title: ClimbVis: Investigating In-situ Visualizations for Understanding Climbing Movements by Demonstration

abstract: Rock climbing involves complex movements and therefore requires guidance when acquiring a new technique. The classic approach is mimicking the movements of a more experienced climber. However, the trainee has to remember every nuance of the climb, since the sequence of movements cannot be performed in parallel to the experienced climber. As a solution to this problem, we present a video recording and replay system for climbing. The replay component allows for different in-situ video feedback methods. We investigated the video feedback component of the system by studying two example visualization techniques, i.e. a life-sized in-place projection and a real-time third-person view of the climber, augmented by a video showing a successful ascent. The latter is presented to the user on both Google Glass and a projected display. The results indicate that a life-sized projection was perceived as easiest to follow, while most of the climbers had problems with the context switches between the augmented video and the climbing wall. These findings can aid in the design of assistance systems that teach complex movements.


Aliveness Detection Using Coupled Hidden Markov Models


title: Modified bagging of maximal information coefficient for genome-wide identification

abstract: A new method, modified Bagging mBagging of Maximal Information Coefficient mBoMIC, was developed for genome-wide identification. Traditional Bagging is inadequate to meet some requirements of genome-wide identification, in terms of statistical performance and time cost. To improve statistical performance and reduce time cost, an mBagging was developed to introduce Maximal Information Coefficient MIC into genome-wide identification. The mBoMIC overcame the weakness of original MIC, i.e., the statistical power is inadequate and MIC values are volatile. The three incompatible measures of Bagging, i.e. time cost, statistical power and false positive rate, were significantly improved simultaneously. Compared with traditional Bagging, mBagging reduced time cost by 80%, improved statistical power by 15%, and decreased false positive rate by 31%. The mBoMIC has sensitivity and university in genome-wide identification. The SNPs identified only by mBoMIC have been reported as SNPs associated with cardiac disease.


title: Threshold-Dependent Camouflaged Cells to Secure Circuits Against Reverse Engineering Attacks

abstract: With current tools and technology, someone who has physical access to a chip can extract the detailed layout of the integrated circuit (IC). By using advanced visual imaging techniques, reverse engineering can reveal details that are meant to be kept secret, such as a secure protocol or novel implementation that offers a competitive advantage. A promising solution to defend against reverse engineering attacks is IC camouflaging. In this work, we propose a new camouflaging technique based on the threshold voltage of the transistors. We refer to these cells as threshold dependent camouflaged cells. Our work differs from current commercial solutions in that the latter use look-alike cells, with the assumption that it is difficult for the reverse engineer to identify the cell's functionality. Yet, if a structural distinction between cells exists, then these are still vulnerable, especially as reverse engineers use more advanced and precise techniques. On the other hand, the proposed threshold dependent standard cells are structurally identical regardless of the cells' functionality. Detailed circuit simulations of our proposed threshold dependent camouflaged cells demonstrate that they can be used to cost-effectively and robustly camouflage large netlists. Corner analysis of process, temperature, and supply voltage (PVT) variations show that our cells operate as expected over all PVT corners simulated.


title: Applying Business Intelligence Concepts to Medicaid Claim Fraud Detection

abstract: U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state’s existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data.


title: Zynq-based SoC implementation of an induction machine control algorithm

abstract: In this paper, a new hardware/software design and implementation of an Induction Machine (IM) drive control topology is presented. Power electronic applications such as three-phase inverter require highly accurate switching frequency. This design uses a System on Chip (SoC) approach and implemented on a Field Programmable Gate Array (FPGA). The on-chip processor is used for high level programing while the FPGA's programmable fabric is used to create precise gating signals for a three-phase inverter. These signals are generated in the hardware side of the design. Floating-point calculations and control flow of the whole design are managed by SoC. This method is suitable for any power electronic application where precise gating signals are required. The methodology used in this solution is explained and experimental results are presented.


title: Coordinating an innovation in supply chain management

abstract: Abstract The importance of a long-term relationship between a manufacturing firm and its supplier(s) has been emphasized in the literature on supply chain management. Essential to such a relationship is the coordination among participants in a supply chain. In order to sustain the relationship, the coordination should enhance the profitability of not only the manufacturer, but also the supplier(s). In this paper, we consider a particular supply chain situation in which the manufacturer coordinates, e.g., supports, its supplier's innovation that can eventually lead to supply cost reduction. Developing a mathematical model, we show that although the coordination could improve the manufacturing firm's own profitability, it might not be attractive to the supplier unless the supply cost reduction should ultimately increase the market demand to a certain extent. Under particular circumstances, if the market demand stays constant, the manufacturer's profit increase due to the coordination equals the amount of profit loss to the supplier. The analysis presents exact mathematical criteria to determine whether the coordination strategy can be agreeable to both the manufacturer and the supplier. Numerical examples are employed to show the applicability of the criteria.


title: Tensor Discriminant Color Space for Face Recognition

abstract: Recent research efforts reveal that color may provide useful information for face recognition. For different visual tasks, the choice of a color space is generally different. How can a color space be sought for the specific face recognition problem? To address this problem, this paper represents a color image as a third-order tensor and presents the tensor discriminant color space (TDCS) model. The model can keep the underlying spatial structure of color images. With the definition of n-mode between-class scatter matrices and within-class scatter matrices, TDCS constructs an iterative procedure to obtain one color space transformation matrix and two discriminant projection matrices by maximizing the ratio of these two scatter matrices. The experiments are conducted on two color face databases, AR and Georgia Tech face databases, and the results show that both the performance and the efficiency of the proposed method are better than those of the state-of-the-art color image discriminant model, which involve one color space transformation matrix and one discriminant projection matrix, specifically in a complicated face database with various pose variations.


title: Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning

abstract: One of the major obstacles that has to be faced when applying automatic emotion recognition to realistic human–machine interaction systems is the scarcity of labeled data for training a robust model. Motivated by this concern, this paper seeks to utmost exploit unlabeled data that are pervasively available in the real-world and easy to be collected, by means of novel semi-supervised learning (SSL) approaches. Conventional SSL methods such as self-training, suffer from their inherent drawback of error accumulation, i.e., the samples that are misclassified by the system are continuously employed to train the model in the following learning iterations. To address this major issue, we first propose an enhanced learning strategy, by which we re-evaluate the previously automatically labeled samples in each learning iteration, in order to update the training set by correcting the mislabeled samples. We further exploit multiple modalities and models in the SSL system, by using collaborative SSL, where all modalities and models are considered simultaneously; samples are selected by means of minimizing the joint entropy. This strategy is supposed to not only improve the performance of the model for data annotation and consequently enhance the trustability of the automatically labeled data, but also to elevate the diversity of selected data. To evaluate the effectiveness of the proposed approaches, we performed extensive experiments on the remote collaborative and affective database, which includes multimodal recordings of spontaneous affective interactions of dyads. The empirical results show that the proposed approaches significantly outperform recently well-established SSL methods.


Issues Affecting User Confidence in Explanation Systems


title: Evaporation-driven microfluidic sample concentration

abstract: A new method for sample concentration within microfluidic devices is presented. Evaporation at an air/liquid interface is used to induce flow. within a microfluidic device and concentrate sample. The practicality of this method was demonstrated with 0.2 /spl mu/m fluorescent microspheres and FITC-labeled bovine serum albumin (BSA). Eighty one percent of a 0.6 /spl mu/L fluorescent sphere suspension was concentrated into a well within a microfluidic device. In the same amount of time, 93% of a 0.6 /spl mu/L FITC-labeled BSA solution was concentrated.


Report for Distributed Systems seminar about Blockchain in distributed systems as a privacy enabler


title: Development of bio-inspired knee joint for power assist suit

abstract: A lot of the power assist suits using the pneumatic artificial muscles for assisting the extension of the knee have been developed. But in almost all of devices their centers of rotation are fixed. The knee joint of human has a complex movement. Flexion and extension motions of knee joint consist of rolling and sliding. Moreover, there also exist rotations in front plane and horizontal plane. Therefore, we developed a new knee joint mechanism with a bio-inspired joint motion, torque adjustment mechanism, and U-shaped springs. We also evaluated the motion of the knee mechanism and the effect of the U-shaped springs. The combination of the sliding mechanism and wire-pulley mechanism fitted the subject's flexion-extension motions. The U-shaped spring worked to reduce unexpected stress with misalignment.


title: Fast Trajectory Optimization for Legged Robots Using Vertex-Based ZMP Constraints

abstract: This paper combines the fast Zero-Moment-Point (ZMP) approaches that work well in practice with the broader range of capabilities of a Trajectory Optimization formulation, by optimizing over body motion, footholds and Center of Pressure simultaneously. We introduce a vertex-based representation of the support-area constraint, which can treat arbitrarily oriented point-, line-, and area-contacts uniformly. This generalization allows us to create motions such quadrupedal walking, trotting, bounding, pacing, combinations and transitions between these, limping, bipedal walking and push-recovery all with the same approach. This formulation constitutes a minimal representation of the physical laws (unilateral contact forces) and kinematic restrictions (range of motion) in legged locomotion, which allows us to generate various motion in less than a second. We demonstrate the feasibility of the generated motions on a real quadruped robot.


Self-Care for Caring Practice: Student Nurses’ Perspectives


title: Switchable Deep Network for Pedestrian Detection

abstract: In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection. The SDN automatically learns hierarchical features, salience maps, and mixture representations of different body parts. Pedestrian detection faces the challenges of background clutter and large variations of pedestrian appearance due to pose and viewpoint changes and other factors. One of our key contributions is to propose a Switchable Restricted Boltzmann Machine (SRBM) to explicitly model the complex mixture of visual variations at multiple levels. At the feature levels, it automatically estimates saliency maps for each test sample in order to separate background clutters from discriminative regions for pedestrian detection. At the part and body levels, it is able to infer the most appropriate template for the mixture models of each part and the whole body. We have devised a new generative algorithm to effectively pretrain the SDN and then fine-tune it with back-propagation. Our approach is evaluated on the Caltech and ETH datasets and achieves the state-of-the-art detection performance.


title: Machine learning applications in cancer prognosis and prediction

abstract: Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.


title: Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm

abstract: In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (@f) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., Borkowski, 1989; Bowring, 1976; Fukushima, 2006; Heikkinen, 1982; Jones, 2002; Zhang, 2005; Borkowski, 1987; Shu, 2010 and Lin, 1995) and Computational-Intelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.


title: Ransomware Inside Out

abstract: Android is currently the most widely used mobile environment. This trend encourages malware writers to develop specific attacks targeting this platform with threats designed to covertly collect data or financially extort victims, the so-called ransomware. In this paper we use formal methods, in particular model checking, to automatically dissect ransomware samples. Starting from manual inspection of few samples, we define a set of rule in order to check whether the behaviours we find are representative of ransomware functionalities.


title: Smart Small Cell with Hybrid Beamforming for 5G: Theoretical Feasibility and Prototype Results

abstract: In this article, we present a real-time 3D hybrid beamforming approach for 5G wireless networks. One of the key concepts in 5G cellular systems is the small cell network, which settles the high mobile traffic demand and provides uniform user-experienced data rates. The overall capacity of the small cell network can be enhanced with the enabling technology of 3D hybrid beamforming. This study validates the feasibility of 3D hybrid beamforming, mostly for link-level performance, through the implementation of a real-time testbed using a SDR platform and fabricated antenna array. Based on the measured data, we also investigate system-level performance to verify the gain of the proposed smart small cell system over LTE systems by performing system-level simulations based on a 3D ray-tracing tool.


title: A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture

abstract: Clustering is a fundamental problem in many data-driven application domains, and clustering performance highly depends on the quality of data representation. Hence, linear or non-linear feature transformations have been extensively used to learn a better data representation for clustering. In recent years, a lot of works focused on using deep neural networks to learn a clustering-friendly representation, resulting in a significant increase of clustering performance. In this paper, we give a systematic survey of clustering with deep learning in views of architecture. Specifically, we first introduce the preliminary knowledge for better understanding of this field. Then, a taxonomy of clustering with deep learning is proposed and some representative methods are introduced. Finally, we propose some interesting future opportunities of clustering with deep learning and give some conclusion remarks.


title: A Controlled Pilot-Outcome Study of Sensory Integration (SI) in the Treatment of Complex Adaptation to Traumatic Stress

abstract: This study tested whether sensory integration (SI) treatment combined with psychotherapy would improve symptom outcome over psychotherapy alone in the treatment of complex posttraumatic stress, as measured by the Disorders of Extreme Stress Not Otherwise Specified (DESNOS) clinical construct in a group of 10 adult patients with histories of childhood abuse. DESNOS symptoms were assessed at three time periods (T1, baseline; T2, after experimental group SI treatment; and T3, after wait-list control group SI treatment) using the Structured Interview for Disorders of Extreme Stress (SIDES). The Sensory Learning Program™ (SLP), developed by the Sensory Learning Institute of Boulder, Colorado, was used as the SI treatment modality. Results indicated significant differential improvement for the group treated with SLP in SIDES Total Score (T1/T2 and T2/T3), Self Perception (T1/T2 and T2/T3), Affect Regulation (T2/T3), and Alterations in Meaning (T1/T2).


title: Analog Versus Digital: Extrapolating from Electronics to Neurobiology

abstract: We review the pros and cons of analog and digital computation. We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and informationprocessing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire. Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.


title: Perception of transgenerational family relationships: Comparison of eating-disordered patients and their parents

abstract: BACKGROUND ::: Disturbances in various elements of transgenerational family functioning patterns are not uncommon in studies of eating disorders. We examined the relationship between patients' perception of autonomy and intimacy in their families of origin and that of their parents in their own families of origin. ::: ::: ::: MATERIAL AND METHODS ::: The sample consisted of 112 girls who had a diagnosis of an eating disorder and their parents; 54 of the girls were diagnosed with anorexia nervosa restrictive subtype, 22 as anorexia nervosa binge/purge subtype, and 36 were diagnosed with bulimia nervosa. We had 2 control groups: 1 group consisted of 36 girls diagnosed with a depressive episode, dysthymia, or adjustment disorder with depressed mood and the other group was 85 female students from schools in Cracow, Poland and their parents. We used the the Family of Origin Scale to assess perception of family relationships. Statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS 20.0.PL; Chicago, IL, USA). ::: ::: ::: RESULTS ::: There was a significant association between daughters' and fathers' perceptions of autonomy in their families of origin in all groups. There was no significant association between daughters' and mothers' perceptions in all groups. The strongest correlation was between the non-clinical sample of girls and their fathers and for the bulimic group. ::: ::: ::: CONCLUSIONS ::: We did not detect any link indicating the specificity of transgenerational transmission of autonomy and intimacy in eating disorders. The results point to the importance of the father figure in studies of family systems, including the context of family transmission.


title: Undertaking a literature review: a step-by-step approach

abstract: Nowadays, most nurses, pre- and post-qualification, will be required to undertake a literature review at some point, either as part of a course of study, as a key step in the research process, or as part of clinical practice development or policy. For student nurses and novice researchers it is often seen as a difficult undertaking. It demands a complex range of skills, such as learning how to define topics for exploration, acquiring skills of literature searching and retrieval, developing the ability to analyse and synthesize data as well as becoming adept at writing and reporting, often within a limited time scale. The purpose of this article is to present a step-by-step guide to facilitate understanding by presenting the critical elements of the literature review process. While reference is made to different types of literature reviews, the focus is on the traditional or narrative review that is undertaken, usually either as an academic assignment or part of the research process.


title: ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI

abstract: Abstract Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called l1-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.


title: Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models

abstract: Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.


title: Audio Cover Song Identification using Convolutional Neural Network

abstract: In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the (dis)similarity between the two songs. Based on the observation that there is a meaningful pattern between cover songs and that this can be learned, we have reformulated the cover song identification problem in a machine learning framework. To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs. We then construct the data set composed of cover song pairs and non-cover song pairs, which are used as positive and negative training samples, respectively. The trained CNN outputs the probability of being in the cover song relation given a cross-similarity matrix generated from any two pieces of music and identifies the cover song by ranking on the probability. Experimental results show that the proposed algorithm achieves performance better than or comparable to the state-of-the-art.


A Probabilistic Context-Free Grammar for Melodic Reduction


title: Demand for Cloud Services as an Infrastructure in Higher Education

abstract: Cloud computing is one of the major current trends in IS. It is said to be able to increase work productivity and simultaneously lower costs in organizations. However, institutions of higher education, like universities, remain hesitant to adopt it. The ‘NSA scandal’ and similar revelations lead to a sharp decline in trust (especially in Europe) in commercial cloud computing providers like Dropbox and Google. At the same time, cloud computing solutions often are not customized to students’ and employees’ needs. In this context, we examine the concept of ‘university cloud computing services’ by discussing it from the theoretical viewpoint as an ‘infrastructure.’ We conducted a large-scale survey with over 10,000 student respondents and employees from three universities. Our findings indicate a high demand for cloud computing at those universities and offer valuable implications for designing and managing cloud computing in a higher educational context.


title: Automatic fruit count on coffee branches using computer vision

abstract: A system to count the number of fruits on a coffee branch in field conditions.A technique that detects occluded and unoccluded fruits in field images.A technique that classifies fruits as either harvestable or not harvestable.A model that relates 1-by-1 the number of observed fruits and measurement fruits.A system in field to measure the number of fruits in different moments of the harvest.A technique for small coffee growers, which estimates coffee production in a farm. In this article, a non-destructive method is proposed to count the number of fruits on a coffee branch by using information from digital images of a single side of the branch and its growing fruits. In order to do this, 1018 coffee branches at different ripening stages. They had different numbers of fruits, harvest dates, were of different varieties, and were at different stages of coffee trees life. A Machine Vision System (MVS) was constructed, which was capable of counting and identifying harvestable and not harvestable fruits in a set of images corresponding to a specific coffee branch was constructed. This MVS consists of an image acquisition system, based on mobile devices (it does not require to control of the environmental conditions), and an image processing algorithm to classify and detect each one of the fruits in the acquired images. After obtaining information regarding the number of fruits identified by the MVS, linear estimation models were constructed between the detected fruits automatically and the ones observed on the coffee branch. These models were calculated for fruits in three categories: harvestable, not harvestable, and fruits whose maturation stage were disregarded. These models link the fruits that are counted automatically to the ones actually observed with an R2 higher than 0.93 one-to-one. Not only is the MVS used to estimate the number of fruits on the branch but also to estimate their maturation percentage and weight. The MVS was validated in four Variedad Castillo coffee plots, in different stages of development and with different densities. We found that MVS neither overestimates nor underestimates the number of fruits and that it shows a correlation higher than 0.90 at early stages of crop development, when tree fruits are still not harvestable. The information obtained in this research will spawn a new generation of tools for coffee growers to use. It is an efficient, non-destructive, and low-cost method which offers useful information for them to plan agricultural work and obtain economic benefits from the correct administration of resources.


title: Latent overlapped fingerprint separation: a review

abstract: Fingerprint matching is a widely used process to aid in crime scene investigation, where fingerprint fragments are often found on objects and surfaces. In such cases, the lifted fingerprints (called latents) are usually of poor quality and often appear overlapped and against a noisy background. These aspects make latent fingerprint --- especially overlapped latent fingerprints --- segmentation and enhancement (for subsequent matching) a difficult problem, for which several solutions have been proposed during the past few years. This paper presents an overview of contemporary techniques for overlapped fingerprint separation in the context of latent overlapped fingerprint matching. In addition to explaining the main concepts and surveying the literature in the field, it highlights the importance of the overlapped fingerprint segmentation (ROI extraction) process, a step for which there are no automatic techniques yet.


title: Grass strip corridors in agricultural landscapes enhance nest-site colonization by solitary wasps.

abstract: Corridors that connect otherwise isolated habitats have often been proposed as a management strategy to mitigate negative effects of habitat fragmentation. Non-crop corridors may have the potential to enhance the connectivity for arthropod predators in cropland landscapes, especially for species that require multiple habitats, such as cavity-nesting wasps which use wooded habitat for nesting and grassland habitat for foraging. However, the effects of corridors in nonexperimental landscapes have been rarely examined. We studied the species richness and abundance of cavity-nesting wasps and their parasitoids in standardized trap nests located in three habitat types (forest edge, hedge, grass strip) and in three grass-strip types (connected to a forest edge, slightly isolated, highly isolated from a forest edge). Species richness and the abundance of wasps (Hymenoptera: Sphecidae, Eumenidae, Pompilidae) were highest at forest edges, which provide natural nesting sites, and lowest in grass strips, with few natural nesting sites. Wasp abundance in grass strips connected to forest edges was 270% higher than in slightly isolated grass strips and 600% higher than in highly isolated grass strips. The abundance of caterpillar-hunting eumenid wasps was 600% higher in connected grass strips than in slightly and highly isolated grass strips. Species richness of wasps was enhanced by 180% in connected grass strips compared to highly isolated grass strips. Parasitism rates were not directly influenced by habitat or grass-strip type, but increased with increasing parasitoid diversity that was higher at forest edges than in grass strips. We conclude that grass-strip corridors enhance the colonization of nesting sites, presumably by facilitating wasp movements. In agricultural landscapes, where nesting sites are limited and food availability changes frequently, rapid colonization of nests may enhance population viability. Higher wasp abundance in connected nesting sites may be directly linked to higher biocontrol of pest caterpillars within the foraging range around nests. Although grass strips can reduce the negative effects of habitat fragmentation, non-crop habitats such as forest habitats and hedges providing nesting sites are required within the home range of wasps to allow reproduction in agricultural landscapes.


title: Bias Field Inconsistency Correction of Motion-Scattered Multislice MRI for Improved 3D Image Reconstruction

abstract: A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.


title: Review on cyber-physical security of the smart grid: Attacks and defense mechanisms

abstract: Because of the current power grid limitations, the Smart Grid is proposed as a promising alternative. It supports green energy resources and enhances power generation, delivery and distribution. For better energy management, extensive data exchange between subsystems and the control plane is needed. Since the Smart Grid uses existing technologies for communication, it inherits their defects. Targeting these flaws can lead to confidential data disclosure, cascading failures or blackouts. Therefore, securing the Smart Grid is crucial for achieving the required performance. We first discuss the weaknesses of the Smart Grid, the common attacks and their consequences. We then introduce cyber-physical defense algorithms and summarize the results of their performance evaluation. In all, simulation results performed by many researchers all lead to the same conclusion: security of the Smart Grid depends on combined physical and cyber security.


title: Empowering communities in combating river blindness and the role of NGOs: case studies from Cameroon, Mali, Nigeria, and Uganda

abstract: The control of onchocerciasis is not only a major success story in global health, but also one of the best examples of the power of public-private partnership at the international level as well as at the national level. The onchocerciasis story is also a leading example of the contribution of a group of called Non-Governmental Development Organizations (NGDO) to operational research which resulted in important changes in treatment strategies and policies.The four case studies presented here illustrate some key contributions the NGDOs made to the development of "community directed treatment with ivermectin" -CDTI, in Africa, which became the approved methodology within the African Programme for Onchocerciasis Control (APOC). The partnership between the international, multilateral, government institutions and the NGDO Coordination Group was the backbone of the APOC programme's structure and facilitated progress and scale-up of treatment programmes. Contributions included piloting community-based methodology in Mali and Nigeria; research, collaboration and coordination on treatment strategies and policies, coalition building, capacity building of national health workforce and advocacy at the national and international level. While the Onchocerciasis Control Programme (OCP) and APOC provided leadership, the NGDOs working with the national health authorities played a major role in advocacy evolving the community methodology which led to achieving and maintaining- treatments with ivermectin for at least 20 years and strengthening community health systems.


title: Color-based skin segmentation: An evaluation of the state of the art

abstract: Skin segmentation is widely used, e.g. in face detection and gesture recognition. In the last years, the number of skin segmentation approaches has grown. However, multiple datasets and varying performance measurements make direct comparison difficult. We address these shortcomings and evaluate 5 threshold-based methods, 5 model-based methods, and 2 region-based state-of-the-art skin segmentation methods. We discuss each algorithm and provide the segmentation performance along with the processing time. All methods are evaluated on the ECU dataset which provides a great amount of training data besides other important attributes.


Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems


title: Simultaneous localization and mapping (SLAM): part II

abstract: This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. The paper focuses on three key areas: computational complexity; data association; and environment representation


title: Learning spatial relationships from 3D vision using histograms

abstract: Effective robot manipulation requires a vision system which can extract features of the environment which determine what manipulation actions are possible. There is existing work in this direction under the broad banner of recognising “affordances”. We are particularly interested in possibilities for actions afforded by relationships among pairs of objects. For example if an object is “inside” another or “on top” of another. For this there is a need for a vision system which can recognise such relationships in a scene. We use an approach in which a vision system first segments an image, and then considers a pair of objects to determine their physical relationship. The system extracts surface patches for each object in the segmented image, and then compiles various histograms from looking at relationships between the surface patches of one object and those of the other object. From these histograms a classifier is trained to recognise the relationship between a pair of objects. Our results identify the most promising ways to construct histograms in order to permit classification of physical relationships with high accuracy. This work is important for manipulator robots who may be presented with novel scenes and must identify the salient physical relationships in order to plan manipulation activities.


title: Multiple-Path Selection for new Highway Alignments using Discrete Algorithms

abstract: This paper addresses the problem of finding multiple near-optimal, spatially-dissimilar paths that can be considered as alternatives in the decision making process, for finding optimal corridors in which to construct a new road. We further consider combinations of techniques for reducing the costs associated with the computation and increasing the accuracy of the cost formulation. Numerical results for five algorithms to solve the dissimilar multipath problem show that a"bidirectional approach"yields the fastest running times and the most robust algorithm. Further modifications of the algorithms to reduce the running time were tested and it is shown that running time can be reduced by an average of 56 percent without compromising the quality of the results.


title: MapReduce and Semantics Enabled Event Detection using Social Media

abstract: Abstract Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.


title: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

abstract: Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR ::: .


title: Smart Hospital based on Internet of Things

abstract: In this paper, we propose an architecture and a scheme of smart hospital based on Internet of Things (IOT) in order to overcome the disadvantages of the present hospital information system, such as the fixed information point, inflexible networking mode and so on. The key technologies and construction of smart hospital is presented based on understanding of the connotation and architecture of smart hospital. Furthermore, taking a third grade-A hospital as an example, a scheme of smart hospital is given, and its logic structure, application framework, the construction of basic network environment etc. are described in detail. Experiment proves that deployment of smart hospital can effectively solve the prominent problems existing the diagnosis and treatment of hospital and it brings a positive and profound effect for the present diagnosis and treatment mode in hospital.


title: Gamify Employee Collaboration - A Critical Review of Gamification Elements in Social Software

abstract: Social software solutions in enterprises such as IBM Connections are said to have the potential to support communication and collaboration among employees. However, companies are faced to manage the adoption of such collaborative tools and therefore need to raise the employees' acceptance and motivation. To solve these problems, developers started to implement Gamification elements in social software tools, which aim to increase users' motivation. In this paper, we critically examine the status quo of the current market of leading social software solutions and seek to find out which Gamification approaches are implementated in these collaborative tools. Our findings show, that most of the major social collaboration solutions do not offer Gamification features by default, but leave the integration to a various number of third party plug-in vendors. Furthermore we identify a trend in which Gamification solutions majorly focus on rewarding quantitative improvement of work activities, neglecting qualitative performance. Subsequently, current solutions do not match recent findings in research and ignore risks that can lower the employees' motivation and work performance in the long run.


title: Convex relaxations of structured matrix factorizations

abstract: We consider the factorization of a rectangular matrix $X $ into a positive linear combination of rank-one factors of the form $u v^\top$, where $u$ and $v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode specific structures regarding the factors, such as positivity or sparsity. In this paper, we show that computing the optimal decomposition is equivalent to computing a certain gauge function of $X$ and we provide a detailed analysis of these gauge functions and their polars. Since these gauge functions are typically hard to compute, we present semi-definite relaxations and several algorithms that may recover approximate decompositions with approximation guarantees. We illustrate our results with simulations on finding decompositions with elements in $\{0,1\}$. As side contributions, we present a detailed analysis of variational quadratic representations of norms as well as a new iterative basis pursuit algorithm that can deal with inexact first-order oracles.


title: Compact wideband 3dB branch line coupler with multiple symmetric PI section

abstract: This paper presents the design of a miniaturized 3dB branch line coupler (BLC) for wideband applications. Multiple symmetric PI sections are used in this design to replace the quarter-wavelength transmission lines of the conventional coupler. The design formulas are derived and verified with the simulated and measured results. The circuit area of the proposed BLC is only 20.3% of the conventional one. The fractional bandwidth is greater than 75% at the center frequency of 0.9 GHz.


title: Polymer-fullerene composite solar cells.

abstract: Fossil fuel alternatives, such as solar energy, are moving to the forefront in a variety of research fields. Polymer-based organic photovoltaic systems hold the promise for a cost-effective, lightweight solar energy conversion platform, which could benefit from simple solution processing of the active layer. The function of such excitonic solar cells is based on photoinduced electron transfer from a donor to an acceptor. Fullerenes have become the ubiquitous acceptors because of their high electron affinity and ability to transport charge effectively. The most effective solar cells have been made from bicontinuous polymer-fullerene composites, or so-called bulk heterojunctions. The best solar cells currently achieve an efficiency of about 5%, thus significant advances in the fundamental understanding of the complex interplay between the active layer morphology and electronic properties are required if this technology is to find viable application.


title: Evaluation of an online healthy eating programme

abstract: This study examined the effects of an online healthy eating programme on consumption of fruit and vegetables, saturated fat and added sugars. Participants (82 females, 18 males) were randomised to an intervention or control group and asked to log onto a website every week for 24 weeks to complete health-related measures. They also attended laboratory sessions at baseline, 3 months and 6 months to complete additional physiological and diet measures. At the online sessions, intervention participants received dietary feedback and advice and completed tasks targeting motivation (analysing reasons for the importance of health, thinking about health-related desires and concerns), volition (forming implementation intentions) and maintenance (reviewing tasks, weekly ‘tips’). Ninety-one participants completed 12 or more online sessions. Analysis of their data showed a significant interaction for fruit and vegetable consumption, with intervention participants increasing their intake between baseline and 6 months (3.72–4.17 cups) and control participants decreasing their intake (3.59–3.36 cups). Results also showed overall reductions in saturated fat and added sugar between baseline and 6 months (saturated fat = 19.61–14.96 g, sugar = 39.66–28.29 g) but no interaction with group. The results indicate that the programme was effective at helping individuals increase their fruit and vegetable consumption. The results also provide support for the efficacy of simply monitoring behaviours over time. The absence of additional programme benefits beyond monitoring for fat and sugar may be because intervention participants failed to target these behaviours, or because the programme was more effective for approach than avoidance goals.


title: Declarative memory impairments in Alzheimer's disease and semantic dementia

abstract: Abstract Semantic dementia (SD) and Alzheimer's disease (AD) are both disorders in which early pathology affects the temporal lobe yet they produce distinct syndromes of declarative memory impairment—loss of established semantic knowledge with relatively preserved episodic memory in the former and the converse in the latter. Groups with mild SD and mild AD who showed a double dissociation in these two aspects of declarative memory were studied—the SD group's episodic memory and the AD group's semantic knowledge each being comparable to controls. Positron emission tomography and volumetric magnetic resonance imaging were used to map deficits in regional cerebral metabolic rate and mesial temporal lobe (MTL) atrophy, respectively. Episodic memory impairment in AD was associated with dysfunction of an integrated network (mesial temporal lobe, mamillary bodies, dorso-mesial thalamus and posterior cingulate). Semantic memory impairment in SD was associated with bilateral rostral temporal lobe hypometabolism. The SD group had comparable MTL atrophy and hypometabolism to that found in AD but the remainder of their limbic–diencephalic network was preserved suggesting that the latter explains their ability to acquire new episodic memories. The results challenge the view that amnesia in early AD can be explained by the degree of MTL damage alone while showing that semantic impairment can occur with damage restricted to the rostral temporal lobes.


title: Haptic Feedback Enhances Force Skill Learning

abstract: This paper explores the use of haptic feedback to teach an abstract motor skill that requires recalling a sequence of forces. Participants are guided along a trajectory and are asked to learn a sequence of one-dimensional forces via three paradigms: haptic training, visual training, or combined visuohaptic training. The extent of learning is measured by accuracy of force recall. We find that recall following visuohaptic training is significantly more accurate than recall following visual or haptic training alone, although haptic training alone is inferior to visual training alone. This suggests that in conjunction with visual feedback, haptic training may be an effective tool for teaching sensorimotor skills that have a force-sensitive component to them, such as surgery. We also present a dynamic programming paradigm to align and compare spatiotemporal haptic trajectories


title: Weighted range sensor matching algorithms for mobile robot displacement estimation

abstract: Introduces a "weighted" matching algorithm to estimate a robot's planar displacement by matching two-dimensional range scans. The influence of each scan point on the overall matching error is weighted according to its uncertainty. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. Based on models of expected sensor uncertainty, our algorithm computes the appropriate weighting for each measurement so as to optimally estimate the displacement between two consecutive poses. By explicitly modeling the various noise sources, we can also calculate the actual covariance of the displacement estimates instead of a statistical approximation of it. A realistic covariance estimate is necessary for further combining the pose displacement estimates with additional odometric and/or inertial measurements within a localization framework. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range finder illustrate that the method is more accurate than prior techniques.


title: Extreme Extraction: Only One Hour per Relation

abstract: Information Extraction (IE) aims to automatically generate a large knowledge base from natural language text, but progress remains slow. Supervised learning requires copious human annotation, while unsupervised and weakly supervised approaches do not deliver competitive accuracy. As a result, most fielded applications of IE, as well as the leading TAC-KBP systems, rely on significant amounts of manual engineering. Even "Extreme" methods, such as those reported in Freedman et al. 2011, require about 10 hours of expert labor per relation. ::: This paper shows how to reduce that effort by an order of magnitude. We present a novel system, InstaRead, that streamlines authoring with an ensemble of methods: 1) encoding extraction rules in an expressive and compositional representation, 2) guiding the user to promising rules based on corpus statistics and mined resources, and 3) introducing a new interactive development cycle that provides immediate feedback --- even on large datasets. Experiments show that experts can create quality extractors in under an hour and even NLP novices can author good extractors. These extractors equal or outperform ones obtained by comparably supervised and state-of-the-art distantly supervised approaches.


title: Probabilistic query expansion using query logs

abstract: Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.


title: (Social) Norm Dynamics

abstract: This chapter is concerned with the dynamics of social norms, that is the way that such norms change. In particular this chapter concentrates on the lifecycle that social norms go through, focusing on the generation of norms, the way that norms spread and stabilize, and the way that norms evolve. We also discuss the cognitive mechanisms behind norm compliance, the role of culture in norm dynamics, and the way that trust affects norm dynamics.


title: A tunable duty-cycle-controlled switched-R-MOSFET-C CMOS filter for low-voltage and high-linearity applications

abstract: A switched-R-MOSFET-C filter with tunable corner frequency is described. The tunability is achieved by varying the clock duty cycle using an automatic tuning circuit. This tuning method does not involve a change in any gate voltage, and is therefore particularly suitable for low-voltage and high-linearity applications. The advantages of the proposed method are illustrated with the design and simulation of a high-Q biquad filter.


title: Graph-based segmentation for colored 3D laser point clouds

abstract: We present an efficient graph-theoretic algorithm for segmenting a colored laser point cloud derived from a laser scanner and camera. Segmentation of raw sensor data is a crucial first step for many high level tasks such as object recognition, obstacle avoidance and terrain classification. Our method enables combination of color information from a wide field of view camera with a 3D LIDAR point cloud from an actuated planar laser scanner. We extend previous work on robust camera-only graph-based segmentation to the case where spatial features, such as surface normals, are available. Our combined method produces segmentation results superior to those derived from either cameras or laser-scanners alone. We verify our approach on both indoor and outdoor scenes.


title: Visible threads: a smart VR interface to digital libraries

abstract: The importance of information as a resource for economic growth and education is steadily increasing. Due to technological advances in computer industry and the explosive growth of the Internet much valuable information will be available in digital libraries. This paper introduces a system that aims to support a user's browsing activities in document sets retrieved from a digital library. Latent Semantic Analysis is applied to extract salient semantic structures and citation patterns of documents stored in a digital library in a computationally expensive batch job. At retrieval time, cluster techniques are used to organize retrieved documents into clusters according to the previously extracted semantic similarities. A modified Boltzman algorithm [1] is employed to spatially organize the resulting clusters and their documents in the form of a three-dimensional information landscape or "i-scape". The i-scape is then displayed for interactive exploration via a multi-modal, virtual reality CAVE interface [8]. Users' browsing activities are recorded and user models are extracted to give newcomers online help based on previous navigation activity as well as to enable experienced users to recognize and exploit past user traces. In this way, the system provides interactive services to assist users in the spatial navigation, interpretation, and detailed exploration of potentially large document sets matching a query.


title: Linear Antenna Array Synthesis with Invasive Weed Optimization Algorithm

abstract: Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently developed metaheuristic algorithm, known as the Invasive Weed Optimization (IWO), to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the IWO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way


title: Fuzzy K-mean clustering in MapReduce on cloud based hadoop

abstract: Clustering is regarded as one of the significant task in data mining which deals with primarily grouping of similar data. To cluster large data is a point of concern. Hadoop is a software framework which deals with distributed processing of huge amount of data across clusters of commodity computers using MapReduce programming model. MapReduce allows a kind of parallelization for solving a problem involving large data sets using computing clusters and is also an attractive mean for data clustering involving large datasets. Mahout, a scalable machine learning library is an approach to Fuzzy K-mean clustering which runs on a Hadoop. This paper focuses on studying the performance of different datasets using Fuzzy K-mean clustering in MapReduce on Hadoop. Experimental results depict the execution time of the approach on a multi-node Hadoop cluster which is build using Amazon Elastic Cloud Computing(Amazon EC2).


title: High-Frequency Equity Index Futures Trading Using Recurrent Reinforcement Learning with Candlesticks

abstract: In 1997, Moody and Wu presented recurrent reinforcement learning (RRL) as a viable machine learning method within algorithmic trading. Subsequent research has shown a degree of controversy with regards to the benefits of incorporating technical indicators in the recurrent reinforcement learning framework. In 1991, Nison introduced Japanese candlesticks to the global research community as an alternative to employing traditional indicators within the technical analysis of financial time series. The literature accumulated over the past two and a half decades of research contains conflicting results with regards to the utility of using Japanese candlestick patterns to exploit inefficiencies in financial time series. In this paper, we combine features based on Japanese candlesticks with recurrent reinforcement learning to produce a high-frequency algorithmic trading system for the E-mini S&P 500 index futures market. Our empirical study shows a statistically significant increase in both return and Sharpe ratio compared to relevant benchmarks, suggesting the existence of exploitable spatio-temporal structure in Japanese candlestick patterns and the ability of recurrent reinforcement learning to detect and take advantage of this structure in a high-frequency equity index futures trading environment.


title: An Improved Time Delay Estimation Algorithm with Robustness

abstract: Acoustic positioning and navigation technology is the main technical means for underwater target location and navigation systems, where the precision of time delay estimation is the key. This paper proposes a time delay estimation algorithm with robustness for time-varying and multipath fading channels. Based on generalized cross correlation algorithm, normalized correlation peak envelope ratio is got. Using time-frequency analysis, it also gets two additional features of signal, which are the short-term energy ratio and sub-band energy ratio. It recognizes the desired signal smartly and detects the direct wave accurately. Under the same detection probability, the false alarm probability is greatly reduced. Simulation and lake test results verify that the detection probability is 100% and probability of false alarm and missed detection is 0% when the signal to noise ratio is larger than-5 dB.


title: Low correlation MIMO antenna for LTE 700MHz band

abstract: A key feature of upcoming 4G wireless communication standards is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas must be low (< 0.5). In this context, we propose a new correlation reduction technique suitable for closely spaced antennas (distance, d < λ/40). This technique reduces mutual coupling between antennas and concurrently uncorrelates the antennas' radiation characteristic by inducing negative group delay in the target frequency band. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700MHz band. Measurement results show that the antenna correlation is reduced more than 37% using the proposed technique.


title: Web accessibility in the Mid-Atlantic United States: a study of 50 homepages

abstract: This paper reports on a study of 50 homepages in the Mid-Atlantic United States to determine what accessibility problems exist. The 50 homepages were evaluated using both the U.S. government's Section 508 guidelines as well as the Web Accessibility Initiative's (WAI) Priority Level 1 of the Web Content Accessibility Guidelines (WCAG). According to both sets of guidelines, 49 out of 50 sites were found to have accessibility problems, although some of the accessibility problems were minor and easy to fix. There are two troubling findings from this study. The Web sites that had the most accessibility problems were organizations in the Web development and information technology field, which ideally should be the leaders in making the Web more accessible. The Web accessibility software testing tools, which are available to assist people in making their Web sites more accessible, are flawed and inconsistent and require large numbers of manual checks, which many developers may not be able to do. More people need to become aware of the topic of Web accessibility, and the testing tools need to be improved so that once people are aware, it is easier for them to move their sites toward full accessibility.


title: Spherical layout and rendering methods for immersive graph visualization

abstract: While virtual reality has been researched in many ways for spatial and scientific visualizations, comparatively little has been explored for visualizations of more abstract kinds of data. In particular, stereoscopic and VR environments for graph visualization have only been applied as limited extensions to standard 2D techniques (e.g. using stereoscopy for highlighting). In this work, we explore a new, immersive approach for graph visualization, designed specifically for virtual reality environments.


title: Omega-Shaped Inchworm-Inspired Crawling Robot With Large-Index-and-Pitch (LIP) SMA Spring Actuators

abstract: This paper proposes three design concepts for developing a crawling robot inspired by an inchworm, called the Omegabot. First, for locomotion, the robot strides by bending its body into an omega shape; anisotropic friction pads enable the robot to move forward using this simple motion. Second, the robot body is made of a single part but has two four-bar mechanisms and one spherical six-bar mechanism; the mechanisms are 2-D patterned into a single piece of composite and folded to become a robot body that weighs less than 1 g and that can crawl and steer. This design does not require the assembly of various mechanisms of the body structure, thereby simplifying the fabrication process. Third, a new concept for using a shape-memory alloy (SMA) coil-spring actuator is proposed; the coil spring is designed to have a large spring index and to work over a large pitch-angle range. This large-index-and-pitch SMA spring actuator cools faster and requires less energy, without compromising the amount of force and displacement that it can produce. Therefore, the frequency and the efficiency of the actuator are improved. A prototype was used to demonstrate that the inchworm-inspired, novel, small-scale, lightweight robot manufactured on a single piece of composite can crawl and steer.


title: Context identification of sentences in research articles: Towards developing intelligent tools for the research community

abstract: Scientific literature is an important medium for disseminating scientific knowledge. However, in recent times, a dramatic increase in research output has resulted in challenges for the research community. An increasing need is felt for tools that exploit the full content of an article and provide insightful services with value beyond quantitative measures such as impact factors and citation counts. However, the intricacies of language and thought, and the unstructured format of research articles present challenges in providing such services. The identification of sentence contexts that encode the role of specific sentences in advancing an article’s scientific argument can facilitate in developing intelligent tools for the research community. This paper describes our research work in this direction. First, we investigate the possibility of identifying contexts associated with sentences and propose a scheme of thirteen context type definitions for sentences, based on the generic rhetorical pattern found in scientific articles. We then present the results of our experiments using sequential classifiers – conditional random fields – for achieving automatic context identification. We also describe our Semantic Web application developed for providing citation context based information services for the research community. Finally, we present a comparison and analysis of our results with similar studies and explain the distinct features of our application.


title: Low loss H-shape SIW hybrid coupler for millimeter-wave phased arrays antenna systems

abstract: A simple low loss H-shape hybrid coupler based on the substrate integrated waveguide technology is presented for millimeter-wave applications. The coupler operation is based on the excitation of two different modes, TE 10 and TE 20 . The coupler S-matrix is calculated by using a full-wave solver that uses the even/odd mode (symmetry) analysis to minimize the computational time and provides more physical insight. The simulated return and insertion losses are better than −20 dB and −3.90 dB, respectively over the operating frequency bandwidth of 39–40.50 GHz.


raiden11@IECSIL-FIRE-2018 : Named Entity Recognition for Indian Languages


title: Low Offset Drift–Low-Noise Orthogonal Fluxgate With Synchronized Polarity Flipping

abstract: In this paper, we present an orthogonal fluxgate in fundamental mode with extremely stable offset and low noise. The typical solution to stabilizing the offset in this type of sensor is to flip the dc bias of its excitation current and subtract the output obtained with opposite polarities. In this case, on the contrary, we flip both the ac and dc components of the excitation current and sum the output voltages. This is performed using fast solid-state switches with low on-resistance. In this way, we achieved a large suppression of the dependence of the offset on temperature, obtaining an average offset drift of 0.012 nT/K, making this sensor on the top class for offset stability. We digitally sampled and numerically demodulated the output voltage of the sensor to extract the first harmonic. The value of the first harmonic shows noise right after switching of the current. Therefore, we numerically erased those few noisy values of the first harmonic before integrating it. In this way, we managed to limit the noise growth given by switching of the current. Finally, we strongly improved the offset stability with minor increment of noise.


title: Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing

abstract: Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.


title: An Underexamined Inequality Cultural and Psychological Barriers to Men’s Engagement With Communal Roles

abstract: Social psychological research has sought to understand and mitigate the psychological barriers that block women’s interest, performance, and advancement in male-dominated, agentic roles (e.g., science, technology, engineering, and math). Research has not, however, correspondingly examined men’s underrepresentation in communal roles, traditionally occupied by women (e.g., careers in health care, early childhood education, and domestic roles including child care). In this article, we seek to provide a roadmap for research on this underexamined inequality by (a) outlining the benefits of increasing men’s representation in communal roles; (b) reviewing cultural, evolutionary, and historical perspectives on the asymmetry in status assigned to men’s and women’s roles; and (c) articulating the role of gender stereotypes in creating social and psychological barriers to men’s interest and inclusion in communal roles. We argue that promoting equal opportunities for both women and men requires a better understanding...


title: Sensory and microbiological quality of yogurt drinks with prebiotics and probiotics

abstract: The popularity of dairy products fortified with prebiotics and probiotics continues to increase as consumers desire flavorful foods that will fulfill their health needs. Our objectives were to assess the sensory profile of drinkable yogurts made with prebiotics and probiotics and to determine the viability of the probiotics in the yogurt drink over the duration of storage. Thirteen trained descriptive panelists evaluated 10 yogurt drinks on a 16-point category scale. Three selected prebiotics, soluble corn fiber, polydextrose, and chicory inulin, were each present individually at an amount to claim an excellent source of fiber (5 g of fiber/serving) or a good source of fiber (2.5 g of fiber/serving) in 6 different yogurt drinks. Three additional yogurt drinks contained 5 g of each of the separate prebiotics along with a mixture of the selected probiotics (Bifidobacterium lactis Bb-12 and Lactobacillus acidophilus LA-5). A control sample with no prebiotics or probiotics was also included in the experimental design. Data were analyzed by ANOVA, Fisher's least significant difference, and principal component analysis. Survival of the probiotics in the yogurt drinks during a 30-d refrigerated storage period was also analyzed. Results showed that clover honey aroma, buttermilk aroma, butter aroma, sweetness, sourness, chalky mouthfeel, and viscosity were identified as significant attributes in the yogurt drinks. Total variance explained by the principal component analysis biplot of factors 1 and 2 was 65%, which showed yogurt drinks with soluble corn fiber and inulin varying by the sweet versus sour attributes and yogurt drinks with polydextrose varying by the mouthfeel attributes. The viability study determined a 2- to 3-log decrease in the survival of probiotics in all of the yogurt treatments during a 30-d refrigerated storage period. Based on the results of the current study, only the polydextrose treatment would be an acceptable vehicle to deliver the probiotic health effects at the end of the 30-d storage period.


title: A survey of machine learning algorithms for big data analytics

abstract: Big data analytics is a booming research area in computer science and many other industries all over the world. It has gained great success in vast and varied application sectors. This includes social media, economy, finance, healthcare, agriculture, etc. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions. A literature survey of different machine learning techniques is provided in this paper. Also a study on commonly used machine learning algorithms for big data analytics is done and presented in this paper.


title: Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel

abstract: This paper describes a new computational finance approach. This approach combines pattern recognition techniques with an evolutionary computation kernel applied to financial markets time series in order to optimize trading strategies. Moreover, for pattern matching a template-based approach is used in order to describe the desired trading patterns. The parameters for the pattern templates, as well as, for the decision making rules are optimized using a genetic algorithm kernel. The approach was tested considering actual data series and presents a robust profitable trading strategy which clearly beats the market, S&P 500 index, reducing the investment risk significantly.


title: WHICH PROCESSES DO USERS NOT WANT ONLINE ? - EXTENDING PROCESS VIRTUALIZATION THEORY

abstract: Following the advent of the Internet more and more processes are provided virtually, i.e., without physical interactions between involved people and objects. For instance, E-Commerce has virtualized shopping processes since products are bought without physical inspection and interaction with sales staff. This study is founded on the key idea of process virtualization theory (PVT) that from the users’ perspective not all processes are equally amenable for virtualization. We investigate characteristics of processes, which are causing users’ resistance toward the virtualized process. Surveying 501 individuals regarding 10 processes, this study constitutes the first quantitative test evaluating the prediction capabilities of PVT by analysis of varying processes. Moreover, it introduces and successfully tests the extended PVT (EPVT), which integrates PVT with multiple, related constructs from extant literature in a unified model with multi-order causal relations. Thereby, it clearly enhances our understanding of human behavior with regard to the frequent phenomenon process virtualization.


title: Designing urban media façades: cases and challenges

abstract: Media facades comprise a category of urban computing concerned with the integration of displays into the built environment, including buildings and street furniture. This paper identifies and discusses eight challenges faced when designing urban media facades. The challenges concern a broad range of issues: interfaces, physical integration, robustness, content, stakeholders, situation, social relations, and emerging use. The challenges reflect the fact that the urban setting as a domain for interaction design is characterized by a number of circumstances and socio-cultural practices that differ from those of other domains. In order to exemplify the challenges and discuss how they may be addressed, we draw on our experiences from five experimental design cases, ranging from a 180 m2 interactive building facade to displays integrated into bus shelters.


title: RBAC-SC: Role-Based Access Control Using Smart Contract

abstract: The role-based access control (RBAC) framework is a mechanism that describes the access control principle. As a common interaction, an organization provides a service to a user who owns a certain role that was issued by a different organization. Such trans-organizational RBAC is common in face-to-face communication but not in a computer network, because it is difficult to establish both the security that prohibits the malicious impersonation of roles and the flexibility that allows small organizations to participate and users to fully control their own roles. In this paper, we present an RBAC using smart contract (RBAC-SC), a platform that makes use of Ethereum’s smart contract technology to realize a trans-organizational utilization of roles. Ethereum is an open blockchain platform that is designed to be secure, adaptable, and flexible. It pioneered smart contracts, which are decentralized applications that serve as “autonomous agents” running exactly as programmed and are deployed on a blockchain. The RBAC-SC uses smart contracts and blockchain technology as versatile infrastructures to represent the trust and endorsement relationship that are essential in the RBAC and to realize a challenge-response authentication protocol that verifies a user’s ownership of roles. We describe the RBAC-SC framework, which is composed of two main parts, namely, the smart contract and the challenge-response protocol, and present a performance analysis. A prototype of the smart contract is created and deployed on Ethereum’s Testnet blockchain, and the source code is publicly available.


title: Exploiting an I/OMMU vulnerability

abstract: It is difficult to protect an operating system kernel in an efficient way. Attackers can corrupt or subvert it by two different means: (1) the CPU; (2) the Direct Memory Access (DMA) capability of I/O controllers. DMA-based attacks can be blocked using an I/OMMU. This component, embedded in most of current chipsets, enables the operating system to virtualize the main memory for I/O controllers and to restrict their access to only some memory regions. In this paper, we present different vulnerabilities we identified on Intel VT-d, which implements an I/OMMU. An example of exploitation of one of them is then detailed. Finally, we give some recommendations to prevent these vulnerabilities from being used for malicious purposes.


title: Incidence of alarplasty in primary and revision rhinoplasty in a private practice setting.

abstract: OBJECTIVE ::: To evaluate the incidence of alarplasty in primary and revision rhinoplasty during a 3-year period. ::: ::: ::: METHODS ::: We conducted a retrospective medical record review of patients undergoing primary and revision rhinoplasty between January 1, 2004, and December 31, 2006. Patients were divided into the following categories: primary or revision rhinoplasty, alarplasty, and ethnicity. The incidence of alarplasty was calculated and compared for primary vs revision rhinoplasty and for ethnicity. Statistically significant differences were confirmed using the chi(2) test. ::: ::: ::: RESULTS ::: A total of 168 patients underwent rhinoplasty with minimum follow-up of 6 months; 116 patients had primary rhinoplasty and 52 revision rhinoplasty. All alarplasties were bilateral. Of 50 alarplasties performed, 35 were in the primary group and 15 in the revision group. The incidence of alarplasty was 30.2% in the primary group and 28.8% in the revision group. Nonwhite patients had a higher overall incidence of alarplasty (63.0%) than did white patients (25.4%). Each ethnic category identified had a higher incidence of alarplasty than the white group. ::: ::: ::: CONCLUSIONS ::: Alarplasty is an important technique in rhinoplasty. The high incidence of alarplasty in revision rhinoplasty procedures suggests that alarplasty is underused by many surgeons currently performing rhinoplasty. One should consider alarplasty for all patients' noses, not just those of nonwhite ethnic rhinoplasty patients.


title: “I regretted the minute I pressed share”: A Qualitative Study of Regrets on Facebook

abstract: We investigate regrets associated with users' posts on a popular social networking site. Our findings are based on a series of interviews, user diaries, and online surveys involving 569 American Facebook users. Their regrets revolved around sensitive topics, content with strong sentiment, lies, and secrets. Our research reveals several possible causes of why users make posts that they later regret: (1) they want to be perceived in favorable ways, (2) they do not think about their reason for posting or the consequences of their posts, (3) they misjudge the culture and norms within their social circles, (4) they are in a "hot" state of high emotion when posting, or under the influence of drugs or alcohol, (5) their postings are seen by an unintended audience, (6) they do not foresee how their posts could be perceived by people within their intended audience, and (7) they misunderstand or misuse the Facebook platform. Some reported incidents had serious repercussions, such as breaking up relationships or job losses. We discuss methodological considerations in studying negative experiences associated with social networking posts, as well as ways of helping users of social networking sites avoid such regrets.


title: Exploiting Performance Portability in Search Algorithms for Autotuning

abstract: Autotuning seeks the best configuration of an application by orchestrating hardware and software knobs that affect performance on a given machine. Autotuners adopt various search techniques to efficiently find the best configuration, but they often ignore lessons learned on one machine when tuning for another machine. We demonstrate that a surrogate model built from performance results on one machine can speedup the autotuning search by 1.6X to 130X on a variety of modern architectures.


title: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease

abstract: The past two decades have witnessed an explosion in the identification, largely by positional cloning, of genes associated with mendelian diseases. The roughly 1,200 genes that have been characterized have clarified our understanding of the molecular basis of human genetic disease. The principles derived from these successes should be applied now to strategies aimed at finding the considerably more elusive genes that underlie complex disease phenotypes. The distribution of types of mutation in mendelian disease genes argues for serious consideration of the early application of a genomic-scale sequence-based approach to association studies and against complete reliance on a positional cloning approach based on a map of anonymous single nucleotide polymorphism haplotypes.


title: Cutaneous Findings Mistaken for Physical Abuse: Present but Not Pervasive

abstract: Incorrect diagnoses during child abuse evaluations are serious. Because skin lesions are common in abuse, it is important to consider cutaneous mimics of physical abuse. The current study prospectively identified cutaneous mimics in a cohort of children evaluated for possible physical abuse. This is a secondary analysis of data from the Examining Siblings To Recognize Abuse research network's prospective, observational, cross-sectional study involving 20 U.S. child abuse teams. Subjects were younger than 10 years old and were evaluated by child abuse physicians (CAPs) for concerns of physical abuse. CAPs prospectively documented whether mimics were identified during their physical abuse evaluations. Details of each patient with cutaneous mimics were evaluated to determine the types of mimics, which part of the evaluations identified mimics, and the perceived abuse likelihood. Of 2,890 children evaluated for physical abuse, 137 had at least one mimic identified and 69 had some cutaneous mimic components. Although 985 of 2,753 (39%) subjects without mimics had high levels of abuse concern, only 9 of 137 (6%) children with mimics had high levels of abuse concern (p < 0.001). Of 69 children with cutaneous mimics, 56 (81%) were diagnosed by history and physical examination. Cutaneous abuse mimics were identified in 2.4% of children evaluated for physical abuse. Although it was eventually determined that there was little or no concern for abuse in 84% of children with cutaneous mimics, a small number were physically abused. CAP evaluation may be valuable in recognizing children with cutaneous mimics who also were abused.


title: Invited - Cross-layer approximate computing: from logic to architectures

abstract: We present a survey of approximate techniques and discuss concepts for building power-/energy-efficient computing components reaching from approximate accelerators to arithmetic blocks (like adders and multipliers). We provide a systematical understanding of how to generate and explore the design space of approximate components, which enables a wide-range of power/energy, performance, area and output quality tradeoffs, and a high degree of design flexibility to facilitate their design. To enable cross-layer approximate computing, bridging the gap between the logic layer (i.e. arithmetic blocks) and the architecture layer (and even considering the software layers) is crucial. Towards this end, this paper introduces open-source libraries of low-power and high-performance approximate components. The elementary approximate arithmetic blocks (adder and multiplier) are used to develop multi-bit approximate arithmetic blocks and accelerators. An analysis of data-driven resilience and error propagation is discussed. The approximate computing components are a first steps towards a systematic approach to introduce approximate computing paradigms at all levels of abstractions.


title: Differentiating ability in users of the ReWalkTM powered exoskeleton: An analysis of walking kinematics

abstract: The ReWalkTM powered exoskeleton assists thoracic level motor complete spinal cord injury patients who are paralyzed to walk again with an independent, functional, upright, reciprocating gait. We completed an evaluation of twelve such individuals with promising results. All subjects met basic criteria to be able to use the ReWalkTM - including items such as sufficient bone mineral density, leg passive range of motion, strength, body size and weight limits. All subjects received approximately the same number of training sessions. However there was a wide distribution in walking ability. Walking velocities ranged from under 0.1m/s to approximately 0.5m/s. This variability was not completely explained by injury level The remaining sources of that variability are not clear at present. This paper reports our preliminary analysis into how the walking kinematics differed across the subjects - as a first step to understand the possible contribution to the velocity range and determine if the subjects who did not walk as well could be taught to improve by mimicking the better walkers.


title: Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

abstract: There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database.


Working with Beliefs: AI Transparency in the Enterprise


title: Angle of Arrival Measurement Using Multiple Static Monopole Antennas

abstract: The angle of arrival (AoA) is an important factor in the localization of a wireless sensor network. This paper deals with AoA measurement using omnidirectional antennas. In our case, microstrip monopole antennas are used, which have radiation patterns with two sharp minimums. Therefore, an algorithm based on an approach where an AoA is obtained along a direction where the measured received strength signal indicator is minimal. Multiple stationary microstrip antennas are placed on a printed circuit board and no moving parts are needed for measurement. In comparison with rotating antennas this simple method has lower resolution. We show that the resolution can be improved using interpolations and approximations. When using the proposed algorithm, the experimental results for the outdoor measurements reached a root-mean-square error of <10°, and an indoor environment of <25°.


title: A survey of UNI signaling systems and protocols for ATM networks

abstract: The main aspect covered by signaling systems and protocols for ATM networks concerns the possibility to manage, maintain, and control a user-driven communication between arbitrary ATM end-systems connected to an ATM network. The tasks and procedures defined for, e.g., setting-up an ATM connection, are often very different concerning the rrelevant specifications of various working bodies (such as ITU-T or ATM-Forum) or certain vendors, although the basis to be done for maintaining ATM connections are always principally the same. The reason for this situation is, that the intentions of working bodies are different and each one of them follows specific strategies for certain scenarios (such as for point-to-point unicast or point-to-multipoint multicast). Nevertheless, various types of characteristics (such as addressing, multicast, or interworking) are requested from applications residing on top of ATM networks. Therefore, this survey of signaling systems and protocols for ATM networks identifies for several of selected approaches (such as Q.2931, UNI 3.1, or CMAP) important characteristics and relevant scenarios. Furthermore, a table-based comparison of some approaches has been added


title: Expanding and Refining Design and Criticality in HCI

abstract: The term 'critical design' is on the upswing in HCI. We analyze how discourses around 'critical design' are diverging in Design and HCI. We argue that this divergence undermines HCI's ability to learn from and appropriate the design approaches signaled by this term. Instead, we articulate two ways to broaden and deepen connections between Design and HCI: (1) develop a broader collective understanding of what these design approaches can be, without forcing them to be about 'criticality' or 'critical design,' narrowly construed; and (2) shape a variation of design criticism to better meet Design practices, terms, and ways of knowing.