title: SUMO – Simulation of Urban MObility An Overview

abstract: SUMO is an open source traffic simulation package including net import and demand modeling components. We describe the current state of the package as well as future developments and extensions. SUMO helps to investigate several research topics e.g. route choice and traffic light algorithm or simulating vehicular communication. Therefore the framework is used in different projects to simulate automatic driving or traffic management strategies.


title: Graph Clustering: Block-models and model free results

abstract: Clustering graphs under the Stochastic Block Model (SBM) and extensions are well studied. Guarantees of correctness exist under the assumption that the data is sampled from a model. In this paper, we propose a framework, in which we obtain "correctness" guarantees without assuming the data comes from a model. The guarantees we obtain depend instead on the statistics of the data that can be checked. We also show that this framework ties in with the existing model-based framework, and that we can exploit results in model-based recovery, as well as strengthen the results existing in that area of research.


title: Optimal attack trajectory for Hypersonic Boost-Glide Missile in maximum reachable domain

abstract: Hypersonic Boost-Glide Missile is a new intercontinental guided weapon intends to fly along an optimized trajectory. Its glide warhead with sharp nose needs to endure long-time serious aeroheating to long-range glide in atmosphere. The trajectory optimization problem is an important aspect for HBG missile design. The trajectory optimization problem is characterized by a boost phase followed by a glide phase. A multi-constraints multi-phase trajectory optimization method is adopted to maximize reachable domain and minimize the attack time, under near-real condition. The associated optimal control problem has been solved by selecting a direct method approach. The dynamics has been transcribed to a set of nonlinear constraints and the arising nonlinear programming problem has been solved through a sequential quadratic programming solver. Numerical results represent the optimal trajectory solved is a combined boost-glide-dive trajectory, and the maximum reachable domain can attain 44300000 square kilometer, the ground track of optimal attack trajectory approximates as a S line.


Development of Generalized Photovoltaic Model Using MATLAB/SIMULINK


title: Students’ perception of Kahoot!’s influence on teaching and learning

abstract: Technology is being increasingly integrated into teaching environments in view of enhancing students’ engagement and motivation. In particular, game-based student response systems have been found to foster students’ engagement, enhance classroom dynamics and improve overall students’ learning experience. This article presents outcomes of research that examined students’ experience using a game-based student response system, Kahoot!, in an Information Systems Strategy and Governance course at a research-intensive teaching university in New Zealand. We conducted semi-structured interviews with students to learn about the extent to which Kahoot! influence classroom dynamics, motivation and students’ learning process. Key findings revealed that Kahoot! enriched the quality of student learning in the classroom, with the highest influence reported on classroom dynamics, engagement, motivation and improved learning experience. Our findings also suggest that the use of educational games in the classroom is likely to minimise distractions, thereby improving the quality of teaching and learning beyond what is provided in conventional classrooms. Other factors that contributed to students’ enhanced learning included the creation and integration of appropriate content in Kahoot!, providing students with timely feedback, and game-play (gamification) strategies.


title: Development of HILS simulator for steering control in hot strip finishing mill

abstract: In this paper, hardware-in-the loop-simulation(HILS) simulator for steering control is developed. The simulator describes lateral movement of the strip in hot strip finishing mill. The simulator include strip dynamics, mill model, mill motor and AGC controllers. The factors considered for strip dynamics are initial off-center and wedge difference. The wedge is realized using the expansion of deformation process. To test validation of a simulator, PID controller is tested.


title: TypeSan: Practical Type Confusion Detection

abstract: The low-level C++ programming language is ubiquitously used for its modularity and performance. Typecasting is a fundamental concept in C++ (and object-oriented programming in general) to convert a pointer from one object type into another. However, downcasting (converting a base class pointer to a derived class pointer) has critical security implications due to potentially different object memory layouts. Due to missing type safety in C++, a downcasted pointer can violate a programmer's intended pointer semantics, allowing an attacker to corrupt the underlying memory in a type-unsafe fashion. This vulnerability class is receiving increasing attention and is known as type confusion (or bad-casting). Several existing approaches detect different forms of type confusion, but these solutions are severely limited due to both high run-time performance overhead and low detection coverage. This paper presents TypeSan, a practical type-confusion detector which provides both low run-time overhead and high detection coverage. Despite improving the coverage of state-of-the-art techniques, TypeSan significantly reduces the type-confusion detection overhead compared to other solutions. TypeSan relies on an efficient per-object metadata storage service based on a compact memory shadowing scheme. Our scheme treats all the memory objects (i.e., globals, stack, heap) uniformly to eliminate extra checks on the fast path and relies on a variable compression ratio to minimize run-time performance and memory overhead. Our experimental results confirm that TypeSan is practical, even when explicitly checking almost all the relevant typecasts in a given C++ program. Compared to the state of the art, TypeSan yields orders of magnitude higher coverage at 4--10 times lower performance overhead on SPEC and 2 times on Firefox. As a result, our solution offers superior protection and is suitable for deployment in production software. Moreover, our highly efficient metadata storage back-end is potentially useful for other defenses that require memory object tracking.


title: Deep classification of vehicle makers and models: The effectiveness of pre-training and data enhancement

abstract: Vision-based vehicle detection and classification is an important problem in machine vision and attracted widespread attention due to its wide range of applications in intelligent traffic system. Most of the current vehicle type classification methods require to precisely locate car positions and use the cropped car regions as input. In this paper, we propose deep convolutional neural networks for vehicle makers and models classification, which can take whole image as input without detecting car regions. Our main contributions are two-fold. Firstly, we find pre-train the deep CNN in the task to identify whether vehicle exists in input image can boost the performance of vehicle type classification. Secondly, we show data enhancement can further improve the classification accuracy. Our methods achieve the accuracy of 79% on a large scale Cars dataset, which is comparable with the recent state of the art which requires cropped car image as input.


title: Motivating the academic mind: High-level construal of academic goals enhances goal meaningfulness, motivation, and self-concordance

abstract: How one thinks about or conceptualizes a goal has important consequences for the motivational features of goal pursuit. Two experiments tested the hypothesis, inspired by work on meaning in life, action identification theory, and expectancy-value theory, that high-level construal of an academic goal should enhance motivation to pursue that goal. In each experiment, we manipulated high-level versus low-level construal of an academic goal and assessed several variables related to the goal: the perceived meaningfulness of the goal, motivation to pursue the goal, and goal self-concordance. Supporting the hypothesis, individuals who thought about their academic goal in a high-level manner viewed their goal as more meaningful, reported being more motivated to pursue the goal, and reported the goal to be more self-concordant. Implications and future directions are discussed.


title: Moving towards inclusive design guidelines for socially and ethically aware HCI

abstract: Most people acknowledge that personal computers have enormously enhanced the autonomy and communication capacity of people with special needs. The key factor for accessibility to these opportunities is the adequate design of the user interface which, consequently, has a high impact on the social lives of users with disabilities. The design of universally accessible interfaces has a positive effect over the socialisation of people with disabilities. People with sensory disabilities can profit from computers as a way of personal direct and remote communication. Personal computers can also assist people with severe motor impairments to manipulate their environment and to enhance their mobility by means of, for example, smart wheelchairs. In this way they can become more socially active and productive. Accessible interfaces have become so indispensable for personal autonomy and social inclusion that in several countries special legislation protects people from 'digital exclusion'. To apply this legislation, inexperienced HCI designers can experience difficulties. They would greatly benefit from inclusive design guidelines in order to be able to implement the 'design for all' philosophy. In addition, they need clear criteria to avoid negative social and ethical impact on users. This paper analyses the benefits of the use of inclusive design guidelines in order to facilitate a universal design focus so that social exclusion is avoided. In addition, the need for ethical and social guidelines in order to avoid undesirable side effects for users is discussed. Finally, some preliminary examples of socially and ethically aware guidelines are proposed.


title: Usage of Apriori Algorithm of Data Mining as an Application to Grievous Crimes against Women

abstract: Quantitative data must be converted into qualitative data, for this association algorithm only can apply to it. As association rule deals with frequent item sets as done by many association algorithms such as: Apriori algorithm, that's why in most real life applications Apriori algorithm is used. In this paper author contains the use of association rule mining in extracting patterns that occur frequently within a dataset and showcases the implementation of the Apriori algorithm in mining association rules from a dataset which is manual collection of demeaning crimes against women which is collected from Session court. In this paper author considers the two Association Rule algorithms i.e. Apriori Algorithm and PredictiveApriori Algorithm and compares the result of both the algorithms using WEKA, a data mining tool.As result of rules of both algorithms clearly shows that Apriori algorithm performs better and faster than PredictiveApriori algorithm.


title: Feature line extraction from unorganized noisy point clouds using truncated Fourier series

abstract: The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.


title: An Adaptive Vision System for Tracking Soccer Players from Variable Camera Settings

abstract: In this paper we present ASPOGAMO, a vision system capable of es- timating motion trajectories of soccer players taped on video. The system per- forms well in a multitude of application scenarios because of its adaptivity to various camera setups, such as single or multiple camera settings, static or dy- namic ones. Furthermore, ASPOGAMO can directly process image streams taken from TV broadcast, and extract all valuable information despite scene interrup- tions and cuts between different cameras. The system achieves a high level of robustness through the use of modelbased vision algorithms for camera estima- tion and player recognition and a probabilistic multi-player tracking framework capable of dealing with occlusion situations typical in team-sports. The continu- ous interplay between these submodules is adding to both the reliability and the efficiency of the overall system.


title: The Perceived Impact of E-books on Student Reading Practices: A Local Study

abstract: This study investigates the perceived impact of future e-book collections on student research and recreational reading habits at our institution through three questions: how students currently use library print collections, how students use e-books, and how these factors impact student perception of the effects of future library e-books on their research and recreational reading behavior. Students express a fairly high interest in e-books, although not without raising significant concerns. While students appreciate the ease of access provided by e-books, many imagine that research would be more difficult using e-books. Results will help the library better evaluate e-book options and navigate possible issues related to implementation.


title: Oral lichen planus and HLA DR.

abstract: Oral lichen planus (OLP) is a chronic inflammatory mucocutaneous disease, predominantly seen in middle-aged female patients. Histopathologically, OLP includes parakeratosis with T-lymphocyte subepithelial infiltration. In etiology of OLP, chronically liver and biliary diseases, stress-induced hypertension, inheritance and most commonly carbohydrate metabolism disorders including diabetes mellitus are mentioned. Considering the autoimmune nature of the illness and the role of the HLA DR 3/4 antigens in development of diabetes mellitus we hypothesized that the same antigens could play role in the development of OLP. We examined 50 unrelated Croatian patients (5 males) from Institute of Oral Pathology, Faculty of Dental Medicine, Zagreb, with clinical and histopathological diagnosis of OLP. They were all tested for HLA DR and compared with 1089 Croatian controls that underwent the tissue standardization in fathership cases. OLP patients had blood sugar level controlled, and those with HLA DR 3/4 and those with blood sugar level higher then 5.6 mmol/l additionally underwent standard oral glucose tolerance test. Carbohydrate metabolism disorder was found in 13 OLP patients (26%) while 10 of them (20%) had diabetes mellitus type I. This finding is 400 times more frequent than is appearance of diabetes in healthy controls (0.05%) in Croatia.


Leveraging Supervised Learning and Automated Reasoning for Robot Sequential Decision-Making


title: Dead bodies found in wells.

abstract: Corpses found in wells or lime pits must be identified and the cause and manner of death must be determined. There are several circumstances that may lead to the presence of corpses in wells. In this study, 3940 death examinations and autopsies, performed at the Konya Branch of Forensic Medicine Council (Turkey) between 2000 and 2007, were retrospectively investigated, and it was found that 18 (0.46%) of the bodies had been recovered from wells. The cases were evaluated in terms of their demographic features, manner of death (accidental, suicidal, or homicidal), autopsy findings, cause of death, and the characteristics of the wells in which they were found. The ages of the victims ranged from 4 to 74 years, and the average age was 40. 4 +/- 20.6 years. Of total, 16 cases were males and 2 were females. The manner of death was determined to be accidental in 10 of the cases, suicide in 6 of the cases, and homicide in the remaining 2 cases. In 7 of the cases, death had occurred as a result of drowning in water. A comprehensive scene investigation and autopsy must be performed for corpses recovered from wells and pits for both identification and determination of the cause and manner of death. Wells should be covered and kept closed at all times to reduce the number of accidental deaths resulting from falls into wells.


title: Quantum computation and quantum information

abstract: The paper is intended to be a survey of all the important aspects and results that have shaped the field of quantum computation and quantum information. The reader is first familiarized with those features and principles of quantum mechanics providing a more efficient and secure information processing. Their applications to the general theory of information, cryptography, algorithms, computational complexity and error-correction are then discussed. Prospects for building a practical quantum computer are also analyzed. †This research was supported by the Natural Sciences and Engineering Research Council of Canada.


title: DETERMINATION OF EFFECTIVE DAMPING CHARACTERISTICS OF FIBER-REINFORCED VISCOELASTIC COMPOSITES

abstract: A numerical method for the determination of effective complex moduli and vibration decrements of fiber-reinforced viscoelastic composites is proposed, which is based on the finite element modeling of representative volume element and equation of the elastic and viscous parts of the strain energy of composite and quasi-homogeneous material volumes.


title: Creating school environments that deter antisocial behaviors in youth

abstract: School violence has become of paramount concern in recent years, leaving many schools unprepared to deal with the varied problems students bring to the classroom. Conditions within the school can be readily identified that predict and contribute to problematic behavior. The success of preventive and intervention programs for youth violence hinges upon recognizing and modifying aspects of school climate, teacher/school personnel interactions with students, and school structure. Several of these aspects are identified in this article and suggestions for improving the educational environment to prevent the development of antisocial behaviors in youth are offered. © 2002 Wiley Periodicals, Inc.


title: Weighted Word Pairs for query expansion

abstract: Abstract This paper proposes a novel query expansion method to improve accuracy of text retrieval systems. Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words. Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model. We compared our method with other baseline query expansion schemes and methods. Evaluations performed on TREC-8 demonstrated the effectiveness of the proposed method with respect to the baseline.


title: Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics

abstract: Wearable electronics have been utilized for a number of applications including for ocular use, although their use has been limited to a single function. Here, the authors developed a multifunctional contact lens with wireless electronics for measurement of glucose and intraocular pressure.


title: Bipartite graph partitioning and data clustering

abstract: Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, we propose a new data clustering method based on partitioning the underlying bipartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. We show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. We point out the connection of our clustering algorithm to correspondence analysis used in multivariate analysis. We also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, we apply our clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.


title: Enterprise agility and the enabling role of information technology

abstract: In turbulent environments, enterprise agility, that is, the ability of firms to sense environmental change and respond readily, is an important determinant of firm success. We define and deconstruct enterprise agility, delineate enterprise agility from similar concepts in the business research literature, explore the underlying capabilities that support enterprise agility, explicate the enabling role of information technology (IT) and digital options, and propose a method for measuring enterprise agility. The concepts in this paper are offered as foundational building blocks for the overall research program on enterprise agility and the enabling role of IT.


title: Transfer Learning in Collaborative Filtering for Sparsity Reduction

abstract: Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.


title: Factors for success in customer relationship management (CRM) systems

abstract: The importance of effective customer relationships as a key to customer value and hence shareholder value is widely emphasised. In order to enhance these relationships, the application of IT to marketing through customer relationship management (CRM) software, e-commerce and other initiatives is growing rapidly. This study examines the factors that influence the successful deployment of CRM applications, with particular emphasis on those factors which are distinct from other areas of application. Using the analytic induction method, success factors were derived from five in-depth case studies. Resulting factors underemphasised in previous literature include: the need for project approval procedures which allow for uncertainty; the need to leverage models of best practice; the importance of prototyping new processes, not just IT; and the need to manage for the delivery of the intended benefits, rather than just implementing the original specification.


title: Single-pole double-throw CMOS switches for 900-MHz and 2.4-GHz applications on p/sup -/silicon substrates

abstract: A 900-MHz single-pole double-throw (SPDT) switch with an insertion loss of 0.5 dB and a 2.4-GHz SPDT switch with an insertion loss of 0.8 dB were implemented using 3.3-V 0.35-/spl mu/m NMOS transistors in a 0.18-/spl mu/m bulk CMOS process utilizing 20-/spl Omega//spl middot/cm p/sup -/ substrates. Impedance transformation was used to reduce the source and load impedances seen by the switch to increase the power handling capability. SPDT switches with 30-/spl Omega/ impedance transformation networks exhibit 0.97-dB insertion loss and 24.3-dBm output P/sub 1dB/ when tuned for 900-MHz operation, and 1.10-dB insertion loss and 20.6-dBm output P/sub 1dB/ when tuned for 2.4-GHz operation. The 2.4-GHz switch is the first bulk CMOS switch which can be used for 802.11b wireless local area network applications.


title: A Review on Knowledge-based Expert System: Concept and Architecture

abstract: A Knowledge-based expert system use human knowledge to solve problems that normally would require human intelligence. Expert systems are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem solving purposes. With the growing importance of human resource management and increasing size of the organizations, maintenance of employee related data and generating appropriate reports are the crucial aspects of any organization. Therefore more and more organizations are adopting computer based human resource management systems (HRMS). This paper explains the architecture, knowledge representation techniques and application areas of knowledge-based expert system in Human Resource.


title: Smart glove for Sign Language communications

abstract: Human beings have a natural ability to see, listen and interact with their external environment. Unfortunately, there are some people who are differently abled and do not have the ability to use their senses to the best extent possible. Such people depend on other means of communication like sign language. This presents a major roadblock for people in the deaf and dumb communities when they try to engage in interaction with others, especially in their educational, social and professional environments. Therefore, it is necessary to have an advance gesture recognition or sign language detection system to bridge this communication gap. Here an effort has been made to develop a smart glove using Intel Galileo Gen 2 IoT kit for real-time gesture recognition. The objective is to create a device which helps the hearing or speech impaired persons to communicate with others.


title: Efficient regression of general-activity human poses from depth images

abstract: We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints. Experimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities.


title: CPW to Rectangular Waveguide Transition on an ${\hbox{LiNbO}}_{3}$ Substrate

abstract: This paper presents transition from coplanar waveguide (CPW) to rectangular waveguide for W -band (75-110 GHz) operations. The waveguide consists of a cosine-shaped fin extending from the upper waveguide wall onto the CPW signal electrode. The design features direct integration of the waveguide with opto-electronic devices on lithium niobate (LiNbO3) for the applications of millimeter wave to optical signal processing. Simulations and measurements show that the model works very well over the entire W-band. Measurements of fabricated devices have yielded return loss of more than 10 dB and insertion loss of about 2.8 dB over the operating bandwidth.


title: Comparisons of CFD simulations and in-service data for the self propelled performance of an Autonomous Underwater Vehicle

abstract: A blade element momentum theory propeller model is coupled with a commercial RANS solver. This allows the fully appended self propulsion of the autonomous underwater vehicle Autosub 3 to be considered. The quasi-steady propeller model has been developed to allow for circumferential and radial variations in axial and tangential inflow. The non-uniform inflow is due to control surface deflections and the bow-down pitch of the vehicle in cruise condition. The influence of propeller blade Reynolds number is included through the use of appropriate sectional lift and drag coefficients. Simulations have been performed over the vehicles operational speed range (Re = 6.8 × 106 to 13.5 × 106). A workstation is used for the calculations with mesh sizes up to 2x106 elements. Grid uncertainty is calculated to be 3.07% for the wake fraction. The initial ::: comparisons with in service data show that the coupled RANS-BEMT simulation under predicts the drag of the vehicle and consequently the required propeller rpm. However, when an appropriate correction is made for the effect on resistance of various protruding sensors the predicted propulsor rpm matches well with that of in-service rpm measurements for vessel speeds (1m/s - 2m/s). The developed analysis captures ::: the important influence of the propeller blade and hull Reynolds number on overall system efficiency.


title: The chemical abstract machine

abstract: We introduce a new kind of abstract machine based on the chemical metaphor used in the Γ language of Banâtre & al . States of a machine are chemical solutions where floating molecules can interact according to reaction rules. Solutions can be stratified by encapsulating subsolutions within membranes that force reactions to occur locally. We illustrate the use of this model by describing the operational semantics of the TCCS and CCS process calculi. We also show how to extract a higher-order concurrent λ-calculus out of the basic concepts of the chemical abstract machine.


title: Ricci curvature for metric-measure spaces via optimal transport

abstract: We define a notion of a measured length space X having nonnegative N-Ricci curvature, for N finite, or having infinity-Ricci curvature bounded below by K, for K a real number. The definitions are in terms of the displacement convexity of certain functions on the associated Wasserstein space P_2(X) of probability measures. We show that these properties are preserved under measured Gromov-Hausdorff limits. We give geometric and analytic consequences.


title: Multimodal Interaction with Multiple Co-located Drones in Search and Rescue Missions

abstract: We present a multimodal interaction framework suitable for a human rescuer that operates in proximity with a set of co-located drones during search missions. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse, incomplete, although high-value, instructions to the robots. This operative scenario requires a human-interaction framework that supports multimodal communication along with an effective and natural mixed-initiative interaction between the human and the robots. In this work, we illustrate the domain and the proposed multimodal interaction framework discussing the system at work in a simulated case study.


RF MEMS from a device perspective


title: A survey of Internet-of-Things: Future vision, architecture, challenges and services

abstract: Internet-of-Things (IoT) is the convergence of Internet with RFID, Sensor and smart objects. IoT can be defined as “things belonging to the Internet” to supply and access all of real-world information. Billions of devices are expected to be associated into the system and that shall require huge distribution of networks as well as the process of transforming raw data into meaningful inferences. IoT is the biggest promise of the technology today, but still lacking a novel mechanism, which can be perceived through the lenses of Internet, things and semantic vision. This paper presents a novel architecture model for IoT with the help of Semantic Fusion Model (SFM). This architecture introduces the use of Smart Semantic framework to encapsulate the processed information from sensor networks. The smart embedded system is having semantic logic and semantic value based Information to make the system an intelligent system. This paper presents a discussion on Internet oriented applications, services, visual aspect and challenges for Internet of things using RFID, 6lowpan and sensor networks.


ow does hotel pricing influence guest satisfaction by the moderating nfluence of room occupancy ? hiang


title: Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

abstract: As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.


title: Poisoning Complete-Linkage Hierarchical Clustering

abstract: Clustering algorithms are largely adopted in security applications as a vehicle to detect malicious activities, although few attention has been paid on preventing deliberate attacks from subverting the clustering process itself. Recent work has introduced a methodology for the security analysis of data clustering in adversarial settings, aimed to identify potential attacks against clustering algorithms and to evaluate their impact. The authors have shown that single-linkage hierarchical clustering can be severely affected by the presence of a very small fraction of carefully-crafted poisoning attacks into the input data, highlighting that the clustering algorithm may be itself the weakest link in a security system. In this paper, we extend this analysis to the case of complete-linkage hierarchical clustering by devising an ad hoc poisoning attack. We verify its effectiveness on artificial data and on application examples related to the clustering of malware and handwritten digits.


title: In situ-forming hydrogels—review of temperature-sensitive systems

abstract: Abstract In the past few years, an increasing number of in situ-forming systems have been reported in the literature for various biomedical applications, including drug delivery, cell encapsulation, and tissue repair. There are several possible mechanisms that lead to in situ gel formation: solvent exchange, UV-irradiation, ionic cross-linkage, pH change, and temperature modulation. The thermosensitive approach can be advantageous for particular applications as it does not require organic solvents, co-polymerization agents, or an externally applied trigger for gelation. In the last 2 decades, several thermosensitive formulations have been proposed. This manuscript focuses on aqueous polymeric solutions that form implants in situ in response to temperature change, generally from ambient to body temperature. It mainly reviews the characterization and use of polysaccharides, N -isopropylacrylamide copolymers, poly(ethylene oxide- b -propylene oxide- b -ethylene oxide) (poloxamer) and its copolymers, poly(ethylene oxide)/( d , l -lactic acid- co -glycolic acid) copolymers, and thermosensitive liposome-based systems.


Frozen Tissue Examination: Is It really no Longer of Use in Parathyroid Surgery? Single-center Retrospective Study on 97 Patients treated by minimally Invasive Approach


title: Model reactions and natural occurrence of furans from hypersaline environments

abstract: Volatile organic compounds like furan and its derivatives are important for atmospheric properties and reactions. In this work the known abiotic formation of furan from catechol under Fenton-like conditions with Fe 3+ sulfate was revised by the use of a bispidine Fe 2+ complex as a model compound for iron with well-known characteristics. While total yields were comparable to those with the Fe 3+ salt, the bispidine Fe 2+ complex is a better catalyst as the turnover numbers of the active iron species were higher. Additionally, the role of iron and pH is discussed in relation to furan formation from model compounds and in natural sediment and water samples collected from the Dead Sea and several salt lakes in Western Australia. Various alkylated furans and even traces of halogenated furans (3-chlorofuran and 3-bromofuran) were found in some Australian samples. 3-chlorofuran was found in three sediments and four water samples, whereas 3-bromofuran was detected in three water samples. Further, the emission of furans is compared to the abundance of several possible precursors such as isoprene and aromatic hydrocarbons as well as to the related thiophenes. It is deduced that the emissions of volatile organic compounds such as furans contribute to the formation of ultra-fine particles in the vicinity of salt lakes and are important for the local climate.


title: Temporal Difference Models and Reward-Related Learning in the Human Brain

abstract: Temporal difference learning has been proposed as a model for Pavlovian conditioning, in which an animal learns to predict delivery of reward following presentation of a conditioned stimulus (CS). A key component of this model is a prediction error signal, which, before learning, responds at the time of presentation of reward but, after learning, shifts its response to the time of onset of the CS. In order to test for regions manifesting this signal profile, subjects were scanned using event-related fMRI while undergoing appetitive conditioning with a pleasant taste reward. Regression analyses revealed that responses in ventral striatum and orbitofrontal cortex were significantly correlated with this error signal, suggesting that, during appetitive conditioning, computations described by temporal difference learning are expressed in the human brain.


title: Illustrative parallel coordinates

abstract: Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edgebundling through splines; (b) visualization of "branched" clusters to reveal the distribution of the data; (c) opacitybased hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.


title: Automatic generation of oracles for exceptional behaviors

abstract: Test suites should test exceptional behavior to detect faults in error-handling code. However, manually-written test suites tend to neglect exceptional behavior. Automatically-generated test suites, on the other hand, lack test oracles that verify whether runtime exceptions are the expected behavior of the code under test. This paper proposes a technique that automatically creates test oracles for exceptional behaviors from Javadoc comments. The technique uses a combination of natural language processing and run-time instrumentation. Our implementation, Toradocu, can be combined with a test input generation tool. Our experimental evaluation shows that Toradocu improves the fault-finding effectiveness of EvoSuite and Randoop test suites by 8% and 16% respectively, and reduces EvoSuite’s false positives by 33%.


title: Managing the Crowd: Towards a Taxonomy of Crowdsourcing Processes

abstract: Crowdsourcing is an umbrella term for a variety of approaches that tap into the potential of a large and open crowd of people. So far, there is no systematic understanding of the processes used to source and aggregate contributions from the crowd. In particular, crowdsourcing organizations striving to achieve a specific goal should be able to evaluate the mechanisms that impact these processes. Following a method of IS taxonomy development we propose a new taxonomic framework for crowdsourcing processes. In contrast to previous work, this classification scheme focuses exclusively on an organizational perspective and on the mechanisms available to these organizations. The resulting dimensions are preselection of contributors, accessibility of peer contributions, aggregation of contributions, and remuneration for contributions. By classifying the processes of 46 crowdsourcing examples, we identify 19 distinct process types. A subsequent cluster analysis shows general patterns among these types and indicates a link to certain applications of crowdsourcing.


title: Network hub location problems: The state of the art

abstract: Hubs are special facilities that serve as switching, transshipment and sorting points in many-to-many distribution systems. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin-destination pairs. In this paper we classify and survey network hub location models. We also include some recent trends on hub location and provide a synthesis of the literature.


title: Bird's-eye view vision system for vehicle surrounding monitoring

abstract: Blind spots usually lead to difficulties for drivers to maneuver their vehicles in complicated environments, such as garages, parking spaces, or narrow alleys. This paper presents a vision system which can assist drivers by providing the panoramic image of vehicle surroundings in a bird's-eye view. In the proposed system, there are six fisheye cameras mounted around a vehicle so that their views cover the whole surrounding area. Parameters of these fisheye cameras were calibrated beforehand so that the captured images can be dewarped into perspective views for integration. Instead of error-prone stereo matching, overlapping regions of adjacent views are stitched together by aligning along a seam with dynamic programming method followed by propagating the deformation field of alignment with Wendland functions. In this way the six fisheye images can be integrated into a single, panoramic, and seamless one from a look-down viewpoint. Our experiments clearly demonstrate the effectiveness of the proposed image-stitching method for providing the bird's eye view vision for vehicle surrounding monitoring.


title: Performance Analysis of the Advanced Infrared (AIr) CSMA/CA MAC Protocol for Wireless LANs

abstract: Advanced Infrared (AIr) is a proposed standard of the Infrared Data Association (IrDA) for indoor infrared LANs. AIr Medium Access Control (MAC) employs Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) techniques with Request To Send/Clear To Send (RTS/CTS) frame exchange to address the hidden station problem. A long Collision Avoidance Slot (CAS) duration, that includes the beginning of the CTS frame, is defined to cope with collisions caused from hidden stations. AIr MAC employs linear adjustment of the Contention Window (CW) size to minimize delays emerging from the long CAS duration. This paper provides a simple and accurate analytical model for the linear CW adjustment that calculates AIr throughput assuming a finite number of stations and error free channel transmissions. Validity of the model is verified by comparing analysis with simulation results. By examining the first derivative of the throughput equation, we derive the optimum CW size that maximizes throughput as a function of the network size. In the case of the AIr protocol, where a collision lasts exactly one CAS, different conclusions result for maximum throughput as compared with the corresponding conclusions for the similar IEEE 802.11 protocol. Using the proposed model, we present an extensive AIr throughput performance evaluation. The effectiveness of physical and link layer parameters on throughput performance is explored. The proposed long CAS duration combined with CW linear adjustment are proven quite effective. Linear CW adjustment combined with the long CAS duration offer an efficient collision avoidance scheme that does not suffer from collisions caused from hidden stations.


title: Dynamic simulation of buoyancy engine of underwater glider based on experimentation

abstract: As a key component for underwater gliders, the buoyancy engine plays an important role in the operation of gliders, the performance of which directly affects the monolithic reliability. In this paper, to lower costs of research on the buoyancy engine, a dynamic simulation is established by AMEsim software, in which the performance of the low-pressure boost gear pump and that of the high-pressure hydraulic axial piston pump are analyzed. And based on the simulation results, an experiment is designed to verify validity of the simulation. The method of buoyancy engine simulation in this paper may provide a valuable reference for designers.


title: Challenges of Computational Processing of Code-Switching

abstract: This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed. It refers to code-switching which has become more popular in our daily life and therefore obtains an increasing amount of attention from the research community. We report our experience that cov- ers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency parsing but also more downstream ones such as machine translation and automatic speech recognition. We highlight and discuss the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work.


title: Compact aspect embedding for diversified query expansions

abstract: Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query. We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.


abstract: Anthropomorphic Agents as a User Interface Paradigm: Experimental Findings and a Framework for Research Richard Catrambone (rc7@prism.gatech.edu) School of Psychology, Georgia Institute of Technology, 274 5th Street Atlanta, GA 30332-0170 USA John Stasko (stasko@cc.gatech.edu) Jun Xiao (junxiao@cc.gatech.edu) College of Computing, Georgia Institute of Technology Atlanta, GA 30332-0280 USA Abstract Research on anthropomorphic agent interfaces has produced widely divergent results. We suggest that this is due to insufficient consideration of key factors that influence the perception and effectiveness of agent-based interfaces. We propose a framework for studying anthropomorphic agents that can systematize the research. The framework emphasizes features of the agent, the user, and the task the user is performing. Our initial experiment within this framework manipulated the agent’s appearance (lifelike versus iconic) and the nature of the user’s task (carrying out procedures versus providing opinions). We found that the perception of the agent was strongly influenced by the task while features of the agent that we manipulated had little effect. Introduction If you could ask for assistance from a smart, spoken natural language help system, would that be an improvement over an on-line reference manual? Presumably the answer, in most cases, is yes, for two reasons. First, the spoken natural language aspect would allow you to speak your questions rather than having to type them. Generally this is a faster approach for most people. Second, the smart aspect would improve the chance of the help system finding the information you want even if you do not state the query using the correct or most appropriate terms. The state of the art in this style of interface is a human user consultant. Does it matter that the user consultant has a face and that the face can have expressions and convey a personality? Would a face affect you in terms of your comfort and satisfaction with the interaction? Would the presence of a face make the help or advice you receive more persuasive? The answers to such questions have implications for the design of training systems, customer service, information kiosks, and many other applications. Many people believe that anthropomorphic computer interfaces have great potential to be beneficial for a number of reasons. Agents could act as smart assistants, much like travel agents or investment advisors, helping people manage the ever-growing amount of information encountered today (Lyman & Varian, 2002). Further, a conversational interface appears to be a natural dialog style in which the user does not have to learn complex command structure and functionality (Laurel, 1990). An anthropomorphic interface could use intonation, gaze patterns, and facial expressions, in addition to words, for conveying information and affect. The human face seems to occupy a privileged position for conveying a great deal of information, including relatively subtle information, efficiently (Fridlund & Gilbert, 1985). Anthropomorphic interfaces could make a computer more human-like, engaging, entertaining, approachable, and understandable to the user, thus harboring potential to build trust and establish relationships with users, and make them feel more comfortable with computers. These potential advantages are balanced by strong negatives. Anthropomorphic agent interfaces are viewed by some researchers as being impractical and inappropriate. Current speech recognition, natural language understanding, and learning capabilities of computers still fall far short of any human assistant. More specifically, it has been proposed that agent systems disempower users by clouding issues such as who is responsible for a system’s actions (Lanier, 1995). Others feel that user interfaces are more beneficial when they clearly reflect the commands available to a user and present the objects that a user can act upon (Shneiderman, 1997). Furthermore, critics argue that agent interfaces may mislead both users and designers, increase user anxiety, reduce user control, undermine user responsibility, and destroy a user’s sense of accomplishment (Shneiderman & Maes, 1997). Many current anthropomorphic or personified interfaces are viewed as being annoying, silly characters who hinder rather than enhance productivity (e.g., the Microsoft Paper Clip). Although strong opinions have been voiced on both sides of this issue, relatively little careful empirical research on anthropomorphic interfaces has been conducted, and the results from this research have been contradictory or equivocal (Cassell, 2000). Our goal is to develop a framework to systematically evaluate and understand the autonomous agent as a user interface paradigm. The present paper outlines the framework and an initial study that examines two issues within this framework. The first is whether the degree to which an interface agent is anthropomorphic has a measurable effect on users. Note that anthropomorphism is not a dichotomy but rather a continuum. One can think of interfaces with full fidelity video or 3D images of people to


title: A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets

abstract: Inspired by recent high performance patch-based image inpainting algorithms using Point Integral Method (PIM) and Low Dimension Manifold Model (LDMM), we propose in this paper an image representation scheme combining the local and nonlocal characterization of patches in an image. By resolving an image as patches averaged out on their overlapping pixels, our representation scheme can be shown as equivalent to a tight frame constructed from convolving local bases (wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g. spectral basis induced by nonlinear dimension reduction on patches) and each component is called a {\it convolution framelet}. This insight leads to a novel interpretation of LDMM as a weighted $\ell_2$-regularization on the coefficients obtained by decomposing images into linear combinations of convolution framelets; based on this understanding, we extend the original LDMM to a reweighted version that yields improved inpainting results. In addition, we establish the energy concentration property of convolution framelet coefficients, where given a nonlocal basis the paired local basis is constructed from a linear reconstruction framework; a generalization of this framework to the setting of unions of local embedding is natural for interpreting BM3D, one of the state-of-the-art image denoising algorithms.


Village Life in Old China; A Community Study of Kao Yao, Yunnan.


title: Bilateral Filtering Based User-Controllable Multi-exemplars Texture Synthesis

abstract: Texture synthesis is a core process of computer graphics applications, which can enhance the realistic rendering greatly. With the rapidly increasing demands of realistic rendering, single texture synthesis cannot meet the needs. Multi-exemplars synthesis is a challenging research topic to increase the richness of texture details. In this paper, we use exemplar graph to realize Multi-exemplars texture synthesis under users' control. Further to improve the quality of synthesis results, we adopt bilateral filtering technology to improve the exemplar analysis stage. It can maintain the boundary of the hybrids of multi-exemplars well while making the texture elements in the composite image more clear at the same time.


title: New techniques for the detection of microplastics in sediments and field collected organisms

abstract: Microplastics have been reported in marine environments worldwide. Accurate assessment of quantity and type is therefore needed. Here, we propose new techniques for extracting microplastics from sediment and invertebrate tissue. The method developed for sediments involves a volume reduction of the sample by elutriation, followed by density separation using a high density NaI solution. Comparison of this methods’ efficiency to that of a widely used technique indicated that the new method has a considerably higher extraction efficiency. For fibres and granules an increase of 23% and 39% was noted, extraction efficiency of PVC increased by 100%. The second method aimed at extracting microplastics from animal tissues based on chemical digestion. Extraction of microspheres yielded high efficiencies (94–98%). For fibres, efficiencies were highly variable (0–98%), depending on polymer type. The use of these two techniques will result in a more complete assessment of marine microplastic concentrations.


title: Statistical Models for Frame-Semantic Parsing

abstract: We present a brief history and overview of statistical methods in frame-semantic parsing – the automatic analysis of text using the theory of frame semantics. We discuss how the FrameNet lexicon and frameannotated datasets have been used by statistical NLP researchers to build usable, state-of-the-art systems. We also focus on future directions in frame-semantic parsing research, and discuss NLP applications that could benefit from this line of work. 1 Frame-Semantic Parsing Frame-semantic parsing has been considered as the task of automatically finding semantically salient targets in text, disambiguating their semantic frame representing an event and scenario in discourse, and annotating arguments consisting of words or phrases in text with various frame elements (or roles). The FrameNet lexicon (Baker et al., 1998), an ontology inspired by the theory of frame semantics (Fillmore, 1982), serves as a repository of semantic frames and their roles. Figure 1 depicts a sentence with three evoked frames for the targets “million”, “created” and “pushed” with FrameNet frames and roles. Automatic analysis of text using framesemantic structures can be traced back to the pioneering work of Gildea and Jurafsky (2002). Although their experimental setup relied on a primitive version of FrameNet and only made use of “exemplars” or example usages of semantic frames (containing one target per sentence) as opposed to a “corpus” of sentences, it resulted in a flurry of work in the area of automatic semantic role labeling (Marquez et al., 2008). However, the focus of semantic role labeling (SRL) research has mostly been on PropBank (Palmer et al., 2005) conventions, where verbal targets could evoke a “sense” frame, which is not shared across targets, making the frame disambiguation setup different from the representation in FrameNet. Furthermore, it is fair to say that early research on PropBank focused primarily on argument structure prediction, and the interaction between frame and argument structure analysis has mostly been unaddressed (Marquez et al., 2008). There are exceptions, where the verb frame has been taken into account during SRL (Meza-Ruiz and Riedel, 2009; Watanabe et al., 2010). Moreoever, the CoNLL 2008 and 2009 shared tasks also include the verb and noun frame identification task in their evaluations, although the overall goal was to predict semantic dependencies based on PropBank, and not full argument spans (Surdeanu et al., 2008; Hajic et al., 2009). The SemEval 2007 shared task (Baker et al., 2007) attempted to revisit the frame-semantic analysis task based on FrameNet. It introduced a larger FrameNet lexicon (version 1.3), and also a larger corpus with full-text annotations compared to prior work, with multiple targets annotated per sentence. The corpus allowed words and phrases with noun, verb, adjective, adverb, number, determiner, conjunction and preposition syntactic categories to serve as targets and evoke frames, unlike any other single dataset; it also allowed targets from different syntactic categories share frames, and therefore roles. The repository of semantic role types was also much richer than PropBankstyle lexicons, numbering in several hundreds. Most systems participating in the task resorted to a cascade of classifiers and rule-based modules: identifying targets (a non-trivial subtask), disambiguating frames, identifying potential arguments, and then labeling them with roles. The system described by Johansson and Nugues (2007) performed the best in this shared task. Next, we focus on its performance, and subsequent improvements made by the research community on this task.


title: Human scabies acquired from a pet puppy.

abstract: Scabies is a very old contagious skin disease caused by itch mite, Sarcoptes scabiei which burrows into the stratum corneum. Nowadays, human scabies is one of the health problems among the school children. This paper dealt with a case of scabies in a preschool aged child who acquired the infestation from his pet puppy.


title: A case study of applying boosting naive Bayes to claim fraud diagnosis

abstract: We apply the weight of evidence reformulation of AdaBoosted naive Bayes scoring due to Ridgeway et al. (1998) to the problem of diagnosing insurance claim fraud. The method effectively combines the advantages of boosting and the explanatory power of the weight of evidence scoring framework. We present the results of an experimental evaluation with an emphasis on discriminatory power, ranking ability, and calibration of probability estimates. The data to which we apply the method consists of closed personal injury protection (PIP) automobile insurance claims from accidents that occurred in Massachusetts (USA) during 1993 and were previously investigated for suspicion of fraud by domain experts. The data mimic the most commonly occurring data configuration, that is, claim records consisting of information pertaining to several binary fraud indicators. The findings of the study reveal the method to be a valuable contribution to the design of intelligible, accountable, and efficient fraud detection support.


title: Architecture-Based Approach to Reliability Assessment of Software Systems

abstract: Abstract With the growing emphasis on reuse, software development process moves toward component-based software design. As a result, there is a need for modeling approaches that are capable of considering the architecture of the software and estimating the reliability by taking into account the interactions between the components, the utilization of the components, and the reliabilities of the components and of their interfaces with other components. This paper details the state of the architecture-based approach to reliability assessment of component based software and describes how it can be used to examine software behavior right from the design stage to implementation and final deployment. First, the common requirements of the architecture-based models are identified and the classification is proposed. Then, the key models in each class are described in detail and the relation among them is discussed. A critical analysis of underlying assumptions, limitations and applicability of these models is provided which should be helpful in determining the directions for future research.


title: Mind Controlled Wheelchair

abstract: The paper deals with engineering an electric wheelchair from a common wheelchair and the ::: development of a BCI system (Brain-Computer Interface) between the electric wheelchair and the human ::: brain. The target populations for the mind controlled wheelchair are the patients who are paralyzed below the ::: neck and are unable to use conventional wheelchair interfaces. A portable EEG headset, MCU and fi rmware ::: signal processing together facilitate the movement of the wheelchair by processing the user’s brain activity ::: and eye blinks’ frequency. This paper reports the creation of a cost effi cient solution, later intended to be ::: distributed as an add-on conversion kit for a common manual wheelchair. A Neurosky Mind wave headset ::: is used to pick up EEG signals from the brain. This is a commercialized version of the Open-EEG Project. ::: The signal obtained from EEG sensor is processed by the ARM microcontroller FRDM KL-25Z, a Freescale board. The microcontroller takes decision for determining the direction of motion of wheelchair based on floor detection and obstacle avoidance sensors mounted on wheelchair’s footplate. The MCU shows real time information on a color LCD interfaced to it. Joystick control of the wheelchair is also provided as an additional interface option that can be chosen from the menu system of the project.


title: Pathways for irony detection in tweets

abstract: Posts on Twitter allow users to express ideas and opinions in a very dynamic way. The volume of data available is incredibly high in this support, and it may provide relevant clues regarding the judgement of the public on certain product, event, service etc. While in standard sentiment analysis the most common task is to classify the utterances according to their polarity, it is clear that detecting ironic senses represent a big challenge for Natural Language Processing. By observing a corpus constitued by tweets, we propose a set of patterns that might suggest ironic/sarcastic statements. Thus, we developed special clues for irony detection, through the implementation and evaluation of a set of patterns.


title: A preamplifier-latch comparator for high accuracy high speed switched-capacitors pipelined ADC

abstract: In this paper, a gain-boosting preamplifier-latch comparator with minimum input referred offset voltage is proposed. The preamplifier utilizes telescope structure with cross-coupled active loading, which enhances the gain of preamplifier. In order to minimize the delay time of comparator, the input DC range and the ratio of PMOS/NMOS are selected carefully. The circuit is fabricated in TSMC 0.18um with 1.8V power supply.


title: Event detection with spatial latent Dirichlet allocation

abstract: A large number of news articles are generated every day on the Web. Automatically identifying events from a large document collection is a challenging problem. In this paper, we propose two event detection approaches using generative models. We combine the popular LDA model with temporal segmentation and spatial clustering. In addition, we adapt an image segmentation model, SLDA, for spatial-temporal event detection on text. The results of our experiments show that both approaches outperform the traditional content-based clustering approaches on our datasets.


title: Facial Expression Recognition Using Salient Features and Convolutional Neural Network

abstract: A depth camera-based novel method is proposed here for efficient facial expression recognition. For each pixel in a depth image, eight local directional strengths are obtained and ranked. Once the rank of all pixels is obtained, eight histograms are developed for the eight surrounding directions. The histograms are then concatenated to represent features for a depth image of a face. This approach is named local directional rank histogram pattern (LDRHP). To combine with LDRHP features, one more robust feature extraction technique named local directional strength pattern (LDSP) is proposed. Typical local directional pattern (LDP) considers only absolute values of edge strengths for a pixel. This generalization in LDP may generate the same patterns for two different kinds of edge pixels. LDSP can overcome this problem. It considers the binary values of the position with the directions representing the highest and lowest original strengths. The highest strength indicates the strongest direction on the bright side of a pixel and the lowest one indicates the strongest direction in the dark side of that pixel. Hence, combining binary positions of these two directions can generate more robust patterns than LDP. Besides, LDSP pattern of a pixel is of six bits, whereas traditional LDP-based patterns are of eight bits (e.g., local directional deviation-based pattern and local directional position pattern). Thus, LDSP reduces the dimension of features with the same time adding robustness. For a depth image in a depth video, LDSP features are augmented with LDRHP features followed by Kernel principal component analysis and generalized discriminant analysis to generate more robust features. At last, the features are trained with a deep learning approach and convolutional neural network for successful facial expression recognition. The proposed approach is compared and shown to outperform the traditional expression recognition methods.


title: Detection of hemorrhages in diabetic retinopathy analysis using color fundus images

abstract: Diabetic retinopathy (DR), a complication due to diabetes, is the leading cause of blindness. Diagnosis of DR at its earliest is thus very necessary. In this paper, we present an automated computer aided method for detection of hemorrhages using fundus images. In the first stage, blood vessel (BV) is detected and eliminated. In the following stage hemorrhage candidate is detected. The method is applied on HRF, DIARETDB0, DIARETDB1, MESSIDOR and Local databases which have both normal and pathological images. The proposed method shows an overall sensitivity of 97.3% and specificity of 98.92% in hemorrhage detection.


title: You are what you tweet…pic! gender prediction based on semantic analysis of social media images

abstract: We propose a method to extract user attributes from the pictures posted in social media feeds, specifically gender information. While traditional approaches rely on text analysis or exploit visual information only from the user profile picture or colors, we propose to look at the distribution of semantics in the pictures coming from the whole feed of a person to estimate gender. In order to compute such semantic distribution, we trained models from existing visual taxonomies to recognize objects, scenes and activities, and applied them to the images in each user's feed. Experiments conducted on a set of ten thousand twitter users and their collection of half a million images revealed that the gender signal can indeed be extracted from the users image feed (75.6% accuracy). Furthermore, the combination of visual cues resulted almost as strong as textual analysis in predicting gender, while providing complementary information that can be employed to further boost gender prediction accuracy to 88% when combined with textual data. As a byproduct of our investigation, we were also able to extrapolate the semantic categories of posted pictures mostly correlated to males and females.


title: Design and Validation of a Rehabilitation Robotic Exoskeleton for Tremor Assessment and Suppression

abstract: Exoskeletons are mechatronic systems worn by a person in such a way that the physical interface permits a direct transfer of mechanical power and exchange of information. Upper limb robotic exoskeletons may be helpful for people with disabilities and/or limb weakness or injury. Tremor is the most common movement disorder in neurological practice. In addition to medication, rehabilitation programs, and deep brain stimulation, biomechanical loading has appeared as a potential tremor suppression alternative. This paper introduces the robotic exoskeleton called WOTAS (wearable orthosis for tremor assessment and suppression) that provides a means of testing and validating nongrounded control strategies for orthotic tremor suppression. This paper describes in detail the general concept for WOTAS, outlining the special features of the design and selection of system components. Two control strategies developed for tremor suppression with exoskeletons are described. These two strategies are based on biomechanical loading and notch Altering the tremor through the application of internal forces. Results from experiments using these two strategies on patients with tremor are summarized. Finally, results from clinical trials are presented, which indicate the feasibility of ambulatory mechanical


title: EarPut: augmenting ear-worn devices for ear-based interaction

abstract: One of the pervasive challenges in mobile interaction is decreasing the visual demand of interfaces towards eyes-free interaction. In this paper, we focus on the unique affordances of the human ear to support one-handed and eyes-free mobile interaction. We present EarPut, a novel interface concept and hardware prototype, which unobtrusively augments a variety of accessories that are worn behind the ear (e.g. headsets or glasses) to instrument the human ear as an interactive surface. The contribution of this paper is three-fold. We contribute (i) results from a controlled experiment with 27 participants, providing empirical evidence that people are able to target salient regions on their ear effectively and precisely, (ii) a first, systematically derived design space for ear-based interaction and (iii) a set of proof of concept EarPut applications that leverage on the design space and embrace mobile media navigation, mobile gaming and smart home interaction.


title: A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective

abstract: Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT.


title: IMPaSTo: a realistic, interactive model for paint

abstract: We present a paint model for use in interactive painting systems that captures a wide range of styles similar to oils or acrylics. The model includes both a numerical simulation to recreate the physical flow of paint and an optical model to mimic the paint appearance.Our physical model for paint is based on a conservative advection scheme that simulates the basic dynamics of paint, augmented with heuristics that model the remaining key properties needed for painting. We allow one active wet layer, and an unlimited number of dry layers, with each layer being represented as a height-field.We represent paintings in terms of paint pigments rather than RGB colors, allowing us to relight paintings under any full-spectrum illuminant. We also incorporate an interactive implementation of the Kubelka-Munk diffuse reflectance model, and use a novel eight-component color space for greater color accuracy.We have integrated our paint model into a prototype painting system, with both our physical simulation and rendering algorithms running as fragment programs on the graphics hardware. The system demonstrates the model's effectiveness in rendering a variety of painting styles from semi-transparent glazes, to scumbling, to thick impasto.


title: Emerging Rumor Identification for Social Media with Hot Topic Detection

abstract: A rumor is commonly defined as a statement whose true value is unverifiable. As rumor can spread misinformation around people, causing social problems such as panic, and the rapid growth of online social media has made it possible for rumors to spread more quickly, it is important to automatically identify rumors for social media. Existing methods on rumor detection always concentrate on telling rumor from truth with handcrafted regular expressions, dealing with out of date rumor related message. To solve this problem, we introduce a novel hot topic detection method combining bursty term identification and multi-dimension sentence modeling to automatically detect emerging hot topics for rumor identification. We conduct a comprehensive set of experiments on two data sets from real-world social media. Experiment results show that our emerging rumor identification for social media with hot topic detection work well both in news data set and twitter data set, and combining the hot topic detection with the rumor detection is possible to finish real-time rumor identification. We believe our method to automatically detect rumor will open new dimensions in analyzing online misinformation and other aspects of social media mining.


title: Argo: Architecture-aware graph partitioning

abstract: The increasing popularity and ubiquity of various large graph datasets has caused renewed interest for graph partitioning. Existing graph partitioners either scale poorly against large graphs or disregard the impact of the underlying hardware topology. A few solutions have shown that the nonuniform network communication costs may affect the performance greatly. However, none of them considers the impact of resource contention on the memory subsystems (e.g., LLC and Memory Controller) of modern multicore clusters. They all neglect the fact that the bandwidth of modern high-speed networks (e.g., Infiniband) has become comparable to that of the memory subsystems. In this paper, we provide an in-depth analysis, both theoretically and experimentally, on the contention issue for distributed workloads. We found that the slowdown caused by the contention can be as high as 11x. We then design an architecture-aware graph partitioner, Argo, to allow the full use of all cores of multicore machines without suffering from either the contention or the communication heterogeneity issue. Our experimental study showed (1) the effectiveness of Argo, achieving up to 12x speedups on three classic workloads: Breadth First Search, Single Source Shortest Path, and PageRank; and (2) the scalability of Argo in terms of both graph size and the number of partitions on two billion-edge real-world graphs.


title: Search Challenges in Natural Language Generation with Complex Optimization Objectives

abstract: Automatic natural language generation (NLG) is a difficult problem already when merely trying to come up with natural-sounding utterances. Ubiquituous applications, in particular companion technologies, pose the additional challenge of flexible adaptation to a user or a situation. This requires optimizing complex objectives such as information density, in combinatorial search spaces described using declarative input languages. We believe that AI search and planning is a natural match for these problems, and could substantially contribute to solving them effectively. We illustrate this using a concrete example NLG framework, give a summary of the relevant optimization objectives, and provide an initial list of research challenges.


title: Automata simulator

abstract: Software simulation tools supporting a teaching process are highly accepted by both teachers and students. We discuss the possibility of using automata simulators in theoretical computer science courses. The main purpose of this article is to propose key features and requirements of well designed automata simulator and to present our tool SimStudio -- integrated simulator of finite automaton, pushdown automaton, Turing machine, RAM with extension and abacus machine. The aim of this paper is to report our experiences with using of our automata simulators in teaching of the course "Fundamentals of Theoretical Computer Science" in bachelor educational program in software engineering held at Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava.


title: Soft Pneumatic Actuator Fascicles for High Force and Reliability

abstract: Soft pneumatic actuators (SPAs) are found in mobile robots, assistive wearable devices, and rehabilitative technologies. While soft actuators have been one of the most crucial elements of technology leading the development of the soft robotics field, they fall short of force output and bandwidth requirements for many tasks. In addition, other general problems remain open, including robustness, controllability, and repeatability. The SPA-pack architecture presented here aims to satisfy these standards of reliability crucial to the field of soft robotics, while also improving the basic performance capabilities of SPAs by borrowing advantages leveraged ubiquitously in biology; namely, the structured parallel arrangement of lower power actuators to form the basis of a larger and more powerful actuator module. An SPA-pack module consisting of a number of smaller SPAs will be studied using an analytical model and physical prototype. Experimental measurements show an SPA pack to generate over 112 N linear force, while the model indicates the benefit of parallel actuator grouping over a geometrically equivalent single SPA scale as an increasing function of the number of individual actuators in the group. For a module of four actuators, a 23% increase in force production over a volumetrically equivalent single SPA is predicted and validated, while further gains appear possible up to 50%. These findings affirm the advantage of utilizing a fascicle structure for high-performance soft robotic applications over existing monolithic SPA designs. An example of high-performance soft robotic platform will be presented to demonstrate the capability of SPA-pack modules in a complete and functional system.


title: The role of biological system other than auditory air-conduction in the emergence of the hypersonic effect

abstract: Abstract Although human beings cannot perceive elastic vibrations in the frequency range above 20 kHz, nonstationary sounds containing a wealth of inaudible high-frequency components (HFC) above the human audible range activate deep-lying brain structures, including the brainstem and thalamus and evoke various physiological, psychological, and behavioral responses. In the previous reports, we have called these phenomena collectively “the hypersonic effect.” It remains unclear, however, if vibratory stimuli above the audible range are transduced and perceived solely via the conventional air-conducting auditory system or if other mechanisms also contribute to mediate transduction and perception. In the present study, we have examined the emergence of the hypersonic effect when inaudible HFC and audible low-frequency components (LFC) were presented selectively to the ears, the entrance of an air-conducting auditory system, or to the body surface including the head which might contain some unknown vibratory sensing mechanisms. We used two independent measurements based on differing principles; one physiological (alpha 2 frequency of spontaneous electroencephalogram [alpha-EEG]) and the other behavioral (the comfortable listening level [CLL]). Only when the listener's entire body surface was exposed to HFC, but not when HFC was presented exclusively to the air-conducting auditory system, did both the alpha-EEG and the CLL significantly increase compared to the presentation of LFC alone, that is to say, there was an evident emergence of the hypersonic effect. The present findings suggest that the conventional air-conducting auditory system alone does not bring about the hypersonic effect. We may need to consider the possible involvement of a biological system distinct from the conventional air-conducting auditory nervous system in sensing and transducing high-frequency elastic vibration above the human audible range.


Identifying informative messages in disaster events using Convolutional Neural Networks


Statistical Pattern Recognition of the Iris


title: Theory, evidence and Intervention Mapping to improve behavior nutrition and physical activity interventions

abstract: BackgroundThe present paper intends to contribute to the debate on the usefulness and barriers in applying theories in diet and physical activity behavior-change interventions.DiscussionSince behavior theory is a reflection of the compiled evidence of behavior research, theory is the only foothold we have for the development of behavioral nutrition and physical activity interventions. Application of theory should improve the effectiveness of interventions. However, some of the theories we use lack a strong empirical foundation, and the available theories are not always used in the most effective way. Furthermore, many of the commonly-used theories provide at best information on what needs to be changed to promote healthy behavior, but not on how changes can be induced. Finally, many theories explain behavioral intentions or motivation rather well, but are less well-suited to explaining or predicting actual behavior or behavior change.For more effective interventions, behavior change theory needs to be further developed in stronger research designs and such change-theory should especially focus on how to promote action rather than mere motivation. Since voluntary behavior change requires motivation, ability as well as the opportunity to change, further development of behavior change theory should incorporate environmental change strategies.ConclusionIntervention Mapping may help to further improve the application of theories in nutrition and physical activity behavior change.


STUDY OF REMOTE SENSING IMAGE FUSION AND ITS APPLICATION IN IMAGE CLASSIFICATION


title: A 0.05- to 10-GHz, 19- to 22-GHz, and 38- to 44-GHz Frequency Synthesizer for Software-Defined Radios in 0.13- $\mu\mbox{m}$ CMOS Process

abstract: This brief presents a fully integrated frequency synthesizer for software-defined radios covering not only all the existing wireless standards from 47 MHz to 10 GHz [including 14-band multiband orthogonal frequency-division modulation (MB-OFDM) ultrawideband (UWB)] but also the gigabits per second wireless communication around 60 GHz. A dual-band quadrature output voltage-controlled oscillatior reconfigurable as either a one-port oscillator for low power consumption or a two-port oscillator for low phase noise is employed as the core to generate all the frequencies from 47 MHz to 6 GHz. A multimode x3/x5/x7 injection-locked frequency multiplier and a single-sideband mixer with a tunable transformer-based narrow-band load are proposed to generate the carrier frequencies for the 14 MB-OFDM and impulse UWB bands with fast settling time, low power, and small chip area. An automatic amplitude calibration (APC) technique is proposed to greatly increase the locking range of the subharmonic injection-locked oscillators with achieving a given output amplitude. Implemented in a 0.13- $\mu\mbox{m}$ CMOS process, the synthesizer prototype occupies an active area of 3 $\mu\mbox{m}^{2}$ , consumes 33–83 mW in total, and measures phase noise of −139.6 dBc/Hz at 3-MHz offset from a 1.7-GHz carrier.


title: Voltage Doubler/Tripler Current-Mode Charge Pump Topology with simple "Gear Box"

abstract: DC to DC converters which are realized as a switched capacitor type are very common in display and lighting low power solutions. This applications requires a lot of different supply voltage rails with different demand on the output power. E.g. a thin-film-transistor (TFT) LCD panel requires 24V - 35V at 10mA - 50mA to switch pixel transistors. A current-mode regulated charge pump generates this rail from a 12V -18V input supply. The selected charge pump topology uses a simple "gear box" controlled by a single feedback amplifier to select between doubler and tripler modes of operation. This feedback amplifier simultaneously regulates output voltage and selects the operating mode. The new topology allows less circuit complexity compare to common "gear box" solutions. Therefore this converter system with flexible conversion ratios to support different VIN/VOUT ratios and load currents is the ideal solution to be most efficient. Also the advantages of current-mode control where one transistor in the output stage operates as a regulated current source on the low voltage rail which can theoretically support any power stage topology will be demonstrated. The proposed charge pump is realized in a BICMOS process with HV extension. Finally the measurement results especially regarding mode transitions will be shown.


title: Diverse Image-to-Image Translation via Disentangled Representations

abstract: Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: (1) the lack of aligned training pairs and (2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for producing diverse outputs without paired training images. To achieve diversity, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Using the disentangled features as inputs greatly reduces mode collapse. To handle unpaired training data, we introduce a novel cross-cycle consistency loss. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks. We validate the effectiveness of our approach through extensive evaluation.


title: Chaff Bugs: Deterring Attackers by Making Software Buggier

abstract: Sophisticated attackers find bugs in software, evaluate their exploitability, and then create and launch exploits for bugs found to be exploitable. Most efforts to secure software attempt either to eliminate bugs or to add mitigations that make exploitation more difficult. In this paper, we introduce a new defensive technique called chaff bugs, which instead target the bug discovery and exploit creation stages of this process. Rather than eliminating bugs, we instead add large numbers of bugs that are provably (but not obviously) non-exploitable. Attackers who attempt to find and exploit bugs in software will, with high probability, find an intentionally placed non-exploitable bug and waste precious resources in trying to build a working exploit. We develop two strategies for ensuring non-exploitability and use them to automatically add thousands of non-exploitable bugs to real-world software such as nginx and libFLAC; we show that the functionality of the software is not harmed and demonstrate that our bugs look exploitable to current triage tools. We believe that chaff bugs can serve as an effective deterrent against both human attackers and automated Cyber Reasoning Systems (CRSes).


title: Blockchain-Based System for Secure Data Storage with Private Keyword Search

abstract: Traditional cloud storage has relied almost exclusively on large storage providers, who act as trusted third parties to transfer and store data. This model poses a number of issues including data availability, high operational cost, and data security. In this paper, we introduce a system that leverages blockchain technology to provide a secure distributed data storage with keyword search service. The system allows the client to upload their data in encrypted form, distributes the data content to cloud nodes and ensures data availability using cryptographic techniques. It also provides the data owner a capability to grant permission for others to search on her data. Finally, the system supports private keyword search over the encrypted dataset.


title: Bidirectional LSTM-CRF Models for Sequence Tagging

abstract: In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag information thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or close to) accuracy on POS, chunking and NER data sets. In addition, it is robust and has less dependence on word embedding as compared to previous observations.


title: Developing trust: First impressions and experience

abstract: Using the repeated Trust Game, we investigated how first impressions and experience affect trusting dispositions, beliefs, and behaviors. As in previous research, trusting beliefs and trust-related behaviors were greater at the start of the game for partners with trustworthy faces; and higher later in the game for partners who reciprocated. Three additional findings extended beyond the previous research. First, by measuring the discrete components of trusting beliefs rather than an umbrella “trustworthiness” measure, we confirmed that first impressions and experience influence judgments of competence, benevolence, and integrity. Moreover, we found suggestive evidence that perceptions of benevolence and integrity updated more quickly with experience than perceptions of competence. Second, by looking at trusting beliefs at the start of two consecutive repeated Trust Games, we found that judgments of competence, benevolence, and integrity continue to be influenced by trustworthy facial appearances, even after previous beliefs based on facial appearances were disconfirmed. Third, we found increased investment with a partner at the start of a second repeated Trust Game, even when participants expected their partners to betray them. Overall, our results clarify our understanding of how first impressions and experience influence trusting beliefs; provides evidence that changes in the repeated Trust Game represents learning about a specific partner rather than revisions of trusting dispositions; and highlights important distinctions between trusting beliefs and trust-related behaviors.


title: Unique Features of Mobile Commerce

abstract: While the market potentials and impacts of web-based e-commerce are still in the ascendant, the advances in wireless technologies and mobile networks have brought about a new business opportunity and research attention, what is termed mobile commerce. Commonly, mobile commerce is considered to be another new application of existing web-based e-commerce onto wireless networks, but as an independent business area, mobile commerce has its own advantages and challenges as opposed to traditional e-commerce applications. This paper focuses on exploring the unique features of mobile commerce as compared with traditional e-commerce. Also, there are still some limitations arisen in m-commerce in contrast to web-based e-commerce. Finally, current state of mobile commerce in Japan is presented in brief, with an introduction of several cases involving mobile commerce applications in today's marketplace.


title: On the Number of RF Chains and Phase Shifters, and Scheduling Design With Hybrid Analog–Digital Beamforming

abstract: This paper considers hybrid beamforming (HB) for downlink multiuser massive multiple-input multiple-output (MIMO) systems with frequency selective channels. The proposed HB design employs sets of digitally controlled phase (fixed phase) paired phase shifters (PSs) and switches. For this system, first we determine the required number of radio frequency (RF) chains and PSs such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes $N$ (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing $r_t$ RF chains and $2r_t(N-r_t + \text{1})$ PSs, where $r_t \leq N$ is the rank of the combined digital precoder matrices of all subcarriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only ${20}{-}{40}$ PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains $(N_a) $ is less than $r_t$ , we propose a simple user scheduling algorithm to select the best set of users in each subcarrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.


title: Integrative approaches to qualitative and quantitative evidence

abstract: A process and apparatus for continuous copper smelting, slag conversion, and production of blister copper in the same furnace by enriched oxygen containing gas injection.


title: Further Pharmacological Evidence of Nuclear Factor-κB Pathway Involvement in Bradykinin B1 Receptor-Sensitized Responses in Human Umbilical Vein

abstract: Bradykinin (BK) B 1 receptors are thought to exert a pivotal role in maintaining and modulating inflammatory processes. They are not normally present under physiological situations but are induced under physiopathological conditions. In isolated human umbilical vein (HUV), a spontaneous BK B 1 receptor up-regulation and sensitization process has been demonstrated. Based on pyrrolidine-dithiocarbamate inhibition, it has been proposed that this phenomenon is dependent on nuclear factor-κB (NF-κB) activation. The aim of this study was to further evaluate the NF-κB pathway involvement on BK B 1 receptor sensitization in isolated HUV, using several pharmacological tools. In 5-h incubated rings, either the I-κB kinase inhibitor 3-(4-methylphenylsulfonyl)-2-propenenitrile (Bay 11–7082) or the proteasome activity inhibitor Z-Leu-Leu-Leu-CHO (MG-132) inhibited the development of the BK B 1 receptor-sensitized contractile responses. Furthermore, pro-inflammatory cytokine interleukin-6 (IL-6) produced a leftward shift of the concentration-response curve to the BK B 1 receptor agonist, whereas anti-inflammatory cytokines interleukin-4 (IL-4) and tumor growth factor-β1 (TGF-β1) produced a rightward shift of the responses to des-Arg 9 -BK in our preparations. Taken together, these results point to NF-κB as a key intermediary in the activation of the expression of BK B 1 receptor-sensitized responses in HUV and support the role of inflammatory mediators in the modulation of this process.