title: Towards privacy preserving social recommendation under personalized privacy settings

abstract: Privacy leakage is an important issue for social relationships-based recommender systems (i.e., social recommendation). Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users’ information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using personalized privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to achieve privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel privacy-preserving social recommendation framework, in which user can model user feedbacks and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive feedbacks, we can protect users’ privacy against untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users’ privacy while being able to retain effectiveness of the underlying recommender system.


title: A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

abstract: Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the...


title: Information Systems Success: The Quest for the Dependent Variable

abstract: A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I/S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.


title: Zinc Inhibits HIF-Prolyl Hydroxylase Inhibitor-Aggravated VSMC Calcification Induced by High Phosphate

abstract: Vascular calcification is a life-threatening clinical condition in chronic kidney disease (CKD) and is associated with reduced zinc serum levels. Anemia is another frequent complication of CKD. Hypoxia-inducible factor (HIF) stabilizers, also known as HIF prolyl hydroxylase inhibitors (PHI), are promising candidates to treat CKD-associated anemia by increasing erythropoietin synthesis. Recent evidence suggests that HIFs play a pivotal role in vascular calcification. Our study explored feasible impacts of HIF PHI on phosphate (Pi)-induced calcification of vascular smooth muscle cells (VSMCs) and tested whether zinc might inhibit this mineralization process. Treatment of VSMCs with PHI aggravated Pi-induced calcium deposition and Pi uptake. PHI promoted Pi-induced loss of smooth muscle cell markers (ACTA-2, MYH11, SM22α) and enhanced osteochondrogenic gene expression (Msx-2, BMP-2, Sp7) triggering osteochondrogenic phenotypic switch of VSMCs. These effects of PHI paralleled with increased pyruvate dehydrogenase kinase 4 (PDK4) expression, decreased Runx2 Ser451 phosphorylation, and reduced cell viability. Zinc inhibited Pi-induced mineralization of VSMCs in a dose-dependent manner and also attenuated the pro-calcification effect of PHI in Pi-induced mineralization. Zinc inhibited osteochondrogenic phenotypic switch of VSMCs reflected by lowering Pi uptake, decreasing the expressions of Msx-2, BMP-2, and Sp7 as well as the loss of smooth muscle cell-specific markers. Zinc preserved phosphorylation state of Runx2 Ser451, decreased PDK4 level, and restored cell viability. PHI alone reduced the expression of smooth muscle markers without inducing mineralization, which was also inhibited by zinc. In addition, we observed a significantly lower serum zinc level in CKD as well as in patients undergoing carotid endarterectomy compared to healthy individuals. Conclusion - PHI promoted the loss of smooth muscle markers and augmented Pi-induced osteochondrogenic phenotypic switch leading to VSMCs calcification. This mineralization process was attenuated by zinc. Enhanced vascular calcification is a potential risk factor during PHI therapy in CKD which necessitates the strict follow up of vascular calcification and zinc supplementation.


title: TOWARDS SMART HOME: CONTROL ELECTRICAL DEVICES ONLINE

abstract: The rapid growth of the World Wide Web, has made the web is not just use for delivering information. Currently, Web is used to carry information in a borderless way since one of its features is stateless. The extension of the technology has discovered a web also could delivered signals. Since the introduction to the smart home concept, a web plays an important roles to control almost every electrical devices connected to internet that enable the user can control it every where. This paper presents on development of web-based controller for control electrical device. The prototype developed using Microsoft Visual Studio. NET and Circuit design. The methodology used in this project consists of three main phases which are Analysis, Design and Implementation. For future advancement, the prototype intends to include SMS module so that controlling can be done through SMS.


title: Distributed Processing Applications for UAV/drones: A Survey

abstract: Distributed Processing Systems are the ones that include multiple devices (which could be of many types, such as PC computers, mobile devices etc.) that have computational and communication capabilities. Their computational power is jointly used for collaborative processing of variety of tasks – and this processing is realized in distributed manner. UAV - Unmanned Aerial Vehicles (also called drones) gain significant attention over recent years. They have been employed to realize multiple tasks such as surveillance or environmental monitoring. First implementations were based on single UAV, later the potential of multiple UAVs collaborating in a team was noticed. Many applications were implemented in distributed manner, using multiple collaborative UAVs and the distributed processing systems principles. In this paper we survey the applications implemented over cooperative teams of UAVs that operate as distributed processing systems.


title: Fast In-Memory SQL Analytics on Typed Graphs

abstract: We study a class of graph analytics SQL queries, which we call relationship queries. These queries involving aggregation, join, semijoin, intersection and selection are a wide superset of fixed-length graph reachability queries and of tree pattern queries. We present real-world OLAP scenarios, where efficient relationship queries are needed. However, row stores, column stores and graph databases are unacceptably slow in such OLAP scenarios. ::: ::: We propose a GQ-Fast database, which is an indexed database that roughly corresponds to efficient encoding of annotated adjacency lists that combines salient features of column-based organization, indexing and compression. GQ-Fast uses a bottom-up fully pipelined query execution model, which enables (a) aggressive compression (e.g., compressed bitmaps and Huffman) and (b) avoids intermediate results that consist of row IDs (which are typical in column databases). GQ-Fast compiles query plans into executable C++ source code. Besides achieving runtime efficiency, GQ-Fast also reduces main memory requirements because, unlike column databases, GQ-Fast selectively allows dense forms of compression including heavy-weight compressions, which do not support random access. ::: ::: We used GQ-Fast to accelerate queries for two OLAP dashboards in the biomedical field. GQ-Fast outperforms PostgreSQL by 2--4 orders of magnitude and MonetDB, Vertica and Neo4j by 1--3 orders of magnitude when all of them are running on RAM. Our experiments dissect GQ-Fast's advantage between (i) the use of compiled code, (ii) the bottom-up pipelining execution strategy, and (iii) the use of dense structures. Other analysis and experiments show the space savings of GQ-Fast due to the appropriate use of compression methods. We also show that the runtime penalty incurred by the dense compression methods decreases as the number of CPU cores increases.


title: Fashion image classification on mobile phones using layered deep convolutional neural networks

abstract: Toward implementation of fashion recommendation system based on photos taken with mobile phones, we propose a framework to recognize hierarchical categories of a fashion item. To classify an arbitrary photo of clothes robustly, (1) we collected two kind of dataset: (I) 120K datasets of clothes images on EC sites to train the classifier and (II) the clothing image set composed of photos taken by participants with their mobile phones. (2) we proposed Layered Deep Convolutional Neural Networks (LDCNNs) which is specialized in classifying images into hierarchical categories: hoodie is a lower in hierarchy in tops category. Experimental result shows proposed LDCNNs obtained mean accuracy of 92.7% for datasets from EC sites and 96.9% for those from mobile phones. This results are better than those (84.9%, 90.6 % respectively) for MLR+CNN in classification accuracy.


title: THE EFFECTS OF TEN WEEKS OF LOWER-BODY UNSTABLE SURFACE TRAINING ON MARKERS OF ATHLETIC PERFORMANCE

abstract: Cressey, E.M., C.A. West, D.P. Tiberio, W.J. Kraemer, and C.M. Maresh. The effects of ten weeks of lower-body unstable surface training on markers of athletic performance. J. Strength Cond. Res. 21(2):561-567. 2007.-Initially reserved for rehabilitation programs, unstable surface training (UST) has recently grown in popularity in strength and conditioning and general exercise scenarios. Nonetheless, no studies to date have examined the effects of UST on performance in healthy, trained individuals. The purpose of this study was to determine the effects of 10 weeks of lower-body UST on performance in elite athletes. Nineteen healthy, trained members (ages 18-23 years) of a National Collegiate Athletic Association Division I collegiate men's soccer team participated. The experimental (US) group (n = 10) supplemented their normal conditioning program with lower-body exercises on inflatable rubber discs; the control (ST) group (n = 9) performed the same exercises on stable surfaces. Bounce drop jump (BDJ) and countermovement jump (CMJ) heights, 40- and 10-yard sprint times, and T-test (agility) times were assessed before and after the intervention. The ST group improved significantly on predicted power output on both the BDJ (3.2%) and CMJ (2.4%); no significant changes were noted in the US group. Both groups improved significantly on the 40- (US = ?1.8%, ST = ?3.9%) and 10-yard sprint times (US = ?4.0%, ST = ?7.6%). The ST group improved significantly more than the US group in 40-yard sprint time; a trend toward greater improvement in the ST group was apparent on the 10-yard sprint time. Both groups improved significantly (US = 2.9%, ST = ?4.4%) on T-test performance; no statistically significant changes were apparent between the groups. These results indicate that UST using inflatable rubber discs attenuates performance improvements in healthy, trained athletes. Such implements have proved valuable in rehabilitation, but caution should be exercised when applying UST to athletic performance and general exercise scenarios.


title: A soft robotic approach to robust and dexterous grasping

abstract: In this paper, we present a compliant robotic gripper, Edgy-2, with 4-DOF dexterity, enabling four grasping modes: parallel grasping, power grasping, finger-tip pinch and fully-actuated grasping. The roboticfinger is based on soft-rigid-hybrid structures, combining fiber-reinforced soft pneumatic actuators with rigid joints, which exhibit reliable structural rigidity and grasping robustness while maintaining excellent adaptability and inherent compliance. With both grasping dexterity and grasping robustness, the Edgy-2 achieves excellent grasping reliability with various daily objects, from a fragile cherry to a 2 kg water bottled water. The relationship of design parameters and grasping strength is presented with analytical models. The performance of a prototype Edgy-2 is verified by dedicated experiments. The proposed hybrid dexterous grasping approach can be easily extended into different end-effector designs according to application requirements. The proposed mechanism for grasping provides excellent human-robot interaction safety and reliability.


title: A MATLAB differentiation matrix suite

abstract: A software suite consisting of 17 MATLAB functions for solving differential equations by the spectral collocation (i.e., pseudospectral) method is presented. It includes functions for computing derivatives of arbitrary order corresponding to Chebyshev, Hermite, Laguerre, Fourier, and sinc interpolants. Auxiliary functions are included for incorporating boundary conditions, performing interpolation using barycentric formulas, and computing roots of orthogonal polynomials. It is demonstrated how to use the package for solving eigenvalue, boundary value, and initial value problems arising in the fields of special functions, quantum mechanics, nonlinear waves, and hydrodynamic stability.


title: Differential mechanisms of tumor progression in clones from a single heterogeneous human melanoma.

abstract: We used vertical growth phase (VGP) human VMM5 melanoma cells to ask whether the tumor microenvironment could induce matrix metalloproteinase-1 (MMP-1) in vivo, and whether this induction correlated with metastasis. We isolated two clones from parental VMM5 cells: a low MMP-1 producing clone (C4) and high producing clone (C9). When these clones were injected orthotopically (intradermally) into nude mice, both were equally tumorigenic and produced equivalent and abundant amounts of MMP-1. However, the tumors from the C4 clones displayed different growth kinetics and distinct profiles of gene expression from the C9 population. The C4 tumors, which had low MMP-1 levels in vitro, appeared to rely on growth factors and cytokines in the microenvironment to increase MMP-1 expression in vivo, while MMP-1 levels remained constant in the C9 tumors. C9 cells, but not C4 cells, grew as spheres in culture and expressed higher levels of JARID 1B, a marker associated with melanoma initiating cells. We conclude that VMM5 melanoma cells exhibit striking intra-tumor heterogeneity, and that the tumorigenicity of these clones is driven by different molecular pathways. Our data suggest that there are multiple mechanisms for melanoma progression within a tumor, which may require different therapeutic strategies.


title: Adaptive Courseware : A Literature Review

abstract: This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e- learning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.


title: Total variation based interpolation

abstract: We propose an reversible interpolation method for signals or images, in the sense that the original image can be deduced from its interpolation by a sub-sampling. This imposes some constraints on the Fourier coefficients of the interpolated image. Now, there still exist many possible interpolations that satisfy these constraints. The zero-padding method is one of them, but gives very oscillatory images. We propose to choose among all possibilities, the one which is the most "regular". We justify the total variation of the image as a good candidate as measure of regularity. This yields to minimise a regularisation functional defined in space domain, with a constraint defined in the frequency domain.


title: Hand Gesture-based Visual User Interface for Infotainment

abstract: We present a real-time vision-based system that discriminates hand gestures performed by in-vehicle front-row seat occupants for accessing the infotainment system. The hand gesture-based visual user interface may be more natural and intuitive to the user than the current tactile interaction interface. Consequently, it may encourage a gaze-free interaction, which can alleviate driver distraction without limiting the user's infotainment experience. The system uses visible and depth images of the dashboard and center-console area in the vehicle. The first step in the algorithm uses the representation of the image area given by a modified histogram-of-oriented-gradients descriptor and a support vector machine (SVM) to classify whether the driver, passenger, or no one is interacting with the region of interest. The second step extracts gesture characteristics from temporal dynamics of the features derived in the initial step, which are then inputted to a SVM in order to perform gesture classification from a set of six classes of hand gestures. The rate of correct user classification into one of the three classes is 97.9% on average. Average hand gesture classification rates for the driver and passenger using color and depth input are above 94%. These rates were achieved on in-vehicle collected data over varying illumination conditions and human subjects. This approach demonstrates the feasibility of the hand gesture-based in-vehicle visual user interface.


title: Analysis of one-best MCQs: the difficulty index, discrimination index and distractor efficiency.

abstract: OBJECTIVE ::: To investigate the relationship of items having good difficulty and discrimination indices with their distractor efficiency to find how 'ideal questions' can be affected by non-functioning distractors (NF-Ds). ::: ::: ::: METHODS ::: The cross-sectional study was conducted at Fatima Jinnah Dental College, Karachi, during Jan-Jun 2009, with 102 First Year dental students (17-20 years). Physiology paper of the first semester, given after 22 weeks of teaching general topics of physiology, was analysed. The paper consisted of 50 one-best MCQs, having 5 options each. The MCQs were analysed for difficulty index (p-value), discrimination index (DI), and distractor efficiency (DE). Items having p-value between 30-70 and DI > or = 0.25 were considered as having good difficulty and discrimination indices respectively. Effective distractors were considered as the ones selected by at least 5% of the students. ::: ::: ::: RESULTS ::: The mean score was 27.31 +/- 5.75 (maximum 50 marks). Mean p-value and DI were 54.14 +/- 17.48 and 0.356 +/- 0.17, respectively. Seventy eight per cent items were of average (recommended) difficulty (mean p-value = 51.44 +/- 11.11) and having DE = 81.41%. Sixty two per cent items had excellent DI (0.465 +/- 0.083) with DE = 83.06%. Combining the two indices, 32 (64%) items could be called as 'ideal' (p-value = 30 to 70; DI > 0.24) and had DE = 85.15%. Overall 42% items had no Non-functioning Distractors (NF-D), while 12% had 3 NF-Ds. Excellent discrimination (DI = 0.427) was achieved with items having one NF-D, while items with 2 NF-D and no NF-D had nearly equal but lower DI (0.365 and 0.351 respectively). ::: ::: ::: CONCLUSION ::: One-best MCQs having average difficulty and high discrimination with three functioning distractors should be incorporated into future tests to improve the test score and properly discriminate among the students. Items with two NF-Ds, though easier, are better discriminators than items with no NF-D.


title: Capturing QP-relevant Information from Natural Language Text

abstract: Understanding descriptions of physical phenomena starts with the identification of continuous parameters that are involved in the physical processes. Descriptions of physical phenomena typically contain abundant references to physical quantities. The extraction of information about continuous parameters is therefore an essential step in building models of physical processes (Kuehne, 2003). People can learn about the physical world from textbooks and develop an understanding of physical phenomena from simple descriptions. As part of our ongoing investigation of the extraction and representation of knowledge about physical processes found in natural language text, we describe a natural language system that captures information about instances of physical processes from paragraph-sized descriptions through a deep semantic interpretation process as a set of interconnected frame structures. We have previously shown that natural language descriptions of physical phenomena can contain abundant QP-relevant information (Kuehne & Forbus, 2002). If one looks carefully at the descriptions of physical processes, given either as a concrete example or as generalized knowledge, one can identify parts of the natural language description that correspond to certain elements of QP Theory. In the same context, we have also proposed that QP theory can provide a knowledge representation language for aspects of natural language semantics concerned with continuous parameters and continuous causation. We have outlined a representational scheme that recasts the theoretical framework of QP theory in terms of frame semantics. QP frames use a representational scheme that is compatible with the notions of frames and frame elements in FrameNet (Baker, Fillmore, & Lowe, 1998; Fillmore, Wooters, & Baker, 2001). They form an intermediate representational layer between the natural language input and the representations that can be used in qualitative reasoning, e.g. model fragments in CML (Falkenhainer et al., 1994). Introduction When people read descriptions of physical phenomena in textbooks they usually have certain expectations about the information and mentally construct appropriate models of the described phenomena. This construction is an idiosyncratic process, because the readers need to interpret the author’s description, building their own model of it. Readers have to use their background knowledge to eliminate potentially ambiguous interpretations and to fill gaps left by the natural language description. In other words, reading about physical processes involves interpreting the text by constructing a model. In the best case, it is an exact reconstruction of the author’s intended model of the process. QP theory (Forbus, 1984) concerns the structure of a class of physical theories, and has been successfully used in a variety of reasoning systems (Forbus, 1996). The hypothesis is that many mental models of physical phenomena can be expressed in this formalism. QP theory has been used to develop a wide range of models of phenomena, including economics, ecology and medicine in addition to physical models. This makes it an excellent candidate for a component in a larger system of natural language semantics. In this paper we describe a natural language system that captures QP-relevant information from descriptions of instances of physical processes. It uses a deep semantic interpretation process and represents the contents as a set of interconnected QP frame structures. The system combines the results of our previous work, it is fully implemented, and has been tested on a dozen paragraphsized natural language descriptions (Kuehne, 2004). We begin with an overview of the system and its individual components. A detailed example illustrates the interpretation process, followed by a comparison of the information captured in terms of QP frame structures with a manually constructed process model. Finally, we discuss a number of problems that we encountered in using the system to capture QP-relevant information from natural language text and plans for future work. The fact that humans can learn about the physical world from textbooks and other sources leads to a number of interesting questions about the connections between our conceptual understanding of the physical world and how it is reflected in natural language. If students can learn from simple descriptions of physical phenomena, can the knowledge included in these texts be extracted to automatically construct models of the underlying physical processes?


title: Rotary Transformer Design for Brushless Electrically Excited Synchronous Machines

abstract: In modern automotive drive applications, Electrically Excited Synchronous Machines (ESM) have become an alternative to PSMs. Until now, ESMs need slip-rings to supply the rotor winding. However, carbon brush slip-ring systems are sensitive to environmental influences and produce conductive carbon dust during operation. This paper discusses the construction of a contactless energy transfer system to replace the slip-ring system in an electrical machine. The focus is laid on the power transferring device, a rotary transformer. Basic loss modelling methods are discussed, and an analytical model for winding losses is created. The model's range of validity is increased by scaling rules, and it is verified by FEM software and practical measurements. The results were used for the dimensioning of a contactless power transfer system.


title: Calliphora vicina larvae grow at different rates on different body tissues.

abstract: Compared with the rate on pig's liver, larval growth of the blowfly, Calliphora vicina is significantly faster by as much as 2 days on lung, kidney, heart or brain tissue. Potentially this has major implications when laboratory growth rates of larvae fed on one food substrate (often liver) are used to calculate the amount of development, and therefore the postmortem interval in a forensic case.


title: NeuCoin: the First Secure, Cost-efficient and Decentralized Cryptocurrency

abstract: NeuCoin is a decentralized peer-to-peer cryptocurrency derived from Sunny King’s Peercoin, which itself was derived from Satoshi Nakamoto’s Bitcoin. As with Peercoin, proof-of-stake replaces proof-of-work as NeuCoin’s security model, effectively replacing the operating costs of Bitcoin miners (electricity, computers) with the capital costs of holding the currency. Proof-of-stake also avoids proof-of-work’s inherent tendency towards centralization resulting from competition for coinbase rewards among miners based on lowest cost electricity and hash power.NeuCoin increases security relative to Peercoin and other existing proof-of-stake currencies in numerous ways, including: (1) incentivizing nodes to continuously stake coins over time through substantially higher mining rewards and lower minimum stake age; (2) abandoning the use of coin age in the mining formula; (3) causing the stake modifier parameter to change over time for each stake; and (4) utilizing a client that punishes nodes that attempt to mine on multiple branches with duplicate stakes.This paper demonstrates how NeuCoin’s proof-of-stake implementation addresses all commonly raised “nothing at stake” objections to generic proof-of-stake systems. It also reviews many of the flaws of proof-of-work designs to highlight the potential for an alternate cryptocurrency that solves these flaws.


title: Software and attack centric integrated threat modeling for quantitative risk assessment

abstract: One step involved in the security engineering process is threat modeling. Threat modeling involves understanding the complexity of the system and identifying all of the possible threats, regardless of whether or not they can be exploited. Proper identification of threats and appropriate selection of countermeasures reduces the ability of attackers to misuse the system. This paper presents a quantitative, integrated threat modeling approach that merges software and attack centric threat modeling techniques. The threat model is composed of a system model representing the physical and network infrastructure layout, as well as a component model illustrating component specific threats. Component attack trees allow for modeling specific component contained attack vectors, while system attack graphs illustrate multi-component, multi-step attack vectors across the system. The Common Vulnerability Scoring System (CVSS) is leveraged to provide a standardized method of quantifying the low level vulnerabilities in the attack trees. As a case study, a railway communication network is used, and the respective results using a threat modeling software tool are presented.


title: Be Careful What You Backpropagate: A Case For Linear Output Activations&Gradient Boosting

abstract: In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This exponential gradient boosting is shown to speed up convergence and improve generalization. To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the latter case, using the state-of-the-art neural network architecture, the model converged 33% faster with our method (roughly two days of training less) than with the standard softmax activation, and with a slightly better performance to boot.


title: Reducing Ambiguities in Requirements Specifications Via Automatically Created Object-Oriented Models

abstract: In industry, reviews and inspections are the primary methods to identify ambiguities, inconsistencies, and under specifications in natural language (NL) software requirements specifications (SRSs). However, humans have difficulties identifying ambiguities and tend to overlook inconsistencies in a large NL SRS. This paper presents a three-step, semi-automatic method, supported by a prototype tool, for identifying inconsistencies and ambiguities in NL SRSs. The method combines the strengths of automation and human reasoning to overcome difficulties with reviews and inspections. First, the tool parses a NL SRS according to a constraining grammar. Second, from relationships exposed in the parse, the tool creates the classes, methods, variables, and associations of an object-oriented analysis model of the specified system. Third, the model is diagrammed so that a human reviewer can use the model to detect ambiguities and inconsistencies. Since a human finds the problems, the tool has to have neither perfect recall nor perfect precision. The effectiveness of the approach is demonstrated by applying it and the tool to a widely published example NL SRS. A separate study evaluates the tool's domain-specific term detection.


title: Study of Planar Inverted-F Antenna ( PIFA ) for Mobile Devices

abstract: Now-a-days more and more radios are being integrated into a single wireless platform to allow maximum connectivity. In this paper a theoretical study of Planar Inverted F Antenna is presented. PIFA as a low profile antenna is widely used in several application areas due to several advantages. Research and experiments done on PIFA structures are discussed and categorized. Some methods that can be used to improve efficiency, bandwidth & reduce volume of an antenna are also discussed which includes effect of dielectric substrate, dimensions of the antenna etc.


title: A practical guide to endodontic access cavity preparation in molar teeth

abstract: The main objective of access cavity preparation is to identify the root canal entrances for subsequent preparation and obturation of the root canal system. Access cavity preparation can be one of the most challenging and frustrating aspects of endodontic treatment, but it is the key to successful treatment. Inadequate access cavity preparation may result in difficulty locating or negotiating the root canals. This may result in inadequate cleaning, shaping and filling of the root canal system. It may also contribute to instrument separation and aberrations of canal shape. These factors may ultimately lead to failure of treatment. Good access cavity design and preparation is therefore imperative for quality endodontic treatment, prevention of iatrogenic problems, and prevention of endodontic failure.


title: CANTrack: Enhancing automotive CAN bus security using intuitive encryption algorithms

abstract: In this paper, the proposed CANTrack solution is described and its implementation is thoroughly explained. CANTrack is a sophisticated Computer Aided Design (CAD) tool that supports the entire development process for automotive networked systems. CANTrack differentiates itself from other competitive tools; by not only supporting planning, development, testing and finally starting-up; but also incorporating the unique features of CAN bus security enhancement and tool customization upon customer needs. The Controller Area Network (CAN) bus security feature is implemented using an intuitive algorithm that encrypts the 8-byte payload data using a symmetric key that is being dynamically changed using synchronized key generators across all nodes. This algorithm makes the encrypted message unique at any instance of time to avoid replay attacks. The algorithm is tested thoroughly on an experimental setup using Vector Canoe; with the results of two use cases presented.


title: Fusing active orientation models and mid-term audio features for automatic depression estimation

abstract: In this paper, we predict a human's depression level in the BDI-II scale, using facial and voice features. Active orientation models (AOM) and several voice features were extracted from the video and audio modalities. Long-term and mid-term features were computed and a fusion is performed in the feature space. Videos from the Depression Recognition Sub-Challenge of the 2014 Audio-Visual Emotion Challenge and Workshop (AVEC 2014) were used and support vector regression models were trained to predict the depression level. We demonstrated that the fusion of AOMs with audio features leads to better performance compared to individual modalities. The obtained regression results indicate the robustness of the proposed technique, under different settings, as well as an RMSE improvement compared to the AVEC 2014 video baseline.


title: Single Image Super-Resolution With Multiscale Similarity Learning

abstract: Example learning-based image super-resolution (SR) is recognized as an effective way to produce a high-resolution (HR) image with the help of an external training set. The effectiveness of learning-based SR methods, however, depends highly upon the consistency between the supporting training set and low-resolution (LR) images to be handled. To reduce the adverse effect brought by incompatible high-frequency details in the training set, we propose a single image SR approach by learning multiscale self-similarities from an LR image itself. The proposed SR approach is based upon an observation that small patches in natural images tend to redundantly repeat themselves many times both within the same scale and across different scales. To synthesize the missing details, we establish the HR-LR patch pairs using the initial LR input and its down-sampled version to capture the similarities across different scales and utilize the neighbor embedding algorithm to estimate the relationship between the LR and HR image pairs. To fully exploit the similarities across various scales inside the input LR image, we accumulate the previous resultant images as training examples for the subsequent reconstruction processes and adopt a gradual magnification scheme to upscale the LR input to the desired size step by step. In addition, to preserve sharper edges and suppress aliasing artifacts, we further apply the nonlocal means method to learn the similarity within the same scale and formulate a nonlocal prior regularization term to well pose SR estimation under a reconstruction-based SR framework. Experimental results demonstrate that the proposed method can produce compelling SR recovery both quantitatively and perceptually in comparison with other state-of-the-art baselines.


In Codice Ratio: Scalable Transcription of Historical Handwritten Documents


Theme, Rheme, Topic, and Comment in the Syntax of American Sign Language


title: Global search acceleration in the nested optimization scheme

abstract: Multidimensional unconstrained global optimization problem with objective function under Lipschitz condition is considered. For solving this problem the dimensionality reduction approach on the base of the nested optimization scheme is used. This scheme reduces initial multidimensional problem to a family of one-dimensional subproblems being Lipschitzian as well and thus allows applying univariate methods for the execution of multidimensional optimization. For two well-known one-dimensional methods of Lipschitz optimization the modifications providing the acceleration of the search process in the situation when the objective function is continuously differentiable in a vicinity of the global minimum are considered and compared. Results of computational experiments on conventional test class of multiextremal functions confirm efficiency of the modified methods.


title: Gesture and voice prototyping for early evaluations of social acceptability in multimodal interfaces

abstract: Interaction techniques that require users to adopt new behaviors mean that designers must take into account social acceptability and user experience otherwise the techniques may be rejected by users as they are too embarrassing to do in public. This research uses a set of low cost prototypes to study social acceptability and user perceptions of multimodal mobile interaction techniques early on in the design process. We describe 4 prototypes that were used with 8 focus groups to evaluate user perceptions of novel multimodal interactions using gesture, speech and nonspeech sounds, and gain feedback about the usefulness of the prototypes for studying social acceptability. The results of this research describe user perceptions of social acceptability and the realities of using multimodal interaction techniques in daily life. The results also describe key differences between young users (18-29) and older users (70-95) with respect to evaluation and approach to understanding these interaction techniques.


title: PaperPoint: A Paper-Based Presentation and Interactive Paper Prototyping Tool

abstract: Recent developments in digital pen and paper solutions enable, not only the digital capture of handwriting, but also paper to be used as an interactive medium that links to digital information and services. We present a tool that builds on technologies for interactive paper to enable PowerPoint presentations to be controlled from printed slide handouts. Furthermore, slides can be easily annotated during presentations by simply drawing on the printed version of the slide. As well as discussing the advantages of such a paper-based interface and initial findings on its use, we describe how we were also able to exploit it to provide a general prototyping tool for interactive paper applications.


title: Reflectarray antennas

abstract: This paper reviews some innovative concepts and techniques, architectures and realisations developed for both passive and re-configurable reflectarray antennas, mostly for space applications. Some trends and perspectives are discussed in the conclusion.


title: Pano2Vid: Automatic Cinematography for Watching 360$$^{\circ }$$ Videos

abstract: We introduce the novel task of Pano2Vid — automatic cinematography in panoramic 360\(^{\circ }\) videos. Given a 360\(^{\circ }\) video, the goal is to direct an imaginary camera to virtually capture natural-looking normal field-of-view (NFOV) video. By selecting “where to look” within the panorama at each time step, Pano2Vid aims to free both the videographer and the end viewer from the task of determining what to watch. Towards this goal, we first compile a dataset of 360\(^{\circ }\) videos downloaded from the web, together with human-edited NFOV camera trajectories to facilitate evaluation. Next, we propose AutoCam, a data-driven approach to solve the Pano2Vid task. AutoCam leverages NFOV web video to discriminatively identify space-time “glimpses” of interest at each time instant, and then uses dynamic programming to select optimal human-like camera trajectories. Through experimental evaluation on multiple newly defined Pano2Vid performance measures against several baselines, we show that our method successfully produces informative videos that could conceivably have been captured by human videographers.


title: Deeprn: A Content Preserving Deep Architecture for Blind Image Quality Assessment

abstract: This paper presents a blind image quality assessment (BIQA) method based on deep learning with convolutional neural networks (CNN). Our method is trained on full and arbitrarily sized images rather than small image patches or resized input images as usually done in CNNs for image classification and quality assessment. The resolution independence is achieved by pyramid pooling. This work is the first that applies a fine-tuned residual deep learning network (ResNet-101) to BIQA. The training is carried out on a new and very large, labeled dataset of 10, 073 images (KonIQ-10k) that contains quality rating histograms besides the mean opinion scores (MOS). In contrast to previous methods we do not train to approximate the MOS directly, but rather use the distributions of scores. Experiments were carried out on three benchmark image quality databases. The results showed clear improvements of the accuracy of the estimated MOS values, compared to current state-of-the-art algorithms. We also report on the quality of the estimation of the score distributions.


title: A utility-based power-control scheme in wireless cellular systems

abstract: Distributed power-control algorithms for systems with hard signal-to-interference ratio (SIR) constraints may diverge when infeasibility arises. In this paper, we present a power-control framework called utility-based power control (UBPC) by reformulating the problem using a softened SIR requirement (utility) and adding a penalty on power consumption (cost). Under this framework, the goal is to maximize the net utility, defined as utility minus cost. Although UBPC is still noncooperative and distributed in nature, some degree of cooperation emerges: a user will automatically decrease its target SIR (and may even turn off transmission) when it senses that traffic congestion is building up. This framework enables us to improve system convergence and to satisfy heterogeneous service requirements (such as delay and bit error rate) for integrated networks with both voice users and data users. Fairness, adaptiveness, and a high degree of flexibility can be achieved by properly tuning parameters in UBPC.


title: Pruning subsequence search with attention-based embedding

abstract: Searching a large database to find a sequence that is most similar to a query can be prohibitively expensive, particularly if individual sequence comparisons involve complex operations such as warping. To achieve scalability, "pruning" heuristics are typically employed to minimize the portion of the database that must be searched with more complex matching. We present an approximate pruning technique which involves embedding sequences in a Euclidean space. Sequences are embedded using a convolutional network with a form of attention that integrates over time, trained on matching and non-matching pairs of sequences. By using fixed-length embeddings, our pruning method effectively runs in constant time, making it many orders of magnitude faster than full dynamic time warping-based matching for large datasets. We demonstrate our approach on a large-scale musical score-to-audio recording retrieval task.


title: Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli

abstract: In natural vision both stimulus features and task-demands affect an observer's attention. However, the relationship between sensory-driven (“bottom-up”) and task-dependent (“top-down”) factors remains controversial: Can task-demands counteract strong sensory signals fully, quickly, and irrespective of bottom-up features? To measure attention under naturalistic conditions, we recorded eye-movements in human observers, while they viewed photographs of outdoor scenes. In the first experiment, smooth modulations of contrast biased the stimuli's sensory-driven saliency towards one side. In free-viewing, observers' eye-positions were immediately biased toward the high-contrast, i.e., high-saliency, side. However, this sensory-driven bias disappeared entirely when observers searched for a bull's-eye target embedded with equal probability to either side of the stimulus. When the target always occurred in the low-contrast side, observers' eye-positions were immediately biased towards this low-saliency side, i.e., the sensory-driven bias reversed. Hence, task-demands do not only override sensory-driven saliency but also actively countermand it. In a second experiment, a 5-Hz flicker replaced the contrast gradient. Whereas the bias was less persistent in free viewing, the overriding and reversal took longer to deploy. Hence, insufficient sensory-driven saliency cannot account for the bias reversal. In a third experiment, subjects searched for a spot of locally increased contrast (“oddity”) instead of the bull's-eye (“template”). In contrast to the other conditions, a slight sensory-driven free-viewing bias prevails in this condition. In a fourth experiment, we demonstrate that at known locations template targets are detected faster than oddity targets, suggesting that the former induce a stronger top-down drive when used as search targets. Taken together, task-demands can override sensory-driven saliency in complex visual stimuli almost immediately, and the extent of overriding depends on the search target and the overridden feature, but not on the latter's free-viewing saliency.


title: In vitro effects of thallium on mouse neuroblastoma cells

abstract: To compare the effects of thallium at different cellular levels, cultured mouse neuroblastoma cells (Neuro-2A) were exposed for 24 hr to thallium(I) acetate. The following toxic indicators were assessed in the in vitro test system: cell proliferation by quantification of total protein content of the culture; cytoplasmic membrane integrity to cytosolic lactate dehydrogenase (LDH) leakage; lysosomal hexosaminidase release; lactate dehydrogenase activity; mitochondrial succinate dehydrogenase activity; relative neutral red uptake by lysosomes; lysosomal hexosaminidase sphingolipid degradation activity; acetylcholinesterase activity. The effects of thallium on the various indicators differed. Neural acetylcholinesterase activity was extremely sensitive to T1 inhibition. In contrast, hexosaminidase, an enzyme involved in glycosphingolipid degradation, was stimulated prior to cytoplasmic membrane disruption detected as LDH leakage. Relative neutral red uptake was slightly more sensitive than cell growth inhibition and the reduction in hexosaminidase release suggests an interaction with lysosomes. The low degree of sensitivity of cell proliferation, as judged by the protein content of the cultures, may reflect inhibition of protein degradation. LDH glycolytic activity was severely inhibited, but succinate dehydrogenase activity in the citric acid cycle was increased, probably owing to the mitochondrial accumulation of thallium.


title: A 10 MHz to 100 GHz LTCC CPW-to-stripline vertical transition

abstract: A broadband 50Ω coplanar waveguide (CPW)-to-stripline transition is presented which is capable of operation up to 100 GHz. This vertical transition is fabricated in low-temperature co-fired ceramic (LTCC), and it is appropriate for system in package (SiP) module packaging. It was designed for wafer probing an LTCC module containing an embedded stripline. This compact transition occupies only two layers of nominal 5 mil thick tape. Both simulated and measured s parameter results are shown for back-to-back transitions up to 110 GHz. Measured insertion loss for a single transition is less than 0.3 dB near 60 GHz and less than 1 dB up to 100 GHz.


title: Adaptivity based on Felder-Silverman Learning Styles Model in E-Learning Systems

abstract: Adaptivity in e-learning systems is based on modeling the learners. Learners are modeled based on their individual differences and preferences. In this context, learning styles models are generally used for adaptivity purposes, as they model how the learners learn. Felder and Silverman learning styles model is the most widely used learning styles model in the e-learning field. How this model can be used to provide adaptivity in e-learning systems is an important research subject. In this paper, this research subject is investigated, by using some of the recent reviews on learning styles based adaptive e-learning systems as the basis of the paper. Results show that automatic classification of learners by their learning styles is an important area that needs further research. Evaluation of the attributes used in the classification mechanisms is another research area that needs to be supported with more research studies. Therefore, it can be concluded that more research is needed to fully realize the potential of adaptive e-learning systems based on Felder and Silverman learning styles model.


title: Design of a scalable InfiniBand topology service to enable network-topology-aware placement of processes

abstract: Over the last decade, InfiniBand has become an increasingly popular interconnect for deploying modern super-computing systems. However, there exists no detection service that can discover the underlying network topology in a scalable manner and expose this information to runtime libraries and users of the high performance computing systems in a convenient way. In this paper, we design a novel and scalable method to detect the InfiniBand network topology by using Neighbor-Joining techniques (NJ). To the best of our knowledge, this is the first instance where the neighbor joining algorithm has been applied to solve the problem of detecting InfiniBand network topology. We also design a network-topology-aware MPI library that takes advantage of the network topology service. The library places processes taking part in the MPI job in a network-topology-aware manner with the dual aim of increasing intra-node communication and reducing the long distance inter-node communication across the InfiniBand fabric.


title: Interpreting and Executing Recipes with a Cooking Robot

abstract: The creation of a robot chef represents a grand challenge for the field of robotics. Cooking is one of the most important activities that takes place in the home, and a robotic chef capable of following arbitrary recipes would have many applications in both household and industrial environments. The kitchen environment is a semi-structured proving ground for algorithms in robotics. It provides many computational challenges, such as accurately perceiving ingredients in cluttered environments, manipulating objects, and engaging in complex activities such as mixing and chopping.


title: How does grit impact college students' academic achievement in science?

abstract: Research has suggested that achievement is not solely based on the cognitive abilities of the learner, but rather on the combination of cognitive ability and personality traits. This paper explores how grit affects student academic performance and success in first-year college physics students in the context of a Quebec College d'enseignement general et professionnel (CEGEP). Two hierarchical linear regressions were performed using scores on a 12-item 5point Likert scale survey measure of Grit on student cumulative GPA and on final exam mark for a gateway physics course, controlling for gender and high school GPA. Prior academic performance was found to be a significant predictor of college performance, as well as success in the gateway physics course. Grit was not found to be a significant predictor of student academic achievement or course success.


title: 60-GHz Two-Dimensionally Scanning Array Employing Wideband Planar Switched Beam Network

abstract: A two-dimensionally (2-D) scanning array employing a planar switched beam network (SBN) is proposed for 60-GHz high data rate wireless communication. SBNs offer a number of advantages including low cost, multibeam operation, simple direction finding, and minimal power consumption. Unlike existing switched beam arrays operating at 60 GHz, the proposed array scans in both planes. Also, in contrast to 2-D scanning stacks of Butler matrices and Rotman lenses, the proposed SBN is uniplanar and, hence, leads to low-cost manufacturing and integration with 60-GHz radios. The network produces eight 2-D beam states and demonstrates beamforming capability across the entire band of interest (57-64 GHz) with the aid of dummy crossover structures. It is printed on a Rogers Duroid 5880 substrate along with a 2 × 4 patch array. This letter describes the design, operation, and realization of the beamforming array. Simulated and measured performance results are presented to verify the design.


Principles of sports training : introduction to the theory and methods of training


Pattern Recognition in Time Series


title: Barriers to the Integration of Information Technology within Early Childhood Education and Care Organisations: A Review of the Literature

abstract: Employees of early childhood education and care (ECEC) organisations may experience a wide range of barriers as they attempt to integrate information technology (IT) into their work practices. However, studies within the ECEC organisational literature which attempt to identify and understand these barriers are scant. This literature review is the first to present consolidated findings from the body of knowledge on barriers to the integration of IT within ECEC organisations. In addition to highlighting limitations and gaps in the literature, it proposes a tri-perspective framework to provide for future research to develop a deeper understanding of not only what barriers exist but also how they interrelate and shape the IT integration process and the work practices of ECEC organisational employees.


title: Towards Analogy-based Recommendation: Benchmarking of Perceived Analogy Semantics

abstract: Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however o‰en unable to crisply de€ne what they are looking for. One of the core concepts of natural communication for describing and explaining complex information needs in an intuitive fashion are analogies: e.g., “What is to Christopher Nolan as is 2001: A Space Odyssey to Stanley Kubrick?”. Analogies allow users to explore the item space by formulating queries in terms of items rather than explicitly specifying the properties that they €nd aSractive. One of the core challenges which hamper research on analogy-enabled queries is that analogy semantics rely on consensus on human perception, which is not well represented in current benchmark data sets. Œerefore, in this paper we introduce a new benchmark dataset focusing on the human aspects for analogy semantics. Furthermore, we evaluate a popular technique for analogy semantics (word2vec neuronal embeddings) using our dataset. Œe results show that current word embedding approaches are still not not suitable to su�ciently deal with deeper analogy semantics. We discuss future directions including hybrid algorithms also incorporating structural or crowd-based approaches, and the potential for analogy-based explanations.


title: Comparison among dimensionality reduction techniques based on Random Projection for cancer classification

abstract: Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.


title: VLL: a lock manager redesign for main memory database systems

abstract: Lock managers are increasingly becoming a bottleneck in database systems that use pessimistic concurrency control. In this paper, we introduce very lightweight locking (VLL), an alternative approach to pessimistic concurrency control for main memory database systems, which avoids almost all overhead associated with traditional lock manager operations. We also propose a protocol called selective contention analysis (SCA), which enables systems implementing VLL to achieve high transactional throughput under high-contention workloads. We implement these protocols both in a traditional single-machine multi-core database server setting and in a distributed database where data are partitioned across many commodity machines in a shared-nothing cluster. Furthermore, we show how VLL and SCA can be extended to enable range locking. Our experiments show that VLL dramatically reduces locking overhead and thereby increases transactional throughput in both settings.


title: A compact dual-band monopole antenna for 4G LTE and WIFI utilizations

abstract: In this paper presented a CPW-fed monopole antenna,which design for WiFi and 4G (LTE) utilizations. The proposed antenna contains a U- slot in the circular patch with gradually varied ground to improve the impedance match, and it occupies a compact size of 42*28.38*1.5mm3. Through simulations and measurements, the proposed antenna with dual-band operation is suitable to for 4G LTE and WiFi utilizations in the 2.28GHz to 2.82GHz band and 3.87GHz to 6.0GHz band.


title: NeuFlow: A runtime reconfigurable dataflow processor for vision

abstract: In this paper we present a scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms — neuFlow — and a dataflow compiler — luaFlow — that transforms high-level flow-graph representations of these algorithms into machine code for neuFlow. This system was designed with the goal of providing real-time detection, categorization and localization of objects in complex scenes, while consuming 10 Watts when implemented on a Xilinx Virtex 6 FPGA platform, or about ten times less than a laptop computer, and producing speedups of up to 100 times in real-world applications. We present an application of the system on street scene analysis, segmenting 20 categories on 500 × 375 frames at 12 frames per second on our custom hardware neuFlow.


title: Geometric Models of Rolling-Shutter Cameras

abstract: Cameras with rolling shutters are becoming more common as low-power, low-cost CMOS sensors are being used more frequently in cameras. The rolling shutter means that not all scanlines are exposed over the same time interval. The effects of a rolling shutter are noticeable when either the camera or objects in the scene are moving and can lead to systematic biases in projection estimation. We develop a general projection equation for a rolling shutter camera and show how it is affected by different types of camera motion. In the case of fronto-parallel motion, we show how that camera can be modeled as an X-slit camera. We also develop approximate projection equations for a non-zero angular velocity about the optical axis and approximate the projection equation for a constant velocity screw motion. We demonstrate how the rolling shutter effects the projective geometry of the camera and in turn the structure-from-motion.


title: The Ideological-Conflict Hypothesis: Intolerance Among Both Liberals and Conservatives

abstract: Decades of research in social and political psychology have demonstrated that political conservatives appear more intolerant toward a variety of groups than do political liberals. Recent work from our three independent labs has challenged this conventional wisdom by suggesting that some of the psychological underpinnings of intolerance are not exclusive to people on either end of the political spectrum. These studies have demonstrated that liberals and conservatives express similar levels of intolerance toward ideologically dissimilar and threatening groups. We suggest directions for future research and discuss the psychological and political implications of our conclusions.


title: Molecular and Morphological Characterization of Aphelenchoides fuchsi sp. n. (Nematoda: Aphelenchoididae) Isolated from Pinus eldarica in Western Iran

abstract: To find and prevent the spread of pests comprising pine wood nematode Bursaphelenchus xylophilus (Steiner and Buhrer, 1934) Nickle, 1970 in Iran, bark and wood samples must be sampled and inspected. So, during the past few years, a wide survey on dead or weakening pine trees in northern Iran was conducted and several species belonging to Aphelenchoididae Skarbilovich, 1947 were recovered and described: two new species of the genus Ektaphelenchoides Baujard, 1984 (Pedram et al., 2012; Aliramaji et al., 2014); a new species of the genus Bursaphelenchus Fuchs, 1937 (Pedram et al., 2011); and a new species of the genus Laimaphelenchus Fuchs, 1937 (Asghari et al., 2012). Up to now, B. xylophilus, which has caused serious damage to the coniferous forests, has not been found in Iran. ::: ::: The genus Aphelenchoides Fischer, 1894 contains at present more than 150 nominal species (Hunt, 2008) and tends to be greatly conserved in gross morphology, making species identification a very difficult task. Also, many of the description fall below modern standards. Currently, species discrimination in Aphelenchoides is mainly based on morphology and morphometrics. In recent years, several species of the genus were described related to the pine trees in different countries, e.g., China (Cheng et al., 2009; Zhuo et al., 2010) and the United States (Kaisa, 2000), and some species isolated form packaging wood in South Korea (Cui et al., 2011; Fang et al., 2014b), India (Bina Chanu et al., 2013), South Africa (Wang et al., 2013), and Japan (Fang et al., 2014a). Recently a new species of Aphelenchoides, Aphelenchoides huntensis Esmaeili, Fang, Li, and Heydari, 2016 is described in association with pine trees in Iran. It would be likely to find more Aphelenchoides species if other substrates (e.g., bark beetles) were examined with such attention. ::: ::: During 2013 and 2014, we conducted some inspections on wood and bark samples from dead or weakened pine trees in western Iran. As a result, a new species of Aphelenchoides was isolated from a weakened Mondell pine tree (Pinus eldarica L.), which is described and figured in this paper as Aphelenchoides fuchsi sp. n. It is the second new species of Aphelenchoides that is described from Iran.


title: An overview of image steganography

abstract: The pins of rotating crankshafts are fine-machined by resiliently biasing the workpiece towards a grinding device with a predetermined force.


title: Smart antenna design for wireless communication using adaptive beam-forming approach

abstract: Smart antenna is recognized as promising technologies for higher user capacity in 3G wireless networks by effectively reducing multipath and co-channel interference. Advent of powerful, low-cost, digital processing components and the development of software-based techniques has made smart antenna systems a practical reality for both base station and mobile station of a cellular communications systems in the next generation. The core of smart antenna is the selection of smart algorithms in adaptive array. Using beam forming algorithms the weight of antenna arrays can be adjusted to form certain amount of adaptive beam to track corresponding users automatically and at the same time to minimize interference arising from other users by introducing nulls in their directions. Thus interferences can be suppressed and the desired signals can be extracted. This research work provides description, comparative analysis and utility of various reference signal based algorithms as well as blind adaptive algorithms. Exhaustive simulation study of beam patterns and learning characteristics have proved the efficacies of the proposed work from application point of view.


Ammonia intoxication due to acongenital defect in urea synthesis


title: THE BENEFITS OF OPTIMIZING PRICES TO MANAGE DEMAND IN HOTEL REVENUE MANAGEMENT SYSTEMS

abstract: We investigate the revenue impact of a new Price Setting Method (PSM) and compare it with the industry standard Bid Price Method (BPM). This comparison is performed via a simulation that was validated by a major hotel chain. In 27 out of the 32 cases, the PSM outperformed the BPM based on statistically significant tests. The PSM produces an average revenue increase of 34%, which can be thought of as an upper bound on the realistic revenue increase.


A Call to ARMs: Understanding the Costs and Benefits of JIT Spraying Mitigations


Design of a circular array antenna for generating waves with orbital angular momentum


title: Dynamic decoupling control method for PMSM drive with cross-coupling inductances

abstract: This paper proposes a decoupling control method for high-performance drive of permanent magnet synchronous motors (PMSMs). The conventional decoupling schemes proposed for removing coupling effects on d-q current regulation have the limited performance because the cross-coupling inductances caused by asymmetric structure and saturation are not considered. Those inductances may degrade the decoupling performance and even make the current control unstable by inducing the additional coupling voltages. Therefore, in this paper, a new voltage model including the cross-coupling inductances is developed and the compensation voltages for decoupling based on the model are calculated. The performance of the proposed decoupling method is verified by experimental results.


title: User-centered design, activity-centered design, and goal-directed design: a review of three methods for designing web applications

abstract: When conducting research with users in order to design web applications, the practitioner has a variety of methods from which to choose. This paper examines three such methods'User-Centered Design (UCD), Goal-Directed Design (GDD), and Activity-Centered Design (ACD)'in terms of their foundations, processes, and deliverables.


title: Gaussian Mixture Embeddings for Multiple Word Prototypes

abstract: Recently, word representation has been increasingly focused on for its excellent properties in representing the word semantics. Previous works mainly suffer from the problem of polysemy phenomenon. To address this problem, most of previous models represent words as multiple distributed vectors. However, it cannot reflect the rich relations between words by representing words as points in the embedded space. In this paper, we propose the Gaussian mixture skip-gram (GMSG) model to learn the Gaussian mixture embeddings for words based on skip-gram framework. Each word can be regarded as a gaussian mixture distribution in the embedded space, and each gaussian component represents a word sense. Since the number of senses varies from word to word, we further propose the Dynamic GMSG (D-GMSG) model by adaptively increasing the sense number of words during training. Experiments on four benchmarks show the effectiveness of our proposed model.


title: Chemically Derived Graphene Oxide: Towards Large‐Area Thin‐Film Electronics and Optoelectronics

abstract: Chemically derived graphene oxide (GO) possesses a unique set of properties arising from oxygen functional groups that are introduced during chemical exfoliation of graphite. Large-area thin-film deposition of GO, enabled by its solubility in a variety of solvents, offers a route towards GO-based thin-film electronics and optoelectronics. The electrical and optical properties of GO are strongly dependent on its chemical and atomic structure and are tunable over a wide range via chemical engineering. In this Review, the fundamental structure and properties of GO-based thin films are discussed in relation to their potential applications in electronics and optoelectronics.


title: Classification of motor imagery hands movement using levenberg-marquardt algorithm based on statistical features of EEG signal

abstract: Motor Imagery (MI) is a highly supervised method nowadays for the disabled patients to give them hope. This paper proposes a differentiation method between imagery left and right hands movement using Daubechies wavelet of Discrete Wavelet Transform (DWT) and Levenberg-Marquardt back propagation training algorithm of Neural Network (NN). DWT decomposes the raw EEG data to extract significant features that provide feature vectors precisely. Levenberg-Marquardt Algorithm (LMA) based neural network uses feature vectors as input for classification of the two class data and outcomes overall classification accuracy of 92%. Previously various features and methods used but this recommended method exemplifies that statistical features provide better accuracy for EEG classification. Variation among features indicates differences between neural activities of two brain hemispheres due to two imagery hands movement. Results from the classifier are used to interface human brain with machine for better performance that requires high precision and accuracy scheme.


title: Towards an integration of deep learning and neuroscience

abstract: Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.


title: Adaptive Linear Regression for Appearance-Based Gaze Estimation

abstract: We investigate the appearance-based gaze estimation problem, with respect to its essential difficulty in reducing the number of required training samples, and other practical issues such as slight head motion, image resolution variation, and eye blinking. We cast the problem as mapping high-dimensional eye image features to low-dimensional gaze positions, and propose an adaptive linear regression (ALR) method as the key to our solution. The ALR method adaptively selects an optimal set of sparsest training samples for the gaze estimation via l ::: 1 ::: -optimization. In this sense, the number of required training samples is significantly reduced for high accuracy estimation. In addition, by adopting the basic ALR objective function, we integrate the gaze estimation, subpixel alignment and blink detection into a unified optimization framework. By solving these problems simultaneously, we successfully handle slight head motion, image resolution variation and eye blinking in appearance-based gaze estimation. We evaluated the proposed method by conducting experiments with multiple users and variant conditions to verify its effectiveness.


title: Integrated ${LCC} $ Compensation Topology for Wireless Charger in Electric and Plug-in Electric Vehicles

abstract: This paper presents an integrated $LCC$ compensation topology for electric vehicle/plug-in hybrid electric vehicle wireless chargers. The effect of the coupling between the additional coil and the main coil on the $LCC$ compensation topology is studied. The proposed topology will reduce the size of the additional coil and make the system more compact with extremely high efficiency. The basic characteristics of the proposed topology are analyzed based on fundamental harmonic approximation. Furthermore, based on the steady-state model, three categories of operation modes are presented and analyzed. In order to realize zero-voltage switching, the series capacitor $C_{2} $ on the secondary side is tuned. A numerical method is used to analyze the impact of different values of $\Delta C_{2}$ on the turnoff current, and the best value of $C_{2}$ is chosen to build a prototype to verify the analysis.


title: In-band cellular IoT for smart home applications

abstract: Smart home and its applications are one of the main interests of IoT (internet of things). Recently, 3GPP has introduced cellular IoT systems including LTE-M and NB-IoT (narrowband IoT). In this letter, we propose a unified frame structure for providing the applications of cellular IoTs to smart home scenario and implement using the USRP-RIO. The experimental results verify the performances of cellular IoTs in smart home environments.


title: Real-time 2D/3D Registration via CNN Regression

abstract: In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. The CNN regressors are trained for local zones and applied in a hierarchical manner to break down the complex regression task into simpler sub-tasks that can be learned separately. Our experiment results demonstrate the advantage of the proposed method in computational efficiency with negligible degradation of registration accuracy compared to intensity-based methods.


title: A current ripple cancellation circuit for electrolytic capacitor-less AC-DC LED driver

abstract: This paper presents a bidirectional buck-boost current ripple cancellation circuit applied to electrolytic capacitor-less flyback LED driver with power factor correction (PFC). By operating the flyback converter in discontinuous conduction mode (DCM), high power factor can be obtained under universal input voltage range. The bidirectional buck-boost converter can minimize the twice-line-frequency output current ripple using simple open-loop pulse width modulation (PWM) control without complex control scheme, and modularization is possible for other types of converter at the PFC stage. The output current ripple can be significantly reduced without using electrolytic capacitors (E-Cap) and the power factor is near unity under universal input voltage. Finally, a 700 mA / 20 W hardware prototype is built to verify the feasibility of the proposed method.


title: Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method

abstract: Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation connections of U-Net [1] and short-range residual connections from ResNet [2]. The model was trained using the 130 LiTS training datasets and achieved an average Dice score of 0.67 when evaluated on the 70 test CT scans, which ranked first for the LiTS challenge at the time of the ISBI 2017 conference.


title: To dwell among gamers: Investigating the relationship between social online game use and gaming-related friendships

abstract: Abstract Social online games are a mass phenomenon and, for most of the users, a social activity. Even though there are numerous studies on social online games they do not explicitly investigate the offline contacts of the gamers, which play a significant role for their social embeddedness. Regarding the online gaming-related contacts possible strong ties such as friendship relationships are often neglected. Additionally modality switching between the two spheres is not further investigated. In this paper, we strive to extend the research on the interrelation of social game use and the social embeddedness of social online gamers, as well as modality switching between the online and offline context. In doing so we provide new insights into the emergence and quality of gaming-related relationships. We collected information on everyday life as well as on gaming-related friendships from a representative sample of 2213 gamers and 287 non-gamers in Germany. Our results show that social online gamers do not differ significantly from other gamers or non-gamers regarding the number of their good friends. However, we found a significant impact of social online gaming frequency on the probability of meeting exclusively online friends. Different social motives played an important role for modality switching processes. Players with a pronounced motive to gain social capital and to play in a team had the highest probability to transform their social relations from online to offline context. We found that social online gamers are well integrated and use the game to spend time with old friends—and to recruit new ones. Our results suggest that gaming-related ties might be very useful: especially modality switching between the two spheres might lead to strong ties and accordingly to additional bonding social capital.


title: Anti-phishing based on automated individual white-list

abstract: In phishing and pharming, users could be easily tricked into submitting their username/passwords into fraudulent web sites whose appearances look similar as the genuine ones. The traditional blacklist approach for anti-phishing is partially effective due to its partial list of global phishing sites. In this paper, we present a novel anti-phishing approach named Automated Individual White-List (AIWL). AIWL automatically tries to maintain a white-list of user's all familiar Login User Interfaces (LUIs) of web sites. Once a user tries to submit his/her confidential information to an LUI that is not in the white-list, AIWL will alert the user to the possible attack. Next, AIWL can efficiently defend against pharming attacks, because AIWL will alert the user when the legitimate IP is maliciously changed; the legitimate IP addresses, as one of the contents of LUI, are recorded in the white-list and our experiment shows that popular web sites' IP addresses are basically stable. Furthermore, we use Naive Bayesian classifier to automatically maintain the white-list in AIWL. Finally, we conclude through experiments that AIWL is an efficient automated tool specializing in detecting phishing and pharming.


title: Stratified Transfer Learning for Cross-domain Activity Recognition

abstract: In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets (i.e., OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.


title: Cross-task crowdsourcing

abstract: Crowdsourcing is an effective method for collecting labeled data for various data mining tasks. It is critical to ensure the veracity of the produced data because responses collected from different users may be noisy and unreliable. Previous works solve this veracity problem by estimating both the user ability and question difficulty based on the knowledge in each task individually. In this case, each single task needs large amounts of data to provide accurate estimations. However, in practice, budgets provided by customers for a given target task may be limited, and hence each question can be presented to only a few users where each user can answer only a few questions. This data sparsity problem can cause previous approaches to perform poorly due to the overfitting problem on rare data and eventually damage the data veracity. Fortunately, in real-world applications, users can answer questions from multiple historical tasks. For example, one can annotate images as well as label the sentiment of a given title. In this paper, we employ transfer learning, which borrows knowledge from auxiliary historical tasks to improve the data veracity in a given target task. The motivation is that users have stable characteristics across different crowdsourcing tasks and thus data from different tasks can be exploited collectively to estimate users' abilities in the target task. We propose a hierarchical Bayesian model, TLC (Transfer Learning for Crowdsourcing), to implement this idea by considering the overlapping users as a bridge. In addition, to avoid possible negative impact, TLC introduces task-specific factors to model task differences. The experimental results show that TLC significantly improves the accuracy over several state-of-the-art non-transfer-learning approaches under very limited budget in various labeling tasks.


title: Architecture-driven self-adaptation and self-management in robotics systems

abstract: We describe an architecture-centric design and implementation approach for building self-adapting and self-managing robotics systems. The basis of our approach is the concept of meta-level components, which facilitate adaptation and management of application-level components. Our approach applies two key enhancements to the traditional usage of meta-level components: (1) we utilize three distinct, specialized meta-level components for the three fundamental activities of a robotics system: sensing, computation, and control, and (2) we allow meta-level components to themselves be monitored, managed and adapted by other (higher layer) meta-level components. In this way, our approach flexibly supports adaptive layered architectures of arbitrary depth, the specification of arbitrary system adaptation policies, and the provision of intelligent facilities for constructing adaptation plans on-the-fly. We showcase our approach using a team of autonomous mobile robots that engage in a leader-follower scenario and experience a wide variety of failures, activating distinct recovery mechanisms.


Story Traces and Projections: Exploring the Patterns of Storytelling


title: Anxiety Increases the Feeling of Presence in Virtual Reality

abstract: Given previous studies indicating a significant correlation between anxiety and presence, the purpose of this investigation was to explore the direction of the causal relationship between them. The sample consisted of 31 adults suffering from snake phobia. The study featured a randomized within-between design with two conditions and three counterbalanced immersions: (a) a baseline control immersion (BASELINE), (b) an immersion in a threatening and anxiety-inducing environment (ANX), and (c) an immersion in a nonthreatening environment that should not induce anxiety (NOANX). In the NOANX environment, participants were immersed for 5 min in a virtual Egyptian desert. They were told that the environment was safe and contained no snakes. The ANX immersion was identical, except that participants were led to believe that a multitude of hidden and dangerous snakes were lurking in the environment. A period of distraction (reading a text on relaxation) separated the ANX and NOANX immersions. Experimenters recorded presence and anxiety in the middle of and after each VR immersion. These brief measures of presence supported our hypothesis and were significantly higher in the anxious immersion than in the baseline or the nonanxious immersion. This finding was not corroborated by the presence questionnaire, where scores varied significantly in the opposite direction. The results from the brief one-item measures of presence support the significant contribution of emotions felt during the immersion on the subjective feeling of presence. The mixed results with the presence questionnaire are discussed, along with psychological factors potentially involved in presence.


title: Printed Wide-Slot Antenna for Wideband Applications

abstract: The design and analysis of a novel printed wide-slot antenna, fed by a microstrip line, for wideband communication systems is presented. Detailed simulation and experimental investigations are conducted to understand its behavior and optimize for broadband operation. The designed antenna has a wide operating bandwidth of over 120% (2.8-11.4 GHz) for S11 <-10 dB. In addition to being small in size, the antenna exhibits stable far-field radiation characteristics in the entire operating bandwidth, relatively high gain, and low cross polarization. By properly choosing the suitable slot shape, selecting similar feed shape and tuning their dimensions, the design with very wide operating bandwidth, relatively small size and improved radiation pattern is obtained. A comprehensive parametric study has been carried out to understand the effects of various dimensional parameters and to optimize the performance of the designed antenna. Results show that the impedance matching of this kind of antenna is greatly affected by the feed-slot combination and feed gap width, with the slot shape being a main contributor of the radiation characteristics. The simulated and measured Results for return loss, far-field E and H-plane radiation patterns, and gain of designed antenna are presented and discussed.


title: Ground leakage current suppression in a 50 kW 5-level T-type transformerless PV inverter

abstract: In this paper ground leakage current suppression in a 50 kW 5-level T-type transformerless PV inverter has been presented. Compared to a 3-level T-type PV inverter, this topology allows a simple modulation method such as carrier-based (CB) PWM can be used to suppress the leakage current without the penalty of the traditional methods. Phase disposition (PD) and phase opposition disposition (POD) based CB PWM method has been applied to this 5-level topology. The spectrum analysis has demonstrated that PD and POD will generate the same phase voltage spectrum. In addition, the common-mode (CM) voltage of a 5-level T-type PV inverter has been derived and the CM choke has been designed. The value of the CM choke is a 73% reduction compared to that of a 3-level T-type PV inverter. The simulation and experimental verification have been provided.


title: Home Appliance Energy Disaggregation Using Low Frequency Data and Machine Learning Classifiers

abstract: Home appliance monitoring provides useful information about appliance usage, which can be used to better inform users about their consumption habits and promote energy conservation. However, metering all loads is cost prohibitive. Instead, many have tried to disaggregate loads from aggregate power measurements. Existing approaches that require submetering high resolution power signals of individual appliances during training and testing, are impractical and economically infeasible. In this paper, we introduce a low-cost approach for home appliance monitoring based on observations made on a single circuit. Our approach consists of three steps. First, we apply a neural network classifier to segment the input power signals. Then, we apply another classifier to label each segment as an individual appliance or multiple appliances. Finally, we iteratively disaggregate the multi-appliance segments with the classifier in the previous step. Our proposed approach is evaluated in two experiments. The first experiment uses a fully labeled public dataset consisting of 1,211 segments from 25 student bedrooms on a university campus. The second experiment uses 1,563 segments from the REDD public dataset. The evaluation shows that our approach can accurately detect those appliances that dominate energy consumption (overall accuracy of 86.6% and 91.2%, respectively). We also present an in-depth analysis of the failures and conjecture why they are harder to detect.


title: Skewing of stator windings for reduction of spatial harmonics in concentrated wound PMSM

abstract: In Surface Permanent Magnet (SPM) Synchronous machines with fractional slot concentrated windings (FSCW) configuration, few slot/pole combinations exhibit large spatial harmonic and sub-harmonic fields. Spatial harmonics result in distorted voltage waveforms and cause additional losses thereby deteriorating the machine's performance. From background literature, it has been found that these harmonics are reduced by increasing the number of winding layers, increasing the number of phases, or introducing complicated magnetic flux barriers in the stator core, thereby reducing the overall torque density of the machine. This paper presents a method to minimize the effects of spatial harmonics by skewing the stator windings without reducing the torque producing harmonic component. Further, the impact of stator winding skew is studied on magnetic force, open-circuit phase voltage, inductances and cogging torque using a novel analytical model incorporating higher order harmonics in air-gap flux density and phase inductances. Comparative analysis of skewed machine is performed with the experimentally obtained results for a baseline un-skewed machine.


title: Sign Language Recognition

abstract: This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a precis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.


SPATIO-TEMPORAL PATTERN ANALYSIS OF TAXI TRIPS IN NEW YORK CITY


title: Some practical guidance for the implementation of propensity score matching

abstract: Propensity Score Matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like "what to do if there is choice-based sampling?" or "when to measure effects?" can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.


title: A Single Layer Reflectarray Antenna for C/X/Ka Bands Applications

abstract: This paper proposes a novel tri-band reflectarray antenna design. The antenna consists of three different types of elements: split circular ring for Ka band operation, split square loop for C band operation, and cross-dipole element for X band operation. An advantage of the antenna is that all three types of elements are aligned and fabricated on a single layer of substrate. This single-layer design will be significantly simpler to fabricate with lower cost than the traditional multilayer designs.


10.2417/1201203.004158 Neuromorphic computer vision: overcoming 3D limitations


Analysis of the Saturation Throughput in an IEEE 802.11 Network Using Cognitive Radio