title: Architecture support for disciplined approximate programming

abstract: Disciplined approximate programming lets programmers declare which parts of a program can be computed approximately and consequently at a lower energy cost. The compiler proves statically that all approximate computation is properly isolated from precise computation. The hardware is then free to selectively apply approximate storage and approximate computation with no need to perform dynamic correctness checks. In this paper, we propose an efficient mapping of disciplined approximate programming onto hardware. We describe an ISA extension that provides approximate operations and storage, which give the hardware freedom to save energy at the cost of accuracy. We then propose Truffle, a microarchitecture design that efficiently supports the ISA extensions. The basis of our design is dual-voltage operation, with a high voltage for precise operations and a low voltage for approximate operations. The key aspect of the microarchitecture is its dependence on the instruction stream to determine when to use the low voltage. We evaluate the power savings potential of in-order and out-of-order Truffle configurations and explore the resulting quality of service degradation. We evaluate several applications and demonstrate energy savings up to 43%.


title: Securing RFIDs by randomizing the modulation and channel

abstract: RFID cards are widely used in sensitive applications such as access control and payment systems. Past work shows that an eavesdropper snooping on the communication between a card and its legitimate reader can break their cryptographic protocol and obtain their secret keys. One solution to this problem is to install stronger encryption on the cards. However, RFIDs' size, power, and cost limitations do not allow for strong encryption protocols. Further, changing the encryption on the cards requires revoking billions of cards in consumers' hands, which is impracticable. ::: ::: This paper presents RF-Cloak, a solution that protects RFIDs from the above attacks, without any changes to today's cards. RF-Cloak achieves this performance using a novel transmission system that randomizes both the modulation and the wireless channels. It is the first system that defends RFIDs against MIMO eavesdroppers, even when the RFID reader has no MIMO capability. A prototype of our design built using software radios demonstrates its ability to protect commercial RFIDs from both single-antenna and MIMO eavesdroppers.


Supervised Keyphrase Extraction as Positive Unlabeled Learning


title: CASMACAT: An Open Source Workbench for Advanced Computer Aided Translation

abstract: Abstract We describe an open source workbench that offers advanced computer aided translation (CAT) functionality: post-editing machine translation (MT), interactive translation prediction (ITP), visualization of word alignment, extensive logging with replay mode, integration with eye trackers and e-pen.


title: Evolutionary Computation Meets Machine Learning: A Survey

abstract: Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.


title: Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package

abstract: It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. ::: Efficient implementations of score-based structure learning benefit from past and current research in optimisation theory, which can be adapted to the task by using the network score as the objective function to maximise. This is not true for approaches based on conditional independence tests, called constraint-based learning algorithms. The only optimisation in widespread use, backtracking, leverages the symmetries implied by the definitions of neighbourhood and Markov blanket. ::: In this paper we illustrate how backtracking is implemented in recent versions of the bnlearn R package, and how it degrades the stability of Bayesian network structure learning for little gain in terms of speed. As an alternative, we describe a software architecture and framework that can be used to parallelise constraint-based structure learning algorithms (also implemented in bnlearn) and we demonstrate its performance using four reference networks and two real-world data sets from genetics and systems biology. We show that on modern multi-core or multiprocessor hardware parallel implementations are preferable over backtracking, which was developed when single-processor machines were the norm.


title: Interactions between Pseudomonas aeruginosa and Staphylococcus aureus during co-cultivations and polymicrobial infections

abstract: Pseudomonas aeruginosa and Staphylococcus aureus are versatile bacterial pathogens and common etiological agents in polymicrobial infections. Microbial communities containing both of these pathogens are shaped by interactions ranging from parasitic to mutualistic, with the net impact of these interactions in many cases resulting in enhanced virulence. Polymicrobial communities of these organisms are further defined by multiple aspects of the host environment, with important implications for disease progression and therapeutic outcomes. This mini-review highlights the impact of these interactions on the host and individual pathogens, the molecular mechanisms that underlie these interactions, and host-specific factors that drive interactions between these two important pathogens.


title: Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble

abstract: In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm CMOS process, it is demonstrated that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors due to process variations in near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% . In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we propose an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric to further enhance robustness. Simulation results confirm that the error weighted voting achieves a P_{det} that varies by only 1.4%, which is 12X lower compared to SVM.


title: Stigma: ignorance, prejudice or discrimination?

abstract: The term stigma refers to problems of knowledge (ignorance), attitudes (prejudice) and behaviour (discrimination). Most research in this area has been based on attitude surveys, media representations of mental illness and violence, has only focused upon schizophrenia, has excluded direct participation by service users, and has included few intervention studies. However, there is evidence that interventions to improve public knowledge about mental illness can be effective. The main challenge in future is to identify which interventions will produce behaviour change to reduce discrimination against people with mental illness.


title: A Low-Cost 60-GHz Switched-Beam Patch Antenna Array With Butler Matrix Network

abstract: In this letter, a low-cost 60-GHz switched-beam patch antenna array with Butler matrix network is developed and experimentally demonstrated. In order to improve integration with the patch elements, a 4 times 4 planar Butler matrix is implemented in a low-dielectric substrate. The four rectangular patches fed by inset microstrip lines are connected to the outputs of the Butler matrix for the 60-GHz operation. Because of the fabrication tolerances, the operating frequency of the fabricated antenna shifts to 62 GHz. The radiation patterns measured at 62 GHz are in good agreement with the theoretical array factors. The antenna developed in this letter provides a cost-effective approach to implement an adaptive antenna for 60-GHz wireless communications.


title: Modular Representation of Layered Neural Networks

abstract: Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood.In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1)Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network.


title: Arity-generic datatype-generic programming

abstract: Some programs are doubly-generic. For example, map is datatype-generic in that many different data structures support a mapping operation. A generic programming language like Generic Haskell can use a single definition to generate map for each type. However, map is also arity-generic because it belongs to a family of related operations that differ in the number of arguments. For lists, this family includes repeat, map, zipWith, zipWith3, zipWith4, etc. With dependent types or clever programming, one can unify all of these functions together in a single definition. However, no one has explored the combination of these two forms of genericity. These two axes are not orthogonal because the idea of arity appears in Generic Haskell: datatype-generic versions of repeat, map and zipWith have different arities of kind-indexed types. In this paper, we define arity-generic datatype-generic programs by building a framework for Generic Haskell-style generic programming in the dependently-typed programming language Agda 2.


title: Replication-Based Fault-Tolerance for Large-Scale Graph Processing

abstract: The increasing algorithm complexity and dataset sizes necessitate the use of networked machines for many graph-parallel algorithms, which also makes fault tolerance a must due to the increasing scale of machines. Unfortunately, existing large-scale graph-parallel systems usually adopt a distributed checkpoint mechanism for fault tolerance, which incurs not only notable performance overhead but also lengthy recovery time. This paper observes that the vertex replicas created for distributed graph computation can be naturally extended for fast in-memory recovery of graph states. This paper proposes Imitator, a new fault tolerance mechanism, that supports cheaply maintenance of vertex states by replicating vertex states to their replicas during normal message exchanges, and provides fast in-memory reconstruction of failed vertices from replicas in other machines. Imitator has been implemented by extending Hama, a popular open-source clone of Pregel. Evaluation shows that Imitator incurs negligible performance overhead (less than 5% for all cases) and can recover from failures of more than one million of vertices with less than 3.4 seconds.


title: Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator

abstract: This article presents the development of an autonomous motion planning algorithm for a soft planar grasping manipulator capable of grasp-and-place operations by encapsulation with uncertainty in the position and shape of the object. The end effector of the soft manipulator is fabricated in one piece without weakening seams using lost-wax casting instead of the commonly used multilayer lamination process. The soft manipulation system can grasp randomly positioned objects within its reachable envelope and move them to a desired location without human intervention. The autonomous planning system leverages the compliance and continuum bending of the soft grasping manipulator to achieve repeatable grasps in the presence of uncertainty. A suite of experiments is presented that demonstrates the system's capabilities.


title: Detection of breast cancer by mammogram image segmentation

abstract: An important approach for describing a region is to quantify its structure content. In this paper the use of functions for computing texture based on statistical measures is prescribed. MPM (Maximizer of the posterior margins) algorithm is employed. The segmentation based on texture feature would classify the breast tissue under various categories. The algorithm evaluates the region properties of the mammogram image and thereby would classify the image into important segments. Images from mini-MIAS data base (Mammogram Image Analysis Society database (UK)) have been considered to conduct our experiments. The segmentation thus obtained is comparatively better than the other normal methods. The validation of the work has been done by visual inspection of the segmented image by an expert radiologist. This is our basic step for developing a computer aided detection (CAD) system for early detection of breast cancer.


title: Deep Representation for Finger-Vein Image-Quality Assessment

abstract: Finger-vein biometrics has been extensively investigated for personal authentication. One of the open issues in finger-vein verification is the lack of robustness against image-quality degradation. Spurious and missing features in poor-quality images may degrade the system’s performance. Despite recent advances in finger-vein quality assessment, current solutions depend on domain knowledge. In this paper, we propose a deep neural network (DNN) for representation learning to predict image quality using very limited knowledge. Driven by the primary target of biometric quality assessment, i.e., verification error minimization, we assume that low-quality images are falsely rejected in a verification system. Based on this assumption, the low- and high-quality images are labeled automatically. We then train a DNN on the resulting data set to predict the image quality. To further improve the DNN’s robustness, the finger-vein image is divided into various patches, on which a patch-based DNN is trained. The deepest layers associated with the patches form together a complementary and an over-complete representation. Subsequently, the quality of each patch from a testing image is estimated and the quality scores from the image patches are conjointly input to probabilistic support vector machines (P-SVM) to boost quality-assessment performance. To the best of our knowledge, this is the first proposed work of deep learning-based quality assessment, not only for finger-vein biometrics, but also for other biometrics in general. The experimental results on two public finger-vein databases show that the proposed scheme accurately identifies high- and low-quality images and significantly outperforms existing approaches in terms of the impact on equal error-rate decrease.


title: Review and exploration of metadata management in data warehouse

abstract: With the rapid development of information technology, how to extract and retrieve required information from a large amount of data has become an important issue that managers and policymakers concern. As a kind of structured data environment based on data management, data warehouse shows its advantage in analysis and decision-making. It can provide current and historical data for decision support, which is hardly obtained in traditional operational database. In the construction of data warehouse system, metadata and its management are of great importance to make the system more effective. In this paper, a brief history of data management's development from database to data warehouse is presented. Metadata and its management are introduced from several aspects, i.e. definition, structure, and influence. The typical framework and construction of metadata management system are concluded to give some guidances for enterprises. At last, the prospect of metadata management in data warehouse systems is discussed.


title: Economic models for resource management and scheduling in Grid computing

abstract: The accelerated development in peer-to-peer and Grid computing has positioned them as promising next-generation computing platforms. They enable the creation of virtual enterprises for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply-and-demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod-G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker, with deadline and cost constrained scheduling for two different optimization strategies, on the World-Wide Grid testbed that has resources distributed across five continents.


Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients


title: Mining the search trails of surfing crowds: identifying relevant websites from user activity

abstract: The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.


title: Lithium: virtual machine storage for the cloud

abstract: To address the limitations of centralized shared storage for cloud computing, we are building Lithium, a distributed storage system designed specifically for virtualization workloads running in large-scale data centers and clouds. Lithium aims to be scalable, highly available, and compatible with commodity hardware and existing application software. The design of Lithium borrows ideas and techniques originating from research into Byzantine Fault Tolerance systems and popularized by distributed version control software, and demonstrates their practical applicability to the performance-sensitive problem of VM hosting. To our initial surprise, we have found that seemingly expensive techniques such as versioned storage and incremental hashing can lead to a system that is not only more robust to data corruption and host failures, but also often faster than naive approaches and, for a relatively small cluster of just eight hosts, performs well compared with an enterprise-class Fibre Channel disk array.


title: Nasal bone length throughout gestation: normal ranges based on 3537 fetal ultrasound measurements

abstract: Objective To establish normal ranges for nasal bone length measurements throughout gestation and to compare measurements in two subsets of patients of different race (African-American vs. Caucasian) to determine whether a different normal range should be used in these populations. Method Normalnasalbone lengthreferencerangeswere generated using prenatal measurements by a standardized technique in 3537 fetuses. Results The nasal bone lengths were found to correlate positively with advancing gestation (R 2 = 0.77, secondorder polynomial). No statistical difference was found between African-American and Caucasian subjects. Conclusion These reference ranges may prove to be useful in prenatal screening and diagnosis of syndromes known to be associated with nasal hypoplasia. Different normal ranges for African-American and Caucasian women are not required. Copyright  2003 ISUOG. Published by John Wiley & Sons, Ltd.


title: Child Safety & Tracking Management System by Using GPS, Geo-Fencing & Android Application: An Analysis

abstract: Today, technology is growing rapidly and providingall essential and effective solutions for every requirement. Nowa day's child security is an important area of concern. Thismodel is developed to rectify the worries of parents regardingtheir child security. In this scenario, Our system ensuresmaximum security and ensures live tracking for their kidsbecause parent worries are genuine. This paper proposed amodel for child safety through smart phones that provides theoption to track the location of their children as well as in caseof emergency children is able to send a quick message and itscurrent location via Short Message services. This proposedsystem is validated by testing on the Android platform.


title: Automatic Parking Management System and Parking Fee Collection Based on Number Plate Recognition

abstract: This paper discussed on automatic parking system ::: and electronic parking fee collection based on vehicle number ::: plate recognition. The aim of this research is to develop and ::: implement an automatic parking system that will increase ::: convenience and security of the public parking lot as well as ::: collecting parking fee without hassles of using magnetic card. ::: The auto parking system will able to have less interaction of ::: humans and use no magnetic card and its devices. In additions ::: to that, it has parking guidance system that can show and guide ::: user towards a parking space. The system used image ::: processing of recognizing number plates for operation of ::: parking and billing system. Overall, the systems run with ::: pre-programmed controller to make minimum human ::: involvement in parking system and ensure access control in ::: restricted places. This paper presents algorithm technology ::: based method for license plate extraction from car images ::: followed by the segmentation of characters and reorganization ::: and also develop electronics parking fee collection system based ::: on number plate information.


title: Some Advances in Transformation-Based Part of Speech Tagging

abstract: Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger was described that obtained performance comparable to that of stochastic taggers, but captured relevant linguistic information in a small number of simple non-stochastic rules. In this paper, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical relations in tagging that stochastic taggers are currently unable to express. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger-can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.


VIRTUAL PRODUCT EXPERIENCE: THE EFFECTS OF INTERACTIVITY AND TASK ON PRESENCE PERCEPTIONS


title: Ieee 802.11ad: introduction and performance evaluation of the first multi-gbps wifi technology

abstract: Multi-Gbps communication is the next frontier in high-speed local and personal wireless technologies, which will offer the necessary foundation for a new wave of applications such as wireless display, high-speed device synchronization, and the evolution of Wi-Fi. The wide harmonized spectrum in the unlicensed millimeter-wave (60 GHz) band is considered the most prominent candidate to support the evolution towards multi-Gbps data rates. As such, the industry is in the process of defining new 60 GHz PHY and MAC technologies that can serve a wide variety of applications and usages, as to avoid the proliferation of non-coexistent devices operating in this unoccupied spectrum. The most promising activity is taking place under the auspices of the IEEE 802.11ad task group, which is defining amendments to the 802.11 standard for operation in the 60GHz band. In this paper we describe the main components of the MAC and PHY amendments included in the current IEEE 802.11ad draft standard and that enable multi-Gbps data rates. We also provide a comprehensive set of simulation results for typical use cases, and argue that 802.11ad is poised to be the standard that will enable mass market adoption of multi-Gbps wireless communication in the 60 GHz spectrum band.


title: Brands and Branding: Research Findings and Future Priorities

abstract: Branding has emerged as a top management priority in the last decade due to the growing realization that brands are one of the most valuable intangible assets that firms have. Driven in part by this intense industry interest, academic researchers have explored a number of different brand-related topics in recent years, generating scores of papers, articles, research reports, and books. This paper identifies some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand-equity measurement, brand growth, and brand management. The paper also outlines some gaps that exist in the research of branding and brand equity and formulates a series of related research questions. Choice modeling implications of the branding concept and the challenges of incorporating main and interaction effects of branding as well as the impact of competition are discussed.


title: Zero-Shot Learning by Convex Combination of Semantic Embeddings

abstract: Abstract: Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image transformation. In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage. Proponents of these image embedding systems have stressed their advantages over the traditional \nway{} classification framing of image understanding, particularly in terms of the promise for zero-shot learning -- the ability to correctly annotate images of previously unseen object categories. In this paper, we propose a simple method for constructing an image embedding system from any existing \nway{} image classifier and a semantic word embedding model, which contains the $\n$ class labels in its vocabulary. Our method maps images into the semantic embedding space via convex combination of the class label embedding vectors, and requires no additional training. We show that this simple and direct method confers many of the advantages associated with more complex image embedding schemes, and indeed outperforms state of the art methods on the ImageNet zero-shot learning task.


title: Dealing with Behavioral Ambiguity in Textual Process Descriptions

abstract: Textual process descriptions are widely used in organizations since they can be created and understood by virtually everyone. The inherent ambiguity of natural language, however, impedes the automated analysis of textual process descriptions. While human readers can use their context knowledge to correctly understand statements with multiple possible interpretations, automated analysis techniques currently have to make assumptions about the correct meaning. As a result, automated analysis techniques are prone to draw incorrect conclusions about the correct execution of a process. To overcome this issue, we introduce the concept of a behavioral space as a means to deal with behavioral ambiguity in textual process descriptions. A behavioral space captures all possible interpretations of a textual process description in a systematic manner. Thus, it avoids the problem of focusing on a single interpretation. We use a compliance checking scenario and a quantitative evaluation with a set of 47 textual process descriptions to demonstrate the usefulness of a behavioral space for reasoning about a process described by a text. Our evaluation demonstrates that a behavioral space strikes a balance between ignoring ambiguous statements and imposing fixed interpretations on them.


title: The critical success factors for e-government implementation in Jordan

abstract: The implementation of e-government initiatives has become one of the main goals of many countries around the worlds.However, e-government implementation is not straightforward.Projects encounter different problems, be it in developed or developing countries.Many studies have focused on failed e-government implementation, while some focus on the factors leading to success.This paper reports the Critical Success Factors (CSFs) that could spur the growth and success of e-government initiates in Jordan. Ten key CSFs were identified using Nvivo and content analysis of 30 interviews with 30 mangers from three Jordanian ministries.These CSFs have practical implementation and must be incorporated in e-government implementation plans.


title: Learning Rapid-Temporal Adaptations

abstract: A hallmark of human intelligence and cognition is its flexibility. One of the long-standing goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation - the ability to adapt quickly to new environments or tasks - that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.


title: Fast approximations for global illumination on dynamic scenes

abstract: An innovative lighting algorithm is presented that allows scenes to be displayed with approximate global illumination including ambient occlusion and sky-light effects at real-time rates. The method is scalable for high polygonal scenes and requires a small amount of pre-computation. The presented technique can be successfully applied to dynamic and animated sequences, and displays a striking aesthetic style by reducing traditional constraints of physical correctness and a standard lighting model.


title: A variable physical damping actuator (VPDA) for compliant robotic joints

abstract: This paper introduces the development of a semi-active friction based variable physical damping actuator (VPDA) unit. The realization of this unit aims to facilitate the control of compliant robotic joints by providing physical variable damping on demand assisting on the regulation of the oscillations induced by the introduction of compliance. The mechatronics details and the dynamic model of the damper are introduced. The proposed variable damper mechanism is evaluated on a simple 1-DOF compliant joint linked to the ground through a torsion spring. This flexible connection emulates a compliant joint, generating oscillations when the link is perturbed. Preliminary results are presented to show that the unit and the proposed control scheme are capable of replicating simulated relative damping values with good fidelity.


title: Active and Passive Devices for Tuning Impedance in Wearable Robotics

abstract: Abstract : Wearable robots, i.e. active orthoses, exoskeletons, and mechatronic prostheses, represent a class of biomechatronic systems posing severe constraints in terms of safety and controllability. Additionally, whenever the worn system is required to establish a well-tuned dynamic interaction with the human body, in order to exploit emerging dynamical behaviours, the possibility of having modular joints, able to produce a controllable viscoelastic behaviour, becomes crucial. Controllability is a central issue in wearable robotics applications, because it impacts robot safety and effectiveness. Under this regard, DC motors offer very good performances, provided that a proper mounting scheme is used in order to mimic the typical viscoelastici behaviour exhibited by biological systems, as required by the selected application. In this paper we report on the design of two compact devices for controlling the active and passive torques applied to the joint of a wearable robot for the lower limbs. The first device consists of a rotary Serial Elastic Actuator (SEA), incorporating a custom made torsion spring. The second device is a purely mechanical passive viscoelastici joint, functionally equivalent to a torsion spring mounted in parallel to a rotary viscous damper. The torsion stiffness and the damping coefficient can be easily tuned by acting on specific elements, thanks to the modular design of the device. The working principles and basic design choices regarding the overall architectures and the single components are presented and discussed.


title: The mobile photographic stress meter (MPSM): a new way to measure stress using images

abstract: Stress is an important aspect of human psychology. Many measures of stress have been developed over the years, but different issues exist for most of these measures. This study introduces the Mobile Photographic Stress Meter (MPSM), a new way to measure stress in which a user simply selects an image that best captures his or her stress level. Such a tool allows researchers to quickly measure stress in real time and in natural environments less onerous for the user. Our results show that MPSM is a valid measure of stress. Users find it easy and actually enjoyable to use. Our results also show that MPSM is strongly correlated with the Perceived Stress Scale, a validated multi-item stress scale. The correlation has an r-value of 0.5559 and a p-value of less than 0.001. We conclude that MPSM can be applied effectively in research experiments and advance the research on stress.


title: Using BabelNet to improve OOV coverage in SMT

abstract: Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies ::: different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic ::: dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named ::: entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods – ::: using direct training data, domain-adaptation techniques and the BabelNet API – are proposed in this paper to obtain translations for ::: OOVs to improve system performance. Experimental results on English–Polish and English–Chinese language pairs show that domain ::: adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is ::: a really useful tool for improving translation performance of SMT systems.


title: Explaining perceived performance of the World Wide Web: uncertainty and the task‐technology fit model

abstract: The performance of the World Wide Web is evaluated as an information resource in a specific information domain. The theoretical framework underpinning this approach recognises the contribution of information‐seeking behaviour from the discipline of information science and models of information systems success from the discipline of information systems in explaining World Wide Web usage as an information resource. A model integrating the construct of uncertainty and the task‐technology fit model is presented. A questionnaire‐based empirical study is used to test this integrated approach. Our results confirm that richer models representing the broad context of World Wide Web usage are required to evaluate usage of the World Wide Web as an information resource. Usage for travel tasks, uncertainty reduction, the Web as an information resource, and mediation all have a significant impact on users' perception of performance, explaining 46 per cent of the variance.


title: Social semantic query expansion

abstract: Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.


title: OntoSTEP: Enriching product model data using ontologies

abstract: The representation and management of product lifecycle information is critical to any manufacturing organization. Different modeling languages are used at different lifecycle stages, for example STEP's EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. It is necessary to consolidate product information created using these different languages to build a coherent knowledge base. In this paper, we present an approach to enable the translation of STEP schema and its instances to Ontology Web Language (OWL). This gives a model-which we call OntoSTEP-that can easily be integrated with any OWL ontologies to create a semantically rich model. As an example, we combine geometry information represented in STEP with non-geometry information, such as function and behavior, represented using the NIST's Core Product Model (CPM). A plug-in for Protege is developed to automate the different steps of the translation. As additional benefits, reasoning, inference procedures, and queries can be performed on enriched legacy CAD models. We describe the rules for the translation from EXPRESS to OWL, and illustrate the benefits of OWL translation with an example. We will also describe how these mapping rules can be implemented through meta-model based transformations, which can be used to map other languages to OWL.


title: Detecting emotional stress from facial expressions for driving safety

abstract: Monitoring the attentive and emotional status of the driver is critical for the safety and comfort of driving. In this work a real-time non-intrusive monitoring system is developed, which detects the emotional states of the driver by analyzing facial expressions. The system considers two negative basic emotions, anger and disgust, as stress related emotions. We detect an individual emotion in each video frame and the decision on the stress level is made on sequence level. Experimental results show that the developed system operates very well on simulated data even with generic models. An additional pose normalization step reduces the impact of pose mismatch due to camera setup and pose variation, and hence improves the detection accuracy further.


title: Probability Risk Identification Based Intrusion Detection System for SCADA Systems

abstract: As Supervisory Control and Data Acquisition (SCADA) systems control several critical infrastructures, they have connected to the internet. Consequently, SCADA systems face different sophisticated types of cyber adversaries. This paper suggests a Probability Risk Identification based Intrusion Detection System (PRI-IDS) technique based on analysing network traffic of Modbus TCP/IP for identifying replay attacks. It is acknowledged that Modbus TCP is usually vulnerable due to its unauthenticated and unencrypted nature. Our technique is evaluated using a simulation environment by configuring a testbed, which is a custom SCADA network that is cheap, accurate and scalable. The testbed is exploited when testing the IDS by sending individual packets from an attacker located on the same LAN as the Modbus master and slave. The experimental results demonstrated that the proposed technique can effectively and efficiently recognise replay attacks.


title: A Graphical User Interface (GUI) Testing Methodology

abstract: Software testing in general and graphical user interface (GUI) testing in particular is one of the major challenges in the lifecycle of any software system. GUI testing is inherently more difficult than the traditional and command-line interface testing. Some of the factors that make GUI testing different from the traditional software testing and significantly more difficult are: a large number of objects, different look and feel of objects, many parameters associated with each object, progressive disclosure, complex inputs from multiple sources, and graphical outputs. The existing testing techniques for the creation and management of test suites need to be adapted/enhanced for GUIs, and new testing techniques are desired to make the creation and management of test suites more efficient and effective. In this article, a methodology is proposed to create test suites for a GUI. The proposed methodology organizes the testing activity into various levels. The tests created at a particular level can be reused at higher levels. This methodology extends the notion of modularity and reusability to the testing phase. The organization and management of the created test suites resembles closely to the structure of the GUI under test.


title: An Indoor Visible Light Positioning System Based on Optical Camera Communications

abstract: We experimentally demonstrate an indoor visible light positioning system based on optical camera communications, in which the transmitted coordinate data are spatially separated and demodulated by a camera. The receiver’s position is calculated based on the coordinates of light-emitting diodes in the real word and in the image. The experimental results show that the proposed system with under-sampled phase shift keying modulation offers an error free data transmission and mean positioning errors of 5.0 and 6.6 cm for $h$ of 120 and 180 cm, respectively.


title: Malware detection using assembly and API call sequences

abstract: One of the major problems concerning information assurance is malicious code. To evade detection, malware has also been encrypted or obfuscated to produce variants that continue to plague properly defended and patched networks with zero day exploits. With malware and malware authors using obfuscation techniques to generate automated polymorphic and metamorphic versions, anti-virus software must always keep up with their samples and create a signature that can recognize the new variants. Creating a signature for each variant in a timely fashion is a problem that anti-virus companies face all the time. In this paper we present detection algorithms that can help the anti-virus community to ensure a variant of a known malware can still be detected without the need of creating a signature; a similarity analysis (based on specific quantitative measures) is performed to produce a matrix of similarity scores that can be utilized to determine the likelihood that a piece of code under inspection contains a particular malware. Two general malware detection methods presented in this paper are: Static Analyzer for Vicious Executables (SAVE) and Malware Examiner using Disassembled Code (MEDiC). MEDiC uses assembly calls for analysis and SAVE uses API calls (Static API call sequence and Static API call set) for analysis. We show where Assembly can be superior to API calls in that it allows a more detailed comparison of executables. API calls, on the other hand, can be superior to Assembly for its speed and its smaller signature. Our two proposed techniques are implemented in SAVE) and MEDiC. We present experimental results that indicate that both of our proposed techniques can provide a better detection performance against obfuscated malware. We also found a few false positives, such as those programs that use network functions (e.g. PuTTY) and encrypted programs (no API calls or assembly functions are found in the source code) when the thresholds are set 50% similarity measure. However, these false positives can be minimized, for example by changing the threshold value to 70% that determines whether a program falls in the malicious category or not.


A Deep Learning model for scene segmentation of images captured by drones


title: pix2code: Generating Code from a Graphical User Interface Screenshot

abstract: Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).


title: Towards Crawling the Web for Structured Data: Pitfalls of Common Crawl for E-Commerce

abstract: In the recent years, the publication of structured data inside HTML content of Web sites has become a mainstream feature of commercial Web sites. In particular, e-commerce sites have started to add RDFa or Microdata markup based on schema.org and GoodRelations vocabularies. For many potential usages of this huge body of data, we need to crawl the sites and extract the data from the markup. Unfortunately, a lot of markup can be found in very deep branches of the sites, namely in the product detail pages. Such pages are difficult to crawl because of their sheer number and because they often lack links pointing to them. In this paper, we conduct a small-sized experiment where we compare the Web pages from a popular Web crawler, Common Crawl, with the URLs in sitemap files of respective Web sites. We show that Common Crawl fails to detect most of the product detail pages that hold a majority of the data, and that an approach as simple as a sitemap crawl yields much more product pages. Based on our insights gained in this paper, we conclude that a rethinking of state-of-the-art crawling strategies is necessary in order to cater for e-commerce scenarios.


title: Automatic recognition of fingerspelled words in British Sign Language

abstract: We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and contains signs which are ambiguous from the observer's viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues; (ii) robust visual features for hand shape recognition; (iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality webcam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%.


title: Solution of n-Queen problem using ACO

abstract: In this paper, a solution is proposed for n-Queen problem based on ACO (Ant Colony Optimization). The n-Queen problem become intractable for large values of ‘n’ and thus placed in NP (Non-Deterministic Polynomial) class problem. The n-Queen problem is basically a generalized form of 8-Queen problem. In 8-Queen problem, the goal is to place 8 queens such that no queen can kill the other using standard chess queen moves. So, in this paper, the proposed solution will be applied to 8-Queen problem. The solution can very easily be extended to the generalized form of the problem for large values of ‘n’. The paper contains the detail discussion of problem background, problem complexity, Ant Colony Optimization (Swarm Intelligence) and a fair amount of experimental graphs.


title: Design and mode switching of wheel-leg adaptable amphibious robot

abstract: A large diameter wheel-leg adaptable amphibious hybrid robot of a new type is proposed in this paper. Firstly, the robot adopts the large diameter wheel-legged mechanism; the wheel has a composition of wheel plates. Secondly, the three wheel plate joint are distributed in different planes. The mode switching mechanism is easy to implement and there is no redundant mechanism. The drive system is distributed in closed space. In the aspect of control, a full-duplex conversion half-duplex control circuit is designed to solve the problem that the full-duplex control board can't directly drive the servo machine with half-duplex circuit communication; two wheel-leg adaptable movements of robot are planned. Adams and GeoGebra5.0 are used for simulation. Finally, this method is verified by the experiment.


title: Pseudomonas spinosa n. sp.

abstract: ABSTRACT ::: A new species of Pseudomonas is described which in its native river-water habitat characteristically has a curved soma with many straight spine-like structures, but no flagella. In pure culture the organism has a very long polar flagellum, a fairly straight soma with several round inclusions. It is psychrophilic and rather halophobic and shows a slight oxidative metabolism of some carbohydrates.


title: Reducing the metabolic cost of walking with an ankle exoskeleton: interaction between actuation timing and power

abstract: Background ::: Powered ankle-foot exoskeletons can reduce the metabolic cost of human walking to below normal levels, but optimal assistance properties remain unclear. The purpose of this study was to test the effects of different assistance timing and power characteristics in an experiment with a tethered ankle-foot exoskeleton.


title: The cyber threat landscape: Challenges and future research directions

abstract: Cyber threats are becoming more sophisticated with the blending of once distinct types of attack into more damaging forms. Increased variety and volume of attacks is inevitable given the desire of financially and criminally-motivated actors to obtain personal and confidential information, as highlighted in this paper. We describe how the Routine Activity Theory can be applied to mitigate these risks by reducing the opportunities for cyber crime to occur, making cyber crime more difficult to commit and by increasing the risks of detection and punishment associated with committing cyber crime. Potential research questions are also identified.


title: Active Discriminative Network Representation Learning

abstract: Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.


title: Multiple Features With Extreme Learning Machines For Clothing Image Recognition

abstract: Clothing image recognition has recently received considerable attention from many communities, such as multimedia information processing and computer vision, due to its commercial and social applications. However, the large variations in clothing images’ appearances and styles and their complicated formation conditions make the problem challenging. In addition, a generic treatment with convolutional neural networks (CNNs) cannot provide a satisfactory solution considering the training time and recognition performance. Therefore, how to balance those two factors for clothing image recognition is an interesting problem. Motivated by the fast training and straightforward solutions exhibited by extreme learning machines (ELMs), in this paper, we propose a recognition framework that is based on multiple sources of features and ELM neural networks. In this framework, three types of features are first extracted, including CNN features with pre-trained networks, histograms of oriented gradients and color histograms. Second, those low-level features are concatenated and taken as the inputs to an autoencoder version of the ELM for deep feature-level fusion. Third, we propose an ensemble of adaptive ELMs for decision-level fusion using the previously obtained feature-level fusion representations. Extensive experiments are conducted on an up-to-date large-scale clothing image data set. Those experimental results show that the proposed framework is competitive and efficient.


title: LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

abstract: Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an important role in the acceleration of Machine Learning applications. Initial specification of machine learning applications are often done using a high-level Python-oriented framework such as Tensorflow, followed by a manual translation to either C or RTL for synthesis using vendor tools. This manual translation step is time-consuming and requires expertise that limit the applicability of FPGAs in this important domain. In this paper, we present an open-source tool-flow that maps numerical computation models written in Tensorflow to synthesizable hardware. Unlike other tools, which are often constrained by a small number of inflexible templates, our flow uses Google's XLA compiler which emits LLVM code directly from a Tensorflow specification. This LLVM code can then be used with a high-level synthesis tool to automatically generate hardware. We show that our flow allows users to generate Deep Neural Networks with very few lines of Python code.


title: Implementation of Unsupervised k-Means Clustering Algorithm Within Amazon Web Services Lambda

abstract: This work demonstrates how an unsupervised learning algorithm based on k-Means Clustering with Kaufman Initialization may be implemented effectively as an Amazon Web Services Lambda Function, within their serverless cloud computing service. It emphasizes the need to employ a lean and modular design philosophy, transfer data efficiently between Lambda and DynamoDB, as well as employ Lambda Functions within mobile applications seamlessly and with negligible latency. This work presents a novel application of serverless cloud computing and provides specific examples that will allow readers to develop similar algorithms. The author provides compares the computation speed and cost of machine learning implementations on traditional PC and mobile hardware (running locally) as well as implementations that employ Lambda.


title: LIFE-COURSE DESISTERS? TRAJECTORIES OF CRIME AMONG DELINQUENT BOYS FOLLOWED TO AGE 70*

abstract: Linking recently collected data to form what is arguably the longest longitudinal study of crime to date, this paper examines trajectories of offending over the life course of delinquent boys followed from ages 7 to 70. We assess whether there is a distinct offender group whose rates of crime remain stable with increasing age, and whether individual differences, childhood characteristics, and family background can foretell long-term trajectories of offending. On both counts, our results come back negative. Crime declines with age sooner or later for all offender groups, whether identified prospectively according to a multitude of childhood and adolescent risk factors, or retrospectively based on latent-class models of trajectories. We conclude that desistance processes are at work even among active offenders and predicted life-course persisters, and that childhood prognoses account poorly for long-term trajectories of offending.


title: Acne vulgaris

abstract: Acne is a chronic inflammatory disease of the pilosebaceous unit resulting from androgen-induced increased sebum production, altered keratinisation, inflammation, and bacterial colonisation of hair follicles on the face, neck, chest, and back by Propionibacterium acnes. Although early colonisation with P acnes and family history might have important roles in the disease, exactly what triggers acne and how treatment affects the course of the disease remain unclear. Other factors such as diet have been implicated, but not proven. Facial scarring due to acne affects up to 20% of teenagers. Acne can persist into adulthood, with detrimental effects on self-esteem. There is no ideal treatment for acne, although a suitable regimen for reducing lesions can be found for most patients. Good quality evidence on comparative effectiveness of common topical and systemic acne therapies is scarce. Topical therapies including benzoyl peroxide, retinoids, and antibiotics when used in combination usually improve control of mild to moderate acne. Treatment with combined oral contraceptives can help women with acne. Patients with more severe inflammatory acne usually need oral antibiotics combined with topical benzoyl peroxide to decrease antibiotic-resistant organisms. Oral isotretinoin is the most effective therapy and is used early in severe disease, although its use is limited by teratogenicity and other side-effects. Availability, adverse effects, and cost, limit the use of photodynamic therapy. New research is needed into the therapeutic comparative effectiveness and safety of the many products available, and to better understand the natural history, subtypes, and triggers of acne.


title: Research on Cooperative Learning and Achievement: What We Know, What We Need to Know☆

abstract: Research on cooperative learning is one of the greatest success stories in the history of educational research. While there was some research on this topic from the early days of this century, the amount and quality of that research greatly accelerated in the early 1970's, and continues unabated today, a quarter-century later. Hundreds of studies have compared cooperative learning to various control methods on a broad range of measures, but by far the most frequent objective of this research is to determine the effects of cooperative learning on student achievement. Studies of the achievement effects of cooperative learning have taken place in every major subject, at all grade levels, in all types of schools in many countries. Both field studies and laboratory studies have produced a great deal of knowledge about the effects of many types of cooperative interventions and about the mechanisms responsible for these effects. Further, cooperative learning is not only a subject of research and theory; it is used at some level by millions of teachers. A recent national survey (Puma, Jones, Rock, & Fernandez, 1993) found that 79% of elementary teachers and 62% of middle school teachers reported making some sustained use of cooperative learning.


title: The Constrained Laplacian Rank algorithm for graph-based clustering

abstract: Graph-based clustering methods perform clustering on a fixed input data graph. If this initial construction is of low quality then the resulting clustering may also be of low quality. Moreover, existing graph-based clustering methods require post-processing on the data graph to extract the clustering indicators. We address both of these drawbacks by allowing the data graph itself to be adjusted as part of the clustering procedure. In particular, our Constrained Laplacian Rank (CLR) method learns a graph with exactly k connected components (where k is the number of clusters). We develop two versions of this method, based upon the L1-norm and the L2-norm, which yield two new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new graph-based clustering method.


title: Applications and Challenges of Text Mining with Patents

abstract: This paper gives insight into our current research on three text mining tools for patents designed for information professionals. The first tool identifies numeric properties in the patent text and normalises them, the second extracts a list of keywords that are relevant and reveal the invention in the patent text, and the third tool attempts to segment the patent’s description into it’s sections. Our tools are used in the industry and could be applied in research as well.


title: Decision support methods for the detection of adverse events in post-marketing data

abstract: Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.


title: Customer repurchase intention: A general structural equation model

abstract: This paper develops a general service sector model of repurchase intention from the consumer theory literature. A key contribution of the structural equation model is the incorporation of customer perceptions of equity and value and customer brand preference into an integrated repurchase intention analysis. The model describes the extent to which customer repurchase intention is influenced by seven important factors – service quality, equity and value, customer satisfaction, past loyalty, expected switching cost and brand preference. The general model is applied to customers of comprehensive car insurance and personal superannuation services. The analysis finds that although perceived quality does not directly affect customer satisfaction, it does so indirectly via customer equity and value perceptions. The study also finds that past purchase loyalty is not directly related to customer satisfaction or current brand preference and that brand preference is an intervening factor between customer satisfaction and repurchase intention. The main factor influencing brand preference was perceived value with customer satisfaction and expected switching cost having less influence.


title: Tree-Based State Tying For High Accuracy Modelling

abstract: A process for increasing the proportion of a gas in a gaseous mixture comprising such gas and at least two other constituents. The gaseous mixture in a substantially unpressurized condition is drawn successively through two beds of absorbent which adsorbs the two other constituents respectively by the action of a reduced pressure applied to an outlet of the second adsorbent bed in the series. The beds are then regenerated prior to a further admission of gaseous mixture thereto.


title: The Tropical Biominer Project: mining old sources for new drugs.

abstract: The Tropical Biominer Project is a recent initiative from the Federal University of Minas Gerais (UFMG) and the Oswaldo Cruz foundation, with the participation of the Biominas Foundation (Belo Horizonte, Minas Gerais, Brazil) and the start-up Homologix. The main objective of the project is to build a new resource for the chemogenomics research, on chemical compounds, with a strong emphasis on natural molecules. Adopted technologies include the search of information from structured, semi-structured, and non-structured documents (the last two from the web) and datamining tools in order to gather information from different sources. The database is the support for developing applications to find new potential treatments for parasitic infections by using virtual screening tools. We present here the midpoint of the project: the conception and implementation of the Tropical Biominer Database. This is a Federated Database designed to store data from different resources. Connected to the database, a web crawler is able to gather information from distinct, patented web sites and store them after automatic classification using datamining tools. Finally, we demonstrate the interest of the approach, by formulating new hypotheses on specific targets of a natural compound, violacein, using inferences from a Virtual Screening procedure.


title: Evacuation proctography: An investigation of rectal expulsion in 20 subjects without defecatory disturbance

abstract: Evacuation proctography is a dynamic investigation of rectal expulsion that records the voluntary evacuation of thick barium paste on videotape. Evacuation is a passive phenomenon in a defined zone of the rectum, associated with pelvic floor descent of 3 cm from a resting position of the anorectal junction <2 cm above the plane of the ischial tuberosities. The anal canal does not open immediately; it takes about 4.5 sec to open to a maximum diameter of 1.5 cm, with rectal emptying in 11 sec. Anterior rectoceles commonly invert over the anal canal as the rectum collapses in at the end of evacuation.


title: BeBoP: A cost effective predictor infrastructure for superscalar value prediction

abstract: Up to recently, it was considered that a performance-effective implementation of Value Prediction (VP) would add tremendous complexity and power consumption in the pipeline, especially in the Out-of-Order engine and the predictor infrastructure. Despite recent progress in the field of Value Prediction, this remains partially true. Indeed, if the recent EOLE architecture proposition suggests that the OoO engine need not be altered to accommodate VP, complexity in the predictor infrastructure itself is still problematic. First, multiple predictions must be generated each cycle, but multi-ported structures should be avoided. Second, the predictor should be small enough to be considered for implementation, yet coverage must remain high enough to increase performance. To address these remaining concerns, we first propose a block-based value prediction scheme mimicking current instruction fetch mechanisms, BeBoP. It associates the predicted values with a fetch block rather than distinct instructions. Second, to remedy the storage issue, we present the Differential VTAGE predictor. This new tightly coupled hybrid predictor covers instructions predictable by both VTAGE and Stride-based value predictors, and its hardware cost and complexity can be made similar to those of a modern branch predictor. Third, we show that block-based value prediction allows to implement the checkpointing mechanism needed to provide D-VTAGE with last computed/predicted values at moderate cost. Overall, we establish that EOLE with a 32.8KB block-based D-VTAGE predictor and a 4-issue OoO engine can significantly outperform a baseline 6-issue superscalar processor, by up to 62.2% and 11.2% on average (gmean), on our benchmark set.


The Emotional GAN : Priming Adversarial Generation of Art with Emotion


title: An alternative softmax operator for reinforcement learning

abstract: A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice.


title: Bandwidth and storage sharing performance in information centric networking

abstract: Internet usage has dramatically evolved towards content dissemination and retrieval, whilst the underlying infrastructure remains tied up to hosts interconnection. Information centric networking (ICN) proposals have recently emerged to rethink Internet foundations and design a natively content-centric network environment. Important features of such networks are the availability of built-in network storage and of receiver-driven chunk-level transport, whose interaction significantly impacts overall system and user performance. In the paper, we provide an analytical characterization of statistical bandwidth and storage sharing, under fairly general assumption on total demand, topology, content popularity and limited network resources. A closed-form expression for average content delivery time is derived and its accuracy confirmed by event-driven simulations. Finally, we present some applications of our model, leveraging on explicit formulae for the optimal dimensioning and localization of storage resources.


title: Ridesourcing Car Detection by Transfer Learning

abstract: Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.


title: A time-continuous bus-feedback LIN transceiver in 0.14 µm high-voltage SOI CMOS

abstract: This paper presents a novel LIN transceiver designed and fabricated in a 0.14 µm high-voltage SOI CMOS technology that uses a time-continuous bus-feedback technique. The architecture of the LIN transceiver takes advantage of the advanced BCD process as a waveshaping DAC uses a dense control logic circuit while the high-voltage capability enhances the robustness of the transmitter and the receiver. The bus-feedback technique allows to reproduce the shape of a low-EME signal on the LIN bus and to be insensitive to bus-load variations. The architecture, and the feedback loop in particular, are designed to be robust against severe RF disturbances so that both low EME and high EMI can be achieved on LIN and battery supply nodes. EMC performance was evaluated according to automotive industry standards. Excellent EME and EMI compliance with German car OEMs requirements was achieved.


title: Random partial paired comparison for subjective video quality assessment via hodgerank

abstract: Subjective visual quality evaluation provides the groundtruth and source of inspiration in building objective visual quality metrics. Paired comparison is expected to yield more reliable results; however, this is an expensive and timeconsuming process. In this paper, we propose a novel framework of HodgeRank on Random Graphs (HRRG) to achieve efficient and reliable subjective Video Quality Assessment (VQA). To address the challenge of a potentially large number of combinations of videos to be assessed, the proposed methodology does not require the participants to perform the complete comparison of all the paired videos. Instead, participants only need to perform a random sample of all possible paired comparisons, which saves a great amount of time and labor. In contrast to the traditional deterministic incomplete block designs, our random design is not only suitable for traditional laboratory and focus-group studies, but also fit for crowdsourcing experiments on Internet where the raters are distributive over Internet and it is hard to control with precise experimental designs. Our contribution in this work is three-fold: 1) a HRRG framework is proposed to quantify the quality of video; 2) a new random design principle is investigated to conduct paired comparison based on Erdos-Renyi random graph theory; 3) Hodge decomposition is introduced to derive, from incomplete and imbalanced data, quality scores of videos and inconsistency of participants'judgments. We demonstrate the effectiveness of the proposed framework on LIVE Database. Equipped with random graph theory and HodgeRank, our scheme has the following advantages over the traditional ones: 1) data collection is simple and easy to handle, and thus is more suitable for crowdsourcing on Internet; 2) workload on participants is lower and more flexible; 3) the rating procedure is efficient, labor-saving, and more importantly, without jeopardizing the accuracy of the results.


title: Surfactant in children with malignancies, immunosuppression, fever and pulmonary infiltrates

abstract: In children with malignancies and immunosuppression, significant morbidity and mortality result from respiratory complications. The aim of the present study was to investigate whether or not this is associated with altered surfactant components or functions. Bronchoalveolar lavage fluid from 24 children with malignancies, immunosuppression, pulmonary infiltrates and fever unresponsive to empirical antibiotic treatment were compared to that from 24 healthy children. Levels of surfactant protein (SP) A and D and their binding capacity for Pseudomonas aeruginosa , as well as levels of SP-B and SP-C, were assessed by enzyme-linked immunosorbent assay. The large and small surfactant aggregate forms were separated and the biophysical activity of large surfactant aggregates was determined using a pulsating bubble surfactometer. Compared to healthy controls, SP-A levels were increased four-fold, the increase being most pronounced in those children with pathogens recovered from their bronchoalveolar lavage fluid. In children with malignancies, levels of SP-C were increased two-fold and of small surfactant aggregates five-fold. No differences were observed in levels of SP-B or SP-D, binding capacity of SP-A or SP-D or the surface activity of large surfactant aggregates. The increased levels of surfactant protein A, particularly in children with recovered microorganisms, and unchanged binding capacity of surfactant protein A are consistent with upregulated local host defence mechanisms. Increased surfactant protein A and C may also be responsible for the conserved biophysical activity of surfactant in children with malignancies, immunosuppression, pulmonary infiltrates and fever.


title: Strategic Alignment Between Business and Information Technology: A Knowledge-Based View of Behaviors, Outcome, and Consequences

abstract: Senior executives continue to be concerned about factors influencing the business effect of information technology (IT). Prior research has argued that business-IT strategic alignment facilitates business effect of IT and that contextual factors affect business-IT alignment. However, the role of knowledge considerations in the relationship between contextual factors and alignment, and the role of IT projects in the relationship between alignment and business effects of IT, have not been explicitly examined. Therefore, this paper pursues the following two research questions: (1) Based on knowledge considerations, how do planning behaviors (specifically, IT managers' participation in business planning and business managers' participation in IT planning) and top management knowledge of IT mediate the effects of two contextual factors-organizational emphasis on knowledge management and centralization of IT decisions-on business-IT strategic alignment? (2) How do aspects of IT projects (specifically, quality of IT project planning and implementation problems in IT projects) mediate the relationship between business-IT strategic alignment and business effects of IT? ::: ::: Results from a survey of 274 senior information officers indicate that organizational emphasis on knowledge management and centralization of IT decisions affect top managers' knowledge of IT, which facilitates business managers' participation in strategic IT planning and IT managers' participation in business planning, and both of these planning behaviors affect business-IT strategic alignment. Moreover, the results indicate that quality of IT project planning and implementation problems in IT projects mediate the relationship between business-IT strategic alignment and business effect of IT. These findings highlight the importance of considering the planning and implementation of IT projects when examining the effects of business-IT strategic alignment, and highlight the importance of considering shared domain knowledge (i.e., top managers' knowledge of IT) and planning behaviors when examining the effects of contextual factors on business-IT strategic alignment. Managers can use these results to develop more comprehensive action plans for achieving greater business-IT strategic alignment, and for translating alignment into enhanced IT effects on business performance.


title: FHE over the Integers: Decomposed and Batched in the Post-Quantum Regime

abstract: Fully homomorphic encryption over the integers FHE-OI is currently the only alternative to lattice-based FHE. FHE-OI includes a family of schemes whose security is based on the hardness of different variants of the approximate greatest common divisor AGCD problem. A lot of effort was made to port techniques from second generation lattice-based FHE using tensoring to FHE-OI. Gentry, Sahai and Waters Crypto 13 showed that third generation techniques which were later formalized using the "gadget matrix" can also be ported. However, the majority of these works was based on the noise-free variant of AGCD which is potentially weaker than the general one. In particular, the noise-free variant relies on the hardness of factoring and is thus vulnerable to quantum attacks. ::: ::: In this work, we propose a comprehensive study of applying third generation FHE techniques to the regime of FHE-OI. We present and analyze a third generation FHE-OI based on decisional AGCD without the noise-free assumption. We proceed to showing a batch version of our scheme where each ciphertext can encode a vector of messages and operations are performed coordinate-wise. We use a similar AGCD variant to Cheon et al.i¾?Eurocrypt 13 who suggested the batch approach for second generation FHE, but we do not require the noise-free component or a subset sum assumption. However, like Cheon et al., we do require circular security for our scheme, even for bounded homomorphism. Lastly, we discuss some of the obstacles towards efficient implementation of our schemes and discuss a number of possible optimizations.


title: ' s personal copy Production , Manufacturing and Logistics Deriving the DEA frontier for two-stage processes

abstract: Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently DEA has been extended to examine the efficiency of two-stage processes, where all the outputs from the first stage are intermediate measures that make up the inputs to the second stage. The resulting two-stage DEA model provides not only an overall efficiency score for the entire process, but as well yields an efficiency score for each of the individual stages. Due to the existence of intermediate measures, the usual procedure of adjusting the inputs or outputs by the efficiency scores, as in the standard DEA approach, does not necessarily yield a frontier projection. The current paper develops an approach for determining the frontier points for inefficient DMUs within the framework of two-stage DEA.


title: Automated detection in complex objects using a tracking algorithm in multiple X-ray views

abstract: We propose a new methodology to detect parts of interest inside of complex objects using multiple X-ray views. Our method consists of two steps: ‘structure estimation’, to obtain a geometric model of the multiple views from the object itself, and ‘parts detection’, to detect the object parts of interest. The geometric model is estimated by a bundle adjustment algorithm on stable SIFT keypoints across multiple views that are not necessary sorted. The detection of the object parts of interest is performed by an ad-hoc segmentation algorithm (application dependent) followed by a tracking algorithm based on geometric and appearance constraints. It is not required that the object parts have to be segmented in all views. Additionally, it is allowed to obtain false detections in this step. The tracking is used to eliminate the false detections without discriminating the object parts of interest. In order to illustrate the effectiveness of the proposed method, several applications — like detection of pen tips, razor blades and pins in pencil cases and detection of flaws in aluminum die castings used in the au-tomative industry — are shown yielding a true positive rate of 94.3% and a false positive rate of 5.6% in 18 sequences from 4 to 8 views.


title: A Novel Planar Printed Dual-Band Magneto-Electric Dipole Antenna

abstract: In this paper, a novel planar printed dual-band magneto-electric dipole antenna is proposed. The proposed antenna is composed of a conventional bow-tie patch as an electric dipole, a semi-circular loop that operates as a magnetic dipole, a coplanar ground plane, and a wideband microstrip-to-coplanar-stripline transition balun. By etching a pair of complementary capacitively loaded loop slots on the bow-tie radiation patch, a notched band is introduced to form the dual-band antenna. And a coplanar ground is adopted to make the entire antenna become a planar printed structure. Additionally, the proposed antenna loads the periodical interdigital capacitance structure on the semi-circular loop for the purpose of making the current flowing along the loop maintain the same phase and improving the performances of the magnetic dipole. The proposed antenna was fabricated and measured. The measured results keep in good accordance with the simulated ones. Consequently, the proposed antenna achieves a dual-band operation at 2.37~2.82 GHz (17.3%) and 3.14~4.10 GHz (26.5%) with the Voltage Standing Wave Ratio (VSWR) less than 2, so it can be applied for WLAN (2.4–2.484 GHz) and WiMAX (2.5–2.69 GHz/3.4–3.69 GHz). Meanwhile, the stable gain, symmetrical, and stable unidirectional radiation patterns, low cross polarization, and low back lobe are also obtained at the dual bands.


Ecg Steganography Based Privacy Protection of Medical Datas for Telemedicine Application


title: 2012 Presidential Elections on Twitter -- An Analysis of How the US and French Election were Reflected in Tweets

abstract: The main objective of this paper is to compare the sentiments that prevailed before and after the presidential elections, held in both US and France in the year 2012. To achieve this objective we extracted the content information from a social medium such as Twitter and used the tweets from electoral candidates and the public users (voters), collected by means of crawling during the course of election. In order to gain useful insights about the US elections, we scored the sentiments for each tweet using different metrics and performed a time series analysis for candidates and different topics (identified by specific keywords). In addition to this, we compared some of our insights obtained from the US election with what we have observed for the French election. This deep dive analysis was done in order to understand the inherent nature of elections and to bring out the influence of social media on elections.


title: The Role of Institutions in Growth and Development

abstract: In this paper authors argue that the main determinants of differences in prosperity across countries are differences in economic institutions. To solve the problem of development will entail reforming these institutions. Unfortunately, this is difficult because economic institutions are collective choices that are the outcome of a political process. The economic institutions of a society depend on the nature of political institutions and the distribution of political power in society. As yet, authors only have a highly preliminary understanding of the factors that lead a society into a political equilibrium which supports good economic institutions. However, it is clear that it is the political nature of an institutional equilibrium that makes it very difficult to reform economic institutions. The authors illustrate this with a series of pitfalls of institutional reforms. The author's analysis reveals challenges for those who would wish to solve the problem of development and poverty. That such challenges exist is hardly surprising and believe that the main reason for such challenges is the forces authors have outlined in this paper. Better development policy will only come when authors recognize this and understand these forces better. Nevertheless, some countries do undergo political transitions, reform their institutions, and move onto more successful paths of economic development.


title: Development and application of a set of breeder-friendly SNP markers for genetic analyses and molecular breeding of rice (Oryza sativa L.)

abstract: Single nucleotide polymorphisms (SNPs) are the most abundant DNA markers in plant genomes. In this study, based on 54,465 SNPs between the genomes of two Indica varieties, Minghui 63 (MH63) and Zhenshan 97 (ZS97) and additional 20,705 SNPs between the MH63 and Nipponbare genomes, we identified and confirmed 1,633 well-distributed SNPs by PCR and Sanger sequencing. From these, a set of 372 SNPs were further selected to analyze the patterns of genetic diversity in 300 representative rice inbred lines from 22 rice growing countries worldwide. Using this set of SNPs, we were able to uncover the well-known Indica-Japonica subspecific differentiation and geographic differentiations within Indica and Japonica. Furthermore, our SNP results revealed some common and contrasting patterns of the haplotype diversity along different rice chromosomes in the Indica and Japonica accessions, which suggest different evolutionary forces possibly acting in specific regions of the rice genome during domestication and evolution of rice. Our results demonstrated that this set of SNPs can be used as anchor SNPs for large scale genotyping in rice molecular breeding research involving Indica-Japonica and Indica-Indica crosses.


title: Problematizing cultural appropriation

abstract: Cultural appropriation in games entails the taking of knowledge, artifacts or expression from a culture and recontextualizing it within game structures. While cultural appropriation is a pervasive practice in games, little attention has been given to the ethical issues that emerge from such practices with regards to how culture is portrayed. This paper problematizes cultural appropriation in the context of a serious game for children inspired by Dia de los Muertos, a Mexican festival focused on remembrance of the dead. Taking a research through design approach, we demonstrate that recontextualised cultural elements can retain their basic, original meaning. However, we also find that cultural appropriation is inevitable and its ethical implications can be far reaching. In our context, ethical concerns arose as a result of children's beliefs that death affects prominent others and their destructive ways of coping with death. We argue that revealing emergent ethical concerns is imperative before deciding how and in what way to encourage culturally authentic narratives.


title: Prejudice as self-image maintenance: Affirming the self through derogating others.

abstract: The authors argue that self-image maintenance processes play an important role in stereotyping and prejudice. Three studies demonstrated that when individuals evaluated a member of a stereotyped group, they were less likely to evaluate that person negatively if their self-images had been bolstered through a self-affirmation procedure, and they were more likely to evaluate that person stereotypically if their self-images had been threatened by negative feedback.' Moreover, among those individuals whose self-image had been threatened, derogating a stereotyped target mediated an increase in their self-esteem. The authors suggest that stereotyping and prejudice may be a common means to maintain one's self-image, and they discuss the role of self-image-maintenance processes in the context of motivational, sociocultural, and cognitive approaches to stereotyping and prejudice.


Manipulating Opinion Diffusion in Social Networks


title: Pronoun Translation and Prediction with or without Coreference Links

abstract: The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronoun-focused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: gram matical constraints from the hypothesized coreference links, and candidate translations from an SMT decoder. The system for the second sub-task avoids hypothesizing a coreference link, and uses instead a large set of source-side and target-side features from the noun phrases surrounding the pronoun to train a pronoun predictor.


title: Microstrip patch antenna miniaturization using artificial magnetic conductor

abstract: This paper deals with an alternative technique for microstrip patch antenna miniaturization is investigated. The technique employs a 2 layer structures with an artificial magnetic conductor (AMC) structure used as the bottom layer replacing the ground plane. The AMC structure is constructed using a textured high impedance surface (HIS) that takes form of a doubly periodic square patches on top of a dielectric substrate. The miniaturization effect that is introduced especially in the reduction of patch size by use of this 2 layer structures will be numerically investigated. The proposed antenna is designed to have a resonant frequency of 2.45MHz suitable for wireless local area network (WLAN) application and is deployed on an FR-4 Epoxy substrate. To obtain the optimum design, numerical analysis is carried out using 3D simulation software. To verify the miniaturization effect, the proposed antenna will be compared to some reference antenna resonates at the same resonant frequency placed on a 2 layers FR-4 Epoxy substrate with the same thickness but without AMC structure. From the result, it is shown that the incorporation of AMC structure can reduce the size of antenna patch by more than 31%.


title: Identifying Relations for Open Information Extraction

abstract: Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-of-the-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the ReVerb Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TextRunner and woepos. More than 30% of ReVerb's extractions are at precision 0.8 or higher---compared to virtually none for earlier systems. The paper concludes with a detailed analysis of ReVerb's errors, suggesting directions for future work.


title: A probabilistic framework for representing dialog systems and entropy-based dialog management through dynamic stochastic state evolution

abstract: In this paper, we present a probabilistic framework for goal-driven spoken dialog systems. A new dynamic stochastic state (DS-state) is then defined to characterize the goal set of a dialog state at different stages of the dialog process. Furthermore, an entropy minimization dialog management (EMDM) strategy is also proposed to combine with the DS-states to facilitate a robust and efficient solution in reaching a user’s goals. A song-on-demand task, with a total of 38 117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach. In an ideal simulation, assuming no errors, the EMDM strategy is the most efficient goal-seeking method among all tested approaches, returning the correct song within 3.3 dialog turns on average. Furthermore, in a practical scenario, with top five candidates to handle the unavoidable automatic speech recognition (ASR) and natural language understanding (NLU) errors, the results show that only 61.7% of the dialog goals can be successfully obtained in 6.23 dialog turns on average when random questions are asked by the system, whereas if the proposed DS-states are updated with the top five candidates from the SLU output using the proposed EMDM strategy executed at every DS-state, then a 86.7% dialog success rate can be accomplished effectively within 5.17 dialog turns on average. We also demonstrate that entropy-based DM strategies are more efficient than non-entropy based DM. Moreover, using the goal set distributions in EMDM, the results are better than those without them, such as in sate-of-the-art database summary DM.