title: A novel low profile high-gain UHF antenna using High-Impedance Surfaces

abstract: A High-Impedance Surfaces (HIS) analysis is presented to study the feasibility of these elements to be used in the novel design of low-profile planar antennas for UHF applications. The proposed design is based on a wideband monopole antenna in close proximity to an artificial ground plane, composed of an array of square metal loops at the top and a ground plane at the bottom, without grounded vias. Low-cost substrates are employed in the design, rigid PVC for the antenna, and foamed PVC for the artificial ground plane. PVC substrate is ecologic, recyclable, long lasting and cheap, what makes it desirable for this application. The advantages of using a HIS structure as a reflecting surface are the reduction of backradiation, the increase of the antenna gain, the broadening of bandwidth, and the mitigation of the effects of parasitic surface waves. Details of the proposed antenna design are described, and simulation results together with a comparison to a conventional antenna with PEC ground plane are presented.


title: A comprehensive approach for the development of modular software architecture description languages

abstract: Research over the past decade has revealed that modeling software architecture at the level of components and connectors is useful in a growing variety of contexts. This has led to the development of a plethora of notations for representing software architectures, each focusing on different aspects of the systems being modeled. In general, these notations have been developed without regard to reuse or extension. This makes the effort in adapting an existing notation to a new purpose commensurate with developing a new notation from scratch. To address this problem, we have developed an approach that allows for the rapid construction of new architecture description languages (ADLs). Our approach is unique because it encapsulates ADL features in modules that are composed to form ADLs. We achieve this by leveraging the extension mechanisms provided by XML and XML schemas. We have defined a set of generic, reusable ADL modules called xADL 2.0, useful as an ADL by itself, but also extensible to support new applications and domains. To support this extensibility, we have developed a set of reflective syntax-based tools that adapt to language changes automatically, as well as several semantically-aware tools that provide support for advanced features of xADL 2.0. We demonstrate the effectiveness, scalability, and flexibility of our approach through a diverse set of experiences. First, our approach has been applied in industrial contexts, modeling software architectures for aircraft software and spacecraft systems. Second, we show how xADL 2.0 can be extended to support the modeling features found in two different representations for modeling product-line architectures. Finally, we show how our infrastructure has been used to support its own development. The technical contribution of our infrastructure is augmented by several research contributions: the first decomposition of an architecture description language into modules, insights about how to develop new language modules and a process for integrating them, and insights about the roles of different kinds of tools in a modular ADL-based infrastructure.


title: The Minimal Bounding Volume Hierarchy

abstract: Bounding volume hierarchies (BVH) are a commonly used method for speeding up ray tracing. Even though the memory footprint of a BVH is relatively low compared to other acceleration data structures, they still can consume a large amount of memory for complex scenes and exceed the memory bounds of the host system. This can lead to a tremendous performance decrease on the order of several magnitudes. In this paper we present a novel scheme for construction and storage of BVHs that can reduce the memory consumption to less than 1% of a standard BVH. We show that our representation, which uses only 2 bits per node, is the smallest possible representation on a per node basis that does not produce empty space deadlocks. Our data structure, called the Minimal Bounding Volume Hierarchy (MVH) reduces the memory requirements in two important ways: using implicit indexing and preset surface reduction factors. Obviously, this scheme has a non-negligible computational overhead, but this overhead can be compensated to a large degree by shooting larger ray bundles instead of single rays, using a simpler intersection scheme and a two-level representation of the hierarchy. These measure enable interactive ray tracing performance without the necessity to rely on out-of-core techniques that would be inevitable for a standard BVH.


title: Lipids on the frontier: a century of cell-membrane bilayers

abstract: Our present picture of cell membranes as lipid bilayers is the legacy of a century's study that concentrated on the lipids and proteins of cell-surface membranes. Recent work is changing the picture and is turning the snapshot into a video.


title: Nonbacterial Causes of Lymphangitis with Streaking.

abstract: BACKGROUND ::: Lymphangitic streaking, characterized by linear erythema on the skin, is most commonly observed in the setting of bacterial infection. However, a number of nonbacterial causes can result in lymphangitic streaking. We sought to elucidate the nonbacterial causes of lymphangitic streaking that may mimic bacterial infection to broaden clinicians' differential diagnosis for patients presenting with lymphangitic streaking. ::: ::: ::: METHODS ::: We performed a review of the literature, including all available reports pertaining to nonbacterial causes of lymphangitic streaking. ::: ::: ::: RESULTS ::: Various nonbacterial causes can result in lymphangitic streaking, including viral and fungal infections, insect or spider bites, and iatrogenic etiologies. ::: ::: ::: CONCLUSION ::: Awareness of potential nonbacterial causes of superficial lymphangitis is important to avoid misdiagnosis and delay the administration of appropriate care.


title: Performance analysis of mesh routing protocols for UAV swarming applications

abstract: Unmanned Aerial Vehicles (UAVs) are an emerging technology offering new opportunities for innovative applications and efficient overall process management in the areas of public security, cellular networks and surveying. A key factor for the optimizations yielded by this technology is an advanced mesh network design for fast and reliable information sharing between UAVs. In this paper, we analyze the performance of four available mesh routing protocol implementations (open80211s, BATMAN, BATMAN Advanced and OLSR) in the context of swarming applications for UAVs. The protocols are analyzed by means of goodput in one static and one mobile scenario using the same embedded hardware platform installed at UAVs in current research projects. Our results show that layer-2 protocols suit better for mobile applications in comparison to layer-3. On the other hand, they often cause routing flippings, which are unwanted route changes, in static scenarios imposing a small performance decrease. Hence, given the aforementioned routing protocols, we recommend to currently use open80211s or batman-advanced to establish a reliable multi-hop mesh network for swarming applications.


title: Fine-Grained Record Integration and Linkage Tool

abstract: BACKGROUND: As part of the surveillance program to monitor the occurrence of birth defects in the metropolitan Atlanta area, we developed a record linkage software tool that provides latitude in the choice of linkage parameters, allows for efficient and accurate linkages, and enables objective assessments of the quality of the linked data. METHODS: We developed and implemented a Java-based fine-grained probabilistic record integration and linkage tool (FRIL) that incorporates a rich collection of record distance metrics, search methods, and analysis tools. Along its workflow, FRIL provides a rich set of user-tunable parameters augmented with graphic visualization tools to assist users in understanding the effects of parameter choices. We used this software tool to link data from vital records (n 5 1.25 million) with birth defects surveillance records (n 5 12,700) from the metropolitan Atlanta Congenital Defects Program (MACDP) for the birth years 1967–2006. RESULTS: Compared with the data linkage performed by conventional algorithms, the data linkage of birth certificates with birth defect records in MACDP using FRIL was more efficient. The linkage based on FRIL was also accurate, showing 99% precision and 95% recall. Based on positive user feedback, new features continue to be developed, and the tool is being adopted in several other data linkage projects in MACDP. CONCLUSIONS: A software tool that allows significant user interaction and control, such as FRIL, can provide accurate data linkages for birth defect surveillance programs and allows an objective assessment of the quality of linked data. Birth Defects Research (Part A) 82:822–829, 2008. 2008 Wiley-Liss, Inc.


title: Bitcoin: Bubble or Blockchain

abstract: This paper sets out a brief, deliberately non-technical, overview of Bitcoin, a new, but becoming more mainstream, crypto currency, generated and managed by a distributed multi–agent system. Bitcoin was developed in late 2008 by “Satoshi Nakamoto”. The nature of Bitcoin as a disruptive currency, payments system and asset, is juxtaposed against the potential for its transactional ledger, the blockchain, to usher in a revolutionary way of recording “digital truth”. The main contribution of this paper is to progress the debate around Bitcoin beyond the technical and towards legal and ethical issues and the nature of money and memory itself.


title: A Novel Technique for Detecting Plagiarism in Documents Exploiting Information Sources

abstract: Plagiarism takes place when we use any person’s work without giving due acknowledgment. There are several fields where the text similarity is involved like web document retrieval, information mining, and searching related articles. Several approaches have been introduced for detecting plagiarism in the text documents based on the syntactic structure of the text, string similarity, fingerprinting, semantic meaning underlying the text, etc. The basic limitation of plagiarism detection systems these days is that they fail to detect tough cases of plagiarism. The proposed plagiarism detection approach is the hybrid of semantic and syntactic similarity between the text documents. This novel approach exploits linguistic information sources non-linearly using the lexical database for finding the relatedness between text documents. The proposed approach uses semantic knowledge to perform cognitive-inspired computing. The framework is capable of detecting intelligent plagiarism cases like a verbatim copy, paraphrasing, rewording in a sentence, and sentence transformation. The approach has been evaluated on the standard PAN-PC-11 dataset. The experiments show that our technique has outperformed other strong baseline techniques in terms of precision, recall, F-measure, and plagiarism detection (PlagDet) score.


title: Dynamic Intention-Aware Recommendation System

abstract: Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recommender systems. In this paper, we propose a dynamic intention-aware recommender system to better facilitate users to find desirable products and services. Compare to prior work, our proposal possesses the following advantages: (1) it takes user intentions and demands into account through intention mining techniques. By unearthing user intentions from the historical user-item interactions, and various user digital traces harvested from social media and Internet of Things, it is capable of delivering more satisfactory recommendations by leveraging rich online and offline user data; (2) it embraces the benefits of embedding heterogeneous source information and shared representations of multiple domains to provide accurate and effective recommendations comprehensively; (3) it recommends products or services proactively and timely by capturing the dynamic influences, which can significantly reduce user involvements and efforts.


title: A Sensor Network Simulator for the Contiki OS

abstract: This report introduces a new sensor network simulator for the Contiki OS - the COOJA Simulator. ::: ::: The Contiki OS is a portable operating system designed specifically for resource limited devices such as sensor nodes. It is built around an event-driven kernel but supports pre-emptive multithreading at a per-process basis. It also supports a full TCP/IP stack via uIP and the programming abstraction Protothreads. ::: ::: The main design goal of the COOJA Simulator is extendibility, for which Interfaces and Plugins are used. An Interface represents a sensor node property or device, such as a position, a button or a radio transmitter. A Plugin is used to interact with a simulation, for example to control the simulation speed or to watch all network traffic between the simulated nodes. Both new Plugins and Interfaces can easily be created and added to the simulation environment. A number of other parts of the simulator, for example a radio medium responsible for forwarding radio network data, can also be implemented and added to the simulator. And by supporting several different simulation environments at the same time in one simulation, different underlying hardware platforms can be simulated in heterogeneous networks. ::: ::: Java Native Interface is used to connect the new simulator with Contiki, allowing simulated applications to run in a real Contiki system. By using this approach, any simulated application can then be run on a real sensor node unaltered.


title: Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns.

abstract: BACKGROUND ::: Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving. ::: ::: ::: METHODS ::: Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns. ::: ::: ::: RESULTS ::: The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values. ::: ::: ::: CONCLUSIONS ::: Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.


title: Human-aided Multi-Entity Bayesian Networks Learning from Relational Data

abstract: An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability to reason under uncertainty about the situation. Multi-Entity Bayesian Networks (MEBNs) combines First-Order Logic with Bayesian Networks for representing and reasoning about uncertainty in complex, knowledge-rich domains. MEBN goes beyond standard Bayesian networks to enable reasoning about an unknown number of entities interacting with each other in various types of relationships, a key requirement for the OODA process of an AI system. MEBN models have heretofore been constructed manually by a domain expert. However, manual MEBN modeling is labor-intensive and insufficiently agile. To address these problems, an efficient method is needed for MEBN modeling. One of the methods is to use machine learning to learn a MEBN model in whole or in part from data. In the era of Big Data, data-rich environments, characterized by uncertainty and complexity, have become ubiquitous. The larger the data sample is, the more accurate the results of the machine learning approach can be. Therefore, machine learning has potential to improve the quality of MEBN models as well as the effectiveness for MEBN modeling. In this research, we study a MEBN learning framework to develop a MEBN model from a combination of domain expert's knowledge and data. To evaluate the MEBN learning framework, we conduct an experiment to compare the MEBN learning framework and the existing manual MEBN modeling in terms of development efficiency.


title: Side-channel vulnerability factor: a metric for measuring information leakage

abstract: There have been many attacks that exploit side-effects of program execution to expose secret information and many proposed countermeasures to protect against these attacks. However there is currently no systematic, holistic methodology for understanding information leakage. As a result, it is not well known how design decisions affect information leakage or the vulnerability of systems to side-channel attacks. In this paper, we propose a metric for measuring information leakage called the Side-channel Vulnerability Factor (SVF). SVF is based on our observation that all side-channel attacks ranging from physical to microarchitectural to software rely on recognizing leaked execution patterns. SVF quantifies patterns in attackers' observations and measures their correlation to the victim's actual execution patterns and in doing so captures systems' vulnerability to side-channel attacks. In a detailed case study of on-chip memory systems, SVF measurements help expose unexpected vulnerabilities in whole-system designs and shows how designers can make performance-security trade-offs. Thus, SVF provides a quantitative approach to secure computer architecture.


title: A Hybrid Radar-Camera Sensing System With Phase Compensation for Random Body Movement Cancellation in Doppler Vital Sign Detection

abstract: This paper presents a Doppler radar vital sign detection system with random body movement cancellation (RBMC) technique based on adaptive phase compensation. An ordinary camera was integrated with the system to measure the subject's random body movement (RBM) that is fed back as phase information to the radar system for RBMC. The linearity of the radar system, which is strictly related to the circuit saturation problem in noncontact vital sign detection, has been thoroughly analyzed and discussed. It shows that larger body movement does not necessarily mean larger radar baseband output. High gain configuration at baseband is required for acceptable SNR in noncontact vital sign detection. The phase compensation at radar RF front-end helps to relieve the high-gain baseband from potential saturation in the presence of large body movement. A simple video processing algorithm was presented to extract the RBM without using any marker. Both theoretical analysis and simulation have been carried out to validate the linearity analysis and the proposed RBMC technique. Two experiments were carried out in the lab environment. One is the phase compensation at RF front end to extract a phantom motion in the presence of another large shaker motion, and the other one is to measure the subject person breathing normally but randomly moving his body back and forth. The experimental results show that the proposed radar system is effective to relieve the linearity burden of the baseband circuit and help compensate the RBM.


A Semantic Similarity Approach to Paraphrase Detection


title: Prolonged exercise training improves the acute type II muscle fibre satellite cell response in healthy older men

abstract: Key points ::: ::: ::: ::: Skeletal muscle stem cells, termed satellite cells, play a crucial role in repair and remodelling of muscle in response to exercise ::: ::: ::: An age‐related decline in satellite cell number and/or function has been hypothesized to be a key factor in the development of sarcopenia and/or the blunted muscle fibre adaptive response to prolonged exercise training in older persons ::: ::: ::: We report that performing prolonged exercise training improves the acute type II muscle fibre satellite cell response following a single bout of resistance exercise in older men. ::: ::: ::: The observed improvement in muscle satellite function is associated with an increase in muscle fibre capillarization following exercise training suggesting a possible functional link between capillarization and satellite cell function.


title: Clustering and visualization of mobile application permissions for end users and malware analysts

abstract: Application permissions at the core of Android security mechanism are the first leading transparent feature for users to assess any mobile application before download or installation and for experts to analyse any malware. Representing vast, dispersed permissions and achieving clarity is not a trivial matter. In this study, we first examined Android permissions, their groups and formal representations with the limitations. We also surveyed limited studies on clustering/visualization of permissions. We grouped 251 Android permissions into 12 clusters semantically and proposed a new visualization approach that looks more conventional to both end users and experts and helps comprehending permissions easily and quickly. We applied the proposed clustering and visualization on calculated discriminative malign permissions concept for malware analysis and demonstrated potential effectiveness of the approach. Our approach improves expressing and understanding of large number of mobile application permissions in a better context, provides more understanding and insight, and helps interpreting or inferring interesting patterns related to permissions for malware classification.


title: City Data Fusion: Sensor Data Fusion in the Internet of Things

abstract: Internet of Things IoT has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors' main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.


title: SBAR: A Shared Mental Model for Improving Communication Between Clinicians

abstract: Article-at-a-Glance Background The importance of sharing a common mental model in communication prompted efforts to spread the use of the SBAR (Situation, Background, Assessment, and Recommendation) tool at OSF St. Joseph Medical Center, Bloomington, Illinois. Case Study An elderly patient was on warfarin sodium (Coumadin) 2.5mg daily. The nurse received a call from the lab regarding an elevated international normalized ratio (INR) but did not write down the results (she was providing care to another patient). On the basis of the previous lab cumulative summary, the physician increased the warfarin dose for the patient; a dangerously high INR resulted. Actions Taken The medical center initiated a collaborative to implement the use of the SBAR communication tool. Education was incorporated into team resource management training and general orientation. Tools included SBAR pocket cards for clinicians and laminated SBAR "cheat sheets" posted at each phone. SBAR became the communication methodology from leadership to the microsystem in all forms of reporting. Discussion Staff adapted quickly to the use of SBAR, although hesitancy was noted in providing the "recommendation" to physicians. Medical staff were encouraged to listen for the SBAR components and encourage staff to share their recommendation if not initially provided.


title: From Gulf to Bridge When Do Moral Arguments Facilitate Political Influence

abstract: Much of contemporary American political rhetoric is characterized by liberals and conservatives advancing arguments for the morality of their respective political positions. However, research suggests such moral rhetoric is largely ineffective for persuading those who do not already hold one’s position because advocates advancing these arguments fail to account for the divergent moral commitments that undergird America’s political divisions. Building on this, we hypothesize that (a) political advocates spontaneously make arguments grounded in their own moral values, not the values of those targeted for persuasion, and (b) political arguments reframed to appeal to the moral values of those holding the opposing political position are typically more effective. We find support for these claims across six studies involving diverse political issues, including same-sex marriage, universal health care, military spending, and adopting English as the nation's official langauge. Mediation and moderation analyses fur...


title: Towards quality metrics for OpenStreetMap

abstract: Volunteered Geographic Information (VGI) is currently a "hot topic" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGL Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a "trusted" source of ground-truth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.


title: vScale: automatic and efficient processor scaling for SMP virtual machines

abstract: SMP virtual machines (VMs) have been deployed extensively in clouds to host multithreaded applications. A widely known problem is that when CPUs are oversubscribed, the scheduling delays due to VM preemption give rise to many performance problems because of the impact of these delays on thread synchronization and I/O efficiency. Dynamically changing the number of virtual CPUs (vCPUs) by considering the available physical CPU (pCPU) cycles has been shown to be a promising approach. Unfortunately, there are currently no efficient mechanisms to support such vCPU-level elasticity. We present vScale, a cross-layer design to enable SMP-VMs to adaptively scale their vCPUs, at the cost of only microseconds. vScale consists of two extremely light-weight mechanisms: i) a generic algorithm in the hypervisor scheduler to compute VMs' CPU extendability, based on their proportional shares and CPU consumptions, and ii) an efficient method in the guest OS to quickly reconfigure the vCPUs. vScale can be tightly integrated with existing OS/hypervisor primitives and has very little management complexity. With our prototype in Xen/Linux, we evaluate vScale's performance with several representative multithreaded applications, including NPB suite, PARSEC suite and Apache web server. The results show that vScale can significantly reduce the VM's waiting time, and thus can accelerate many applications, especially synchronization-intensive ones and I/O-intensive ones.


title: Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images

abstract: Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. This paper presents an atlas-based approach for automatic segmentation of femurs in x-ray images. A robust global alignment method based on consistent sets of edge segments registers the whole atlas to the image under joint constraints. After global alignment, the femur models undergo local refinement to extract detailed contours of the femurs. Test results show that the proposed algorithm is robust and accurate in segmenting the femur contours of different patients.


Cool English: a Grammatical Error Correction System Based on Large Learner Corpora.


title: A Gait Recognition Method for Human Following in Service Robots

abstract: In this paper, we propose a gait recognition method for service robots to conduct human following tasks. A walking sequence segmentation method is designed to extract the consecutive gait cycles from an arbitrary walking sequence. Based on the segmentation results, a novel hybrid gait feature is proposed to capture the static, dynamic, and trajectory features for each segmented key and supplementary gait cycles. A dataset of 25 human subjects is collected to evaluate the proposed method in three different walking paths with various walking directions. Experimental results show that the proposed method achieves satisfactory performance in terms of identification accuracy and ${F} _{\text {comb}}$ indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.


title: Bargains Old and New: Multinational Corporations in Global Governance

abstract: This paper outlines an approach for understanding the role of multinational corporations (MNCs) in global governance. We develop a typology of regime types with two dimensions, the goal of the regime, which can be market enabling or regulatory, and the location of authority, which can be national, regional, or international, with public and private elements. MNCs tend to support the creation of market enabling regimes at the international level, and prefer to keep social or environmental regulation under national or private authority. However, these are only generalizations and MNCs develop preferences based on their relative influence in various arenas, the costs of political participation, and competitive considerations. We argue that institutions of global governance represent the outcome of a series of negotiations among corporations, states, and non-state actors. The preferences and power of MNCs vary across issues and sectors, and from one negotiating forum to another, accounting for the uneven and fragmented nature of the resulting system. Our approach differs from the traditional FDI bargaining framework in that it recognizes the multi-party nature of negotiations and multiple sources of power. Moreover, the complexity and dynamic nature of the process results in a somewhat indeterminate process.


title: Text joins in an RDBMS for web data integration

abstract: The integration of data produced and collected across autonomous, heterogeneous web services is an increasingly important and challenging problem. Due to the lack of global identifiers, the same entity (e.g., a product) might have different textual representations across databases. Textual data is also often noisy because of transcription errors, incomplete information, and lack of standard formats. A fundamental task during data integration is matching of strings that refer to the same entity. In this paper, we adopt the widely used and established cosine similarity metric from the information retrieval field in order to identify potential string matches across web sources. We then use this similarity metric to characterize this key aspect of data integration as a join between relations on textual attributes, where the similarity of matches exceeds a specified threshold. Computing an exact answer to the text join can be expensive. For query processing efficiency, we propose a sampling-based join approximation strategy for execution in a standard, unmodified relational database management system (RDBMS), since more and more web sites are powered by RDBMSs with a web-based front end. We implement the join inside an RDBMS, using SQL queries, for scalability and robustness reasons. Finally, we present a detailed performance evaluation of an implementation of our algorithm within a commercial RDBMS, using real-life data sets. Our experimental results demonstrate the efficiency and accuracy of our techniques.


title: Oil-importing optimal decision considering country risk with extreme events: A multi-objective programming approach

abstract: From perspective of energy security, this study focuses on oil-importing optimal decision based on multi-objective programming approach. Different from other models, country risk is considered as the main objective to minimize risk exposure of importing disruption. What is more, this model connects emergency management with programming, and optimal decisions are solved under different scenarios of emergency, where one given kind of extreme events break out and impact exporting regions to different degrees. Specifically, two main steps are involved in the proposed methodology, including impact analysis of the extreme events and optimization programming under scenarios of emergency. The first step is to statistically analyze whether and to what extent the given extreme events impact country risk of oil-exporting sources. Secondly, a multi-objective programming model is formulated, and optimal decision is simulated under different scenarios with extreme events. For illustration, China's oil-importing optimization is performed to verify the practicability of the novel methodology. The experimental results suggest that wars in Middle East may significantly enhance country risk of Middle East; and China's oil-importing optimal plan should be changed correspondingly. This further indicates that the proposed methodology can be utilized as an effective tool to adjust oil-importing plan according to certain extreme events.


title: Imaging of Total Knee Arthroplasty

abstract: Painful total knee arthroplasty (TKA) represents a diagnostic challenge for the clinician and radiologist, as there is a wide variety of potential etiologies, with a broad range of clinical presentations, and the abnormalities on imaging studies are often subtle, absent, or nonspecific. Imaging findings of normal TKA are reviewed, in addition to a variety of complications such as loosening, infection, instability, osteolysis, heterotopic ossification, extensor mechanism disruption, and fracture. Although imaging evaluation of painful TKA is usually limited to conventional radiographs and nuclear imaging, examples of the utility of computed tomography are also illustrated, and suggested imaging strategies and algorithms are discussed.


title: An RCT into the effects of neurofeedback on neurocognitive functioning compared to stimulant medication and physical activity in children with ADHD

abstract: Neurofeedback (NFB) is a potential alternative treatment for children with ADHD that aims to optimize brain activity. Whereas most studies into NFB have investigated behavioral effects, less attention has been paid to the effects on neurocognitive functioning. The present randomized controlled trial (RCT) compared neurocognitive effects of NFB to (1) optimally titrated methylphenidate (MPH) and (2) a semi-active control intervention, physical activity (PA), to control for non-specific effects. Using a multicentre three-way parallel group RCT design, children with ADHD, aged 7–13, were randomly allocated to NFB (n = 39), MPH (n = 36) or PA (n = 37) over a period of 10–12 weeks. NFB comprised theta/beta training at CZ. The PA intervention was matched in frequency and duration to NFB. MPH was titrated using a double-blind placebo controlled procedure to determine the optimal dose. Neurocognitive functioning was assessed using parameters derived from the auditory oddball-, stop-signal- and visual spatial working memory task. Data collection took place between September 2010 and March 2014. Intention-to-treat analyses showed improved attention for MPH compared to NFB and PA, as reflected by decreased response speed during the oddball task [η p 2 = 0.21, p < 0.001], as well as improved inhibition, impulsivity and attention, as reflected by faster stop signal reaction times, lower commission and omission error rates during the stop-signal task (range η p 2 = 0.09–0.18, p values <0.008). Working memory improved over time, irrespective of received treatment (η p 2 = 0.17, p < 0.001). Overall, stimulant medication showed superior effects over NFB to improve neurocognitive functioning. Hence, the findings do not support theta/beta training applied as a stand-alone treatment in children with ADHD.


title: Robust Image Hashing With Ring Partition and Invariant Vector Distance

abstract: Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination.


title: A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-Of-Speech Tagging

abstract: In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.


title: A License Plate-Recognition Algorithm for Intelligent Transportation System Applications

abstract: In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low


ROBOTICS PROJECT B 31 XP Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles


title: Electronic structure of nickel silicides Ni/sub 2/Si, NiSi, and NiSi/sub 2/

abstract: Synchrotron-radiation photoemission studies of bulk samples of Ni/sub 2/Si, NiSi, and NiSi/sub 2/ show valence-band emission dominated by Ni 3d-derived features. These d bands shift toward E/sub F/ and broaden with increasing Ni concentration and Ni-Ni interaction, falling at -3.2 eV for NiSi/sub 2/ and -1.3 eV for Ni/sub 2/Si. In each case, the density of states near E/sub F/ is very low. These results are interpreted in terms of recent calculations which are shown to forecast correctly the general trends and modifications of the silicide electronic structure. Further, they indicate that the d-band features observed in photoemission reflect d states which are not directly involved in the Ni--Si bonds. Core-level studies show that charge transfer plays a minor role in the chemical bond, but changes in the electronic configuration account for the observed shifts in the Ni 3p binding energy.


title: On the Duration, Addressability, and Capacity of Memory-Augmented Recurrent Neural Networks

abstract: Memory-augmented recurrent neural networks (M-RNNs) have demonstrated empirically that they are very attractive for many applications, but a good theoretical understanding of their behaviors is unclear yet. In this paper, three analytical indicators named duration, addressability, and capacity of general forms of the additional memory in M-RNNs are formalized. The analysis results of the interactions among these indicators reveal that it is hard for an M-RNN to simultaneously provide good performance on more than two out of three of indicators. Meanwhile, the duration, addressability, and capacity are applied to analyze and compare two M-RNNs: long short term memory and neural turing machine for different cases. The comparison results show that none of the models has better performance on one indicator than the other model all the time. Moreover, it is found that separating memory system into sub-memories can bring the increasing duration and addressability and the decreasing capacity for the whole memory system.


title: Not All Emotions Are Created Equal: The Negativity Bias in Social-Emotional Development

abstract: There is ample empirical evidence for an asymmetry in the way that adults use positive versus negative information to make sense of their world; specifically, across an array of psychological situations and tasks, adults display a negativity bias, or the propensity to attend to, learn from, and use negative information far more than positive information. This bias is argued to serve critical evolutionarily adaptive functions, but its developmental presence and ontogenetic emergence have never been seriously considered. The authors argue for the existence of the negativity bias in early development and that it is evident especially in research on infant social referencing but also in other developmental domains. They discuss ontogenetic mechanisms underlying the emergence of this bias and explore not only its evolutionary but also its developmental functions and consequences. Throughout, the authors suggest ways to further examine the negativity bias in infants and older children, and they make testable predictions that would help clarify the nature of the negativity bias during early development.


title: BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams

abstract: Supporting sequential pattern mining from data streams is nowadays a relevant problem in the area of data stream mining research. Actual proposals available in the literature are based on the well-known PrefixSpan approach and are, indeed, able to effectively bound the error of discovered patterns. This approach foresees the idea of dividing the target stream in a collection of manageable chunks, i.e., pieces of stream, in order to gain into effectiveness and efficiency. Unfortunately, mining patterns from stream chunks indeed introduce additional errors with respect to the basic application scenario where the target stream is mined continuously, in a non-batch manner. This is due to several reasons. First, since batches are processed individually, patterns that contain items from two consecutive batches are lost. Secondly, in most batch-based approaches, the decision about the frequency of a pattern is done locally inside a single batch. Thus, if a pattern is frequent in the stream but its items are scattered over different batches, it will be continuously pruned out and will never become frequent due to the algorithm’s lack of the “complete-picture” perspective. In order to address so-delineated pattern mining problems, this paper introduces and experimentally assesses BFSPMiner, a Batch-Free Sequential Pattern Miner algorithm for effectively and efficiently mining patterns in streams without being constrained to the traditional batch-based processing. This allows us, for instance, to discover frequent patterns that would be lost according to alternative batch-based stream mining processing models. We complement our analytical contributions by means of a comprehensive experimental campaign of BFSPMiner against real-world data stream sets and in comparison with current batch-based stream sequential pattern mining algorithms.


title: A quick assessment tool for human-directed aggression in pet dogs

abstract: Many test series have been developed to assess dog temperament and aggressive behavior, but most of them have been criticized for their relatively low predictive validity or being too long, stressful, and/or problematic to carry out. We aimed to develop a short and effective series of tests that corresponds with (a) the dog's bite history, and (b) owner evaluation of the dog's aggressive tendencies. Seventy-three pet dogs were divided into three groups by their biting history; non-biter, bit once, and multiple biter. All dogs were exposed to a short test series modeling five real-life situations: friendly greeting, take away bone, threatening approach, tug-of-war, and roll over. We found strong correlations between the in-test behavior and owner reports of dogs' aggressive tendencies towards strangers; however, the test results did not mirror the reported owner-directed aggressive tendencies. Three test situations (friendly greeting, take-away bone, threatening approach) proved to be effective in evoking specific behavioral differences according to dog biting history. Non-biters differed from biters, and there were also specific differences related to aggression and fear between the two biter groups. When a subsample of dogs was retested, the test revealed consistent results over time. We suggest that our test is adequate for a quick, general assessment of human-directed aggression in dogs, particularly to evaluate their tendency for aggressive behaviors towards strangers. Identifying important behavioral indicators of aggressive tendencies, this test can serve as a useful tool to study the genetic or neural correlates of human-directed aggression in dogs. Aggr. Behav. 9999:XX-XX, 2013. © 2013 Wiley Periodicals, Inc. Language: en


title: Importance of Computer Assisted Teaching & Learning Methods for Chemistry

abstract: Understanding the basic concepts of chemistry is very important for the students of secondary school level and university level. The Computer Assisted Teaching and Learning (CATL) methods are marked by the usage of computers in teaching and learning processes. Usage of WORD, EXCEL, POWERPOINT, ACCESS, PHOTOSHOP etc., as well as the use of specialized packages such as CHEMDRAW, SCIFINDER etc., can be worth mentioning. The role of internet in feeding the thirst of students is comparably far better than the classroom teaching. By the use of CATL methods, students can acquire high quality of mental models.


title: Thermal Cycling Reliability of Lead-Free Solders (SAC305 and Sn3.5Ag) for High-Temperature Applications

abstract: Applications with temperatures higher than the melting point of eutectic tin-lead solder (183°C) require high-melting-point solders. However, they are expensive and not widely available. With the adoption of lead-free legislation, first in Europe and then in many other countries, the electronics industry has transitioned from eutectic tin-lead to lead-free solders that have higher melting points. This higher melting point presents an opportunity for the manufacturers of high-temperature electronics to shift to mainstream lead-free solders. In this paper, ball grid arrays (BGAs), quad flat packages, and surface mount resistors assembled with SAC305 (96.5%Sn+3.0%Ag+0.5Cu) and Sn3.5Ag (96.5%Sn+3.5%Ag) solder pastes were subjected to thermal cycling from -40°C to 185°C. Commercially available electroless nickel immersion gold board finish was compared to custom Sn-based board finish designed for high temperatures. The data analysis showed that the type of solder paste and board finish used did not have an impact on the reliability of BGA solder joints. The failure analysis revealed the failure site to be on the package side of the solder joint. The evolution of intermetallic compounds after thermal cycling was analyzed.


title: AUTOMATIC DETECTION AND SEVERITY ANALYSIS OF BRAIN TUMORS USING GUI IN MATLAB

abstract: Medical image processing is the most challenging an d emerging field now a day’s processing of MRI imag es is one of the parts of this field. The quantitative analysis of MRI brain tumor allows obtaining useful key indicators of disease progression. This is a computer aided diagnosis systems for detecting malignant tex ture in biological study. This paper presents an ap proach in computer-aided diagnosis for early prediction of brain cancer usin g Texture features and neuro classification logic. This paper describes the proposed strategy for dete ction; extraction and classification of brain tumou r from MRI scan images of brain; which incorporates segmentation and morpholo gical functions which are the basic functions of im age processing. Here we detect the tumour, segment the tumour and we calcul ate the area of the tumour. Severity of the disease can be known, through classes of brain tumour which is done through neuro fuzzy cand creating a user friendly environment using GUI in MATLAB. In this paper cases of 10 patients is taken and severity of disease is shown and different features of images are calculated.


Towards autonomous vehicular clouds


title: Zab: High-performance broadcast for primary-backup systems

abstract: Zab is a crash-recovery atomic broadcast algorithm we designed for the ZooKeeper coordination service. ZooKeeper implements a primary-backup scheme in which a primary process executes clients operations and uses Zab to propagate the corresponding incremental state changes to backup processes1. Due the dependence of an incremental state change on the sequence of changes previously generated, Zab must guarantee that if it delivers a given state change, then all other changes it depends upon must be delivered first. Since primaries may crash, Zab must satisfy this requirement despite crashes of primaries.


title: Android Malware Detection using Deep Learning on API Method Sequences

abstract: Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1K to 33K malware apps, and 38K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%-99% and a false positive rate of 0.06%-2%, under all tested datasets and settings.


Distributed Representation-based Recommender Systems in E-commerce


Efficient embedded software development using QEMU


title: Effects of sleep deprivation on cognitive functions

abstract: Sleep deprivation (SD) is a common condition that afflicts many people in modern life. Deficits in daytime performance due to SD are experienced universally. Recent evidence indicates that SD causes impairments in cognitive functions. However, the mechanisms that SD impairs cognitive functions are not clear. This review will focus on the behavioral and neural effects of SD with the aim to elucidate the possible mechanisms of SD-induced deterioration in cognitive functions and to identify directions for future research. 现代社会, 睡眠剥夺(sleep deprivation, SD)是一种常见现象, 困扰着很多人, 引起人的日间工作能力降低。 近年来的研究结果显示, SD 可以使认知功能受损, 但是其损害机制尚不清楚。 本综述将重点阐述 SD 对行为和中枢系统的影响, 进而阐明 SD 引起认知功能受损的可能机制, 并且展望未来的研究方向。


title: 2D slot array antenna in ridge gap waveguide technology

abstract: This paper describes a new design of a two dimensional slot antenna array on ridge gap waveguide (RGW) technology. The designed antenna operated in Ku frequency band at 13.5 GHz with a bandwidth of about 800 MHz. The antenna consists of an RGW power divider and two branches transmission lines which feed a series of two parallel slots. The slots are elliptical shaped that provided wide impedance matching. The antenna characteristics, including return loss, radiation patter and efficiency are presented.


SqueezedText: A Real-Time Scene Text Recognition by Binary Convolutional Encoder-Decoder Network


title: Robust Image and Video Dehazing with Visual Artifact Suppression via Gradient Residual Minimization

abstract: Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of Li et al. (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips.


title: The Dark (Patterns) Side of UX Design

abstract: Interest in critical scholarship that engages with the complexity of user experience (UX) practice is rapidly expanding, yet the vocabulary for describing and assessing criticality in practice is currently lacking. In this paper, we outline and explore the limits of a specific ethical phenomenon known as "dark patterns," where user value is supplanted in favor of shareholder value. We assembled a corpus of examples of practitioner-identified dark patterns and performed a content analysis to determine the ethical concerns contained in these examples. This analysis revealed a wide range of ethical issues raised by practitioners that were frequently conflated under the umbrella term of dark patterns, while also underscoring a shared concern that UX designers could easily become complicit in manipulative or unreasonably persuasive practices. We conclude with implications for the education and practice of UX designers, and a proposal for broadening research on the ethics of user experience.


title: Training Deep and Recurrent Networks with Hessian-Free Optimization

abstract: In this chapter we will first describe the basic HF approach, and then examine well-known performance-improving techniques such as preconditioning which we have found to be beneficial for neural network training, as well as others of a more heuristic nature which are harder to justify, but which we have found to work well in practice. We will also provide practical tips for creating efficient and bug-free implementations and discuss various pitfalls which may arise when designing and using an HF-type approach in a particular application.


title: MetaMorph: An Adaptive Agent-Based Architecture for Intelligent Manufacturing

abstract: Global competition and rapidly changing customer requirements are forcing major changes in the production styles and configuration of manufacturing organizations. Traditional centralized manufacturing systems are not able to meet such requirements. This paper proposes an agent-based approach for dynamically creating and managing agent communities in such widely distributed and everchanging manufacturing environments. After reviewing the research literature, an adaptive multi-agent manufacturing system architecture called MetaMorph is presented and its main features are described. Such architecture facilitates multi-agent coordination by minimizing communication and processing overheads. Adaptation is facilitated through organizational structural change and two learning mechanisms: learning from past experiences and learning future agent interactions by simulating future dynamic, emergent behaviours. The MetaMorph architecture also addresses other specific requirements for next generation manufacturing sys...


title: The mediating effect of perceived usefulness towards tax service quality and the continuance usage intention of the filing system in Malaysia

abstract: This study examines the mediating effect of perceived usefulness on the relationship between tax service quality ::: (correctness, response time, system support) and continuance usage intention of e-filing system in Malaysia. A total of 116 data ::: was analysed using Partial Least Squared Method (PLS). The result showed that Perceived Usefulness has a partial mediating ::: effect on the relationship between tax service quality (Correctness, Response Time) with the continuance usage intention and tax ::: service quality (correctness) has significant positive relationship with continuance usage intention. Perceived usefulness was ::: found to be the most important predictor of continuance usage intention


title: Effect of Atmospheric Scintillation on SNR of Differential Absorption Lidar System

abstract: In this paper, the effect of scintillation on system SNR is discussed. Atmospheric scintillation is described and log amplitude variance in slant path is given. In addition, the simulations of system SNR at different propagation path and different height are obtained. The results from simulation show that atmospheric scintillation has an important effect on SNR of DIAL system, especially near the ground.


title: Fast and Scalable Range Query Processing With Strong Privacy Protection for Cloud Computing

abstract: Privacy has been the key road block to cloud computing as clouds may not be fully trusted. This paper is concerned with the problem of privacy-preserving range query processing on clouds. Prior schemes are weak in privacy protection as they cannot achieve index indistinguishability, and therefore allow the cloud to statistically estimate the values of data and queries using domain knowledge and history query results. In this paper, we propose the first range query processing scheme that achieves index indistinguishability under the indistinguishability against chosen keyword attack (IND-CKA). Our key idea is to organize indexing elements in a complete binary tree called PBtree, which satisfies structure indistinguishability (i.e., two sets of data items have the same PBtree structure if and only if the two sets have the same number of data items) and node indistinguishability (i.e., the values of PBtree nodes are completely random and have no statistical meaning). We prove that our scheme is secure under the widely adopted IND-CKA security model. We propose two algorithms, namely PBtree traversal width minimization and PBtree traversal depth minimization, to improve query processing efficiency. We prove that the worst-case complexity of our query processing algorithm using PBtree is $O(\vert R\vert\log n)$ , where $n$ is the total number of data items and $R$ is the set of data items in the query result. We implemented and evaluated our scheme on a real-world dataset with 5 million items. For example, for a query whose results contain 10 data items, it takes only 0.17 ms.


title: Statistical modeling of complex backgrounds for foreground object detection

abstract: This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features , at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results.


title: Safe Exploration for Active Learning with Gaussian Processes

abstract: In this paper, the problem of safe exploration in the active learning context is considered. Safe exploration is especially important for data sampling from technical and industrial systems, e.g. combustion engines and gas turbines, where critical and unsafe measurements need to be avoided. The objective is to learn data-based regression models from such technical systems using a limited budget of measured, i.e. labelled, points while ensuring that critical regions of the considered systems are avoided during measurements. We propose an approach for learning such models and exploring new data regions based on Gaussian processes GP's. In particular, we employ a problem specific GP classifier to identify safe and unsafe regions, while using a differential entropy criterion for exploring relevant data regions. A theoretical analysis is shown for the proposed algorithm, where we provide an upper bound for the probability of failure. To demonstrate the efficiency and robustness of our safe exploration scheme in the active learning setting, we test the approach on a policy exploration task for the inverse pendulum hold up problem.


title: Low permittivity cladding to improve the performance of dielectric rod waveguides and dielectric end-fire antennas

abstract: A low loss dielectric rod waveguide design is proposed for use at Ku band. Measured waveguide performance from 10 to 18 GHz is presented and validated using full wave numerical simulations. The proposed design uses a low permittivity cladding to improve performance at the low end of the operating frequency band. Moreover, an improvement of the waveguide isolation due to the cladding is analyzed as well. The proposed ABS cladding is also implemented on a dielectric end-fire antenna obtaining an improvement in return loss, gain and half power beamwidth at the low end of the Ku band; the measured peak gain at 12.2 GHz is 14.6 dBi and 9.8 dBi with and without the cladding, respectively, and the 3 dB beamwidth changes from 22 to 61 degrees.


title: Cooperative autonomous driving: intelligent vehicles sharing city roads

abstract: The paper presents the Intelligent Control System Laboratory's (ICSL) Cooperative Autonomous Mobile Robot technologies and their application to intelligent vehicles for cities. The deployed decision and control algorithms made the road-scaled vehicles capable of undertaking cooperative autonomous maneuvers. Because the focus of ICSL's research is in decision and control algorithms, it is therefore reasonable to consider replacing or upgrading the sensors used with more recent road sensory concepts as produced by other research groups. While substantial progress has been made, there are still some issues that need to be addressed such as: decision and control algorithms for navigating roundabouts, real-time integration of all data, and decision-making algorithms to enable intelligent vehicles to choose the driving maneuver as they go. With continued research, it is feasible that cooperative autonomous vehicles will coexist alongside human drivers in the not-too-distant future.


title: Appearance-based person reidentification in camera networks: problem overview and current approaches

abstract: Recent advances in visual tracking methods allow following a given object or individual in presence of significant clutter or partial occlusions in a single or a set of overlapping camera views. The question of when person detections in different views or at different time instants can be linked to the same individual is of fundamental importance to the video analysis in large-scale network of cameras. This is the person reidentification problem. The paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Methods that effectively address the challenges associated with changes in illumination, pose, and clothing appearance variation are discussed. More specifically, the development of a set of models that capture the overall appearance of an individual and can effectively be used for information retrieval are reviewed. Some of them provide a holistic description of a person, and some others require an intermediate step where specific body parts need to be identified. Some are designed to extract appearance features over time, and some others can operate reliably also on single images. The paper discusses algorithms for speeding up the computation of signatures. In particular it describes very fast procedures for computing co-occurrence matrices by leveraging a generalization of the integral representation of images. The algorithms are deployed and tested in a camera network comprising of three cameras with non-overlapping field of views, where a multi-camera multi-target tracker links the tracks in different cameras by reidentifying the same people appearing in different views.


Image-Based e-Document Authentication


title: Signaling Pathways in Melanogenesis

abstract: Melanocytes are melanin-producing cells found in skin, hair follicles, eyes, inner ear, bones, heart and brain of humans. They arise from pluripotent neural crest cells and differentiate in response to a complex network of interacting regulatory pathways. Melanins are pigment molecules that are endogenously synthesized by melanocytes. The light absorption of melanin in skin and hair leads to photoreceptor shielding, thermoregulation, photoprotection, camouflage and display coloring. Melanins are also powerful cation chelators and may act as free radical sinks. Melanin formation is a product of complex biochemical events that starts from amino acid tyrosine and its metabolite, dopa. The types and amounts of melanin produced by melanocytes are determined genetically and are influenced by a variety of extrinsic and intrinsic factors such as hormonal changes, inflammation, age and exposure to UV light. These stimuli affect the different pathways in melanogenesis. In this review we will discuss the regulatory mechanisms involved in melanogenesis and explain how intrinsic and extrinsic factors regulate melanin production. We will also explain the regulatory roles of different proteins involved in melanogenesis.


title: Hydraulic networked control of four wheel steering agricultural robot

abstract: In a joint project among research institutions and a private company is being designed and built a multifunctional and modular robotics platform for information acquisition in Precision Agriculture. The robotic platform has as main characteristics four wheels propulsion and independent steering, air suspension and break, adjustable width, span of 1,80m in height, diesel engine, hydraulic system and a CAN-based electronic control system. Several modules can be coupled to the platform: autonomous localization and navigation, computer vision, communication, agricultural parameters sensing, robotic arm, and stabilized stand. The proposed paper presents the four wheels steering distributed control system, based on the difference between the desired and actual position and considering the angular speed of the wheels. The results demonstrate that the distributed control system was simple and efficient, providing suitable steering performance for the platform guidance, and also indicate future work possibilities for the system improvement.


title: A 1.0–4.0-Gb/s All-Digital CDR With 1.0-ps Period Resolution DCO and Adaptive Proportional Gain Control

abstract: This paper describes the design and implementation of an all-digital clock and data recovery circuit (ADCDR) for multigigabit/s operation. The proposed digitally-controlled oscillator (DCO) incorporating a supply-controlled ring oscillator with a digitally-controlled resistor (DCR) generates wide-frequency-range multiphase clocks with fine resolution. With an adaptive proportional gain controller (APGC) which continuously adjusts a proportional gain, the proposed ADCDR recovers data with a low-jitter clock and tracks large input jitter rapidly, resulting in enhanced jitter performance. A digital frequency-acquisition loop with a proportional control greatly reduces acquisition time. Fabricated in a 0.13-μm CMOS process with a 1.2-V supply, the ADCDR occupies 0.074 mm2 and operates from 1.0 Gb/s to 4.0 Gb/s with a bit error rate of less than 10-14. At a 3.0-Gb/s 231 - 1 PRBS, the measured jitter in the recovered clock is 3.59 psrms and 29.4 pspp, and the power consumption is 11.4 mW.


title: A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM

abstract: The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to improve the accuracy of classification in medical diagnosis problems. To this end, studies were carried out on 3 different datasets. These datasets are heart disease, Parkinson's disease (PD) and BUPA liver disorders. Key feature of these datasets is that they have a linearly non-separable distribution. A new method entitled k-medoids clustering-based attribute weighting (kmAW) has been proposed as a data preprocessing method. The support vector machine (SVM) was preferred in the classification phase. In the performance evaluation stage, classification accuracy, specificity, sensitivity analysis, f-measure, kappa statistics value and ROC analysis were used. Experimental results showed that the developed hybrid system entitled kmAW?+?SVM gave better results compared to other methods described in the literature. Consequently, this hybrid intelligent system can be used as a useful medical decision support tool.


title: Emerging technologies: quantitative identification and measurement

abstract: Emerging technologies present both challenges and opportunities for national technology strategies. National governments may therefore want to monitor the technological horizon on a systematic basis. This article outlines the quantitative approaches available for such monitoring. Among the standard types of bibliometric data, proposals and publications are most likely to be useful for this purpose since they capture information earlier in the cycle of technology development. Patents, in contrast, trail behind. Analysis can proceed with keywords or citations, and algorithms are available to use the information structure inherent in these kinds of data to identify and measure emerging areas. There are limitations, however, in all the available approaches and the authors therefore recommend using them in conjunction with expert methods by focusing the qualitative assessment in particular areas.


title: Bayesian Active Learning for Classification and Preference Learning

abstract: Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classication with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classication problem, we extend our algorithm to Gaussian Process preference learning.


title: Fault injection at host-compiled level with static fault set reduction for SoC firmware robustness testing

abstract: Decreasing hardware reliability makes robust firmware imperative for safety-critical applications. Hence, ensuring correct handling of errors in peripherals is a key objective during firmware design. To adequately support robustness considerations of firmware designers during implementation, an efficient qualitative fault injection method is required. This paper presents a high-speed fault injection technique based on host-compiled firmware simulation that is suitable to analyze the impact of transient faults on firmware behavior. Additionally, fault set reduction by static code analysis avoids unnecessary injection of masked and equivalent faults. Application of the proposed fault injection technique on an industrial safety-relevant automotive system-on-chip (SoC) firmware demonstrates at least three orders of magnitude speedup compared to instruction set level. In addition, a fault set reduction by 78% is achieved. While significantly reducing the required fault injection time, the presented techniques provide as accurate feedback to the designer as existing state-of-the-art approaches.


title: Database research in computer games

abstract: This tutorial presents an overview of the data management issues faced by computer games today. While many games do not use databases directly, they still have to process large amounts of data, and could benefit from the application of database technology. Other games, such as massively multiplayer online games (MMOs), must communicate with commercial databases and have their own unique challenges. In this tutorial we will present the state-of-the-art of data management in games that we learned from our interaction with various game studios. We will show how the issues involved motivate current research, and illustrate several possibilities for future work. Our tutorial will start with a description of data-driven design, which is the source of many of the data management issues that games face. We will show some of the tools that game developers use to create and manage content. We will discuss how this type of design can affect performance, and the data structures and techniques that developers use to ensure that the game is responsive. We will discuss the problem of consistency in games, and how games ensure that players all share the same view of the world. Finally, we will examine some of the engineering issues that game developers have to deal with when interacting with traditional databases. This tutorial is intended to be self-contained, and provides the background necessary for understanding how databases and database technology are relevant to computer games. This tutorial is accessible to students and researchers who, while perhaps not hardcore gamers themselves, are interested in ways in which they can use their expertise to solve problems in computer games.


title: Tunable VHF Miniaturized Helical Filters

abstract: In this paper, a miniaturization technique and a tuning concept that enable the realization of highly miniaturized and frequency reconfigurable helical filters are reported for the first time. The proposed filter design is based on tunable helical resonators with an internal volume of only 8.6 cm 3 and variable capacitive loading. A second-order bandpass filter was built and measured as a proof-of-concept demonstrator. The helical filter is manufactured with a CNC process and tuning is achieved through piezoelectrically actuated tuners, which are bonded over the helix apex. A measured center frequency tuning between 257-365 MHz was realized with an insertion loss between 0.3-3.2 dB including connector losses. The measured filter bandwidth varied between 6%-7.7%. Tunable resonators were also manufactured so as to extract the unloaded quality factor. It was measured between 392-639 for a center frequency tuning of 2.5:1 (156.5-395 MHz).


title: On the assessment of landmark salience for human navigation

abstract: In this paper, we propose a conceptual framework for assessing the salience of landmarks for navigation. Landmark salience is derived as a result of the observer’s point of view, both physical and cognitive, the surrounding environment, and the objects contained therein. This is in contrast to the currently held view that salience is an inherent property of some spatial feature. Salience, in our approach, is expressed as a three-valued Saliency Vector. The components that determine this vector are Perceptual Salience, which defines the exogenous (or passive) potential of an object or region for acquisition of visual attention, Cognitive Salience, which is an endogenous (or active) mode of orienting attention, triggered by informative cues providing advance information about the target location, and Contextual Salience, which is tightly coupled to modality and task to be performed. This separation between voluntary and involuntary direction of visual attention in dependence of the context allows defining a framework that accounts for the interaction between observer, environment, and landmark. We identify the low-level factors that contribute to each type of salience and suggest a probabilistic approach for their integration. Finally, we discuss the implications, consider restrictions, and explore the scope of the framework.


title: Effectiveness of Switching to ziprasidone for Stable but symptomatic outpatients with schizophrenia

abstract: Background: Many outpatients with schizophrenia experience persistent symptoms or side effects on their current antipsychotic regimen. Few studies have prospectively examined the effects of the prior medication or switching method on the safety and efficacy of a newly available antipsychotic. Efficacy and tolerability of ziprasidone were evaluated in patients with DSM-IV schizophrenia or schizoaffective disorder who were switched from conventional or atypical antipsychotics in three 6-week, multicenter, randomized, open-label, parallel-group trials. Method: Stable outpatients with persistent symptoms or troublesome side effects on (1) conventional antipsychotic (N = 108), (2) olanzapine (N = 104), or (3) risperidone (N = 58) therapy were switched to an open-label, 6-week, flexible-dose trial of ziprasidone (40-160 mg/day). Patients were randomly assigned at baseline to 1 of 3 switching schedules during the first week of ziprasidone therapy. Baseline and outcome assessments included Positive and Negative Syndrome Scale (PANSS) and Clinical Global Impressions of Severity (CGI-S) ratings. Results: All 3 switching strategies were well tolerated for all 3 patient groups. After 6 weeks on ziprasidone therapy, significant (p <.05) improvements were observed on all major symptom measures and almost all subscales for all switched subgroups. Conclusion: Switching stable but symptomatic outpatients from their previous antipsychotic to ziprasidone was generally well tolerated and was associated with symptom improvements 6 weeks later. Improvements occurred in patients recently on other first-line atypical antipsychotic, as well as in those on conventional antipsychotic, treatment. While limitations of switching study designs do not permit interpretation of comparative efficacy, these studies suggest that outpatients who partially respond to conventional antipsychotics, risperidone, or olanzapine may experience improved control of psychotic symptoms following a switch to ziprasidone.


Generating Apt Metaphor Ideas for Pictorial Advertisements


title: Design methodology using inversion coefficient for low-voltage low-power CMOS voltage reference

abstract: This paper presents an analog design methodology, using the selection of inversion coefficient of MOS devices, to design low voltage and low-power (LVLP) CMOS voltage references. These circuits often work under subthreshold operation. Hence, there is a demand for analog design methods that optimize the sizing process of transistors working in weak and moderate inversion. The advantage of the presented method -- compared with the traditional approach to design circuits -- is the reduction of design cycle time and minimization of trial-and-error simulations, if the proposed equations are used. As a case study, a LVLP voltage reference based on subthreshold MOSFETs with supply voltage of 0.7 V was designed for 0.18-¼m CMOS technology.


The adaptive place advisor: A conversational recommendation system


title: Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

abstract: This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.


title: Personality and parenting style in parents of adolescents

abstract: Since parental personality traits are assumed to play a role in parenting behaviors, the current study examined the relation between parental personality and parenting style among 688 Dutch parents of adolescents in the SMILE study. The study assessed Big Five personality traits and derived parenting styles (authoritative, authoritarian, indulgent, and uninvolved) from scores on the underlying dimensions of support and strict control. Regression analyses were used to determine which personality traits were associated with parenting dimensions and styles. As regards dimensions, the two aspects of personality reflecting interpersonal interactions (extraversion and agreeableness) were related to supportiveness. Emotional stability was associated with lower strict control. As regards parenting styles, extraverted, agreeable, and less emotionally stable individuals were most likely to be authoritative parents. Conscientiousness and openness did not relate to general parenting, but might be associated with more content-specific acts of parenting.


title: Fault prediction under the microscope: a closer look into HPC systems

abstract: A large percentage of computing capacity in today's large high-performance computing systems is wasted because of failures. Consequently current research is focusing on providing fault tolerance strategies that aim to minimize fault's effects on applications. By far the most popular technique is the checkpoint-restart strategy. A complement to this classical approach is failure avoidance, by which the occurrence of a fault is predicted and preventive measures are taken. This requires a reliable prediction system to anticipate failures and their locations. Thus far, research in this field has used ideal predictors that were not implemented in real HPC systems. In this paper, we merge signal analysis concepts with data mining techniques to extend the ELSA (Event Log Signal Analyzer) toolkit and offer an adaptive and more efficient prediction module. Our goal is to provide models that characterize the normal behavior of a system and the way faults affect it. Being able to detect deviations from normality quickly is the foundation of accurate fault prediction. However, this is challenging because component failure dynamics are heterogeneous in space and time. To this end, a large part of the paper is focused on a detailed analysis of the prediction method, by applying it to two large-scale systems and by investigating the characteristics and bottlenecks of each step of the prediction process. Furthermore, we analyze the prediction's precision and recall impact on current checkpointing strategies and highlight future improvements and directions for research in this field.


title: A Real-Time Health Monitoring System for Remote Cardiac Patients Using Smartphone and Wearable Sensors

abstract: Online telemedicine systems are useful due to the possibility of timely and efficient healthcare services. These systems are based on advanced wireless and wearable sensor technologies. The rapid growth in technology has remarkably enhanced the scope of remote health monitoring systems. In this paper, a real-time heartmonitoring systemis developed considering the cost, ease of application, accuracy, and data security. The system is conceptualized to provide an interface between the doctor and the patients for two-way communication. The main purpose of this study is to facilitate the remote cardiac patients in getting latest healthcare services which might not be possible otherwise due to low doctor-to-patient ratio.The developed monitoring system is then evaluated for 40 individuals (aged between 18 and 66 years) using wearable sensors while holding an Android device (i.e., smartphone under supervision of the experts). The performance analysis shows that the proposed system is reliable and helpful due to high speed. The analyses showed that the proposed system is convenient and reliable and ensures data security at low cost. In addition, the developed system is equipped to generate warning messages to the doctor and patient under critical circumstances.


title: eSports as An Emerging Research Context at CHI: Diverse Perspectives on Definitions

abstract: This paper represents our first endeavor to explore how to better understand the complex nature, scope, and practices of eSports. Our goal is to explore diverse perspectives on what defines eSports as a starting point for further research. Specifically, we critically reviewed existing definitions/understandings of eSports in different disciplines. We then interviewed 26 eSports players and qualitatively analyzed their own perceptions of eSports. We contribute to further exploring definitions and theories of eSports for CHI researchers who have considered online gaming a serious and important area of research, and highlight opportunities for new avenues of inquiry for researchers who are interested in designing technologies for this unique genre.


title: DT-SLAM: Deferred Triangulation for Robust SLAM

abstract: Obtaining a good baseline between different video frames is one of the key elements in vision-based monocular SLAM systems. However, if the video frames contain only a few 2D feature correspondences with a good baseline, or the camera only rotates without sufficient translation in the beginning, tracking and mapping becomes unstable. We introduce a real-time visual SLAM system that incrementally tracks individual 2D features, and estimates camera pose by using matched 2D features, regardless of the length of the baseline. Triangulating 2D features into 3D points is deferred until key frames with sufficient baseline for the features are available. Our method can also deal with pure rotational motions, and fuse the two types of measurements in a bundle adjustment step. Adaptive criteria for key frame selection are also introduced for efficient optimization and dealing with multiple maps. We demonstrate that our SLAM system improves camera pose estimates and robustness, even with purely rotational motions.


title: Predicting future learning from baseline network architecture

abstract: Abstract Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.


title: Inverse Reward Design

abstract: Autonomous agents optimize the reward function we give them. What they don't know is how hard it is for us to design a reward function that actually captures what we want. When designing the reward, we might think of some specific training scenarios, and make sure that the reward will lead to the right behavior in those scenarios. Inevitably, agents encounter new scenarios (e.g., new types of terrain) where optimizing that same reward may lead to undesired behavior. Our insight is that reward functions are merely observations about what the designer actually wants, and that they should be interpreted in the context in which they were designed. We introduce inverse reward design (IRD) as the problem of inferring the true objective based on the designed reward and the training MDP. We introduce approximate methods for solving IRD problems, and use their solution to plan risk-averse behavior in test MDPs. Empirical results suggest that this approach can help alleviate negative side effects of misspecified reward functions and mitigate reward hacking.


title: The Use of a Constructivist Grounded Theory Method to Explore the Role of Socially-Constructed Metadata (Web 2.0) Approaches

abstract: Purpose –This paper provides rationale for using a constructivist grounded theory method for PhD research in Library and Information Research, entitled “Towards a Theory of Digital Library Metadata: The emergence of Enriching and Filtering”. It highlights on the processes of data collection using intensive interviewing and three stages of data analysis, namely: open coding, focused coding andtheoretical coding. In addition, the processes of constructivist grounded theory conceptualisation using memo writing and theoretical saturation are highlighted. Design/methodology/approach –While the actual results of the PhD study will be presented separately, this paper mainly focuses on the best practises and lessons learnt from the adoption of the method. The paper highlights on how the method enabled the researcher to conduct iterative scrutiny of the concepts and categories through the method‟s memo writing and conceptualisation processes four core categories have emerged. It is argued that a constructivist grounded theory approach is fitting to address issues in relation to Web 2.0 and user-driven metadata approaches. Findings –Following a rigorous application of the method, a constructivist grounded theory method is considered appropriate to explore emerging areas of research in library and information science. It is indicated that the novelty of the use of web 2.0 and social media in libraries, the issue of socially-constructed metadata approaches is relatively under-developed and thus there are absence of extant theories, suggesting thus the importance of inductive research methods, such as the constructivist grounded theory method presented in this paper.Taking into account, the potential existence of diversity of views among librarians, LIS researchers, metadata experts and library users with respect of the issues of involving users in metadata creation, from the three approaches to grounded theory, Charmaz‟s (Charmaz, 2006) constructivist approach was considered fitting. Practical implications –The case for the viability of the constructivist grounded theory method for high level research such as PhD, is made. It is believed that LIS researchers who aim to undertake a research on emerging topical issues, such as Web 2.0, would find the method appropriate. The method allows two-way mutual co-constructions of concepts between the researcher and research participant. It also provides the researcher flexibility and rigour to gather views and opinions, through interactive and iterative indepth interviews.It also allows the researcher to analyse and interpret the perspectives of participants‟ through identification of concepts and categories from the data collected. Finally, the method is expected to help to develop a theory that overarches the concepts and categories derived from the data collected.


A study to identify middle school students' perceptions of bullying experiences


title: Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images

abstract: Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand key-points benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evaluations for the performance of these methods on our dataset. In future, these results and our dataset can serve as a useful benchmark for hand keypoint recognition for rapid finger movements.