title: Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters

abstract: We propose ego-splitting, a new framework for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees, that reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem. We can scale community detection to graphs with tens of billions of edges and outperform previous solutions based on ego-nets analysis. More precisely, our framework works in two steps: a local ego-net analysis phase, and a global graph partitioning phase. In the local step, we first partition the nodes' ego-nets using a partitioning algorithm. We then use the computed clusters to split each node into its persona nodes that represent the instantiations of the node in its communities. Finally, in the global step, we partition the newly created graph to obtain an overlapping clustering of the original graph.


title: A Compact Dielectric-Loaded Log-Periodic Dipole Array (LPDA) Antenna

abstract: A compact, dielectric-loaded, and wideband log-periodic dipole array antenna operating between 200 and 803 MHz for VSWR less than 2 is presented in this letter. The antenna consists of sinusoidal dipoles and parallel stripline printed on a substrate partially loading an air layer, as well as two stepped dielectric materials with $\varepsilon _{{{r}}}= 10$ . These two stepped dielectric materials are placed on both sides of the substrate. It is found that a remarkable lower frequency performance is obtained by using these two stepped dielectric materials, and the antenna's impedance matching performance is improved through the air layer partially loaded in the substrate. Good agreement is achieved between simulated and measured results. The antenna is also shown to work normally through the experimental results of VSWR under the 100 W continuous-wave power excitation for 30 min.


A Study of Teacher Educators’ Job Satisfaction and Interest in Teaching


title: Hippocampal and prefrontal processing of network topology to simulate the future

abstract: Topological networks lie at the heart of our cities and social milieu. However, it remains unclear how and when the brain processes topological structures to guide future behaviour during everyday life. Using fMRI in humans and a simulation of London (UK), here we show that, specifically when new streets are entered during navigation of the city, right posterior hippocampal activity indexes the change in the number of local topological connections available for future travel and right anterior hippocampal activity reflects global properties of the street entered. When forced detours require re-planning of the route to the goal, bilateral inferior lateral prefrontal activity scales with the planning demands of a breadth-first search of future paths. These results help shape models of how hippocampal and prefrontal regions support navigation, planning and future simulation.


title: Real-time computer vision with OpenCV

abstract: Mobile computer-vision technology will soon become as ubiquitous as touch interfaces.


title: Visual Semantic Role Labeling

abstract: In this paper we introduce the problem of Visual Semantic Role Labeling: given an image we want to detect people doing actions and localize the objects of interaction. Classical approaches to action recognition either study the task of action classification at the image or video clip level or at best produce a bounding box around the person doing the action. We believe such an output is inadequate and a complete understanding can only come when we are able to associate objects in the scene to the different semantic roles of the action. To enable progress towards this goal, we annotate a dataset of 16K people instances in 10K images with actions they are doing and associate objects in the scene with different semantic roles for each action. Finally, we provide a set of baseline algorithms for this task and analyze error modes providing directions for future work.


title: Image Edge Detection using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics

abstract: Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image’s intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Experimental results are provided to support the superior performance of the proposed approach.


title: An Improved Faster R-CNN for Same Object Retrieval

abstract: An improved faster region-based convolutional neural network (R-CNN) [same object retrieval (SOR) faster R-CNN] is proposed to retrieve the same object in different scenes with few training samples. By concatenating the feature maps of shallow and deep convolutional layers, the ability of Regions of Interest (RoI) pooling to extract more detailed features is improved. In the training process, a pretrained CNN model is fine-tuned using a query image data set, so that the confidence score can identify an object proposal to the object level rather than the classification level. In the query process, we first select the ten images for which the object proposals have the closest confidence scores to the query object proposal. Then, the image for which the detected object proposal has the minimum cosine distance to the query object proposal is considered as the query result. The proposed SOR faster R-CNN is applied to our Coke cans data set and three public image data sets, i.e., Oxford Buildings 5k, Paris Buildings 6k, and INS 13. The experimental results confirm that SOR faster R-CNN has better identification performance than fine-tuned faster R-CNN. Moreover, SOR faster R-CNN achieves much higher accuracy for detecting low-resolution images than the fine-tuned faster R-CNN on the Coke cans (0.094 mAP higher), Oxford Buildings (0.043 mAP higher), Paris Buildings (0.078 mAP higher), and INS 13 (0.013 mAP higher) data sets.


title: Rogowski current sensor design and analysis based on printed circuit boards (PCB)

abstract: This work presents a new Rogowski current sensor design implemented in printed circuit boards. The proposed structure utilizes a modular design for a variety of applications. Feasibility was first demonstrated using standard printed circuit boards. Test results detail performance with high repeatability with less than 0.8% difference in sensed voltages and less than 0.3% difference in sensed voltages with respect to position of installation. In order to have a less complex, more manufacturable and modular Rogowski current sensor, a flexible printed circuit (FPC) solution was also designed and demonstrated. By using the FPC solution, high sensitivity and much higher power density was achieved. Coupling analysis of a 3-phase system was also conducted both from simulation and experiments.


title: License plate detection and character recognition system for commercial vehicles based on morphological approach and template matching

abstract: Though license plate detection and recognition system has a lot of applications but a very little work has been done on Bangla license plate recognition. Variations among the license plate patterns and complex background of Bangladeshi license plates make it more difficult to use the existing algorithms. Because of this, we propose a solution for Bangla license plate detection and recognition. Firstly, the position of the vehicle is determined. In our country, commercial License Plates have the unique color (green) of its own. That's why; we select the portions of green color with the matching RGB intensity of the plate. A boundary based contour algorithm, area and aspect ratio have been proposed to track down the license plate in the vehicle region. License plate rows containing registration information have been separated by using horizontal projection with threshold. The characters and the digits of the rows have been segmented using vertical projection with threshold value. Finally, template matching has been used for recognizing the characters and the digits of the Bangla license plate. We tested our algorithms for over 180 still images captured from the road. We achieve93% success in license plate detection, 98.1% success in segmentation and 88.8% success rate in Bangla license plate recognition.


title: Achieving scalable mutation-based generation of whole test suites

abstract: Without complete formal specification, automatically generated software tests need to be manually checked in order to detect faults. This makes it desirable to produce the strongest possible test set while keeping the number of tests as small as possible. As commonly applied coverage criteria like branch coverage are potentially weak, mutation testing has been proposed as a stronger criterion. However, mutation based test generation is hampered because usually there are simply too many mutants, and too many of these are either trivially killed or equivalent. On such mutants, any effort spent on test generation would per definition be wasted. To overcome this problem, our search-based EvoSuite test generation tool integrates two novel optimizations: First, we avoid redundant test executions on mutants by monitoring state infection conditions, and second we use whole test suite generation to optimize test suites towards killing the highest number of mutants, rather than selecting individual mutants. These optimizations allowed us to apply EvoSuite to a random sample of 100 open source projects, consisting of a total of 8,963 classes and more than two million lines of code, leading to a total of 1,380,302 mutants. The experiment demonstrates that our approach scales well, making mutation testing a viable test criterion for automated test case generation tools, and allowing us to analyze the relationship of branch coverage and mutation testing in detail.


title: Visualization of higher order singularities in vector fields

abstract: Presents an algorithm for the visualization of vector field topology based on Clifford algebra. It allows the detection of higher-order singularities. This is accomplished by first analysing the possible critical points and then choosing a suitable polynomial approximation, because conventional methods based on piecewise linear or bilinear approximation do not allow higher-order critical points and destroy the topology in such cases. The algorithm is still very fast, because of using linear approximation outside the areas with several critical points.


title: Intraradicular bacteria and fungi in root-filled, asymptomatic human teeth with therapy-resistant periapical lesions: A long-term light and electron microscopic follow-up study

abstract: Light and electron microscopy were used to analyze nine therapy-resistant and asymptomatic human periapical lesions, which were removed as block biopsies during surgical treatment of the affected teeth. The cases that required surgery represented about 10% of all of the cases which received endodontic treatment and root fillings during the period 1977 to 1984. These cases revealed periapical lesions when they were examined 4 to 10 yr after treatment. The biopsies were processed for correlated light and electron microscopy. Six of the nine biopsies revealed the presence of microorganisms in the apical root canal. Four contained one or more species of bacteria and two revealed yeasts. Of the four cases in which bacteria were found, only in one biopsy could they be found by light microscope. In the other three specimens, the bacterial presence could be confirmed only after repeated electron microscopic examination of the apical root canal by serial step-cutting technique. Among the three cases in which no microorganisms could be encountered, one showed histopathological features of a foreign body giant cell granuloma. These findings suggest that in the majority of root-filled human teeth with therapy-resistant periapical lesions, microorganisms may persist and may play a significant role in endodontic treatment failures. In certain instances such lesions may also be sustained by foreign body giant cell type of tissue responses at the periapex of root-filled teeth.


title: Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders.

abstract: Objective: This study was designed to determine the effectiveness ofa group stress reduction program based on mindfulness meditation for patients with anxiety disorders. Method: The 22 study participants were screened with a structured clinical interview and found to meet the DSM-IH-R criteria for generalized anxiety disorder or panic disorder with or without agoraphobia. Assessments, including self-ratings and therapists’ ratings, were obtained weekly bef ore and during the meditation-based stress reduction and relaxation program and monthly during the 3-month follow-up period. Results: Repeated measures analyses ofvariance documented significant reductions in anxiety and depression scores after treatment for 20 of the subjects-changes that were maintained at follow-up. The number of subjects experiencing panic symptoms was also substantially reduced. A comparison of the study subjects with a group ofnonstudy participants in the program who met the initial screening criteria for entry into the study showed that both groups achieved similiar reductions in anxiety scores on the SCL-90-R and on the Medical Symptom Checklist, suggesting generalizability of the study f indings. Conclusions: A group mindfulness meditation training program can effectively reduce symptoms ofanxiety and panic and can help maintain these reductions in patients with


title: The attempted assassination of President Reagan. Medical implications and historical perspective.

abstract: In 1981, President Ronald Reagan became the first incumbent president of the United States to survive being struck by a would-be assassin's bullet. Had President Reagan not survived, the history of this country and the world most certainly would have been changed. This report is the only first-hand account of the details of his medical care and complications following the assassination attempt, an event that emphasizes the vulnerability of presidents to would-be assassins and the importance of readily available expert medical care and facilities to their survival.


title: Integrating cannabis into clinical cancer care.

abstract: Cannabis species have been used as medicine for thousands of years; only since the 1940s has the plant not been widely available for medical use. However, an increasing number of jurisdictions are making it possible for patients to obtain the botanical for medicinal use. For the cancer patient, cannabis has a number of potential benefits, especially in the management of symptoms. Cannabis is useful in combatting anorexia, chemotherapy-induced nausea and vomiting, pain, insomnia, and depression. Cannabis might be less potent than other available antiemetics, but for some patients, it is the only agent that works, and it is the only antiemetic that also increases appetite. Inhaled cannabis is more effective than placebo in ameliorating peripheral neuropathy in a number of conditions, and it could prove useful in chemotherapy-induced neuropathy. A pharmacokinetic interaction study of vaporized cannabis in patients with chronic pain on stable doses of sustained-release opioids demonstrated no clinically significant change in plasma opiates, while suggesting the possibility of synergistic analgesia. Aside from symptom management, an increasing body of in vitro and animal-model studies supports a possible direct anticancer effect of cannabinoids by way of a number of different mechanisms involving apoptosis, angiogenesis, and inhibition of metastasis. Despite an absence of clinical trials, abundant anecdotal reports that describe patients having remarkable responses to cannabis as an anticancer agent, especially when taken as a high-potency orally ingested concentrate, are circulating. Human studies should be conducted to address critical questions related to the foregoing effects.


title: An in vitro comparative evaluation between virtually planned implant positions on interactive implant software versus actual implant positions achieved using sterolithographic open guide system

abstract: Purpose: To evaluate and compare the positional and angular accuracy of virtual implant positions planned on cone-beam computed tomography and final implant positions achieved using a universal open guide system. Materials and Methods: A dual scan of a partially edentulous jaw model along with prosthesis was done, and virtual implant planning was performed. Three implant positions in relation to 35, 36, and 37 were simulated (Group A). In total, 24 implants were placed in eight replaceable bone blocks (Group B) in the same region on the model using an open stereolithographic template. The linear positions and angulation of the placed implants were determined using Vision Measuring Machine. Deviations between virtually planned and surgically placed implants were analyzed in terms of linear and angular measurements. Data were analyzed with the independent-sample t-test with differences P ≤ 0.05 being considered statistically significant. Results: The linear distance (mean ± standard deviation [SD]) in mesiodistal direction between implants in relation to 35 and 36, 36 and 37, 35 and 37 in Group A was 8.79 ± 0 mm, 8.71 ± 0 mm, and 17.50 ± 0 mm, respectively, and in Group B was 7.70 ± 0.58 mm, 8.11 ± 0.30 mm, and 15.80 ± 0.48 mm. All these above values were found to be statistically significant (P ≤ 0.05). The linear distance (mean ± SD) in the vertical direction (mesial) for implants placed in the region of 35, 36, 37 for Group A was 1.51 ± 0 mm, 1.51 ± 0 mm, and 2.47 ± 0 mm, respectively, and for Group B was 1.37 ± 0.32 mm, 1.65 ± 0.48 mm, and 1.79 ± 0.36 mm, respectively. The linear distance (mean ± SD) in the vertical direction (distal) for implants placed in the region of 35, 36, 37 for Group A was 3.37 ± 0 mm, 1.51 ± 0 mm, and 1.51 ± 0 mm, respectively, and for Group B was 1.86 ± 0.48 mm (P ≤ 0.05), 1.56 ± 0.23 mm, and 1.29 ± 0.39 mm (P ≤ 0.05), respectively. The angular deviation (perpendicularity) values for virtually planned implants (Group A) were 90.00° ± 0° and for implants placed in the region of 35, 36, and 37 (Group B) were 84.52° ± 5.4°, 83.57° ± 1.52°, and 80.41° ± 2.37°, respectively, which are highly significant (P ≤ 0.05). Conclusions: The stereolithographic universal open guide used in the study may be considered accurate for placement of implants in mesiodistal position and also in terms of perpendicularity but not in the vertical position. Stereolithographic open guide may be recommended for more accurate implant position, especially for the placement of multiple implants.


title: Bayesian Word Sense Induction

abstract: Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word's contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samples from a multinomial distribution over senses which are in turn characterized as distributions over words. The Bayesian framework provides a principled way to incorporate a wide range of features beyond lexical co-occurrences and to systematically assess their utility on the sense induction task. The proposed approach yields improvements over state-of-the-art systems on a benchmark dataset.


title: Proposal and Evaluation of Serendipitous Recommendation Method Using General Unexpectedness

abstract: systems support users in selecting items and services in an information-rich environment. Although recommender systems have been improved in terms of accuracy, such systems are still insufficient in terms of novelty and serendipity, giving unsatisfactory results to users. Two methods of "serendipitous recommendation" are therefore proposed. However, a method for recommending serendipitous items accurately to users does not yet exist, because what kinds of items are serendipitous is not clearly defined. Accordingly, a human preference model of serendipitous items based on actual data concerning a user's impression collected by questionnaires was devised. Two serendipitous recommendation methods based on the model were devised and evaluated according to a user's actual impression. The evaluation results show that one of these recommendation methods, the one using general unexpectedness independent of user profiles, can recommend the serendipitous items accurately.


title: Real-time robust human tracking based on Lucas-Kanade optical flow and deep detection for embedded surveillance

abstract: Object tracking is one of the most important functions in surveillance systems, especially in the system with Pan/Tilt/Zoom Camera. In this paper, we propose a real-time robust human tracking method for embedded surveillance. The proposed human tracking method tracks human objects based on Lucas-Kanade (LK) optical flow algorithm [1], rectifies tracking error due to accumulation or object missing by readjusting tracked human object bounding boxes periodically. Human localization information is obtained from a reliable deep learning-based human. It also handles occlusion by combination of LK information and human detector information. In order to achieve fast and robust processing, computationally light but reliable human detector is developed based on YOLOv2 object detector [2] model. Through experiments in comparison with other state-of-the-art tracking methods, it is shown that the proposed human tracking method operates fast and reliably with occlusion handling, and that performs better than or comparable to others.


title: Control of bandstop response of Hi-Lo microstrip low-pass filter using slot in ground plane

abstract: This paper examines the effect of the geometrical shapes of a defected ground-plane structure (DGS) slot on performance of the Hi-Lo microstrip low-pass filter (LPF). A three-pole LPF based on an arrowhead DGS slot has 67% less length as compared to a conventional three-pole LPF. It has insertion loss of 0.5 dB and 15-dB rejection in the stopband up to three times the cutoff frequency.


title: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

abstract: Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.


title: Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction

abstract: Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modelling. In this work we present a feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on these feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it outperforms most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which outperforms the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Even though these datasets are only 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets.


title: Frequency and Outcome of Pulmonary Polymethylmethacrylate Embolism during Percutaneous Vertebroplasty

abstract: BACKGROUND AND PURPOSE: During percutaneous polymethylmethacrylate (PMMA) vertebroplasty (PV), PMMA cement may migrate into the venous system and subsequently be transported to the pulmonary arteries. Frequency, outcome, and imaging findings of PMMA pulmonary embolism are poorly understood. We retrospectively assessed the frequency and outcome of PMMA embolism during PV in a large patient cohort and evaluated the relationship of the volume of injected PMMA to the occurrence of pulmonary PMMA embolism. MATERIALS AND METHODS: Between 2001 and 2007, 532 osteoporotic compression fractures in 299 consecutive patients were treated with PV. PMMA embolism was defined as venous PMMA migration toward the lungs visible on biplane fluoroscopy during PV. CT was performed immediately and 1 year after PMMA migration. RESULTS: Venous PMMA migration occurred during 11 PVs in 11 patients (2.1%, 95% confidence interval, 1.1–3.7%). CT in 8 patients demonstrated small peripheral pulmonary PMMA emboli. All 11 patients remained asymptomatic during 1-year follow-up. Repeat CT scanning after 1 year in 6 patients demonstrated unchanged pulmonary PMMA deposits without late reactive changes. Mean injected cement volume in patients with and without PMMA embolism was not different (3.6 ± 1.06 mL versus 3.3 ± 1.16 mL, P = .43). Similar comparison for thoracic and thoracolumbar vertebrae yielded P values of .07 and .9. CONCLUSION: Pulmonary PMMA embolism during PV is an infrequent complication without permanent clinical sequelae. After 1 year, no pulmonary reaction was seen on CT. No definite relationship of PMMA emboli with injected cement volume could be established.


title: Novel High Step-Up DC–DC Converter for Fuel Cell Energy Conversion System

abstract: A novel high step-up dc-dc converter for fuel cell energy conversion is presented in this paper. The proposed converter utilizes a multiwinding coupled inductor and a voltage doubler to achieve high step-up voltage gain. The voltage on the active switch is clamped, and the energy stored in the leakage inductor is recycled. Therefore, the voltage stress on the active switch is reduced, and the conversion efficiency is improved. Finally, a 750-W laboratory prototype converter supplied by a proton exchange membrane fuel cell power source and an output voltage of 400 V is implemented. The experimental results verify the performances, including high voltage gain, high conversion efficiency, and the effective suppression of the voltage stress on power devices. The proposed high step-up converter can feasibly be used for low-input-voltage fuel cell power conversion applications.


title: Diabetes in Asia and the Pacific: Implications for the Global Epidemic

abstract: The last three decades have witnessed an epidemic rise in the number of people with diabetes, especially type 2 diabetes, and particularly in developing countries, where more than 80% of the people with diabetes live. The rise of type 2 diabetes in South Asia is estimated to be more than 150% between 2000 and 2035. Although aging, urbanization, and associated lifestyle changes are the major determinants for the rapid increase, an adverse intrauterine environment and the resulting epigenetic changes could also contribute in many developing countries. The International Diabetes Federation estimated that there were 382 million people with diabetes in 2013, a number surpassing its earlier predictions. More than 60% of the people with diabetes live in Asia, with almost one-half in China and India combined. The Western Pacific, the world’s most populous region, has more than 138.2 million people with diabetes, and the number may rise to 201.8 million by 2035. The scenario poses huge social and economic problems to most nations in the region and could impede national and, indeed, global development. More action is required to understand the drivers of the epidemic to provide a rationale for prevention strategies to address the rising global public health “tsunami.” Unless drastic steps are taken through national prevention programs to curb the escalating trends in all of the countries, the social, economic, and health care challenges are likely to be insurmountable.


title: A Comparative Study of Replication Techniques in Grid Computing Systems

abstract: Abstract — Grid Computing locations, without the overheads of replicating them locally. is a type of parallel and distributed systems that is designed to provide reliable access to data and computational resources in wide area networks. These resources are distributed in different geographical locations, however are organized to provide an integrated service. Effective data management in today`s enterprise environment is an important issue. Also, Performance is one of the challenges of using these environments. For improving the performance of file access and easing the sharing amongst distributed systems, replication techniques are used. Data replication is a common method used in distributed environments, where essential data is stored in multiple locations, so that a user can access the data from a site in his area. In this paper, we present a survey on basic and new replication techniques that have been proposed by other researchers. After that, we have a full comparative study on these replication strategies. Also, at the end of the paper, we summarize the results and points of these replication techniques.


title: Extrinsic dexterity: In-hand manipulation with external forces

abstract: “In-hand manipulation” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation [1], is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and hand-scripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video [2]). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.


title: Counting Crowded Moving Objects

abstract: In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.


title: Incremental Clustering for Semi-Supervised Anomaly Detection applied on Log Data

abstract: Anomaly detection based on white-listing and self-learning has proven to be a promising approach to detect customized and advanced cyber attacks. Anomaly detection aims at detecting significant deviations from normal system and network behavior. A well-known method to classify anomalous and normal system behavior is clustering of log lines. However, this approach has been applied for forensic purposes only, where log data dumps are investigated retrospectively. In order to make this concept applicable for on-line anomaly detection, i.e., at the time the log lines are produced, some major extensions to existing approaches are required. Especially distance based clustering approaches usually fail building the required large distance matrices and rely on time-consuming recalculations of the cluster-map on every arriving log line. An incremental clustering approach seems suitable to solve this issues. Thus, we introduce a semi-supervised concept for incremental clustering of log data that builds the basis for a novel on-line anomaly detection solution based on log data streams. Its operation is independent from the syntax and semantics of the processed log lines, which makes it generally applicable. We demonstrate that that the introduced anomaly detection approach allows to achieve both a high recall and a high precision while maintaining linear complexity.


title: Development of a body joint angle measurement system using IMU sensors

abstract: This paper presents an approach for measuring and monitoring human body joint angles using inertial measurement unit (IMU) sensors. This type of monitoring is beneficial for therapists and physicians because it facilitates remote assessment of patient activities. In our approach, two IMUs are mounted on the upper leg and the lower leg to measure the Euler angles of each segment. The Euler angles are sent via Bluetooth protocols to a pc for calculating the knee joint angle. In our experiments, we utilized a motion capture system to accurately measure the knee joint angle and used this as the ground truth to assess the accuracy of the IMU system. The range of average error of the system across a variety of motion trials was 0.08 to 3.06 degrees. In summary, the accuracy of the IMU measurement system currently outperforms existing wearable systems such as conductive fiber optic sensors and flex-sensors.


title: Scalable Network Function Virtualization for Heterogeneous Middleboxes

abstract: Over the past decade, a wide-ranging collection of network functions in middleboxes has been used to accommodate the needs of network users. Although the use of general-purpose processors has been shown to be feasible for this purpose, the serial nature of microprocessors limits network functional virtualization (NFV) performance. In this paper, we describe a new heterogeneous hardware-software approach to NFV construction that provides scalability and programmability, while supporting significant hardware-level parallelism and reconfiguration. Our computing platform uses both field-programmable gate arrays (FPGA) and microprocessors to implement numerous NFV operations that can be dynamically customized to specific network flow needs. As the number of required functions and their characteristics change, the hardware in the FPGA is automatically reconfigured to support the updated requirements. Traffic management and hardware reconfiguration functions are performed by a global coordinator which allows for the rapid sharing of middlebox state and continuous evaluation of network function needs. To evaluate our approach, a series of software tools and NFV modules have been implemented. Our system is shown to be scalable for collections of network functions exceeding one million shared states.


title: Aesthetic activities and aesthetic attitudes: Influences of education, background and personality on interest and involvement in the arts

abstract: There have been few studies of why some people are frequently involved in aesthetic activities such as going to the theatre, reading or playing musical instruments, whereas others are less involved. This study assesses the broad roles of education, personality and demographic factors such as social class, age and sex. More aesthetic activity was associated with music and art education, whereas science education had a substantial negative relationship with aesthetic activity, both directly and also indirectly via reduced art education. More aesthetic activity was particularly related to higher scores on the personality factor of openness, and also to lower scores on agreeableness and conscientiousness. Higher parental social class was also associated with more aesthetic activity, as also was lower age. Sex had no relationship to aesthetic activity, as neither did masculinity 2 femininity. Positive aesthetic attitudes were also related moderately to aesthetic activity, but were particularly strongly related to openness to experience, and somewhat less to extraversion. Class, age and sex had no direct relationship to aesthetic attitudes. The arts, defined in their broadest sense as music in its multifarious forms, the visual arts of representation, decoration and adornment, the performance arts of theatre and dance and the literary arts of novel-writing, poetry and other forms of text, as well as a huge range of applied arts, consume large amounts of time and effort in all societies. In many cases they are also the basis of major industries, as with the music industry, cinema, television, publishing and other media, all of which are of economic consequence in Western societies. Individually, there are few people who do not spend time on most days in some form of aesthetic consumption, even if it consists only of listening to the radio while in the car, looking at advertising hoardings, painting the bathroom, choosing new clothes, cooking food or watching television. Given the ubiquity of the arts in human life, and particularly given that they are often claimed to be such a peculiarly human activity, stretching back perhaps 30,000 years or more into prehistory, one of the surprising things is how little they have been


title: Zee: zero-effort crowdsourcing for indoor localization

abstract: Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.


title: Understanding the Factors that Impact the Popularity of GitHub Repositories

abstract: Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which can help them on building and evolving systems in a competitive software market.


title: Dual-band bandpass filter using helical resonators

abstract: This paper demonstrates non-uniform pitch helical resonators for realization of dual-band bandpass filters. By modifying the pitch and coil diameter of conventional uniform pitch helical resonators, the desired dual-band frequencies can be obtained. The external coupling and inter-resonator coupling structure are also illustrated. A non-uniform pitch helical resonator was designed and fabricated with unloaded quality-factor (>900) for both passbands. A 2nd order Butterworth filter that has passbands at 840 MHz and 2590 MHz has been designed. The simulation results show that the filter has a good selectivity with 6% and 4.3% fractional bandwidth at the passbands, respectively. The good power handling capability also suggests that the filter is applicable for microcell/picocell LTE base stations.


Ensembles as a Sequence of Classifiers


Estimating a woman's risk of having a pregnancy associated with Down's syndrome using her age and serum alpha-fetoprotein level


title: Template-based Question Answering over RDF Data

abstract: As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.


title: Face Recognition on Drones: Issues and Limitations

abstract: Drones, as known as unmanned aerial vehicles (UAV), are aircrafts which can perform autonomous pilot. They can easily reach locations which are too difficult to reach or dangerous for human beings and collect images from bird's-eye view through aerial photography. Enabling drones to identify people on the ground is important for a variety of applications, such as surveillance, people search, and remote monitoring. Since faces are part of inherent identities of people, how well face recognition technologies can be used by drones becomes essential for future development of the above applications. In this paper, we conduct empirical studies to evaluate several factors that may influence the performance of face detection and recognition techniques on drones. Our findings show that the current face recognition technologies are capable of recognizing faces on drones with some limits in distance and angle, especially when drones take pictures in high altitudes and the face image is taken from a long distance and with a large angle of depression. We also find that augmenting face models with 3D information may help to boost recognition performance in the case of large angles of depression.


title: Compact Environment-Invariant Codes for Robust Visual Place Recognition

abstract: Robust visual place recognition (VPR) requires scene representations that are invariant to various environmental challenges such as seasonal changes and variations due to ambient lighting conditions during day and night. Moreover, a practical VPR system necessitates compact representations of environmental features. To satisfy these requirements, in this paper we suggest a modification to the existing pipeline of VPR systems to incorporate supervised hashing. The modified system learns (in a supervised setting) compact binary codes from image feature descriptors. These binary codes imbibe robustness to the visual variations exposed to it during the training phase, thereby, making the system adaptive to severe environmental changes. Also, incorporating supervised hashing makes VPR computationally more efficient and easy to implement on simple hardware. This is because binary embeddings can be learned over simple-to-compute features and the distance computation is also in the low-dimensional hamming space of binary codes. We have performed experiments on several challenging data sets covering seasonal, illumination and viewpoint variations. We also compare two widely used supervised hashing methods of CCAITQ and MLH and show that this new pipeline out-performs or closely matches the state-of-the-art deep learning VPR methods that are based on high-dimensional features extracted from pre-trained deep convolutional neural networks.


title: Document Neural Autoregressive Distribution Estimation

abstract: We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution Estimator(NADE) model, which has been shown to be a good estimator of the distribution of discrete-valued igh-dimensional vectors. In this paper, we present how NADE can successfully be adapted to the case of textual data, retaining from NADE the property that sampling or computing the probability of observations can be done exactly and efficiently. The approach can also be used to learn deep representations of documents that are competitive to those learned by the alternative topic modeling approaches. Finally, we describe how the approach can be combined with a regular neural network N-gram model and substantially improve its performance, by making its learned representation sensitive to the larger, document-specific context.


title: Hybrid Hexagonal/Classical Tiling for GPUs

abstract: Time-tiling is necessary for the efficient execution of iterative stencil computations. Classical hyper-rectangular tiles cannot be used due to the combination of backward and forward dependences along space dimensions. Existing techniques trade temporal data reuse for inefficiencies in other areas, such as load imbalance, redundant computations, or increased control flow overhead, therefore making it challenging for use with GPUs. We propose a time-tiling method for iterative stencil computations on GPUs. Our method does not involve redundant computations. It favors coalesced global-memory accesses, data reuse in local/shared-memory or cache, avoidance of thread divergence, and concurrency, combining hexagonal tile shapes along the time and one spatial dimension with classical tiling along the other spatial dimensions. Hexagonal tiles expose multi-level parallelism as well as data reuse. Experimental results demonstrate significant performance improvements over existing stencil compilers.


title: Flood flow forecasting using ANN, ANFIS and regression models

abstract: Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R 2. The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m3 s−1 reduction in RMSE). Overall, the MNLR was shown to be superior (R 2 = 0.81 and RMSE = 0.145 m3 s−1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.


title: Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy

abstract: We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gradient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthennore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typically faster than EM, and usually requires about half as many iterations as EM.


title: Advanced street lighting maintenance using GPS, light intensity measuring and incremental cost-effectiveness ratio

abstract: System for light intensity measuring and GPS positioning of street lighting in Novi Sad and its neighboring villages is presented in this paper. Information obtained by measurements will help improve the quality of street lighting and make the maintenance work more efficient. An overview of intelligent lighting system with proposals to improve the quality of street lighting for a relatively low investment is presented. Elements of cost-effectiveness analysis are presented.


title: Metabolic Response and Fatigue in Soccer

abstract: The physical demands in soccer have been studied intensively, and the aim of the present review is to provide an overview of metabolic changes during a game and their relation to the development of fatigue. Heart-rate and body-temperature measurements suggest that for elite soccer players the average oxygen uptake during a match is around 70% of maximum oxygen uptake (VO2max). A top-class player has 150 to 250 brief intense actions during a game, indicating that the rates of creatine-phosphate (CP) utilization and glycolysis are frequently high during a game, which is supported by findings of reduced muscle CP levels and severalfold increases in blood and muscle lactate concentrations. Likewise, muscle pH is lowered and muscle inosine monophosphate (IMP) elevated during a soccer game. Fatigue appears to occur temporarily during a game, but it is not likely to be caused by elevated muscle lactate, lowered muscle pH, or change in muscle-energy status. It is unclear what causes the transient reduced ability of players to perform maximally. Muscle glycogen is reduced by 40% to 90% during a game and is probably the most important substrate for energy production, and fatigue toward the end of a game might be related to depletion of glycogen in some muscle fibers. Blood glucose and catecholamines are elevated and insulin lowered during a game. The blood free-fatty-acid levels increase progressively during a game, probably reflecting an increasing fat oxidation compensating for the lowering of muscle glycogen. Thus, elite soccer players have high aerobic requirements throughout a game and extensive anaerobic demands during periods of a match leading to major metabolic changes, which might contribute to the observed development of fatigue during and toward the end of a game.


title: Fuzzy Graph Theory: A Survey

abstract: A fuzzy graph (f-graph) is a pair G : ( σ, �) where σ is a fuzzy subset of a set S andis a fuzzy relation on σ. A fuzzy graph H : ( τ, υ) is called a partial fuzzy subgraph of G : ( σ, �) if τ (u) ≤ σ(u) for every u and υ (u, v) ≤ �(u, v) for every u and v . In particular we call a partial fuzzy subgraph H : ( τ, υ) a fuzzy subgraph of G : ( σ, � ) if τ (u) = σ(u) for every u in τ * and υ (u, v) = �(u, v) for every arc (u, v) in υ*. A connected f-graph G : ( σ, �) is a fuzzy tree(f-tree) if it has a fuzzy spannin g subgraph F : (σ, υ), which is a tree, where for all arcs (x, y) not i n F there exists a path from x to y in F whose strength is more than �(x, y). A path P of length n is a sequence of disti nct nodes u0, u 1, ..., u n such that �(u i1 , u i) > 0, i = 1, 2, ..., n and the degree of membershi p of a weakest arc is defined as its strength. If u 0 = u n and n ≥ 3, then P is called a cycle and a cycle P is called a fuzzy cycle(f-cycle) if it cont ains more than one weakest arc . The strength of connectedness between two nodes x and y is defined as the maximum of the strengths of all paths between x and y and is denot ed by CONN G(x, y). An x y path P is called a strongest x y


title: Neural Network Approximation Of Piecewise Continuous Functions: Application To Friction Compensation

abstract: One of the most important properties of neural nets (NNs) for control purposes is the universal approximation property. Unfortunately,, this property is generally proven for continuous functions. In most real industrial control systems there are nonsmooth functions (e.g., piecewise continuous) for which approximation results in the literature are sparse. Examples include friction, deadzone, backlash, and so on. It is found that attempts to approximate piecewise continuous functions using smooth activation functions require many NN nodes and many training iterations, and still do not yield very good results. Therefore, a novel neural-network structure is given for approximation of piecewise continuous functions of the sort that appear in friction, deadzone, backlash, and other motion control actuator nonlinearities. The novel NN consists of neurons having standard sigmoid activation functions, plus some additional neurons having a special class of nonsmooth activation functions termed "jump approximation basis function." Two types of nonsmooth jump approximation basis functions are determined- a polynomial-like basis and a sigmoid-like basis. This modified NN with additional neurons having "jump approximation" activation functions can approximate any piecewise continuous function with discontinuities at a finite number of known points. Applications of the new NN structure are made to rigid-link robotic systems with friction nonlinearities. Friction is a nonlinear effect that can limit the performance of industrial control systems; it occurs in all mechanical systems and therefore is unavoidable in control systems. It can cause tracking errors, limit cycles, and other undesirable effects. Often, inexact friction compensation is used with standard adaptive techniques that require models that are linear in the unknown parameters. It is shown here how a certain class of augmented NN, capable of approximating piecewise continuous functions, can be used for friction compensation.


Color image processing [basics and special issue overview]


title: Automatic Number Plate Recognition (ANPR) system for Indian conditions

abstract: Automatic Number Plate Recognition (ANPR) is a real time embedded system which automatically recognizes the license number of vehicles. In this paper, the task of recognizing number plate for Indian conditions is considered, where number plate standards are rarely followed.


title: Shape-based acetabular cartilage segmentation: application to CT and MRI datasets

abstract: Purpose ::: A new method for acetabular cartilage segmentation in both computed tomography (CT) arthrography and magnetic resonance imaging (MRI) datasets with leg tension is developed and tested.


title: SONEP: A Software-Defined optical Network emulation platform

abstract: Network emulation has been one of the tools of choice for conducting experiments on commodity hardware. In the absence of an easy to use optical network test-bed, researchers can significantly benefit from the availability of a flexible/programmable optical network emulation platform. Exploiting the lightweight system virtualization, which is recently supported in modern operating systems, in this work we present the architecture of a Software-Defined Network (SDN) emulation platform for transport optical networks and investigate its usage in a use-case scenario. To the best of our knowledge, this is for the first time that an SDN-based emulation platform is proposed for modeling and performance evaluation of optical networks. Coupled with recent trend of extension of SDN towards transport (optical) networks, the presented tool can facilitate the evaluation of innovative idea before actual implementations and deployments. In addition to the architecture of SONEP, a use-case scenario to evaluate the quality of transmission (QoT) of alien wavelengths in transport optical networks, along with performance results are reported in this piece of work.


title: Evaluating usage and quality of technical software documentation: an empirical study

abstract: Context: Software documentation is an integral part of any software development process. However, software practitioners are often concerned about the lack of usage and quality of documentation in practice. Unfortunately, in many projects, practitioners find that software documentation artifacts are outdated, incomplete and sometimes not beneficial. Objective: Motivated by the needs of NovAtel Inc. (NovAtel), a world-leading company of GPS software systems, we propose in this paper an approach to analyze the usage and quality of software documentation in development and maintenance phases. Method: The approach incorporates inputs from automated analysis (e.g., mining of project's data) and also experts' opinion extracted from survey-based questionnaire. The approach has been designed based on the "action-research" approach and in close collaboration between industry and academia. Results: To evaluate the feasibility and usefulness of the proposed approach, we have applied it in an industrial setting and results are presented in this paper. One of the results is that, in the context of our case-study, usage of documentation for an implementation purpose is higher than the usage for maintenance purposes. Conclusion: It is concluded that the usage of documentation differs for various purposes and it depends on the type of the information needs as well as the task to be completed (e.g. development and maintenance). In addition, we identify the most important and relevant quality attributes which are critical to improving documentation quality.


title: Fuzzy Logic Application to a Two-wheel Mobile Robot for Balancing Control Performance

abstract: This article presents experimental studies of fuzzy logic application to control a two-wheel mobile robot(TWMR) system. The TWMR system is composed of two systems, an inverted pendulum system and a mobile robot system. Although linear controllers can stabilize the TWMR, fuzzy controllers are expected to have robustness to uncertainties so that the resulting performances are expected to be better. Nominal fuzzy rules are used to control balance and position of TWMR. Fuzzy logic is embedded on a DSP chip to control the TWMR. Balancing performances of the PID controller and the fuzzy controller under disturbances are compared through extensive experimental studies.


INTERNET OF THINGS : 1 A REVIEW OF APPLICATIONS & 2 TECHNOLOGIES


title: New steroidal anti-inflammatory antedrugs bind to macrophage glucocorticoid receptors and inhibit nitric oxide generation

abstract: In continuing efforts to synthesize potent, anti-inflammatory steroids devoid of systemic side effects, methyl 9 alpha-fluoro-11 beta,17 alpha,21-trihydroxy-3,20-dioxo-pregna-1,4-diene-16 alpha-carboxylate (FP16CM) and its 21-acetate derivative (FP16CMAc) were recently synthesized and screened in animal models of inflammation. The compounds have now been assessed for high-affinity glucocorticoid receptor binding and glucocorticoid-mediated inhibition of nitric oxide (NO) generation in an in vitro RAW 264.7 macrophage cell culture system. Relative potencies for glucocorticoid receptor binding were 1, 1.7, and 2.4 for prednisone (P) (IC50 = 287 nM), FP16CM, and FP16CMAc, respectively. Concomitant relative potencies for inhibition of NO generation by macrophages stimulated with lipopolysaccharide were 1, 0.92 and 1.9 for P (IC50 = 126 nM), FP16CM, and FP16CMAc, respectively. Collectively, results suggest that the novel antedrugs are active anti-inflammatory agents. The 9 alpha-fluoro and 21-acetate substituent may contribute to enhanced topical potency, increased receptor binding affinity and inhibitory effects on NO generation. Inhibition of vasoactive NO may be one anti-inflammatory action of the steroidal antedrugs in vivo. Collectively, results suggest that these agents may be useful for topical application in allergic/inflammatory diseases.


title: Hierarchical Change Point Detection on Dynamic Networks

abstract: This paper studies change point detection on networks with community structures. It proposes a framework that can detect both local and global changes in networks efficiently. Importantly, it can clearly distinguish the two types of changes. The framework design is generic and as such several state-of-the-art change point detection algorithms can fit in this design. Experiments on both synthetic and real-world networks show that this framework can accurately detect changes while achieving up to 800X speedup.


title: Vehicle Mobility and Communication Channel Models for Realistic and Efficient Highway VANET Simulation

abstract: Developing real-time safety and nonsafety applications for vehicular ad hoc networks (VANETs) requires understanding of the dynamics of the network topology characteristics since these dynamics determine both the performance of routing protocols and the feasibility of an application over VANETs. Using various key metrics of interest, including node degree, neighbor distribution, number of clusters, and link duration, we provide a realistic analysis of the VANET topology characteristics over time and space for a highway scenario. In this analysis, we integrate real-world road topology and real-time data extracted from the freeway Performance Measurement System (PeMS) database into a microscopic mobility model to generate realistic traffic flows along the highway. Moreover, we use a more realistic, recently proposed, obstacle-based channel model and compare the performance of this sophisticated model to the most commonly used and more simplistic channel models, including unit disk and lognormal shadowing models. Our investigation on the key metrics reveals that both lognormal and unit disk models fail to provide realistic VANET topology characteristics. We therefore propose a matching mechanism to tune the parameters of the lognormal model according to the vehicle density and a correlation model to take into account the evolution of the link characteristics over time. The proposed method has been demonstrated to provide a good match with the computationally expensive and difficult-to-implement obstacle-based model. The parameters of the proposed model have been validated to depend only on the vehicle traffic density based on the real data of four different highways in California.


title: Lay Theories About White Racists: What Constitutes Racism (and What Doesn't):

abstract: Psychological theories of racial bias assume a pervasive motivation to avoid appearing racist, yet researchers know little regarding laypeople's theories about what constitutes racism. By investigating lay theories of White racism across both college and community samples, we seek to develop a more complete understanding of the nature of race-related norms, motivations, and processes of social perception in the contemporary United States. Factor analyses in Studies 1 and 1a indicated three factors underlying the traits laypeople associate with White racism: evaluative, psychological, and demographic. Studies 2 and 2a revealed a three-factor solution for behaviors associated with White racism: discomfort/unfamiliarity, overt racism, and denial of problem. For both traits and behaviors, lay theories varied by participants' race and their race-related attitudes and motivations. Specifically, support emerged for the prediction that lay theories of racism reflect a desire to distance the self from any aspect o...


title: Maternal Gestational Diabetes, Birth Weight, and Adolescent Obesity

abstract: OBJECTIVE ::: Obesity increases risk of many adverse outcomes, but its early origins are obscure. Gestational diabetes mellitus (GDM) reflects a metabolically altered fetal environment associated with high birth weight, itself associated with later obesity. Previous studies of GDM and offspring obesity, however, have been few and conflicting. The objectives of this study were to examine associations of birth weight and GDM with adolescent body mass index (BMI) and to determine the extent to which the effect of GDM is explained by its influence on birth weight or by maternal adiposity. ::: ::: ::: METHODS ::: We conducted a survey of 7981 girls and 6900 boys, 9 to 14 years of age, who are participants in the Growing Up Today Study, a US nationwide study of diet, activity, and growth. In 1996, participants reported height, weight, diet, activity, and other variables by self-administered mailed questionnaire. We linked these data with information reported by their mothers, participants in the Nurses' Health Study II, including GDM, height, current weight, and child's birth weight. We excluded births <34 weeks' gestation and mothers who had preexisting diabetes. We defined overweight as BMI (kg/m(2)) >95th percentile, and at risk for overweight as 85th to 95th percentile, for age and gender from US national data. ::: ::: ::: RESULTS ::: Mean birth weight was 3.4 kg for girls and 3.6 kg for boys. Among the 465 subjects whose mothers had GDM, 17.1% were at risk for overweight and 9.7% were overweight in early adolescence. In the group without maternal diabetes, these estimates were 14.2% and 6.6%, respectively. In multiple logistic regression analysis, controlling for age, gender, and Tanner stage, the odds ratio for adolescent overweight for each 1-kg increment in birth weight was 1.4 (95% confidence interval: 1.2-1.6). Adjustment for physical activity, television watching, energy intake, breastfeeding duration, mother's BMI, and other maternal and family variables reduced the estimate to 1.3 (1.1-1.5). For offspring of mothers with GDM versus no diabetes, the odds ratio for adolescent overweight was 1.4 (1.1-2.0), which was unchanged after controlling for energy balance and socioeconomic factors. Adjustment for birth weight slightly attenuated the estimate (1.3; 0.9-1.9); adjustment for maternal BMI reduced the odds ratio to 1.2 (0.8-1.7). ::: ::: ::: CONCLUSIONS ::: Higher birth weight predicted increased risk of overweight in adolescence. Having been born to a mother with GDM was also associated with increased adolescent overweight. However, the effect of GDM on offspring obesity seemed only partially explained by its influence on birth weight, and adjustment for mother's own BMI attenuated the GDM associations. Our results only modestly support a causal role of altered maternal-fetal glucose metabolism in the genesis of obesity in the offspring. Alternatively, GDM may program risk for a postnatal insult leading to obesity, or it may merely be a risk marker, not in the causal pathway.


Conceptual Blending, Form and Meaning


title: Do collectivists know themselves better than individualists? Cross-cultural studies of the holier than thou phenomenon.

abstract: Collectivists know themselves better than individualists do, in that collectivists provide more accurate self-predictions of future behavior in situations with moral or altruistic overtones. In 3 studies, respondents from individualist cultures overestimated the likelihood that they would act generously in situations involving redistributing a reward (Study 1), donating money (Study 2), or avoiding rude behavior (Study 3), whereas collectivists were, in general, more accurate in their self-predictions. Both groups were roughly accurate in predicting the behavior of their peers. Collectivists were more accurate in their self-predictions than were individualists, even when both groups were sampled from the same cultural group (Study 4). Discussion centers on culturally specific motivations that may bias the accuracy of self-insight and social insight.


title: gCoda: Conditional Dependence Network Inference for Compositional Data

abstract: The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under this logistic normal assumption. We then propose a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data. An effective Majorization-Minimization algorithm is proposed to solve the optimization problem in gCoda. Simulation studies show that gCoda outperforms existing methods (e.g., SPIEC-EASI) in edge recovery of inverse covariance for compositional data under a variety of scenarios. gCoda also performs better than SPIEC-EASI for inferring direct microbial interactions of mouse skin microbiome data.


title: Comparing the effectiveness of hits and salsa

abstract: This paper compares the effectiveness of two well-known query-dependent link-based ranking algorithms, "Hyperlink-Induced Topic Search" (HITS) and the "Stochastic Approach for Link-Structure Analysis" (SALSA). The two algorithms are evaluated on a very large web graph induced by 463 million crawled web pages and a set of 28,043 queries and 485,656 results labeled by human judges. We employed three different performance measures - mean average precision (MAP), mean reciprocal rank (MRR), and normalized discounted cumulative gain (NDCG). We found that as an isolated feature, SALSA substantially outperforms HITS. This is quite surprising, given that the two algorithms operate over the same neighborhood graph induced by the query result set. We also studied the combination of SALSA and HITS with BM25F, a state-of-the-art text-based scoring function that incorporates anchor text. We found that the combination of SALSA and BM25F outperforms the combination of HITS and BM25F. Finally, we broke down our query set by query specificity, and found that SALSA (and to a lesser extent HITS) is most effective for general queries.


title: The WU-Minn Human Connectome Project : An overview

abstract: The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.


title: A standard ontology for smart spaces

abstract: This paper presents a universal ontology for smart environments aiming to overcome the limitations of the existing ontologies. We enrich our ontology by adding new environmental aspects such as the referentiality and environmental change, that can be used to describe domains as well as applications. We show through a case study how our ontology is used and integrated in a self-organising middleware for smart environments.


title: Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions

abstract: Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index, where this real-time system works under varying light conditions (diurnal and nocturnal driving). Examples of different images of drivers taken in a real vehicle are shown to validate the algorithms used.


title: Fast damped gauss-newton algorithm for sparse and nonnegative tensor factorization

abstract: Alternating optimization algorithms for canonical polyadic decomposition (with/without nonnegative constraints) often accompany update rules with low computational cost, but could face problems of swamps, bottlenecks, and slow convergence. All-at-once algorithms can deal with such problems, but always demand significant temporary extra-storage, and high computational cost. In this paper, we propose an all-at-once algorithm with low complexity for sparse and nonnegative tensor factorization based on the damped Gauss-Newton iteration. Especially, for low-rank approximations, the proposed algorithm avoids building up Hessians and gradients, reduces the computational cost dramatically. Moreover, we proposed selection strategies for regularization parameters. The proposed algorithm has been verified to overwhelmingly outperform “state-of-the-art” NTF algorithms for difficult benchmarks, and for real-world application such as clustering of the ORL face database.


title: End-effector Position Analysis of SCORBOT-ER V plus Robot

abstract: This paper features the kinematic analysis of a SCORBOT-ER Vplus robot arm which is used for doing successful robotic manipulation task in its workspace. The SCORBOT-ER Vplus is a 5-dof vertical articulated robot and all the joints are revolute [1]. The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. The Denavit-Harbenterg (D-H) model of representation is used to model robot links and joints in this study along with 4x4 homogeneous matrix. SCORBOT-ER Vplus is a dependable and safe robotic system designed for laboratory and training applications. This versatile system allows students to gain theoretical and practical experience in robotics, automation and control systems. The MATLAB 8.0 is used to solve this mathematical model for a set of joint parameter.


title: Natural Solution to FraCaS Entailment Problems

abstract: Reasoning over several premises is not a common feature of RTE systems as it usually requires deep semantic analysis. On the other hand, FraCaS is a collection of entailment problems consisting of multiple premises and covering semantically challenging phenomena. We employ the tableau theorem prover for natural language to solve the FraCaS problems in a natural way. The expressiveness of a type theory, the transparency of natural logic and the schematic nature of tableau inference rules make it easy to model challenging semantic phenomena. The efficiency of theorem proving also becomes challenging when reasoning over several premises. After adapting to the dataset, the prover demonstrates state-of-the-art competence over certain sections of FraCaS.


title: A study of application performance with non-volatile main memory

abstract: Attaching next-generation non-volatile memories (NVMs) to the main memory bus provides low-latency, byte-addressable access to persistent data that should significantly improve performance for a wide range of storage-intensive workloads. We present an analysis of storage application performance with non-volatile main memory (NVMM) using a hardware NVMM emulator that allows fine-grain tuning of NVMM performance parameters. Our evaluation results show that NVMM improves storage application performance significantly over flash-based SSDs and HDDs. We also compare the performance of applications running on realistic NVMM with the performance of the same applications running on idealized NVMM with the same performance as DRAM. We find that although NVMM is projected to have higher latency and lower bandwidth than DRAM, these difference have only a modest impact on application performance. A much larger drag on NVMM performance is the cost of ensuring data resides safely in the NVMM (rather than the volatile caches) so that applications can make strong guarantees about persistence and consistency. In response, we propose an optimized approach to flushing data from CPU caches that minimizes this cost. Our evaluation shows that this technique significantly improves performance for applications that require strict durability and consistency guarantees over large regions of memory.


An investigation into coupling measures for C++


title: Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm

abstract: Abstract System power reliability under varying weather conditions and the corresponding system cost are the two main concerns for designing hybrid solar–wind power generation systems. This paper recommends an optimal sizing method to optimize the configurations of a hybrid solar–wind system employing battery banks. Based on a genetic algorithm (GA), which has the ability to attain the global optimum with relative computational simplicity, one optimal sizing method was developed to calculate the optimum system configuration that can achieve the customers required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS). The decision variables included in the optimization process are the PV module number, wind turbine number, battery number, PV module slope angle and wind turbine installation height. The proposed method has been applied to the analysis of a hybrid system which supplies power for a telecommunication relay station, and good optimization performance has been found. Furthermore, the relationships between system power reliability and system configurations were also given.


OPTIMUM PERFORMANCE IN THE BADMINTON JUMP SMASH


title: A Modified Particle Swarm Optimizer for the Coordination of Directional Overcurrent Relays

abstract: The coordination of directional overcurrent relays (DOCR) is treated in this paper using particle swarm optimization (PSO), a recently proposed optimizer that utilizes the swarm behavior in searching for an optimum. PSO gained a lot of interest for its simplicity, robustness, and easy implementation. The problem of setting DOCR is a highly constrained optimization problem that has been stated and solved as a linear programming (LP) problem. To deal with such constraints a modification to the standard PSO algorithm is introduced. Three case studies are presented, and the results are compared to those of LP technique to demonstrate the effectiveness of the proposed methodology.


Synthesis, Spectroscopy, Computational and Anticancer Evaluation of Some Novel Isatin Derivatives


title: Types of Machine Learning Algorithms

abstract: • Supervised learning --where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function. • Unsupervised learning --which models a set of inputs: labeled examples are not available. • Semi-supervised learning --which combines both labeled and unlabeled examples to generate an appropriate function or classifier. • Reinforcement learning --where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm. • Transduction --similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs. • Learning to learn --where the algorithm learns its own inductive bias based on previous experience.


title: Generating Text with Deep Reinforcement Learning

abstract: We introduce a novel schema for sequence to sequence learning with a Deep Q-Network (DQN), which decodes the output sequence iteratively. The aim here is to enable the decoder to first tackle easier portions of the sequences, and then turn to cope with difficult parts. Specifically, in each iteration, an encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the input sequence, automatically create features to represent the internal states of and formulate a list of potential actions for the DQN. Take rephrasing a natural sentence as an example. This list can contain ranked potential words. Next, the DQN learns to make decision on which action (e.g., word) will be selected from the list to modify the current decoded sequence. The newly modified output sequence is subsequently used as the input to the DQN for the next decoding iteration. In each iteration, we also bias the reinforcement learning's attention to explore sequence portions which are previously difficult to be decoded. For evaluation, the proposed strategy was trained to decode ten thousands natural sentences. Our experiments indicate that, when compared to a left-to-right greedy beam search LSTM decoder, the proposed method performed competitively well when decoding sentences from the training set, but significantly outperformed the baseline when decoding unseen sentences, in terms of BLEU score obtained.


title: A MATLAB-based simulation program for indoor visible light communication system

abstract: We report a simulation program for indoor visible light communication environment based on MATLAB and Simulink. The program considers the positions of the transmitters and the reflections at each wall. For visible light communication environment, the illumination light-emitting diode is used not only as a lighting device, but also as a communication device. Using the simulation program, the distributions of illuminance and root-mean-square delay spread are analyzed at bottom surface.


title: An Image-Based Approach to Detection of Fake Coins

abstract: We propose a new approach to detect fake coins using their images in this paper. A coin image is represented in the dissimilarity space, which is a vector space constructed by comparing the image with a set of prototypes. Each dimension measures the dissimilarity between the image under consideration and a prototype. In order to obtain the dissimilarity between two coin images, the local keypoints on each image are detected and described. Based on the characteristics of the coin, the matched keypoints between the two images can be identified in an efficient manner. A post-processing procedure is further proposed to remove mismatched keypoints. Due to the limited number of fake coins in real life, one-class learning is conducted for fake coin detection, so only genuine coins are needed to train the classifier. Extensive experiments have been carried out to evaluate the proposed approach on different data sets. The impressive results have demonstrated its validity and effectiveness.


title: Communicating Emotion through a Haptic Link: Design Space and Methodology

abstract: Communication of affect across a distance is not well supported by current technology, despite its importance to interpersonal interaction in modern lifestyles. Touch is a powerful conduit for emotional connectedness, and thus mediating haptic (touch) displays have been proposed to address this deficiency; but suitable evaluative methodology has been elusive. In this paper, we offer a first, structured examination of a design space for haptic support of remote affective communication, by analyzing the space and then comparing haptic models designed to manipulate its key dimensions. In our study, dyads (intimate pairs or strangers) are asked to communicate specified emotions using a purely haptic link that consists of virtual models rendered on simple knobs. These models instantiate both interaction metaphors of varying intimacy, and representations of virtual interpersonal distance. Our integrated objective and subjective observations imply that emotion can indeed be communicated through this medium, and confirm that the factors examined influence emotion communication performance as well as preference, comfort and connectedness. The proposed design space and the study results have implications for future efforts to support affective communication using the haptic modality, and the study approach comprises a first model for systematic evaluation of haptically expressed affect.


title: Attitudes and Persuasion

abstract: Study of attitudes and persuasion remains a defining characteristic of contemporary social psychology. This review outlines recent advances, with emphasis on the relevance of today's work for perennial issues. We reiterate the distinction between attitude formation and change, and show its relevance for persuasion. Single- and dual-process models are discussed, as are current views on dissonance theory. Majority and minority influence are scrutinized, with special emphasis on integrative theoretical innovations. Attitude strength is considered, and its relevance to ambivalence and resistance documented. Affect, mood, and emotion effects are reviewed, especially as they pertain to fear arousal and (un)certainty. Finally, we discuss attitude-behavior consistency, perhaps the reason for our interest in attitudes in the first place, with emphasis on self-interest and the theory of planned behavior. Our review reflects the dynamism and the reach of the area, and suggests a sure and sometimes rapid accumulation...


title: Analogies Explained: Towards Understanding Word Embeddings

abstract: Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to achieve it. Several explanations have been proposed, but each introduces assumptions that do not hold in practice. We derive a probabilistically grounded definition of paraphrasing that we re-interpret as word transformation, a mathematical description of "$w_x$ is to $w_y$". From these concepts we prove existence of linear relationships between W2V-type embeddings that underlie the analogical phenomenon, identifying explicit error terms.


title: DSD-Crasher: A hybrid analysis tool for bug finding

abstract: DSD-Crasher is a bug finding tool that follows a three-step approach to program analysis: D. Capture the program's intended execution behavior with dynamic invariant detection. The derived invariants exclude many unwanted values from the program's input domain. S. Statically analyze the program within the restricted input domain to explore many paths. D. Automatically generate test cases that focus on verifying the results of the static analysis. Thereby confirmed results are never false positives, as opposed to the high false positive rate inherent in conservative static analysis..This three-step approach yields benefits compared to past two-step combinations in the literature. In our evaluation with third-party applications, we demonstrate higher precision over tools that lack a dynamic step and higher efficiency over tools that lack a static step.


title: AsterixDB: A Scalable, Open Source BDMS

abstract: AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B+-tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. ::: ::: Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.


title: Up next: retrieval methods for large scale related video suggestion

abstract: The explosive growth in sharing and consumption of the video content on the web creates a unique opportunity for scientific advances in video retrieval, recommendation and discovery. In this paper, we focus on the task of video suggestion, commonly found in many online applications. The current state-of-the-art video suggestion techniques are based on the collaborative filtering analysis, and suggest videos that are likely to be co-viewed with the watched video. In this paper, we propose augmenting the collaborative filtering analysis with the topical representation of the video content to suggest related videos. We propose two novel methods for topical video representation. The first method uses information retrieval heuristics such as tf-idf, while the second method learns the optimal topical representations based on the implicit user feedback available in the online scenario. We conduct a large scale live experiment on YouTube traffic, and demonstrate that augmenting collaborative filtering with topical representations significantly improves the quality of the related video suggestions in a live setting, especially for categories with fresh and topically-rich video content such as news videos. In addition, we show that employing user feedback for learning the optimal topical video representations can increase the user engagement by more than 80% over the standard information retrieval representation, when compared to the collaborative filtering baseline.


title: An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning

abstract: An Intelligent Tutoring System (ITS) offers personalized education to each student in accordance with his/her learning preferences and his/her background. One of the most fundamental components of an ITS is the student model, that contains all the information about a student such as demographic information, learning style and academic performance. This information enables the system to be fully adapted to the student. Our research work intends to propose a student model and enhance it with semantics by developing (or via) an ontology in order to be exploitable effectively within an ITS, for example as a domain-independent vocabulary for the communication between intelligent agents. The ontology schema consists of two main taxonomies: (a) student’s academic information and (b) student’s personal information. The characteristics of the student that have been included in the student model ontology were derived from an empirical study on a sample of students.


title: Role of mitochondrial oxidative phosphorylation in the maintenance of intracellular pH.

abstract: Abstract The oxidative phosphorylation of isolated rabbit heart mitochondria and the accompanying pH changes were simultaneously measured in the same sample. The nearly constant extramitochondrial proton concentration during State 4 respiration decreased considerably under conditions of oxidative phosphorylation. On the other hand, the proton concentration increased when ATP was hydrolysed by the mitochondrial ATPase. The lysis of mitochondria by Triton X-100 equilibrating the pH difference across the inner membrane did not cause significant change in the proton concentration of the sample neither before nor after State 3 respiration. The accumulation of protons during the extramitochondrial hexokinase reaction was reduced by the mitochondrial oxidative phosphorylation. It is suggested that in the normoxic myocardial cell, the protons generated by the extramitochondrial hydrolysis of ATP are utilized during mitochondrial oxidative phosphorylation.


MEDICAL IMAGE SEGMENTATION USING FUZZY C-MEANS CLUSTERING AND MARKER CONTROLLED WATERSHED ALGORITHM


title: The Complexity of (List) Edge-Coloring Reconfiguration Problem

abstract: Let $G$ be a graph such that each edge has its list of available colors, and assume that each list is a subset of the common set consisting of $k$ colors. Suppose that we are given two list edge-colorings $f_0$ and $f_r$ of $G$, and asked whether there exists a sequence of list edge-colorings of $G$ between $f_0$ and $f_r$ such that each list edge-coloring can be obtained from the previous one by changing a color assignment of exactly one edge. This problem is known to be PSPACE-complete for every integer $k \ge 6$ and planar graphs of maximum degree three, but any complexity hardness was unknown for the non-list variant. In this paper, we first improve the known result by proving that, for every integer $k \ge 4$, the problem remains PSPACE-complete even if an input graph is planar, bounded bandwidth, and of maximum degree three. We then give the first complexity hardness result for the non-list variant: for every integer $k \ge 5$, we prove that the non-list variant is PSPACE-complete even if an input graph is planar, of bandwidth linear in $k$, and of maximum degree $k$.


title: Three-dimensional holographic head mounted display using holographic optical element

abstract: Recently, head mounted displays based on holographic optical elements have been reported. These head mounted displays can be implemented in a compact form factor but the image delivered to user's each eye is still two-dimensional, which causes accommodation-vergence mismatch. In this paper, we propose a holographic three-dimensional head mounted display which displays holographic three-dimensional images to each eye. The proposed configuration can achieve a compact form factor by using holographic optical elements, and solve the accommodation-vergence mismatch by presenting holographic three-dimensional images to each eye.