title: RECENT DEVELOPMENTS IN PAPER CURRENCY RECOGNITION SYSTEM

abstract: Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the various potential applications it has. Monetary tra nsaction is an integral part of our day to day acti vities. However, blind people particularly suffer in monetary transactions. They are not able to effectively distinguish between var ious denominations and are often deceived by other people. Also, a reliable currency recognition system could be used in any sector whe rever monetary transaction is of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper c urrency notes correctly. Currency denomination detection is a vast area of research a nd significant progress had been achieved over the years. This paper presents an extensive survey of research on various development s in recent years in identification of currency den omination. A number of techniques applied by various researchers are discu ssed briefly in order to assess the state of art.


title: Geocoding for texts with fine-grain toponyms: an experiment on a geoparsed hiking descriptions corpus

abstract: Geoparsing and geocoding are two essential middleware services to facilitate final user applications such as location-aware searching or different types of location-based services. The objective of this work is to propose a method for establishing a processing chain to support the geoparsing and geocoding of text documents describing events strongly linked with space and with a frequent use of fine-grain toponyms. The geoparsing part is a Natural Language Processing approach which combines the use of part of speech and syntactico-semantic combined patterns (cascade of transducers). However, the real novelty of this work lies in the geocoding method. The geocoding algorithm is unsupervised and takes profit of clustering techniques to provide a solution for disambiguating the toponyms found in gazetteers, and at the same time estimating the spatial footprint of those other fine-grain toponyms not found in gazetteers. The feasibility of the proposal has been tested with a corpus of hiking descriptions in French, Spanish and Italian.


title: Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images

abstract: Research advancements allow computational systems to automatically caption social media images. Often, these captions are evaluated with sighted humans using the image as a reference. Here, we explore how blind and visually impaired people experience these captions in two studies about social media images. Using a contextual inquiry approach (n=6 blind/visually impaired), we found that blind people place a lot of trust in automatically generated captions, filling in details to resolve differences between an image's context and an incongruent caption. We built on this in-person study with a second, larger online experiment (n=100 blind/visually impaired) to investigate the role of phrasing in encouraging trust or skepticism in captions. We found that captions emphasizing the probability of error, rather than correctness, encouraged people to attribute incongruence to an incorrect caption, rather than missing details. Where existing research has focused on encouraging trust in intelligent systems, we conclude by challenging this assumption and consider the benefits of encouraging appropriate skepticism.


Discovering Customer Journey Maps using a Mixture of Markov Models


title: A low-complexity pre-processing system for restoring low-quality QR code images

abstract: In this paper we propose a complete preprocessing system for restoring low-quality Quick Response code images. The target application for the system is to restore and decode a code image in a cell phone when the image is shown in another cell phone display. Therefore, the system design focuses on low complexity solutions that are effective for a variety of capturing and displaying devices. The system has been designed to deal with low contrast, blurred, rotated, and deformed code images, while keeping complexity low enough to run in real-time. The proposed system takes advantage of intrinsic Quick Response code characteristics in order to restore the code properly and efficiently. The system has been assessed on a realistic database composed of 533 images captured by a cell a phone from several different cell phone displays. After the proposed preprocessing, 97.4% of the images in the database were correctly decoded.


title: Vision System for AGI: Problems and Directions

abstract: What frameworks and architectures are necessary to create a vision system for AGI? In this paper, we propose a formal model that states the task of perception within AGI. We show the role of discriminative and generative models in achieving efficient and general solution of this task, thus specifying the task in more detail. We discuss some existing generative and discriminative models and demonstrate their insufficiency for our purposes. Finally, we discuss some architectural dilemmas and open questions.


title: Deep attention-based classification network for robust depth prediction

abstract: In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018) (http://www.robustvision.net/index.php). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: (a) How to extract more discriminative features for different scenes (compared to a single scene)? (b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.


title: Advances on Single-Phase Line-Start High Efficiency Interior Permanent Magnet Motors

abstract: The past 25 years have been a significant period with advances in the development of interior permanent magnet (IPM) machines. Line-start small IPM synchronous motors have expanded their presence in the domestic marketplace from few specialized niche markets in high efficiency machine tools, household appliances, small utility motors, and servo drives to mass-produced applications. A closer examination reveals that several different knowledge-based technological advancements and market forces as well as consumer demand for high efficiency requirements have combined, sometimes in fortuitous ways, to accelerate the development of the improved new small energy efficient motors. This paper provides a broad explanation of the various factors that lead to the current state of the art of the single-phase interior permanent motor drive technology. A unified analysis of single-phase IPM motor that permits the determination of the steady-state, dynamic, and transient performances is presented. The mathematical model is based on both d-q axis theory and finite-element analysis. It leads to more accurate numerical results and meets the engineering requirements more satisfactorily than any other methods. Finally, some concluding comments and remarks are provided for efficiency improvement, manufacturing, and future research trends of line-start energy efficient permanent magnet synchronous motors.


title: Long-term Outcome After Surgical Treatment of Lipedema

abstract: Lipedema is a condition characterized by abnormal deposition of adipose tissue in the lower extremities leading to circumferential bilateral lower extremity enlargement typically seen extending from the hips to the ankles. Diagnosis of the condition is often challenging, and patients fre- quently undergo a variety of unsuccessful therapies before receiving the proper diagnosis and appropriate management. Patients may experience pain and aching in the lower extremity in addition to distress from the cosmetic appearance of their legs and the resistance of the fatty changes to diet and exercise. We report a case of a patient with lipedema who was treated with suction-assisted lipectomy and use of compression garments, with successful treatment of the lipodystrophy and maintenance of improved aesthetic results at 4-year postoperative follow-up. ower extremity swelling is a common problem that can be caused by a variety of conditions, the majority of which are related to systemic causes, such as congestive heart failure or cirrhosis. However, localized edema can also occur secondary to lymphatic disruption or dysfunction, as in the case of lymphedema, or from fatty deposition, as is seen in lipedema. Lipedema is a condition characterized by abnormal deposition of adipose tissue in the lower extremities leading to circumferential bilateral lower ex- tremity enlargement typically seen extending from the hips to the ankles. Diagnosis of the condition is often challenging and patients frequently undergo a variety of unsuccessful therapies before receiving the proper diagnosis and appropriate management. Patients may expe- rience pain and aching in the lower extremity in addition to distress resulting from the cosmetic appearance of their legs and the resistance of the fatty changes to diet and exercise. We report a case of a patient with lipedema who was treated with suction-assisted lipectomy and use of compression garments, with successful treatment of the lipodystrophy and maintenance of improved aesthetic results at 4-year postoperative follow-up.


title: Abdominal Compartment Syndrome: pathophysiology and definitions

abstract: Abstract"Intra-abdominal hypertension", the presence of elevated intra-abdominal pressure, and "abdominal compartment syndrome", the development of pressure-induced organ-dysfunction and failure, have been increasingly recognized over the past decade as causes of significant morbidity and mortality among critically ill surgical and medical patients. Elevated intra-abdominal pressure can cause significant impairment of cardiac, pulmonary, renal, gastrointestinal, hepatic, and central nervous system function. The significant prognostic value of elevated intra-abdominal pressure has prompted many intensive care units to adopt measurement of this physiologic parameter as a routine vital sign in patients at risk. A thorough understanding of the pathophysiologic implications of elevated intra-abdominal pressure is fundamental to 1) recognizing the presence of intra-abdominal hypertension and abdominal compartment syndrome, 2) effectively resuscitating patients afflicted by these potentially life-threatening diseases, and 3) preventing the development of intra-abdominal pressure-induced end-organ dysfunction and failure. The currently accepted consensus definitions surrounding the diagnosis and treatment of intra-abdominal hypertension and abdominal compartment syndrome are presented.


title: Burnout and Suicidal Ideation among U.S. Medical Students

abstract: BACKGROUND: Little is known about the prevalence of suicidal ideation among U.S. medical students or how it relates to burnout. OBJECTIVE: To assess the frequency of suicidal ideation among medical students and explore its relationship with burnout. DESIGN: Cross-sectional 2007 and longitudinal 2006 to 2007 cohort study. SETTING: 7 medical schools in the United States. PARTICIPANTS: 4287 medical students at 7 medical schools, with students at 5 institutions studied longitudinally. MEASUREMENTS: Prevalence of suicidal ideation in the past year and its relationship to burnout, demographic characteristics, and quality of life. RESULTS: Burnout was reported by 49.6% (95% CI, 47.5% to 51.8%) of students, and 11.2% (CI, 9.9% to 12.6%) reported suicidal ideation within the past year. In a sensitivity analysis that assumed all nonresponders did not have suicidal ideation, the prevalence of suicidal ideation in the past 12 months would be 5.8%. In the longitudinal cohort, burnout (P < 0.001 for all domains), quality of life (P < 0.002 for each domain), and depressive symptoms (P < 0.001) at baseline predicted suicidal ideation over the following year. In multivariable analysis, burnout and low mental quality of life at baseline were independent predictors of suicidal ideation over the following year. Of the 370 students who met criteria for burnout in 2006, 99 (26.8%) recovered. Recovery from burnout was associated with markedly less suicidal ideation, which suggests that recovery from burnout decreased suicide risk. Limitation: Although response rates (52% for the cross-sectional study and 65% for the longitudinal cohort study) are typical of physician surveys, nonresponse by some students reduces the precision of the estimated frequency of suicidal ideation and burnout. CONCLUSION: Approximately 50% of students experience burnout and 10% experience suicidal ideation during medical school. Burnout seems to be associated with increased likelihood of subsequent suicidal ideation, whereas recovery from burnout is associated with less suicidal ideation. Language: en


title: Wast Management System for Western Africa : Analysis of systemssuccessfully applied in the world that may fit the reality faced in Western Africa

abstract: Health and safety have been the most important concerns in waste management formany years. However, nowadays society demands that as well as being safe, waste managementmust also be sustainable. Th ...


title: Depth estimation and image restoration using defocused stereo pairs

abstract: We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene.


title: Formal representations of uncertainty

abstract: The recent development of uncertainty theories that account for the notion of belief is linked to the emergence, in the XXth century, of Decision Theory and Artificial Intelligence. Nevertheless, this topic was dealt with very differently by each area. Decision Theory insisted on the necessity to found representations on the empirical observation of individuals choosing between courses of action, regardless of any other type of information. Any axiom in the theory should be liable of empirical validation. Probabilistic representations of uncertainty can then be justified with a subjectivist point of view, without necessary reference to frequency. Degrees of probability then evaluate to what extent an agent believes in the occurrence of an event or in the truth of a proposition. In contrast, Artificial Intelligence adopted a more introspective approach aiming at formalizing intuitions, reasoning processes, through the statement of reasonable axioms, often without reference to probability. Actually, until the nineties Artificial Intelligence essentially focused on purely qualitative and ordinal (in fact, logical) representations.


title: Random Drift Particle Swarm Optimization

abstract: The random drift particle swarm optimization (RDPSO) algorithm, inspired by the free electron model in metal conductors placed in an external electric field, is presented, systematically analyzed and empirically studied in this paper. The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field. The motivation of the RDPSO algorithm is described first, and the velocity equation of the particle is designed by simulating the thermal motion as well as the drift motion of the electrons, both of which lead the electrons to a location with minimum potential energy in the external electric field. Then, a comprehensive analysis of the algorithm is made, in order to provide a deep insight into how the RDPSO algorithm works. It involves a theoretical analysis and the simulation of the stochastic dynamical behavior of a single particle in the RDPSO algorithm. The search behavior of the algorithm itself is also investigated in detail, by analyzing the interaction between the particles. Some variants of the RDPSO algorithm are proposed by incorporating different random velocity components with different neighborhood topologies. Finally, empirical studies on the RDPSO algorithm are performed by using a set of benchmark functions from the CEC2005 benchmark suite. Based on the theoretical analysis of the particle's behavior, two methods of controlling the algorithmic parameters are employed, followed by an experimental analysis on how to select the parameter values, in order to obtain a good overall performance of the RDPSO algorithm and its variants in real-world applications. A further performance comparison between the RDPSO algorithms and other variants of PSO is made to prove the efficiency of the RDPSO algorithms.


title: A psychological perspective on augmented reality in the mathematics classroom

abstract: Physical objects and virtual information are used as teaching aids in classrooms everywhere, and until recently, merging these two worlds has been difficult at best. Augmented reality offers the combination of physical and virtual, drawing on the strengths of each. We consider this technology in the realm of the mathematics classroom, and offer theoretical underpinnings for understanding the benefits and limitations of AR learning experiences. The paper presents a framework for understanding AR learning from three perspectives: physical, cognitive, and contextual. On the physical dimension, we argue that physical manipulation affords natural interactions, thus encouraging the creation of embodied representations for educational concepts. On the cognitive dimension, we discuss how spatiotemporal alignment of information through AR experiences can aid student's symbolic understanding by scaffolding the progression of learning, resulting in improved understanding of abstract concepts. Finally, on the contextual dimension, we argue that AR creates possibilities for collaborative learning around virtual content and in non-traditional environments, ultimately facilitating personally meaningful experiences. In the process of discussing these dimensions, we discuss examples from existing AR applications and provide guidelines for future AR learning experiences, while considering the pragmatic and technological concerns facing the widespread implementation of augmented reality inside and outside the classroom.


title: Logic for improving integrity checking in relational data bases⋆

abstract: When an "updating" operation occurs on the current state of a data base, one has to ensure the new state obeys the integrity constraints. So, some of them have to be evaluated on this new state. The evaluation of an integrity constraint can be time consuming, but one can improve such an evaluation by taking advantage from the fact that the integrity constraint is satisfied in the current state. Indeed, it is then possible to derive a simplified form of this integrity constraint which is sufficient to evaluate in the new state in order to determine whether the initial constraint is still satisfied in this new state. The purpose of this paper is to present a simplification method yielding such simplified forms for integrity constraints. These simplified forms depend on the nature of the updating operation which is the cause of the state change. The operations of inserting, deleting, updating a tuple in a relation as well as transactions of such operations are considered. The proposed method is based on syntactical criteria and is validated through first order logic. Examples are treated and some aspects of the method application are discussed.


title: Information technology and systems justification: A review for research and applications

abstract: Information technology (IT) such as Electronic Data Interchange (EDI), Radio Frequency Identification Technology (RFID), wireless, the Internet and World Wide Web (WWW), and Information Systems (IS) such as Electronic Commerce (E-Commerce) systems and Enterprise Resource Planning (ERP) systems have had tremendous impact in education, healthcare, manufacturing, transportation, retailing, pure services, and even war. Many organizations turned to IT/IS to help them achieve their goals; however, many failed to achieve the full potential of IT/IS. These failures can be attributed at least in part to a weak link in the planning process. That weak link is the IT/IS justification process. The decision-making process has only grown more difficult in recent years with the increased complexity of business brought about by the rapid growth of supply chain management, the virtual enterprise and E-business. These are but three of the many changes in the business environment over the past 10–12 years. The complexities of this dynamic new business environment should be taken into account in IT/IS justification. We conducted a review of the current literature on IT/IS justification. The purpose of the literature review was to assemble meaningful information for the development of a framework for IT/IS evaluation that better reflects the new business environment. A suitable classification scheme has been proposed for organizing the literature reviewed. Directions for future research are indicated.


Denoising 3D Computed Tomography Images using New Modified Coherence Enhancing Diffusion Model


title: A systematic mapping study of software product lines testing

abstract: ContextIn software development, Testing is an important mechanism both to identify defects and assure that completed products work as specified. This is a common practice in single-system development, and continues to hold in Software Product Lines (SPL). Even though extensive research has been done in the SPL Testing field, it is necessary to assess the current state of research and practice, in order to provide practitioners with evidence that enable fostering its further development. ObjectiveThis paper focuses on Testing in SPL and has the following goals: investigate state-of-the-art testing practices, synthesize available evidence, and identify gaps between required techniques and existing approaches, available in the literature. MethodA systematic mapping study was conducted with a set of nine research questions, in which 120 studies, dated from 1993 to 2009, were evaluated. ResultsAlthough several aspects regarding testing have been covered by single-system development approaches, many cannot be directly applied in the SPL context due to specific issues. In addition, particular aspects regarding SPL are not covered by the existing SPL approaches, and when the aspects are covered, the literature just gives brief overviews. This scenario indicates that additional investigation, empirical and practical, should be performed. ConclusionThe results can help to understand the needs in SPL Testing, by identifying points that still require additional investigation, since important aspects regarding particular points of software product lines have not been addressed yet.


title: Failure analysis of parameter-induced simulation crashes in climate models

abstract: Abstract. Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.


title: Switchable constraints for robust pose graph SLAM

abstract: Current SLAM back-ends are based on least squares optimization and thus are not robust against outliers like data association errors and false positive loop closure detections. Our paper presents and evaluates a robust back-end formulation for SLAM using switchable constraints. Instead of proposing yet another appearance-based data association technique, our system is able to recognize and reject outliers during the optimization. This is achieved by making the topology of the underlying factor graph representation subject to the optimization instead of keeping it fixed. The evaluation shows that the approach can deal with up to 1000 false positive loop closure constraints on various datasets. This largely increases the robustness of the overall SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers.


title: CRAHNs : Cognitive radio ad hoc networks

abstract: Cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capabilities of the cognitive radio, will provide an ultimate spectrum-aware communication paradigm in wireless communications. CR networks, however, impose unique challenges due to the high fluctuation in the available spectrum as well as diverse quality-of-service (QoS) requirements. Specifically, in cognitive radio ad hoc networks (CRAHNs), the distributed multi-hop architecture, the dynamic network topology, and the time and location varying spectrum availability are some of the key distinguishing factors. In this paper, intrinsic properties and current research challenges of the CRAHNs are presented. First, novel spectrum management functionalities such as spectrum sensing, spectrum sharing, and spectrum decision, and spectrum mobility are introduced from the viewpoint of a network requiring distributed coordination. A particular emphasis is given to distributed coordination between CR users through the establishment of a common control channel. Moreover, the influence of these functions on the performance of the upper layer protocols, such as the network layer, and transport layer protocols are investigated and open research issues in these areas are also outlined. Finally, a new direction called the commons model is explained, where CRAHN users may independently regulate their own operation based on pre-decided spectrum etiquette.


title: A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network

abstract: A non-invasive Brain Computer Interface (BCI) based on a Convolutional Neural Network (CNN) is presented as a novel approach for navigation in Virtual Environment (VE). The developed navigation control interface relies on Steady State Visually Evoked Potentials (SSVEP), whose features are discriminated in real time in the electroencephalographic (EEG) data by means of the CNN. The proposed approach has been evaluated through navigation by walking in an immersive and plausible virtual environment (VE), thus enhancing the involvement of the participant and his perception of the VE. Results show that the BCI based on a CNN can be profitably applied for decoding SSVEP features in navigation scenarios, where a reduced number of commands needs to be reliably and rapidly selected. The participant was able to accomplish a waypoint walking task within the VE, by controlling navigation through of the only brain activity.


title: Adjustment following sexual abuse discovery: The role of shame and attributional style.

abstract: This study examined adjustment following sexual abuse as a function of shame and attributional style. One hundred forty-seven participants (83 children and 64 adolescents) were seen at the time of abuse discovery and again 1 year later. Once adjustment at abuse discovery was accounted for, shame and attribution style explained additional variation in subsequent adjustment, whereas abuse severity did not. A pessimistic attribution style at abuse discovery moderated the relation between severity of abuse and subsequent depressive symptoms and self-esteem. The relations between abuse severity and these outcomes were significant only at high levels of pessimistic attribution style. Of note, patterns of change in shame and attribution predicted which children remained at risk or improved in adjustment. In addition, age and gender differences were found in adjustment over time.


title: A Review of Active Filters for Power Quality Improvement

abstract: Active filtering of electric power has now become a mature technology for harmonic and reactive power compensation in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) AC power networks with nonlinear loads. This paper presents a comprehensive review of active filter (AF) configurations, control strategies, selection of components, other related economic and technical considerations, and their selection for specific applications. It is aimed at providing a broad perspective on the status of AF technology to researchers and application engineers dealing with power quality issues. A list of more than 200 research publications on the subject is also appended for a quick reference.


title: On the equivalences and differences of evolutionary algorithms

abstract: Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels.


title: Music and aggression: the impact of sexual-aggressive song lyrics on aggression-related thoughts, emotions, and behavior toward the same and the opposite sex.

abstract: Three studies examined the impact of sexual-aggressive song lyrics on aggressive thoughts, emotions, and behavior toward the same and the opposite sex. In Study 1, the authors directly manipulated whether male or female participants listened to misogynous or neutral song lyrics and measured actual aggressive behavior. Male participants who were exposed to misogynous song lyrics administered more hot chili sauce to a female than to a male confederate. Study 2 shed some light on the underlying psychological processes: Male participants who heard misogynous song lyrics recalled more negative attributes of women and reported more feelings of vengeance than when they heard neutral song lyrics. In addition, men-hating song lyrics had a similar effect on aggression-related responses of female participants toward men. Finally, Study 3 replicated the findings of the previous two studies with an alternative measure of aggressive behavior as well as a more subtle measure of aggressive cognitions. The results are discussed in the framework of the General Aggression Model.


title: Porcelain laminate veneers. A retrospective evaluation after 1 to 10 years of service: Part II--Clinical results.

abstract: Abstract Currently available clinical studies on porcelain laminate veneers seem to support their use as a standard treatment method in modern restorative dentistry. The purpose of this study, which comprises 3 parts, was to evaluate the clinical applicability and the survival probability of porcelain laminate veneers for an observation period of up to 10 years. This first part of the study describes the step-by-step clinical procedure. The reasons for provision of porcelain laminate veneers were mainly for the treatment of worn anterior teeth, surface enamel defects, intrinsic discolorations, and hypoplasia. As the treatment time for try-in, cementation, and finishing of veneers differs significantly from that needed for conventional restorations, the total time required for veneer treatment was evaluated. A total of 205 porcelain laminate veneers, 117 in the maxilla and 88 in the mandible, was placed in 72 patients (43 females and 29 males). Mean cementation time was 38.7 minutes, which increased significantly to 43.9 minutes if single discolored teeth were being restored. Clinical experience and a familiarity with the strict step-by-step procedure followed throughout the study reduced the mean treatment time from 40.8 minutes during the first 5 years to 36.7 minutes in the following years. The porcelain laminate veneer concept provides excellent esthetic and conservative restorations, but it should be stated that the cementation and finishing procedure is highly time consuming.


title: Exploiting Open Data to Improve the Business Intelligence & Business Discovery Experience

abstract: The extent to which data mining tools are able to make efficient use of an open data oriented strategy in a smart city is limited. In a sense that it is not fully automated, incompatible or has to be supervised. These sets of tools may offer the possibility to import a dataset in a certain predefined standardized format, still, they do not make it a part of their workflow and algorithms in a fully unsupervised manner (i.e without ongoing human guidance). In a departure from previous research works, in this paper, we present a middleware architecture that exploits open data as background knowledge by acting as a bridge between data mining tools and open data resources.


title: Performance evaluation of the 1st and 2nd generation Kinect for multimedia applications

abstract: Microsoft Kinect had a key role in the development of consumer depth sensors being the device that brought depth acquisition to the mass market. Despite the success of this sensor, with the introduction of the second generation, Microsoft has completely changed the technology behind the sensor from structured light to Time-Of-Flight. This paper presents a comparison of the data provided by the first and second generation Kinect in order to explain the achievements that have been obtained with the switch of technology. After an accurate analysis of the accuracy of the two sensors under different conditions, two sample applications, i.e., 3D reconstruction and people tracking, are presented and used to compare the performance of the two sensors.


title: An Information-Theoretic Optimality Principle for Deep Reinforcement Learning

abstract: We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly. We furthermore propose a novel scheduling scheme for this Lagrange multiplier to ensure efficient and robust learning. In experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and double deep Q-networks) in terms of both game-play performance and sample complexity. These results remain valid under the recently proposed dueling architecture.


title: Learning Policies for Partially Observable Environments: Scaling Up

abstract: Partially observable Markov decision processes (POMDP's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of POMDP's is motivated by a need to address realistic problems, existing techniques for finding optimal behavior do not appear to scale well and have been unable to find satisfactory policies for problems with more than a dozen states. After a brief review of POMDP's, this paper discusses several simple solution methods and shows that all are capable of finding near- optimal policies for a selection of extremely small POMDP'S taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We find that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains.


title: Applications of MPC and PI controls for liquid level control in coupled-tank systems

abstract: The coupled-Tank (CT) system remains an important tool for research by process control engineers. However, effective control of the system depends largely on the accuracy of the mathematical model that predicts its dynamic behavior. In this work, the nonlinear model for the CT system based on analytical technique has been investigated. Linear models for the CT system were obtained based on analytical and empirical techniques. Accuracy of the linear models were investigated based on integral absolute error (IAE), were empirical model was found to be more accurate. Proportional integral (PI) control systems based on Ziegler Nichols (ZN), Ciancone correlation (CCR), Cohen Coon (CC), IMC and pole placement (PP) tuning methods were designed for liquid level control in the CT system tank 2. IAE results showed that PI controller did not meet all the specified control objectives. To improve the response, a neural network feedforward controller was incorporated to the PI controller and the response was compared to that of a model predictive controller (MPC). Simulations results showed that both PI-plus-feedforward and MPC satisfied all control specifications. However, MPC response was more satisfactory in terms of disturbance handling and time response criteria.


title: The end of the end of ideology

abstract: The "end of ideology" was declared by social scientists in the aftermath of World War II. They argued that (a) ordinary citizens' political attitudes lack the kind of stability, consistency, and constraint that ideology requires; (b) ideological constructs such as liberalism and conservatism lack motivational potency and behavioral significance; (c) there are no major differences in content (or substance) between liberal and conservative points of view; and (d) there are few important differences in psychological processes (or styles) that underlie liberal versus conservative orientations. The end-of-ideologists were so influential that researchers ignored the topic of ideology for many years. However, current political realities, recent data from the American National Election Studies, and results from an emerging psychological paradigm provide strong grounds for returning to the study of ideology. Studies reveal that there are indeed meaningful political and psychological differences that covary with ideological self-placement. Situational variables-including system threat and mortality salience-and dispositional variables-including openness and conscientiousness-affect the degree to which an individual is drawn to liberal versus conservative leaders, parties, and opinions. A psychological analysis is also useful for understanding the political divide between "red states" and "blue states."


title: Using JIRA and Redmine in requirement development for agile methodology

abstract: Today, Agile development has become popular due to the ability to deliver fast and hence reduce cost. It is expected that by 2012, Agile development method will be utilized in 80% of all software development projects. Since 2010, some of software projects in MIMOS have adopted agile methodology. Therefore, this paper will focus on how agile methodology applied in MIMOS has influenced the requirement process. The paper will discuss on the benefits and challenges that MIMOS faced using JIRA and Redmine in requirement development.


An Overview of Checkpointing in Uniprocessor and Distributed Systems, Focusing on Implementation and Performance


Development of a New Animal Component-Free , Protein-Free Medium for Sf 21 and Sf 9 Insect Cell Lines


title: Statically detecting use after free on binary code

abstract: We present GUEB a static tool detecting Use after Free vulnerabilities on disassembled code. This tool has been evaluated on a real vulnerability in the ProFTPD application (CVE-2011-4130).


title: Facial expression recognition using general regression neural network

abstract: Automated facial expression recognition using machine learning techniques is a preliminary step for the future work on the automated emotion detection systems to be used in the real world. In this paper, facial expression recognition using Efficient Local Binary Pattern (LBP) for feature extraction and artificial neural network (ANN) for classification is presented. Facial feature vectors are obtained using Efficient LBP algorithm by considering the blocks of four varied sizes (256 x 256, 128 x 128, 64 x 64 and 32 x 32) of the image dataset. The multiclass classification of the image dataset into the six basic universal emotions is carried out using ANN. We have implemented General Regression Neural Network (GRNN) for classification in this experiment. GRNN trains the network faster and does not require iterative training procedure. The free parameter of the network, spread constant is optimized to reduce the mean square error and thus the efficiency of recognition is improved as seen from the experimental results. The proposed algorithm is tested using widely used standard database such as Japanese Female Facial Expression Database, Taiwanese Facial Expression Database, Cohn-Kanade Expression Database and Indian face database of students. The proposed algorithm with the optimum window sizes of 64x64 improves the recognition rate.


title: Classification performance analysis of MNIST Dataset utilizing a Multi-resolution Technique

abstract: Here, we propose a method for recognition of handwritten English digit utilizing discrete cosine space-frequency transform known as the Discrete Cosine S-Transform (DCST). Experiments have been conducted on the publicly availabe standard MNIST handwritten digit database. The DCST features along with an Artificial Neural Network (ANN) classifier is utilized for solving the classification issues of written by hand digit. The Discrete Cosine S-Transform coefficients are extracted from the standard images of MNIST handwritten isolated digit database. The database consists of a total of 70000 including 60000 training samples and 10000 test samples. To overcome the computational overhead, we have normalized all the images of the MNIST dataset from 28 × 28 to 20 × 20 image size by eliminating the unsought boundary pixels up to width four. Further, the classification of digits has been made by using a back propagation neural network (BPNN). This work has achieved precisely 98.8% of success rate for MNIST database.


title: Consumer RGB-D Cameras and their Applications

abstract: Introduction In November 2010, Microsoft released the Kinect RGB-D sensor as a new Natural User Interface (NUI) for its XBOX 360 gaming platform. The Kinect, like other RGB-D sensors, provides color information as well as the estimated depth for each pixel. At $150, the Kinect is an order of magnitude cheaper than similar sensors that had existed before it. This has dramatically reinvigorated interest in RGB-D sensors and their applications in areas such as Natural User Interfaces, reconstruction and virtual reality, or 3D mapping.


IMPROVING SOFTWARE QUALITY THROUGH DESIGN PATTERNS : A CASE STUDY OF ADAPTIVE GAMES AND AUTO DYNAMIC DIFFICULTY


title: Identifying Careless Responses in Survey Data.

abstract: When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been suggested for identifying careless respondents via indices computed from the data, yet almost no prior work has examined the relationships among these indicators or the types of data patterns identified by each. In 2 studies, we examined several methods for identifying careless responses, including (a) special items designed to detect careless response, (b) response consistency indices formed from responses to typical survey items, (c) multivariate outlier analysis, (d) response time, and (e) self-reported diligence. Results indicated that there are two distinct patterns of careless response (random and nonrandom) and that different indices are needed to identify these different response patterns. We also found that approximately 10%–12% of undergraduates completing a lengthy survey for course credit were identified as careless responders. In Study 2, we simulated data with known random response patterns to determine the efficacy of several indicators of careless response. We found that the nature of the data strongly influenced the efficacy of the indices to identify careless responses. Recommendations include using identified rather than anonymous responses, incorporating instructed response items before data collection, as well as computing consistency indices and multivariate outlier analysis to ensure high-quality data.


title: RFID-enabled Target Tracking and Following with a Mobile Robot Using Direction Finding Antennas

abstract: A stand-alone direction finding RFID reader is developed for mobile robot applications employing a dual-directional antenna. By adding search and localization capabilities to the current state of RFID technology, robots will be able to acquire and dock to a static target in a real environment without requiring a map or landmarks. Furthermore, we demonstrate RFID-enabled tracking and following of a target moving unpredictably with a mobile robot. The RFID reader keeps the robot aware of the direction of arrival (DOA) of the signal of interest toward which the dual-directional antenna faces the target transponder. The simulation results show that the proposed RFID system can track in real time the movement of the target transponder. To verify the effectiveness of the system in a real environment, we perform a variety of experiments in a hallway including target tracking and following with a commercial mobile robot.


title: On The Limits of Steganography

abstract: In this paper, we clarify what steganography is and what it can do. We contrast it with the related disciplines of cryptography and traffic security, present a unified terminology agreed at the first international workshop on the subject, and outline a number of approaches-many of them developed to hide encrypted copyright marks or serial numbers in digital audio or video. We then present a number of attacks, some new, on such information hiding schemes. This leads to a discussion of the formidable obstacles that lie in the way of a general theory of information hiding systems (in the sense that Shannon gave us a general theory of secrecy systems). However, theoretical considerations lead to ideas of practical value, such as the use of parity checks to amplify covertness and provide public key steganography. Finally, we show that public key information hiding systems exist, and are not necessarily constrained to the case where the warden is passive.


title: Programmable aperture photography: multiplexed light field acquisition

abstract: In this paper, we present a system including a novel component called programmable aperture and two associated post-processing algorithms for high-quality light field acquisition. The shape of the programmable aperture can be adjusted and used to capture light field at full sensor resolution through multiple exposures without any additional optics and without moving the camera. High acquisition efficiency is achieved by employing an optimal multiplexing scheme, and quality data is obtained by using the two post-processing algorithms designed for self calibration of photometric distortion and for multi-view depth estimation. View-dependent depth maps thus generated help boost the angular resolution of light field. Various post-exposure photographic effects are given to demonstrate the effectiveness of the system and the quality of the captured light field.


title: Single-Phase Inverter Drive for Interior Permanent Magnet Machines

abstract: This paper proposes a novel drive method for interior permanent magnet machines (IPMMs). In the proposed system, a two-phase IPMM is fed from a single-phase H-bridge inverter for low-cost applications. Its main winding in d -axis is connected to the inverter and the auxiliary winding in q -axis is inherently short circuited. This configuration enables the motor to operate in a wider speed range with higher energy efficiency than most other single-phase drive systems. Also, its torque capability in the high-speed range is as high as that of the three-phase IPMM drive despite the reduced power devices, drivers, and current sensors. Accordingly, the proposed system has benefits such as simple structure, low cost, high-power density, and improved efficiency. In this paper, the system is modeled with an equivalent three-phase machine. Based on the model, its torque characteristics and operating areas are analyzed and compared to those of the conventional IPMM drives. A speed control algorithm is also developed. It includes a maximum torque per ampere control to improve drive efficiency and a flux-weakening control to maximize the feasible inverter output voltage. The proposed system and control algorithm are verified by experimental results.


title: An Integrated Deep Learning Framework Approach for Nail Disease Identification

abstract: Nail Diseases refer to some kind of deformity in the nail unit. Although the nail unit is a skin accessory, it has its own distinct class of diseases as these diseases have their own set of signs, symptoms, causes and effects that may or may not relate to other medical conditions. Recognizing nail diseases still remains an unexplored and a challenging endeavor in itself. This paper proposes a novel deep learning framework to detect and classify nail diseases from images. A distinct class of eleven diseases i.e. onychomycosis, subungulal hematoma, beau's lines, yellow nail syndrome, psoriasis, hyperpigmentation, koilonychias, paroncychia, pincer nails, leukonychia, and onychorrhexis. The framework uses a hybrid of Convolutional Neural Network (CNNs) for feature extraction. Due to the non-existence of a meticulous dataset, a new dataset was built for testing the enactment of our proposed framework. This work has been tested on our dataset and has also been compared with other state-of-the-art algorithms (SVM, ANN, KNN, and RF) that have been shown to have an excelled performance in the area of feature extraction. The results have shown a comparable performance, in terms of differentiating amongst the wide spectrum of nail diseases and are able to recognize them with an accuracy of 84.58%.


title: Objectives, criteria and methods for the design of the SmartHand transradial prosthesis

abstract: This paper presents the requirements, design criteria and methodology used to develop the design of a new self-contained prosthetic hand to be used by transradial amputees. The design is based on users' needs, on authors background and knowledge of the state of the art, and feasible fabrication technology with the aim of replicating as much as possible the functionality of the human hand. The paper focuses on the design approach and methodology which is divided into three steps: (i) the mechanical actuation units, design and actuation distribution; (ii) the mechatronic development and finally (iii) the controller architecture design. The design is presented here and compared with significant commercial devices and research prototypes.


title: Prevalence of kidney stones in the United States.

abstract: BACKGROUND ::: The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history. ::: ::: ::: OBJECTIVE ::: Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110). ::: ::: ::: OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS ::: Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones. ::: ::: ::: RESULTS AND LIMITATIONS ::: The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001; Hispanic: OR: 0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones. ::: ::: ::: CONCLUSIONS ::: Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones.


title: Emotional Regulation and Depression: A Potential Mediator between Heart and Mind

abstract: A narrative review of the major evidence concerning the relationship between emotional regulation and depression was conducted. The literature demonstrates a mediating role of emotional regulation in the development of depression and physical illness. Literature suggests in fact that the employment of adaptive emotional regulation strategies (e.g., reappraisal) causes a reduction of stress-elicited emotions leading to physical disorders. Conversely, dysfunctional emotional regulation strategies and, in particular, rumination and emotion suppression appear to be influential in the pathogenesis of depression and physiological disease. More specifically, the evidence suggests that depression and rumination affect both cognitive (e.g., impaired ability to process negative information) and neurobiological mechanisms (e.g., hypothalamic pituitary adrenal axis overactivation and higher rates of cortisol production). Understanding the factors that govern the variety of health outcomes that different people experience following exposure to stress has important implications for the development of effective emotion-regulation interventional approaches (e.g., mindfulness-based therapy, emotion-focused therapy, and emotion regulation therapy).


title: Abstract Oxytocin is associated with human trustworthiness

abstract: Human beings exhibit substantial interpersonal trust—even with strangers. The neuroactive hormone oxytocin facilitates social recognition in animals, and we examine if oxytocin is related to trustworthiness between humans. This paper reports the results of an experiment to test this hypothesis, where trust and trustworthiness are measured using the sequential anonymous ‘‘trust game’’ with monetary payoffs. We find that oxytocin levels are higher in subjects who receive a monetary transfer that reflects an intention of trust relative to an unintentional monetary transfer of the same amount. In addition, higher oxytocin levels are associated with trustworthy behavior (the reciprocation of trust). Absent intentionality, both the oxytocin and behavioral responses are extinguished. We conclude that perceptions of intentions of trust affect levels of circulating oxytocin.


title: Jan Mikulicz-Radecki (1850–1905): a fundamental contributor to world surgery; surgeon of the head, neck, and esophagus

abstract: We present some of many valuable and unique achievements of Jan Mikulicz-Radecki with special regard to his contribution to laryngology. He constructed esophagogastroscope, and was one of the first to perform endoscopy of esophagus and ventricle. He published several papers describing new approaches to maxillary sinus through inferior meatus, surgical management of tonsillar cancer via lateral pharyngotomy, correction of post-traumatic nasal deformations, and the use of iodophorm in healing wounds. Among Mikulicz’s many celebrated scientific achievements, the most important remains the development of asepsis and creation of a surgical school, which was a modernized continuation of Langenbeck–Billroth achievements.


title: Predicting urban design effects on physical activity and public health: A case study.

abstract: With increasing global concerns about obesity and related health effects, tools to predict how urban form affects population physical activity and health are needed. However, such tools have not been well established. This article develops a computer simulation model for forecasting the health effects of urban features that promote walking. The article demonstrates the model using a proposed small-area plan for a neighborhood of 10,400 residents in Raleigh, North Carolina, one of the fastest-growing and most sprawling U.S. cities. The simulation model predicts that the plan would increase average daily time spent walking for transportation by 17 min. As a result, annual deaths from all causes are predicted to decrease by 5.5%. Annual new cases of diabetes, coronary heart disease, stroke, and hypertension are predicted to decline by 1.9%, 2.3%, 1.3%, and 1.6%, respectively. The present value of these health benefits is $21,000 per resident.


title: AssistOn-Knee: A self-aligning knee exoskeleton

abstract: We present kinematics, actuation, detailed design, characterization results and initial user evaluations of AssistOn-Knee, a novel self-aligning active exoskeleton for robot-assisted knee rehabilitation. AssistOn-Knee can, not only assist flexion/extension movements of the knee joint but also accommodate its translational movements in the sagittal plane. Automatically aligning its joint axes, AssistOn-Knee enables an ideal match between human knee axis and the exoskeleton axis, guaranteeing ergonomy and comfort throughout the therapy. Self-aligning feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time spent on exercises. The proposed exoskeleton actively controls the rotational degree of freedom of the knee through a Bowden cable-driven series elastic actuator, while the translational movements of the knee joints are passively accommodated through use of a 3 degrees of freedom planar parallel mechanism. AssistOn-Knee possesses a lightweight and compact design with significantly low apparent inertia, thanks to its Bowden cable based transmission that allows remote location of the actuator and reduction unit. Furthermore, thanks to its series-elastic actuation, AssistOn-Knee enables high-fidelity force control and active backdrive-ability within its control bandwidth, while featuring passive elasticity for excitations above this bandwidth, ensuring safety and robustness throughout the whole frequency spectrum.


title: Safe distributed motion coordination for second-order systems with different planning cycles

abstract: When multiple robots operate in the same environment, it is desirable for scalability purposes to coordinate their motion in a distributed fashion while providing guarantees about their safety. If the robots have to respect second-order dynamics, this becomes a challenging problem, even for static environments. This work presents a replanning framework where each robot computes partial plans during each cycle, while executing a previously computed trajectory. Each robot communicates with its neighbors to select a trajectory that is safe over an infinite time horizon. The proposed approach does not require synchronization and allows the robots to dynamically change their cycles over time. This paper proves that the proposed motion coordination algorithm guarantees safety under this general setting. This framework is not specific to the underlying robot dynamics, as it can be used with a variety of dynamical systems, nor to the planning or control algorithm used to generate the robot trajectories. The performance of the approach is evaluated using a distributed multi-robot simulator on a computing cluster, where the simulated robots are forced to communicate by exchanging messages. The simulation results confirm the safety of the algorithm in various environments with up to 32 robots governed by second-order dynamics.


title: Multiple-Query Optimization of Regular Path Queries

abstract: Graph databases have become increasingly important with the rise of social networks, and with the growth of the Semantic Web and characterization of biological networks. Regular path queries (RPQs) are a way to explore path patterns in graphs which have become a standard method to explore graph databases. SPARQL 1.1 includes property paths, and so now encompasses RPQs as a fragment. In many environments, such as visual query systems (VQSs), the RPQs are generated visually which may contain many commonalities that can be optimized globally. We introduce SWARMGUIDE, a framework for optimizing multiple regular path queries. The framework detects commonalities among the RPQs, in order to find an execution plan that is globally optimized over the plan spaces of the constituent RPQs.


title: Reporting bias and knowledge acquisition

abstract: Much work in knowledge extraction from text tacitly assumes that the frequency with which people write about actions, outcomes, or properties is a reflection of real-world frequencies or the degree to which a property is characteristic of a class of individuals. In this paper, we question this idea, examining the phenomenon of reporting bias and the challenge it poses for knowledge extraction. We conclude with discussion of approaches to learning commonsense knowledge from text despite this distortion.


title: Neuroimaging of Cerebral Ischemia and Infarction

abstract: The imaging workup for patients with suspected acute ischemic stroke has advanced significantly over the past few years. Evaluation is no longer limited to noncontrast computed tomography, but now frequently also includes vascular and perfusion imaging. Although acute stroke imaging has made significant progress in the last few decades with the development of multimodal approaches, there are still many unanswered questions regarding their appropriate use in the setting of daily patient care. It is important for all physicians taking care of stroke patients to be familiar with current multimodal computed tomography and magnetic resonance imaging techniques, including their strengths, limitations, and their role in guiding therapy.


title: Modeling temporal effects of human mobile behavior on location-based social networks

abstract: The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.


title: Kerfuffle: a web tool for multi-species gene colocalization analysis

abstract: The evolutionary pressures that underlie the large-scale functional organization of the genome are not well understood in eukaryotes. Recent evidence suggests that functionally similar genes may colocalize (cluster) in the eukaryotic genome, suggesting the role of chromatin-level gene regulation in shaping the physical distribution of coordinated genes. However, few of the bioinformatic tools currently available allow for a systematic study of gene colocalization across several, evolutionarily distant species. Kerfuffle is a web tool designed to help discover, visualize, and quantify the physical organization of genomes by identifying significant gene colocalization and conservation across the assembled genomes of available species (currently up to 47, from humans to worms). Kerfuffle only requires the user to specify a list of human genes and the names of other species of interest. Without further input from the user, the software queries the e!Ensembl BioMart server to obtain positional information and discovers homology relations in all genes and species specified. Using this information, Kerfuffle performs a multi-species clustering analysis, presents downloadable lists of clustered genes, performs Monte Carlo statistical significance calculations, estimates how conserved gene clusters are across species, plots histograms and interactive graphs, allows users to save their queries, and generates a downloadable visualization of the clusters using the Circos software. These analyses may be used to further explore the functional roles of gene clusters by interrogating the enriched molecular pathways associated with each cluster.


title: Evidence based guidelines for complex regional pain syndrome type 1

abstract: BackgroundTreatment of complex regional pain syndrome type I (CRPS-I) is subject to discussion. The purpose of this study was to develop multidisciplinary guidelines for treatment of CRPS-I.MethodA multidisciplinary task force graded literature evaluating treatment effects for CRPS-I according to their strength of evidence, published between 1980 to June 2005. Treatment recommendations based on the literature findings were formulated and formally approved by all Dutch professional associations involved in CRPS-I treatment.ResultsFor pain treatment, the WHO analgesic ladder is advised with the exception of strong opioids. For neuropathic pain, anticonvulsants and tricyclic antidepressants may be considered. For inflammatory symptoms, free-radical scavengers (dimethylsulphoxide or acetylcysteine) are advised. To promote peripheral blood flow, vasodilatory medication may be considered. Percutaneous sympathetic blockades may be used to increase blood flow in case vasodilatory medication has insufficient effect. To decrease functional limitations, standardised physiotherapy and occupational therapy are advised. To prevent the occurrence of CRPS-I after wrist fractures, vitamin C is recommended. Adequate perioperative analgesia, limitation of operating time, limited use of tourniquet, and use of regional anaesthetic techniques are recommended for secondary prevention of CRPS-I.ConclusionsBased on the literature identified and the extent of evidence found for therapeutic interventions for CRPS-I, we conclude that further research is needed into each of the therapeutic modalities discussed in the guidelines.


title: Dual-band RFID tag antenna with EBG for glass objects

abstract: The dual-band tag antenna designed for glass objects of HF-RFID (13.56 MHz) and UHF-RFID (920–925 MHz) applications along national standard of Thailand is proposed. The tag antenna has the same size with a credit card. The overall dimension of the tag antenna with EBG is 147 mm × 73.5 mm × 8.3 mm. The purpose of this antenna design is to implement with the RFID for the highway tracking and tolls systems. The tag antenna and EBG are designed on FR-4 substrate with thickness of 0.3 mm and 3 mm, respectively. To achieve the dual band operation, the designed tag antenna with EBG structure is based on the loop (spiral coil), the meander-line structure with double T-matching and mushroom-like EBG structure for HF-, UHF-RFID and EBG, respectively.


title: Does God Class Decomposition Affect Comprehensibility?

abstract: Continuous alterations and extensions of a software system introduce so called god classes, accumulating ever more responsibilities. As god classes make essential steps in program comprehension harder, it is expected that effective and efficient techniques to resolve them will facilitate future maintenance tasks. This work reports on a laboratory experiment with 63 computer science students, in which we verified whether the decomposition of a god class using well-known refactorings can affect comprehensibility of the relevant code part. Five alternative god class decompositions were derived through application of refactorings, by which the responsibilities of a natural god class were increasingly split into a number of collaborating classes. Our results indicate that the derived class decompositions differed significantly with regard to the ability of students to map attributes in the class hierarchy to descriptions of the problem domain. Moreover, this effect has been found to interact with the institution in which the participants were enrolled, confirming that comprehensibility is a subjective notion for which we have to take into account people’s skills and expectations. This work indicates that improving comprehensibility is within the grasp of a single maintainer preparing for future change requests by redistributing the responsibilities of a god class using well-known refactorings.


title: 3D video fragments: dynamic point samples for real-time free-viewpoint video

abstract: Abstract We present 3D video fragments, a dynamic point sample framework for real-time free-viewpoint video. By generalizing 2D video pixels towards 3D irregular point samples we combine the simplicity of conventional 2D video processing with the power of more complex polygonal representations for free-viewpoint video. We propose a differential update scheme exploiting the spatio-temporal coherence of the video streams of multiple cameras. Updates are issued by operators such as inserts and deletes accounting for changes in the input video images. The operators from multiple cameras are processed, merged into a 3D video stream and transmitted to a remote site. We also introduce a novel concept for camera control which dynamically selects the set of relevant cameras for reconstruction. Moreover, it adapts to the processing load and rendering platform. Our framework is generic in the sense that it works with any real-time 3D reconstruction method which extracts depth from images. The video renderer displays free-viewpoint videos using an efficient point-based splatting scheme and makes use of state-of-the-art vertex and pixel processing hardware for real-time visual processing.


title: Genetic Variability, Differentiation, and Founder Effect in Golden Jackals (Canis aureus) from Serbia as Revealed by Mitochondrial DNA and Nuclear Microsatellite Loci

abstract: We analyzed 121 golden jackals (Canis aureus) from six sample sites in Serbia with regard to genetic variability and differentiation as revealed by mitochondrial control region sequences and eight nuclear microsatellite loci. There was no variation at all in the mtDNA sequences, and nuclear variability was very low (average observed and expected heterozygosity of 0.29 and 0.34, respectively). This is in line with the considerable recent range expansion of this species in the Balkans and indicates a strong founder effect in the recently established Serbian population. We did not find evidence of differentiation between the northeastern jackals and those from the plain of Srem or those in between. F-statistics and Bayesian Structure analyses, however, were indicative of a low degree of overall differentiation in the Serbian population. A vagrant Austrian jackal that was also analyzed was genetically indistinguishable from its Serbian conspecifics.


title: Auditory display design for exploration in mobile audio-augmented reality

abstract: In this paper, we compare four different auditory displays in a mobile audio-augmented reality environment (a sound garden). The auditory displays varied in the use of non-speech audio, Earcons, as auditory landmarks and 3D audio spatialization, and the goal was to test the user experience of discovery in a purely exploratory environment that included multiple simultaneous sound sources. We present quantitative and qualitative results from an initial user study conducted in the Municipal Gardens of Funchal, Madeira. Results show that spatial audio together with Earcons allowed users to explore multiple simultaneous sources and had the added benefit of increasing the level of immersion in the experience. In addition, spatial audio encouraged a more exploratory and playful response to the environment. An analysis of the participants' logged data suggested that the level of immersion can be related to increased instances of stopping and scanning the environment, which can be quantified in terms of walking speed and head movement.


title: Improving Event Detection with Abstract Meaning Representation

abstract: Event Detection (ED) aims to identify instances of specified types of events in text, which is a crucial component in the overall task of event extraction. The commonly used features consist of lexical, syntactic, and entity information, but the knowledge encoded in the Abstract Meaning Representation (AMR) has not been utilized in this task. AMR is a semantic formalism in which the meaning of a sentence is encoded as a rooted, directed, acyclic graph. In this paper, we demonstrate the effectiveness of AMR to capture and represent the deeper semantic contexts of the trigger words in this task. Experimental results further show that adding AMR features on top of the traditional features can achieve 67.8% (with 2.1% absolute improvement) F-measure (F1), which is comparable to the state-of-the-art approaches.


Probabilistic Alert Correlation


title: Towards self-assembled structures with mobile climbing robots

abstract: Social insects have evolved to self-assemble ad-hoc structures from their bodies to quickly adapt to unexpected obstacles and situations. Inspired by these natural systems, we present an autonomous tread-based robot which is capable of using its own body as a building block for assembling structures. We analytically assess the optimality of the robot design, and experimentally test its ability to climb over like robots under varying conditions. Finally, using a simple self-assembly algorithm relying on only local sensing, robot prototypes are used to demonstrate the self-assembly of a 2D pyramid structure.


title: Learning hierarchical sparse features for RGB-(D) object recognition

abstract: Recently introduced RGB-D cameras are capable of providing high quality synchronized videos of both color and depth. With its advanced sensing capabilities, this technology represents an opportunity to significantly increase the capabilities of object recognition. It also raises the problem of developing expressive features for the color and depth channels of these sensors. In this paper we introduce hierarchical matching pursuit (HMP) for RGB-D data. As a multi-layer sparse coding network, HMP builds feature hierarchies layer by layer with an increasing receptive field size to capture abstract representations from raw RGB-D data. HMP uses sparse coding to learn codebooks at each layer in an unsupervised way and builds hierarchical feature representations from the learned codebooks in conjunction with orthogonal matching pursuit, spatial pooling and contrast normalization. Extensive experiments on various datasets indicate that the features learned with our approach enable superior object recognition results using linear support vector machines.


title: The sociological study of stress.

abstract: This paper presents a critical overview of current concepts and analytic practices in stress research and considers how they can be changed to make the research more consistent with core sociological interests. An overarching concern of the paper is the analytic use of basic information about people's social and institutional affiliations and statuses. It is important that such information be treated not simply as data that need to be controlled statistically; we must examine the bearing of these data on each domain of the stress process: the exposure to and meaning of stressors, access to stress mediators, and the psychological, physical, and behavioral manifestations of stress. The conceptualization and measurement of stressors should move away from their focus on particular events or chronic strains and should seek instead to observe and assess over time constellations of stressors made up of both events and strains. Moreover, the effects of the mediators-coping and social support-are evaluated most fruitfully in terms of their effects in limiting the number, severity, and diffusion of stressors in these constellations. Finally, sociological stress researchers should not be bound to outcomes that better serve the intellectual interests of those who work with biomedical and epidemiological models of stress, nor should the research be committed exclusively to a single outcome.


title: The Dark Side Of Lean Construction: Exploitation And Ideology

abstract: The emerging concept of lean construction is considered from an overtly critical perspective. It is contended that the current debate is based on a highly selective interpretation of the available literature. The extent to which methods of lean production are applicable beyond the Japanese context remains hotly debated. An extensive body of critical opinion argues that the application of lean methods depends upon the hegemony of management over labour. Whilst the lean rhetoric of flexibility, quality and teamwork is persuasive, critical observers claim that it translates in practice to control, exploitation and surveillance. The accepted research agenda for lean construction is primarily confined to the limited domain of instrumental rationality. Little attention has been directed at the externalities that lie beyond narrow definitions of technical efficiency. In this respect, the activities of construction researchers are seemingly shaped and controlled by the prevailing ideology of neoliberalism. The funding agencies continually exert an insidious pressure to generate outputs that are ‘relevant’ to the needs of industry. The result is an inevitable conservatism whereby the only research that is valued is that which preserves the status quo. This widespread failure to challenge the propaganda that shapes and controls the research community serves to dis-empower the workforce and erode the industry’s intellectual capital. Industry and society at large are becoming increasingly impoverished as dogma triumphs over thoughtfulness.


title: Fatty acid-binding proteins as plasma markers of tissue injury.

abstract: BACKGROUND ::: One of the novel and promising plasma markers for detection of tissue injury is the family of 15 kDa cytoplasmic fatty acid-binding proteins of which various tissue-specific types occur. ::: ::: ::: AIMS AND OBJECTIVES ::: The present status of heart-type fatty acid-binding protein (H-FABP) as a diagnostic and prognostic marker for acute and chronic cardiac injury, as well as the preliminary diagnostic use of other types of FABP for detecting injury in other organs, is reviewed. ::: ::: ::: METHODS ::: This review is based on an overview of the literature on clinical diagnostics of various forms of organ injury, and uses additional literature on physiological aspects relevant for the interpretation of plasma marker concentrations. ::: ::: ::: RESULTS ::: H-FABP not only proves to be an excellent early marker for cardiac injury in acute coronary syndromes, but also allows detection of minor myocardial injury in heart failure and unstable angina. Preliminary results indicate that sensitivity, rule-out power and prognostic value of H-FABP in cardiac injury surpass the performance of the standard early marker myoglobin. The liver only contains liver-type FABP (L-FABP), but co-expression of H-FABP and L-FABP occurs in the kidney. Similarly, intestinal-type FABP (I-FABP) and L-FABP are found in intestines, and brain-type FABP (B-FABP) and H-FABP occur in the brain. Preliminary but promising applications of these proteins have been demonstrated for liver rejection, viability selection of kidneys from non-heart-beating donors (NHBD), inflammatory and ischemic bowel disease, traumatic brain injury and in the prevention of muscle injury in trained athletes. ::: ::: ::: CONCLUSIONS ::: Further study of the diagnostic and prognostic use of various FABP types is warranted, but their clinical application will require further commercialization of automated and rapid assays.


Chapter 30. Nursing Workload and Patient Safety—A Human Factors Engineering Perspective


title: A novel approach to face recognition under various facial expressions, occlusion and tilt angles

abstract: The proposed method on face recognition have been focused on color co-occurrence matrix approach & principal component analysis — applies PCA only on CCM classified Images. Traditionally, to recognize the human face, PCA is performed on the whole facial image database, whereas in the proposed method, Color Co-occurrence is used to extract the certain features of an image with different attribute levels, and PCA is applied only for the classified images to recognize face images under various facial expressions, occlusion and tilt angles. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and less computational time for different facial expressions; further, the proposed method is analyzed with different orientations of face angles which reduces the computational load significantly when the image database is large.


title: Door recognition and deep learning algorithm for visual based robot navigation

abstract: In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient scenes can be recognized and useful information such as the location of door can be identified. The extracted information can be used for robot navigation, so does the robot can approach the target accurately. In the field experiments, detecting doors and predicting the door poses such tasks are designed in the indoor environment to verify the proposed method. The experimental results demonstrate that the doors can be identified with good performance and the deep learning model is suitable for robot navigation.


title: Efficient Video Rectification and Stabilisation for Cell-Phones

abstract: This article presents a method for rectifying and stabilising video from cell-phones with rolling shutter (RS) cameras. Due to size constraints, cell-phone cameras have constant, or near constant focal length, making them an ideal application for calibrated projective geometry. In contrast to previous RS rectification attempts that model distortions in the image plane, we model the 3D rotation of the camera. We parameterise the camera rotation as a continuous curve, with knots distributed across a short frame interval. Curve parameters are found using non-linear least squares over inter-frame correspondences from a KLT tracker. By smoothing a sequence of reference rotations from the estimated curve, we can at a small extra cost, obtain a high-quality image stabilisation. Using synthetic RS sequences with associated ground-truth, we demonstrate that our rectification improves over two other methods. We also compare our video stabilisation with the methods in iMovie and Deshaker.


title: Effects of Medical Interventions on Gender Dysphoria and Body Image: A Follow-Up Study

abstract: Objective The aim of this study from the European Network for the Investigation of Gender Incongruence is to investigate the status of all individuals who had applied for gender confirming interventions from 2007 to 2009, irrespective of whether they received treatment. The current article describes the study protocol, the effect of medical treatment on gender dysphoria and body image, and the predictive value of (pre)treatment factors on posttreatment outcomes. Methods Data were collected on medical interventions, transition status, gender dysphoria (Utrecht Gender Dysphoria Scale), and body image (Body Image Scale for transsexuals). In total, 201 people participated in the study (37% of the original cohort). Results At follow-up, 29 participants (14%) did not receive medical interventions, 36 hormones only (18%), and 136 hormones and surgery (68%). Most transwomen had undergone genital surgery, and most transmen chest surgery. Overall, the levels of gender dysphoria and body dissatisfaction were significantly lower at follow-up compared with clinical entry. Satisfaction with therapy responsive and unresponsive body characteristics both improved. High dissatisfaction at admission and lower psychological functioning at follow-up were associated with persistent body dissatisfaction. Conclusions Hormone-based interventions and surgery were followed by improvements in body satisfaction. The level of psychological symptoms and the degree of body satisfaction at baseline were significantly associated with body satisfaction at follow-up.


title: End-to-End Scene Text Recognition in Videos Based on Multi Frame Tracking

abstract: Text detection and recognition in scene images and videos attract much attention in computer vision recently. However, most existing text detection and recognition methods only focus on static images. In this paper an end-to-end scene text recognition method based on multi frame tracking is proposed for text in videos, in which temporal information is employed to improve performance. First, an end-to-end text recognition method based on a unified deep neural network is used to detect and recognize text in each frame of the input video. Then, multi frame text tracking is employed through associations of texts in current frame and several previous frames to obtain final results. Experiments on ICDAR datasets demonstrate that the proposed method outperforms the state-of-the-art methods in end-to-end video text recognition.


title: Thermal modeling and design on smartphones with heat pipe cooling technique

abstract: While the performance of smartphones becomes much higher, the application processor consumes considerable power. Thus, it is hard to meet thermal constraints by using conventional cooling techniques. Fortunately, since heat pipes can efficiently transfer the thermal energy from hot regions to cool regions, temperatures in hot regions can be reduced greatly. Hence, in the past three years, the heat pipe cooling techniques have been applied to smartphones by industries. However, although the time-consuming commercial simulation tools, such as ANSYS Fluent, can provide accurate thermal maps, they may lead to inefficiency during design stages. Besides, the compact thermal model for bended heat pipes is still underdeveloped. Therefore, efficient thermal simulation for smartphones with bended heat pipes should be developed for the design stage. Furthermore, the routing of bended heat pipe should be optimized to obtain more thermal energy transfer. This work presents a compact thermal model for bended heat pipes and an efficient thermal simulator for smartphones. Moreover, with inconsistent maze routing and dynamic thermal weight assignment, a thermal-driven routing algorithm is proposed for optimizing the design of bended heat pipe. Compared with ANSYS Fluent, the thermal simulation with bended heat pipe thermal model can achieve at least three orders of magnitude speedup with only 3.58% maximum error for all chips. The proposed heat pipe routing algorithm can reduce at least 10.86% of the maximum temperatures for application processors.


title: AutonoVi: Autonomous vehicle planning with dynamic maneuvers and traffic constraints

abstract: We present AutonoVi, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and integrates traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.


title: Discriminative Cellets Discovery for Fine-Grained Image Categories Retrieval

abstract: Fine-grained image categories recognition is a challenging task aiming at distinguishing objects belonging to the same basic-level category, such as leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and etc. Most of the existing methods have difficulties to automatically detect discriminative object components. In this paper, we propose a new fine-grained image categorization model that can be deemed as an improved version spatial pyramid matching (SPM). Instead of the conventional SPM that enumeratively conducts cell-to-cell matching between images, the proposed model combines multiple cells into cellets that are highly responsive to object fine-grained categories. In particular, we describe object components by cellets that connect spatially adjacent cells from the same pyramid level. Straightforwardly, image categorization can be casted as the matching between cellets extracted from pairwise images. Toward an effective matching process, a hierarchical sparse coding algorithm is derived that represents each cellet by a linear combination of the basis cellets. Further, a linear discriminant analysis (LDA)-like scheme is employed to select the cellets with high discrimination. On the basis of the feature vector built from the selected cellets, fine-grained image categorization is conducted by training a linear SVM. Experimental results on the Caltech-UCSD birds, the Leeds butterflies, and the COSMIC insects data sets demonstrate our model outperforms the state-of-the-art. Besides, the visualized cellets show discriminative object parts are localized accurately.


title: Self-Affirmation and Self-Control: Affirming Core Values Counteracts Ego Depletion

abstract: Research has established that acts of self-control deplete a resource required for subsequent self-control tasks. The present investigation revealed that a psychological intervention-self-affirmation-facilitates self-control when the resource has been depleted. Experiments 1 and 2 found beneficial effects of self-affirmation on self-control in a depleted state. Experiments 3 and 4 suggested that self-affirmation improves self-control by promoting higher levels (vs. lower levels) of mental construal. Self-affirmation therefore holds promise as a mental strategy that reduces the likelihood of self-control failure.


title: Autonomous sign reading for semantic mapping

abstract: We consider the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAM-generated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building. Deploying such a system using current text recognition systems, however, is difficult since even state-of-the-art systems have difficulty reading text from non-document images. Despite these difficulties we present a series of additions to the typical mapping pipeline that nevertheless allow us to create highly usable results. In fact, we show how our text detection and recognition system, combined with several other ingredients, allows us to generate an annotated map that enables our robot to recognize named locations specified by a user in 84% of cases.


title: E-PROCUREMENT AN EMERGING SUPPLY-CHAIN MANAGEMENT SYSTEM IN THE HOSPITALITY INDUSTRY: PERSPECTIVES OF HOTELIERS IN THE KUMASI METROPOLIS

abstract: s the hotel industry grows into a more technologyoriented industry, the need to understand and adopt eprocurement practices becomes more important. This research explores the adoption of e-procurement by the hotels in the Kumasi metropolis, focusing more on hoteliers’ perception of the concept of e-procurement and its importance to the hotel industry. The data was collected through survey from eleven selected hotels in Kumasi Metropolis. Results indicated that hoteliers are using e-procurement but not with the internet but through telephone. Benefits derived from the implementation of e-procurement are quality of goods and services, saving time and cost. However some costs which include buying and fixing of technical equipments, consultation fees for service providers, training of existing staff and recruitment of new staff will incur incorporating e-procurement. It was recommended that hoteliers need to ensure the needed systems and tools are in place for the operationalization of eprocurement.


title: The transition to community services in Norway

abstract: The social policy debate in Scandinavia is undergoing ideological changes. The traditionally strong support for the distinctive features of the Scandinavian welfare states, like equality, centralization, and universal social programmes, are gradually declining, and are being replaced by values that emphasize individual freedom and diversity, community and network, and decentralization of public services. There is growing political support for private and voluntary arrangements as alternatives to the public sector. These ideological changes are not merely Scandinavian phenomena. Decentralization and privatization have been general features in most western countries in the 1980s (Goldsmith and Newton, 1988; Kuhnle, 1990), and Communitarianism and the renaissance of the idea of Civil Society seem to be dominant ideologies in the 1990s (Bellah et al., 1991; Seligman, 1992; Selznick, 1992; Coles, 1993; Etzioni, 1993).