ontology (GO) website to access the data provided by the GO Consortium; it can also be downloaded and installed to browse local ontologies and annotations. AmiGO is free open source software developed and maintained by the GO Consortium.


title: Remembering historical victimization: Collective guilt for current ingroup transgressions.

abstract: The authors examined the consequences of remembering historical victimization for emotional reactions to a current adversary. In Experiment 1, Jewish Canadians who were reminded of the Holocaust accepted less collective guilt for their group's harmful actions toward the Palestinians than those not reminded of their ingroup's past victimization. The extent to which the conflict was perceived to be due to Palestinian terrorism mediated this effect. Experiment 2 illustrated that reminding Jewish people, but not non-Jewish people, of the Holocaust decreased collective guilt for current harm doing compared with when the reminder concerned genocide committed against another group (i.e., Cambodians). In Experiments 3 and 4, Americans experienced less collective guilt for their group's harm doing in Iraq following reminders of either the attacks on September 11th. 2001 or the 1941 Japanese attack on Pearl Harbor compared with a historical victimization reminder that was irrelevant to the ingroup. The authors discuss why remembering the ingroup's past affects responses to outgroups in the present.


title: Adaptive Importance Learning for Improving Lightweight Image Super-Resolution Network

abstract: Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that balance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthogonal view, and propose a novel learning strategy to maximize the pixel-wise fitting ability of a given lightweight network architecture. Considering that the initial performance of the lightweight network is very limited, we present an adaptive importance learning scheme for SISR that trains the network with an easy-to-complex paradigm by dynamically updating the importance of image pixels on the basis of the training loss. Specifically, we formulate the network training and the importance learning into a joint optimization problem. With a carefully designed importance penalty function, the importance of individual pixels can be gradually increased through solving a convex optimization problem. The training process thus begins with pixels that are easy to reconstruct, and gradually proceeds to more complex pixels as fitting improves. Furthermore, the proposed learning scheme is able to seamlessly assimilate knowledge from a more powerful teacher network in the form of importance initialization, thus obtaining better initial performance for the network. Through learning the network parameters, and updating pixel importance, the proposed learning scheme enables smaller, lightweight, networks to achieve better performance than has previously been possible. Extensive experiments on four benchmark datasets demonstrate the potential benefits of the proposed learning strategy in lightweight SISR network enhancement. In some cases, our learned network with only \(25\%\) of the parameters and computational complexity can produce comparable or even better results than the corresponding full-parameter network.


title: Unconscious odour conditioning 25 years later: Revisiting and extending ‘Kirk-Smith, Van Toller and Dodd’

abstract: Abstract The pioneering work by Kirk-Smith, Van Toller, and Dodd [Kirk-Smith, M. D., Van Toller, C., & Dodd, G. H. (1983). Unconscious odour conditioning in human subjects. Biological Psychology, 17 , 221–231], established that an unnoticed odorant paired with an emotionally meaningful task can influence mood and attitudes when the odorant alone is re-encountered subsequently. This study was particularly important in highlighting unconscious odour learning processes as they may happen in everyday life. However, it was severely criticized by Black and Smith [Black, S. L., & Smith, D. G. (1994). Has odor conditioning been demonstrated? A critique of “Unconscious odour conditioning in human subjects”. Biological Psychology, 37 , 265–267] on methodological grounds. In the present paper, we replicated and extended Kirk-Smith et al.’s investigation taking into account Black and Smith’s criticisms and carrying out additional controls to improve the validity of the study. The results confirm the initial findings of Kirk-Smith and colleagues by demonstrating the effectiveness of olfactory stimuli in inducing conditioned hedonic reactions even when presented below the level of awareness.


title: Video-based, Real-Time Multi View Stereo

abstract: We investigate the problem of obtaining a dense reconstruction in real-time, from a live video stream. In recent years, multi-view stereo (MVS) has received considerable attention and a number of methods have been proposed. However, most methods operate under the assumption of a relatively sparse set of still images as input and unlimited computation time. Video based MVS has received less attention despite the fact that video sequences offer significant benefits in terms of usability of MVS systems. In this paper we propose a novel video based MVS algorithm that is suitable for real-time, interactive 3d modeling with a hand-held camera. The key idea is a per-pixel, probabilistic depth estimation scheme that updates posterior depth distributions with every new frame. The current implementation is capable of updating 15milliondistributions/s. We evaluate the proposed method against the state-of-the-art real-time MVS method and show improvement in terms of accuracy.


title: Pre-school children with and without developmental delay: behaviour problems and parenting stress over time

abstract: Background Children with intellectual disability are at heightened risk for behaviour problems and diagnosed mental disorder. Methods The present authors studied the early manifestation and continuity of problem behaviours in 205 pre-school children with and without developmental delays. Results Behaviour problems were quite stable over the year from age 36-48 months. Children with developmental delays were rated higher on behaviour problems than their non-delayed peers, and were three times as likely to score in the clinical range. Mothers and fathers showed high agreement in their rating of child problems, especially in the delayed group. Parenting stress was also higher in the delayed group, but was related to the extent of behaviour problems rather than to the child's developmental delay. Conclusion Over time, a transactional model fit the relationship between parenting stress and behaviour problems: high parenting stress contributed to a worsening in child behaviour problems over time, and high child behaviour problems contributed to a worsening in parenting stress. Findings for mothers and fathers were quite similar.


title: on Pension Insurance Ontology Concept Similarity

abstract: Matching key terms is the main method for most engines for information retrieval, but this method cannot meet the needs of people who want to retrieve useful domain information. This is because that it doesn’t consider the semantic relation of resources in the context. Ontology is an effective method that can solve the problem of domain knowledge sharing and semantic understanding. In this paper, an optimization strategy is presented for information retrieval domain, and an algorithm is also proposed for ontology concept similarity calculation. Experiments are carried out on the audit guidance system, and the results show that this method significantly improves the accuracy of information retrieval based on domain ontology


Robust unsupervised feature selection on networked data


title: Haar wavelet method for solving lumped and distributed-parameter systems

abstract: An operational matrix of integration based on Haar wavelets is established, and a procedure for applying the matrix to analyse lumped and distributed-parameters dynamic systems is formulated. The technique can be interpreted from the incremental and multiresolution viewpoint. Crude as well as accurate solutions can be obtained by changing the parameter m; in the mean time, the main features of the solution are preserved. Several nontrivial examples are included for demonstrating the fast, flexible and convenient capabilities of the new method.


The Effect of Analogy-Based Teaching on Students' Achievement and Students' Views about Analogies.


title: Event-based, 6-DOF pose tracking for high-speed maneuvers

abstract: In the last few years, we have witnessed impres-sive demonstrations of aggressive flights and acrobatics using quadrotors. However, those robots are actually blind. They do not see by themselves, but through the “eyes” of an external motion capture system. Flight maneuvers using onboard sensors are still slow compared to those attainable with motion capture systems. At the current state, the agility of a robot is limited by the latency of its perception pipeline. To obtain more agile robots, we need to use faster sensors. In this paper, we present the first onboard perception system for 6-DOF localization during high-speed maneuvers using a Dynamic Vision Sensor (DVS). Unlike a standard CMOS camera, a DVS does not wastefully send full image frames at a fixed frame rate. Conversely, similar to the human eye, it only transmits pixel-level brightness changes at the time they occur with microsecond resolution, thus, offering the possibility to create a perception pipeline whose latency is negligible compared to the dynamics of the robot. We exploit these characteristics to estimate the pose of a quadrotor with respect to a known pattern during high-speed maneuvers, such as flips, with rotational speeds up to 1,200◦/s. Additionally, we provide a versatile method to capture ground-truth data using a DVS.


title: Failure Mode, Mechanism and Effect Analysis for Single Board Computers

abstract: Failure Mode, Mechanism and Effect Analysis (FMMEA) is a reliability analysis method which is used to study possible failure modes, failure mechanisms of each component, and to identify the effects of various failure modes on the components and functions. This paper introduces how to implement FMMEA on the Single Board Computer in detail, including system definition, identification of potential failure modes, analysis of failure cause, failure mechanism, and failure effect analysis. Finite element analysis is carried out for the Single Board Computer, including thermal stress analysis and vibration stress analysis. Temperature distribution and vibration modes are obtained, which are the inputs of physics of failure models. Using a variety of Physics of Failure models, the quantitative calculation of single point failure for the Single Board Computer are carried out. Results showed that the time to failure (TTF) of random access memory chip which is SOP (small outline package) is the shortest and the failure is due to solder joint fatigue failure caused by the temperature cycle. It is the weak point of the entire circuit board. Thus solder joint thermal fatigue failure is the main failure mechanism of the Single Board Computer. In the implementation process of PHM for the Single Board Computer, the failure condition of this position should be monitored.


title: Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures

abstract: Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the ability to use uncertainty for both prediction and learning (e.g. learning from expectations). Probabilistic extensions of OE (Lai and Hockenmaier, 2017) have provided the ability to somewhat calibrate these denotational probabilities while retaining the consistency and inductive bias of ordered models, but lack the ability to model the negative correlations found in real-world knowledge. In this work we show that a broad class of models that assign probability measures to OE can never capture negative correlation, which motivates our construction of a novel box lattice and accompanying probability measure to capture anticorrelation and even disjoint concepts, while still providing the benefits of probabilistic modeling, such as the ability to perform rich joint and conditional queries over arbitrary sets of concepts, and both learning from and predicting calibrated uncertainty. We show improvements over previous approaches in modeling the Flickr and WordNet entailment graphs, and investigate the power of the model.


title: Local Naive Bayes Nearest Neighbor for image classification

abstract: We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class's training descriptors, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. Local NBNN gives a 100 times speed-up over the original NBNN on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features. We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model. However, we find that local NBNN, while competitive with, does not beat state-of-the-art spatial pyramid methods that use local soft assignment and max-pooling.


title: Mechanisms for Leveraging Models at Runtime in Self-adaptive Software.

abstract: Modern software systems are often required to adapt their behavior at runtime in order to maintain or enhance their utility in dynamic environments. Models at runtime research aims to provide suitable abstractions, techniques, and tools to manage the complexity of adapting software systems at runtime. In this chapter, we discuss challenges associated with developing mechanisms that leverage models at runtime to support runtime software adaptation. Specifically, we discuss challenges associated with developing effective mechanisms for supervising running systems, reasoning about and planning adaptations, maintaining consistency among multiple runtime models, and maintaining fidelity of runtime models with respect to the running system and its environment. We discuss related problems and state-of-the-art mechanisms, and identify open research challenges.


title: How Does IT Ambidexterity Impact Organizational Agility?

abstract: Organizational agility is a significant business capability. Though there have been numerous studies about the effects of information technology IT capabilities on organizational agility, there has been limited attention on the enabling effects of IT ambidexterity, namely, the dual capacity to explore and exploit IT resources and practices. We propose that IT ambidexterity enhances organizational agility by facilitating operational ambidexterity, and that the magnitude of facilitation depends on the level of environmental dynamism. We test these relationships utilizing data from a large-scale, matched-pair field survey of business and IT executives. The results confirm that a firm's IT ambidexterity does enhance its organizational agility through the mediated effects of operational ambidexterity, and that the dynamism of a firm's environment affects these relationships.


title: A comparative study of online privacy policies and formats

abstract: Online privacy policies are difficult to understand. Most privacy policies require a college reading level and an ability to decode legalistic, confusing, or jargon-laden phrases. Privacy researchers and industry groups have devised several standardized privacy policy formats to address these issues and help people compare policies. We evaluated three formats in this paper: layered policies, which present a short form with standardized components in addition to a full policy; the Privacy Finder privacy report, which standardizes the text descriptions of privacy practices in a brief bulleted format; and conventional non-standardized human-readable policies. We contrasted six companies’ policies, deliberately selected to span the range from unusually readable to challenging. Based on the results of our online study of 749 Internet users, we found participants were not able to reliably understand companies’ privacy practices with any of the formats. Compared to natural language, participants were faster with standardized formats but at the expense of accuracy for layered policies. Privacy Finder formats supported accuracy more than natural language for harder questions. Improved readability scores did not translate to improved performance. All formats and policies were similarly disliked. We discuss our findings as well as public policy implications.


title: An update on Chinese herbal medicines as adjuvant treatment of anticancer therapeutics

abstract: Numerous studies have indicated that in cancer treatment Chinese herbal medicines in combination with chemo-, radio-, or targeted-therapy can be used to enhance the efficacy of and diminish the side effects and complications caused by these therapies. Therefore, an understanding of Chinese herbal medicines is needed by physicians and other health care providers. This review provides an update on Chinese herbal medicines as adjuvant treatment of anticancer therapeutics. First, some Chinese herbal medicines (e.g. Astragalus, Ginseng, Scutellaria barbata, TJ-41, TJ-48, PHY906, Huachansu injection, and Kanglaite injection) that are commonly used for treating the cancer and/or reducing the toxicity induced by chemo-, radio-, or targeted-therapy are discussed. These Chinese herbal medicines have been shown to possess great advantages in terms of suppressing tumor progression, increasing the sensitivity of chemo-, radio-, or targeted-therapeutics, improving an organism's immune system function, and lessening the damage caused by these therapeutics. Second, some clinical trials using Chinese herbal medicines as adjuvant improving cancer treatment related side effects and complications are reviewed. Some Chinese herbal medicines have a significant effect on reducing cancer-related fatigue and pain, improving peripheral neuropathy and gastrointestinal side effects including diarrhea, nausea, and vomiting, decrease the incidence of bone marrow suppression, protecting anthracycline-induced cardiotoxicity and radiation-induced pneumonitis, and relieving EGFR-TKIs related acneiform eruptions and other side effects. This review of those medicines should contribute to an understanding of Chinese herbal medicines as adjuvant treatment for cancer and provide useful information for the development of more effective anti-cancer drugs. However, rigorously designed trials on potential Chinese herbal medicine must be further examined involving cancer treatment especially molecular targeted-therapy in the future.


title: Dynamic Hybrid Control of a Hexapod Walking Robot: Experimental Verification

abstract: In this paper, a walking robot that consists of its body and six legs is considered for trajectory control. The leg mechanism is a 1-UP&2-UPS (U-universal joint, P-prismatic joint, and S-spherical joint) parallel mechanism with the arm–leg conversion function. Due to the nonlinearity and uncertainties of the robot, it is difficult to obtain its exact dynamic model, therefore this paper utilizes the decentralized control strategy to design a dynamic hybrid controller, in which the outer loop is an impedance controller, the middle and inner loops are designed by using the sliding-mode control and the nonlinear proportional–integral–derivative (PID) control, respectively. The inverse kinematics of one leg is derived for mathematic model, based on which three control methods including the classical PID control, position–velocity loops cascade control, and dynamic hybrid control are examined. Contrast tracking experiments were performed with one robotic leg. The experimental results are shown to identify their differences and prove the effectiveness and applicability of the proposed dynamic hybrid control.


title: Cyber-Physical Systems in the SmartGrid

abstract: Radical changes are expected to occur in the next years in the electricity domain and the grid itself which has been almost unchanged the last 100 years. Value is created when interactions exist and this is the main thrust of the emerging SmartGrid which will heavily rely on IT technologies at several layers for monitoring and control. The basic building blocks are the existing efforts in the domain of the Internet of Things and Internet of Services, that come together with cooperation as the key enabler. The SmartGrid is a complex ecosystem of heterogeneous (cooperating) entities that interact in order to provide the envisioned functionality. Advanced business services will take advantage of the near real-time information flows among all participants. In order to realize the SmartGrid promise we will have to heavily depend on Cyber-Physical Systems (CPS) that will be able to monitor, share and manage information and actions on the business as well as the real world. CPS is seen as an integral part of the SmartGrid, hence several open issues will need to be effectively addressed.


title: A 120-GHz wideband FMCW radar demonstrator based on a fully-integrated SiGe transceiver with antenna-in-package

abstract: This paper presents a fully-integrated D-Band bistatic frequency-modulated continuous-wave radar (FMCW) radar sensor based on a 130nm SiGe BiCMOS technology for short-range applications. The radar sensor includes transmit and receive antennas integrated in an embedded-wafer-level-ball-grid-array (eWLB) package. The transceiver chip is based on ×30 frequency multiplication, which enables the use of off-the-shelf 4GHz phase-locked-loops. A fully-differential IQ receiver is implemented by means of a differential 3-dB branch line coupler. On-wafer measurements show a maximum output power of 0 dBm. The radar sensor is mounted on a low cost PCB and is used in conjunction with an FPGA based baseband board for analog-to-digital conversion, processing and evaluation of the IF signals. In order to verify the operation, FMCW measurements of static targets are performed with a sweep bandwidth of up to 10 GHz, which corresponds to a minimum range-resolution of 3 cm. The radar chip consumes around 560mW from a 3.3V power supply and the package size is 12mm × 6 mm.


title: A Study on Threats Detection and Tracking Systems for Military Applications using WSNs

abstract: In Wireless Sensor Network (WSN) applications it is critical to accurately determine the location of the distributed sensor nodes in order to report the data that is geographically meaningful. Since localization and tracking algorithms have been attracting research and development attention recently, a wide range of existing approaches regarding this topic have emerged. Tracking and localization algorithms have been proposed for different WSN applications including civilian, industrial and safety applications. A few research studies focused on tracking Threats through military applications, such as detecting and tracking Threats through border security area. Therefore this paper studies and investigates the existing WSN based tracking and localization algorithms and summarizes the potential requirements for localizing and tracking Threats through military applications. The existing systems are categorized and discussed. A critical analysis is found in this paper, in order to guide the developer to design and implement a WSN-based tracking system for military applications.


title: Design of Printed-Circuit Board Rogowski Coil for Highly Accurate Current Measurement

abstract: The structure design and optimization of printed-circuit board Rogowski coil (PCBRC) used to highly accurate measurement of alternative current are discussed in detail in this paper. The performance tests of PCBRC show that PCBRC are of excellent linearity, temperature characteristic and rejection of EMI, and can achieve 0.1 or 0.2 s accuracy class (IEC60044-8 standard). It can fulfill the demands of energy measurement in power systems.


title: Plant phenotypic plasticity in a changing climate

abstract: Climate change is altering the availability of resources and the conditions that are crucial to plant performance. One way plants will respond to these changes is through environmentally induced shifts in phenotype (phenotypic plasticity). Understanding plastic responses is crucial for predicting and managing the effects of climate change on native species as well as crop plants. Here, we provide a toolbox with definitions of key theoretical elements and a synthesis of the current understanding of the molecular and genetic mechanisms underlying plasticity relevant to climate change. By bringing ecological, evolutionary, physiological and molecular perspectives together, we hope to provide clear directives for future research and stimulate cross-disciplinary dialogue on the relevance of phenotypic plasticity under climate change.


title: Long term electricity demand forecasting using autoregressive integrated moving average model: Case study of Morocco

abstract: Electricity demand forecasting is vitally important for power production companies. It has many applications, including energy production scheduling, maintenance and operation of electric network, elaboration of accurate investment and development plans for transmission and distribution networks, negotiation of PPAs (Power Purchase Agreements) and purchasing fuels at optimal costs. Over the last decades, a large variety of mathematical models have been developed for load forecasting, including short-term, medium-term and long term models. This article aims at developing a long term load forecasting model for Moroccan electric grid, using Auto-Regressive Moving Average model. The results are compared to official forecasts of ONEE (Office National de l'Eau et d'Electrcite). This model will be a useful tool for decision makers, to better design investment plans and strategies, aiming at reducing the impact of energy bill on Moroccan economy.


title: Business-to-consumer electronic commerce: a longitudinal study

abstract: The objective of the study is to investigate the factors affecting online shopping. A model explaining the impact of different factors on online shopping intentions and behavior is developed based on the Theory of Planned Behavior. The model is then tested empirically in a longitudinal study with two surveys. Data collected from 705 consumers indicate that subjective norms, attitude, and beliefs concerning the consequences of online shopping have significant effects on consumer intentions to buy online. Behavioral control and intentions significantly influenced online shopping behavior. The results also provide strong support for the positive effects of personal innovativeness on attitude and intentions to shop online. The implications of the findings for theory and practice are discussed.


title: All for One: Feature Combination for Highly Channel-Degraded Speech Activity Detection

abstract: Speech activity detection (SAD) on channel transmissions is a critical preprocessing task for speech, speaker and language recognition or for further human analysis. This paper presents a feature combination approach to improve SAD on highly channel degraded speech as part of the Defense Advanced Research Projects Agency’s (DARPA) Robust Automatic Transcription of Speech (RATS) program. The key contribution is the feature combination exploration of different novel SAD features based on pitch and spectro-temporal processing and the standard Mel Frequency Cepstral Coefficients (MFCC) acoustic feature. The SAD features are: (1) a GABOR feature representation, followed by a multilayer perceptron (MLP); (2) a feature that combines multiple voicing features and spectral flux measures (Combo); (3) a feature based on subband autocorrelation (SAcC) and MLP postprocessing and (4) a multiband comb-filter F0 (MBCombF0) voicing measure. We present single, pairwise and all feature combinations, show high error reductions from pairwise feature level combination over the MFCC baseline and show that the best performance is achieved by the combination of all features. Index Terms: speech detection, channel-degraded speech, robust voicing features


title: Remote Health Monitoring System Using IoT

abstract: Today as we can see the population of the country is increasing at a very fast rate, despite our various efforts. The hospitals and medical services available for the rising population is very low. There are still many areas in our country which do not have proper medical help available. But, during these past years, there has been a huge rise in the data service providers, which has provided a huge support for Internet of Things. Hence from the support of the IoT technology, remote and rural areas can also be provided with proper health services. In most of the researches which are done on the topic, there are many loopholes like, no machine learning is implemented, sensors are not secured over the network, etc. Some of these issues have been addressed in this paper with results output of the system. Further, the paper proposes a model based on the integration of machine learning with the current system to predict future diseases in the patient or that remote area.


title: Unsupervised hierarchical image segmentation through fuzzy entropy maximization

abstract: We present an unsupervised multilevel segmentation scheme for automatically segmenting grayscale and color images.Fuzzy 2-partition entropy is combined with Graph Cut to form a bi-level segmentation operator that splits a given region into 2 parts based on both global optimal threshold and local spatial coherence.A multilevel segmentation scheme iteratively performs on selected regions and color channels, producing a coarse-to-fine hierarchy of segments.The presented algorithm is evaluated using the Berkeley Segmentation Database and achieves competitive results compared with the state-of-the-art methods. The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimal thresholds when performing multilevel gray image segmentation. Nevertheless, existing fuzzy c-partition entropy approaches generally have two limitations, i.e., partition number cneeds to be manually tuned for different input and the methods can process grayscale images only. To address these two limitations, an unsupervised multilevel segmentation algorithm is presented in this paper. The core step of our algorithm is a bi-level segmentation operator, which uses binary graph cuts to maximize both fuzzy 2-partition entropy and segmentation smoothness. By iteratively performing this bi-level segmentation operator, multilevel image segmentation is achieved in a hierarchical manner: Starting from the input color image, our algorithm first picks the color channel that can best segment the image into two labels, and then iteratively selects channels to further split each labels until convergence. The experimental results demonstrate the presented hierarchical segmentation scheme can efficiently segment both grayscale and color images. Quantitative evaluations over classic gray images and the Berkeley Segmentation Database show that our method is comparable to the state-of-the-art multi-scale segmentation methods, yet has the advantage of being unsupervised, efficient, and easy to implement.


title: Design and kinematic simulation of a novel exoskeleton rehabilitation hand robot

abstract: The rehabilitation exoskeleton robot is more and more used in the assisting stroke patients in implementing rehabilitation training. In this paper, a novel exoskeleton hand robot which was driven by cable has been proposed to aim at helping varieties of paralyses patients recover motor function. This exoskeleton hand rehabilitation robot system mainly consists of exoskeleton hand robot, EEG system, motor controllers unit, some sensors and a workstation. And the hand exoskeleton mechanism was portable, wearable and adjustable for patients doing home rehabilitation training. Based on the parameters from human hand, the kinematic model of hand robot was established to be used in designing and the kinematic simulation experiments about this exoskeleton hand robot were done. Through the simulation software ADAMS (Automatic Dynamic Analysis of Mechanical Systems), the parameters of position, angle velocity and angle accelerated velocity (PVA) of proximate inter-phalanger (DIP) distal inter-phalanger joint were simulated. And then, the movement situation of the DIP joint was obtained according to the data from the position encoder. So the velocity curve and accelerated velocity curve were obtained by calculating the actual position function derivation. The simulated results showed that this exoskeleton hand robot has high movement ability to finish the continuous passive motion (CPM). Beside that, a comparison test was done to verify whether there were some motion blocks in wearing exoskeleton robot. Through analyzing the experimental data, the error between the actual position and the simulated position of the PIP joint of the finger was very small which verified the small interference of robot. These experiments demonstrated that the exoskeleton hand robot has high efficiency movement ability for stroke patients who need to do the finger rehabilitation training. In the future, the optimization design of the exoskeleton hand robot will be done in order to improve more movement ability.


title: ClimbAX: skill assessment for climbing enthusiasts

abstract: In recent years the sport of climbing has seen consistent increase in popularity. Climbing requires a complex skill set for successful and safe exercising. While elite climbers receive intensive expert coaching to refine this skill set, this progression approach is not viable for the amateur population. We have developed ClimbAX - a climbing performance analysis system that aims for replicating expert assessments and thus represents a first step towards an automatic coaching system for climbing enthusiasts. Through an accelerometer based wearable sensing platform, climber's movements are captured. An automatic analysis procedure detects climbing sessions and moves, which form the basis for subsequent performance assessment. The assessment parameters are derived from sports science literature and include: power, control, stability, speed. ClimbAX was evaluated in a large case study with 53 climbers under competition settings. We report a strong correlation between predicted scores and official competition results, which demonstrate the effectiveness of our automatic skill assessment system.


title: Toward parallel continuum manipulators

abstract: In this paper, we investigate continuum manipulators that are analogous to conventional rigid-link parallel robot designs. These “parallel continuum manipulators” have the potential to inherit some of the compactness and compliance of continuum robots while retaining some of the precision, stability, and strength of rigid-link parallel robots, yet they represent a relatively unexplored area of the broad manipulator design space. We describe the construction of a prototype manipulator structure with six compliant legs connected in a parallel pattern similar to that of a Stewart-Gough platform. We formulate the static forward and inverse kinematics problems for such manipulators as the solution to multiple Cosserat-rod models with coupled boundary conditions, and we test the accuracy of this approach in a set of experiments, including the prediction of leg buckling. An inverse kinematics simulation of slices through the 6 degree-of-freedom (DOF) workspace illustrates the kinematic mapping, range of motion, and force required for actuation, which sheds light on the potential advantages and tradeoffs that parallel continuum manipulators may bring. Potential applications include miniature wrists and arms for endoscopic medical procedures, and lightweight compliant arms for safe interaction with humans.


title: Behavioural and neural evidence for self-reinforcing expectancy effects on pain

abstract: Beliefs and expectations often persist despite evidence to the contrary. Here we examine two potential mechanisms underlying such ‘self-reinforcing’ expectancy effects in the pain domain: modulation of perception and biased learning. In two experiments, cues previously associated with symbolic representations of high or low temperatures preceded painful heat. We examined trial-to-trial dynamics in participants’ expected pain, reported pain and brain activity. Subjective and neural pain responses assimilated towards cue-based expectations, and pain responses in turn predicted subsequent expectations, creating a positive dynamic feedback loop. Furthermore, we found evidence for a confirmation bias in learning: higher- and lower-than-expected pain triggered greater expectation updating for high- and low-pain cues, respectively. Individual differences in this bias were reflected in the updating of pain-anticipatory brain activity. Computational modelling provided converging evidence that expectations influence both perception and learning. Together, perceptual assimilation and biased learning promote self-reinforcing expectations, helping to explain why beliefs can be resistant to change.


title: Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

abstract: In this paper, we propose the novel principal backpropagation networks (PBNets) to revisit the backpropagation algorithms commonly used in training two-stream networks for video action recognition. We content that existing approaches always take all the frames/snippets for the backpropagation not optimal for video recognition since the desired actions only occur in a short period within a video. To remedy these drawbacks, we design a watch-and-choose mechanism. In particular, the watching stage exploits a dense snippet-wise temporal pooling strategy to discover the global characteristic for each input video, while the choosing phase only backpropagates a small number of representative snippets that are selected with two novel strategies, i.e., Max-rule and KL-rule. We prove that with the proposed selection strategies, performing the backpropagation on the selected subset is capable of decreasing the loss of the whole snippets as well. The proposed PBNets are evaluated on two standard video action recognition benchmarks UCF101 and HMDB51, where it surpasses the state of the arts consistently, but requiring less memory and computation to achieve high performance.


title: Ontology-based approach for measuring semantic similarity

abstract: The challenge of measuring semantic similarity between words is to find a method that can simulate the thinking process of human. The use of computers to quantify and compare semantic similarities has become an important area of research in various fields, including artificial intelligence, knowledge management, information retrieval and natural language processing. The development of efficient measures for the computation of concept similarity is fundamental for computational semantics. Several computational measures rely on knowledge resources to quantify semantic similarity, such as the WordNet ? is a ? taxonomy. Several of these measures are based on taxonomical parameters to achieve the best expression possible for the semantics of content. This paper presents a new measure for quantifying the degree of the semantic similarity between concepts and words based on the WordNet hierarchy and using a number of topological parameters related to the "is a" taxonomy. Our proposal combines, in a complementary way, the hyponyms and depth parameters. This measure takes the problem of fine granularity into account. It is argued, however, that WordNet sense distinctions are highly fine-grained even for humans. We, therefore, propose a new method to quantify the hyponyms subgraph of a given concept based on depth distribution. Common nouns datasets (RG65, MC30 and AG203), medical terms dataset (MED38) and verbs dataset (YP130) formed by word pairs are used in the assessment. We start by calculating semantic similarities and then compute the correlation coefficient between human judgement and computational measures. The results demonstrate that, compared to other currently available computational methods, the measure presented in this study yields into better levels of performance. Compared to several measures, it shows good accuracy covering all the pairwises of the verbs dataset YP130.


title: MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering

abstract: MEDLINE is the largest biomedical literature database. It is updated daily with 200–4,000 citations. This permanent growth induces the need of a good MEDLINE abstract clustering to accelerate the procedure of research and information retrieval. Several works have been developed in this context, but clustering MEDLINE abstracts are still an area where researchers are trying to propose new approaches to better clustering. Over the last few years, evolutionary algorithms have been widely applied to clustering problems because of their ability to avoid local optimal solutions and converge to a global one. In this article, a new approach is proposed for clustering MEDLINE abstracts based on an extension of an evolutionary algorithm which is the genetic algorithm combined with a Vector Space Model and an agglomerative algorithm.


title: New Frontiers of Reverse Engineering

abstract: Comprehending and modifying software is at the heart of many software engineering tasks, and this explains the growing interest that software reverse engineering has gained in the last 20 years. Broadly speaking, reverse engineering is the process of analyzing a subject system to create representations of the system at a higher level of abstraction. This paper briefly presents an overview of the field of reverse engineering, reviews main achievements and areas of application, and highlights key open research issues for the future.


title: Space-Alternating Generalized Expectation-Maximization Algorithm

abstract: The expectation-maximization (EM) method can facilitate maximizing likelihood functions that arise in statistical estimation problems. In the classical EM paradigm, one iteratively maximizes the conditional log-likelihood of a single unobservable complete data space, rather than maximizing the intractable likelihood function for the measured or incomplete data. EM algorithms update all parameters simultaneously, which has two drawbacks: 1) slow convergence, and 2) difficult maximization steps due to coupling when smoothness penalties are used. The paper describes the space-alternating generalized EM (SAGE) method, which updates the parameters sequentially by alternating between several small hidden-data spaces defined by the algorithm designer. The authors prove that the sequence of estimates monotonically increases the penalized-likelihood objective, derive asymptotic convergence rates, and provide sufficient conditions for monotone convergence in norm. Two signal processing applications illustrate the method: estimation of superimposed signals in Gaussian noise, and image reconstruction from Poisson measurements. In both applications, the SAGE algorithms easily accommodate smoothness penalties and converge faster than the EM algorithms. >


title: Hyperbolic Wavelet-Fisz Denoising for a Model Arising in Ultrasound Imaging

abstract: We present an algorithm and its fully data-driven extension for noise reduction in ultrasound imaging. The proposed method computes the hyperbolic wavelet transform of the image, before applying a multiscale variance stabilization technique, via a Fisz transformation. This adapts the wavelet coefficients statistics to the wavelet thresholding paradigm. The use of hyperbolic wavelets makes it possible to recover the image while respecting the anisotropic nature of structural details. The data-driven extension obviates the need for any prior knowledge of the noise model parameters by estimating the noise variance using an isotonic Nadaraya–Watson estimator. Experiments on synthetic and real data demonstrate the potential of the proposed algorithm to recover ultrasound images while preserving tissue details. Furthermore, comparisons with other noise-reduction methods show that our technique is competitive with the state-of-the-art OBNLM filter. Finally, the variance estimation procedure is applied to real images emphasizing the noise model.


title: Consistent mesh partitioning and skeletonisation using the shape diameter function

abstract: Mesh partitioning and skeletonisation are fundamental for many computer graphics and animation techniques. Because of the close link between an object’s skeleton and its boundary, these two problems are in many cases complementary. Any partitioning of the object can assist in the creation of a skeleton and any segmentation of the skeleton can infer a partitioning of the object. In this paper, we consider these two problems on a wide variety of meshes, and strive to construct partitioning and skeletons which remain consistent across a family of objects, not a single one. Such families can consist of either a single object in multiple poses and resolutions, or multiple objects which have a general common shape. To achieve consistency, we base our algorithms on a volume-based shape-function called the shape-diameter-function (SDF), which remains largely oblivious to pose changes of the same object and maintains similar values in analogue parts of different objects. The SDF is a scalar function defined on the mesh surface; however, it expresses a measure of the diameter of the object’s volume in the neighborhood of each point on the surface. Using the SDF we are able to process and manipulate families of objects which contain similarities using a simple and consistent algorithm: consistently partitioning and creating skeletons among multiple meshes.


title: An evaluation of streaming digital video resources in on- and off-campus engineering management education

abstract: A recent television documentary on the Columbia space shuttle disaster was converted to streaming digital video format for educational use by on- and off-campus students in an engineering management study unit examining issues in professional engineering ethics. An evaluation was conducted to assess the effectiveness of this new resource. Use of the video was optional, and about half of the class reported using the video, though usage was 90.0% for off-campus students. Most on-campus students accessed the video on-line, while all off-campus students accessed the video via CD-ROM. Off-campus students rated the educational value of the video higher than on-campus students, and were more likely to indicate that the video helped them understand the issues being studied. Most students were able to view the videos without any technical playback problems.