title: Twelve tips to promote excellence in medical teaching

abstract: For medical teachers around the world, teaching duties have expanded beyond the classroom and include teaching small groups, assessment, providing instructional materials beyond the syllabus, problem-based learning, learner-centred teaching, clinical teaching on-the-fly--and the list goes on. Faculty development is essential to train medical faculty in essential educational theory and specific teaching skills as well as to encourage a flexible and learner-centred approach to teaching. Finally, self-reflection and critique of teaching techniques are vital to propel medical schools towards promoting and aiming for uncompromising excellence in medical education. The twelve tips described in this article relating to educating teachers, evaluating teaching and eradicating institutional apathy are simple measures that educational leaders can apply to promote excellence in teaching at their parent institutions. The tips introduce a multi-dimensional approach to improving the overall quality of medical education consisting of measures aimed at individual teachers and those aimed at overhauling the teaching climate at medical institutions.


title: A Robot that Approaches Pedestrians

abstract: When robots serve in urban areas such as shopping malls, they will often be required to approach people in order to initiate service. This paper presents a technique for human–robot interaction that enables a robot to approach people who are passing through an environment. For successful approach, our proposed planner first searches for a target person at public distance zones anticipating his/her future position and behavior. It chooses a person who does not seem busy and can be reached from a frontal direction. Once the robot successfully approaches the person within the social distance zone, it identifies the person's reaction and provides a timely response by coordinating its body orientation. The system was tested in a shopping mall and compared with a simple approaching method. The result demonstrates a significant improvement in approaching performance; the simple method was only 35.1% successful, whereas the proposed technique showed a success rate of 55.9%.


title: Phase measuring deflectometry: a new approach to measure specular free-form surfaces

abstract: We present a new method to measure specular free-form surfaces within seconds. We call the measuring principle `Phase Measuring Deflectometry' (PMD). With a stereo based enhancement of PMD we are able to measure both the height and the slope of the surface. The basic principle is to project sinusoidal fringe patterns onto a screen located remotely from the surface under test and to observe the fringe patterns reflected via the surface. Any slope variations of the surface lead to distortions of the patterns. Using well-known phase-shift algorithms, we can precisely measure these distortions and thus calculate the surface normal in each pixel. We will deduce the method's diffraction-theoretical limits and explain how to reach them. A major challenge is the necessary calibration. We solved this task by combining various photogrammetric methods. We reach a repeatability of the local slope down to a few arc seconds and an absolute accuracy of a few arc minutes. One important field of application is the measurement of the local curvature of progressive eyeglass lenses. We will present experimental results and compare these results with the theoretical limits.


title: Start from Scratch: Towards Automatically Identifying, Modeling, and Naming Visual Attributes

abstract: Higher-level semantics such as visual attributes are crucial for fundamental multimedia applications. We present a novel attribute discovery approach that can automatically identify, model and name attributes from an arbitrary set of image and text pairs that can be easily gathered on the Web. Different from conventional attribute discovery methods, our approach does not rely on any pre-defined vocabularies and human labeling. Therefore, we are able to build a large visual knowledge base without any human efforts. The discovery is based on a novel deep architecture, named Independent Component Multimodal Autoencoder (ICMAE), that can continually learn shared higher-level representations across the visual and textual modalities. With the help of the resultant representations encoding strong visual and semantic evidences, we propose to (a) identify attributes and their corresponding high-quality training images, (b) iteratively model them with maximum compactness and comprehensiveness, and (c) name the attribute models with human understandable words. To date, the proposed system has discovered 1,898 attributes over 1.3 million pairs of image and text. Extensive experiments on various real-world multimedia datasets demonstrate the quality and effectiveness of the discovered attributes, facilitating multimedia applications such as image annotation and retrieval as compared to the state-of-the-art approaches.


title: Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge

abstract: Remote structural health monitoring systems employing a sensor-based quantitative assessment of in-service demands and structural condition are perceived as the future in long-term bridge management programs. However, the data analysis techniques and, in particular, the technology conceived years ago that are necessary for accurately and efficiently extracting condition assessment measures from highway infrastructure have just recently begun maturation. In this study, a large-scale wireless sensor network is deployed for ambient vibration testing of a single-span integral abutment bridge to derive in-service modal parameters. Dynamic behavior of the structure from ambient and traffic loads was measured with accelerometers for experimental determination of the natural frequencies, damping ratios, and mode shapes of the bridge. Real-time data collection from a 40-channel single network operating with a sampling rate of 128 Hz per sensor was achieved with essentially lossless data transmission. Successful acquisition of high-rate, lossless data on the highway bridge validates the proprietary wireless network protocol within an actual service environment. Operational modal analysis is performed to demonstrate the capabilities of the acquisition hardware with additional correlation of the derived modal parameters to a Finite Element Analysis of a model developed using as-built drawings to check plausibility of the mode shapes. Results from this testing demonstrate that wireless sensor technology has matured to the degree that modal analysis of large civil structures with a distributed network is a currently feasible and a comparable alternative to cable-based measurement approaches.


The Comovement of Investor Attention


title: Active Leg Exoskeleton (ALEX) for Gait Rehabilitation of Motor-Impaired Patients

abstract: This paper describes the design and human machine interface of an Active Leg EXoskeleton (ALEX) for gait rehabilitation of patients with walking disabilities. The paper proposes force-field controller which can apply suitable forces on the leg to help it move on a desired trajectory. The interaction forces between the subject and the orthosis are designed to be 'assist-as-needed' for safe and effective gait training. Simulations and experimental results with the force-field controller are presented. Experiments have been performed with healthy subjects walking on a treadmill. It was shown that a healthy subject could be retrained in about 45 minutes with ALEX to walk on a treadmill with a significantly altered gait. In the coming months, this powered orthosis will be used for gait training of stroke patients.


title: Remote path planning and motion control of mobile robot within indoor maze environment

abstract: The paper proposes a wireless navigation mobile robot system for both path planning and trajectory execution within an indoor maze environment. This system consists of the mobile robot, trajectory planner, motion controller, visual sensor (CCD camera), ZigBee wireless communication device and a maze terrain. The camera is used to capture images of the mobile robot within the maze. Developed image processing and analyzing algorithms determine the robot's position and orientation based on color markers recognition. Markers are mounted on the top of the robot. Based on this data the implemented navigation system calculates a trajectory for the mobile robot from a starting point to a target point. The proposed navigation system is an upgrade to our previously developed system. Maze encryption and motion planning modules have been added to the previous system. Breadth First Search (BFS) and modified Depth First Search (DFS) algorithms were used for the trajectory calculation. A developed control algorithm calculates control signals in real time. These signals are sent to the robot via modules for wireless communication, causing robot motion along the calculated trajectory and eventually, the completion of the trajectory. The whole control system is realized and experimental results have been obtained. The experimental results confirm the robustness and effectiveness of the implemented control system.


Redirected Walking in Virtual Reality.


title: Warmstarting of Model-based Algorithm Configuration

abstract: The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.


Quasi-Analytical Prediction of Base Flow-Plume Interaction


title: Bi-level multi-source learning for heterogeneous block-wise missing data

abstract: Abstract Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified “bi-level” learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches.


title: Potential Field Navigation of High Speed Unmanned Ground Vehicles on Uneven Terrain

abstract: This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.


title: Crowdsourced news reporting: supporting news content creation with mobile phones

abstract: As news organizations are moving towards systematically using the power of crowds in news reporting, mobile phones are potential mobile tools for reader reporters. We conducted two user studies to support the development of future mobile crowdsourcing processes and mobile tools for news reporting. In a quasi-experiment on future mobile crowdsourcing process with location-based assignments, SMS messages were experienced as an easy and handy means for news assignments. A customized mobile client prototype was preferred for submission of multimedia content (photo and video), since submission was experienced simple to use and reliable especially for videos. Based on our findings and earlier research we discuss implications for the development of mobile crowdsourcing processes with mobile news reporting assignments.


Proofs-of-delay and randomness beacons in Ethereum


title: Loosely-Coupled Benchmark Framework Automates Performance Modeling on IaaS Clouds

abstract: Cloud computing is under rapid development, which brings the urgent need of evaluation and comparison of cloud systems. The performance testers often struggle in tedious manual operations, when carrying out lots of similar experiments on the cloud systems. However, few of current benchmark tools provide both flexible workflow controlling methodology and extensible workload abstraction at the same time. We present a modeling methodology to compare the performance from multiple aspects based on a loosely coupled benchmark framework, which automates experiments under agile workflow controlling and achieves broad cloud supports, as well as good workload extensibility. With several built-in workloads and scenario templates, we performed a series of tests on Amazon EC2 services and our private Open Stack-based cloud, and analyze the elasticity and scalability based on the performance models. Experiments show the robustness and compatibility of our framework, which makes remarkable guarantee that it can be leveraged in practice for researchers and testers to perform their further study.


title: A survey of cyber crime in India

abstract: Cybercrime is all about the crimes in which communication channel and communication device has been used directly or indirectly as a medium whether it is a Laptop, Desktop, PDA, Mobile phones, Watches, Vehicles. The report titled “Global Risks for 2012”, predicts cyber-attacks as one of the top five risks in the World for Government and business sector. Cyber crime is a crime which is harder to detect and hardest to stop once occurred causing a long term negative impact on victims. With the increasing popularity of online banking, online shopping which requires sensitive personal and financial data, it is a term that we hear in the news with some frequency. Now, in order to protect ourselves from this crime we need to know what it is and how it does works against us. This paper presents a brief overview of all about cyber criminals and crime with its evolution, types, case study, preventive majors and the department working to combat this crime.


title: REQUIREMENTS TRACEABILITY: A SYSTEMATIC REVIEW AND INDUSTRY CASE STUDY

abstract: Requirements traceability enables software engineers to trace a requirement from its emergence to its fulfillment. In this paper we examine requirements traceability definitions, challenges, tools ...


title: A Wireless Health Monitoring System Using Mobile Phone Accessories

abstract: This paper presents the design and prototype of a wireless health monitoring system using mobile phone accessories. We focus on measuring real-time electrocardiogram (ECG) and heart rate monitoring using a smartphone case. With the increasing number of cardiac patients worldwide, this design can be used for early detection of heart diseases. Unlike most of the existing methods that use an optical sensor to monitor heart rate, our approach is to measure real-time ECG with dry electrodes placed on smartphone case. The collected ECG signal can be stored and analyzed in real time through a smartphone application for prognosis and diagnosis. The proposed hardware system consists of a single chip microcontroller (RFduino) embedded with Bluetooth low energy, hence miniaturizing the size and prolonging battery life. The system called “smart case” has been tested in a laboratory environment. We also designed a 3-D printed smartphone case to validate the feasibly of the system. The results demonstrated that the proposed system could be comparable to medical grade devices.


title: Surveillance (Oversight), Sousveillance (Undersight), and Metaveillance (Seeing Sight Itself)

abstract: Surveillance is an established practice that generally involves fixed cameras attached to fixed inanimate objects, or PTZ (Pan Tilt Zoom) cameras at a fixed position. Sur-veillance only provides part of the veillance story, and often only captures a partial truth. Further advances in miniaturization, together with wireless communication technologies, are giving rise to kinematic veillance ("kineveillance"): wearable, portable, and mobile cameras, as well as unpersoned aerial vehicles (UAVs). These additional veillances give us a more complete picture: multiple viewpoints from multiple entities bring us closer to the truth. In contrast to the extensive mathematical and conceptual framework developed around surveillance (e.g. background subtraction, frame-differencing, etc.), now that surveillance is no longer the only veillance, we need new mathematical and conceptual understandings of imaging and image processing. More importantly we need new tools for understanding the many veillances and how they interact. Therefore this paper introduces metaveillance and the veillance wavefunction for metasensing: the sensing of sensors and the sensing of their capacity to sense.


title: Testing Closed-Source Binary Device Drivers with DDT

abstract: DDT is a system for testing closed-source binary device drivers against undesired behaviors, like race conditions, memory errors, resource leaks, etc. One can metaphorically think of it as a pesticide against device driver bugs. DDT combines virtualization with a specialized form of symbolic execution to thoroughly exercise tested drivers; a set of modular dynamic checkers identify bug conditions and produce detailed, executable traces for every path that leads to a failure. These traces can be used to easily reproduce and understand the bugs, thus both proving their existence and helping debug them. We applied DDT to several closed-source Microsoft-certified Windows device drivers and discovered 14 serious new bugs. DDT is easy to use, as it requires no access to source code and no assistance from users. We therefore envision DDT being useful not only to developers and testers, but also to consumers who want to avoid running buggy drivers in their OS kernels.


title: Artificial Intelligence and Software Engineering : Status and Future Trends

abstract: During the last decades the disciplines of Artificial Intelligence (AI) and Software Engineering (SE) have developed separately without much exchange of research results. In AI we researched techniques for the computations that made it possible to perceive, reason, and act. Research in SE was concerned with supporting human beings to develop better software faster. Today, several research directions of both disciplines come closer together and are beginning to build new research areas. Software Agents play an important role as research objects in Distributed AI (DAI) as well as in agentoriented software engineering (AOSE). Knowledge-Based Systems (KBS) are being investigated for learning software organizations (LSO) as well as knowledge engineering. Ambient Intelligence (AmI) is a new research area for distributed, non-intrusive, and intelligent software systems both from the direction of how to build these systems as well as how to design the collaboration between ambient systems. Last but not least, Computational Intelligence (CI) plays an important role in research about software analysis or project management as well as knowledge discovery in databases or machine learning. Furthermore, in the last five to ten years several books, journals, and conferences have focused on the intersection between AI and SE. The international conference and associated journal Automated Software Engineering (ASE) presents research about formal and autonomic approaches to support SE [2]. Similar topics with a stronger focus on KBS and knowledge management are published in the international conference and associated journal of Software Engineering and Knowledge Engineering (IJSEKE) [1]. In this paper, we give a short overview about the status and future trends in the intersection between AI and SE. We focus on the topics software agents, KBS, AmI, and CI as the areas covered by the contributions of this special issue. In Section 2 we describe the disciplines AI and SE. The focused topics are described in more detail in Section 3. Finally, in Section 4 we give an outlook for the next years and present new challenges for both disciplines.


title: Temperature measurements of semiconductor devices - a review

abstract: There are numerous methods for measuring the temperature of an operating semiconductor device. The methods can be broadly placed into three generic categories: electrical, optical, and physically contacting. The fundamentals underlying each of the categories are discussed, and a review of the variety of techniques within each category is given. Some of the advantages and disadvantages as well as the spatial, time, and temperature resolution are also provided.


title: Assisted querying using instant-response interfaces

abstract: We demonstrate a novel query interface that enables users to construct a rich search query without any prior knowledge of the underlying schema or data. The interface, which is in the form of a single text input box, interacts in real-time with the users as they type, guiding them through the query construction. We discuss the issues of schema and data complexity, result size estimation, and query validity; and provide novel approaches to solving these problems. We demonstrate our query interface on two popular applications; an enterprise-wide personnel search, and a biological information database.


title: Multi-label Feature Selection for Graph Classification

abstract: Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously. Extracting good features using multiple labels of the graphs becomes an important step before graph classification. In this paper, we study the problem of multi-label feature selection for graph classification and propose a novel solution, called gMLC, to efficiently search for optimal sub graph features for graph objects with multiple labels. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform multi-label feature selection for graph data in a progressive way together with the sub graph feature mining process. We derive an evaluation criterion, named gHSIC, to estimate the dependence between sub graph features and multiple labels of graphs. Then a branch-and-bound algorithm is proposed to efficiently search for optimal sub graph features by judiciously pruning the sub graph search space using multiple labels. Empirical studies on real-world tasks demonstrate that our feature selection approach can effectively boost multi-label graph classification performances and is more efficient by pruning the sub graph search space using multiple labels.


title: Tools and Methods for Efficiently Designing Serious Games

abstract: Serious games are pedagogical multimedia products made to help learners develop specific skills. Their use has proven to be promising in many domains, but is at present restricted by the time consuming and costly nature of the developing process. When developing Serious Games (SGs) for academic purposes, not only is there a budgetary challenge, but there is also the challenge of integrating enough educational value without sacrificing the fun characteristics. In this article, we detail the designing process of a SG and enumerate the various actors who have to collaborate: project manager, cognitive specialist, domain experts, storyboard writer, artistic director, pedagogical expert, programmers… To help them work together and communicate in an efficient way, we first propose a step by step engineering method that helps the actors collaborate in an efficient and structured way. Each member of the designing team is assigned a set of tasks. Then, we propose authoring tools that can be used to carry out these tasks. They offer the possibility of simultaneously viewing various dimensions of the SG scenario: target knowledge, storyline, learning scenario, fun characteristics… When the authors connect to the platform, each of them is provided with a customized selection of dimensions presented with more or less detail. When interacting with one dimension, the other dimensions react and modify themselves automatically in a synchronized manner. In particular, we present a number of e-learning tools, that have been adapted for SG design and that allow us to specify the target skills that have to be leaned and build the pedagogical learning scenario. The SG authors will also have access to sample SGs and resources to provide them with inspiration for using fun characteristics to captivate the learners and carry them through the learning process. In response to the need felt by many SG authors to store and share their experience, we present a number of tools to help the authors store, find and integrate reusable software components into their SGs.


title: Building a 3-D Line-Based Map Using Stereo SLAM

abstract: This paper presents a graph-based visual simultaneous localization and mapping (SLAM) system using straight lines as features. Compared with point features, lines provide far richer information about the structure of the environment and make it possible to infer spatial semantics from the map. Using a stereo rig as the sole sensor, our proposed system utilizes many advanced techniques, such as motion estimation, pose optimization, and bundle adjustment. We use two different representations to parameterize 3-D lines in this paper: Plucker line coordinates for efficient initialization of newly observed line features and projection of 3-D lines, and orthonormal representation for graph optimization. The proposed system is tested with indoor and outdoor sequences, and it exhibits better reconstruction performance against a point-based SLAM system in line-rich environments.


title: Predictive Maintenance in semiconductor manufacturing

abstract: Over the past two years the Predictive Maintenance (PdM) capability in semiconductor manufacturing has migrated from Proof-of-Concept (PoC) and univariate Fault Detection (FD) extrapolation mechanisms to fab-wide solutions that are (1) robust to typical process and equipment disturbances, (2) extensible so as to provide solution approaches that are portable across instances of a tool type and across tool types, and (3) maintainable so as to provide solutions that are useful for long periods of time. A number of advancements have facilitated this advancement including solutions for porting modeling components across process and equipment types, mechanisms for incorporating process and equipment knowledge into models, mechanisms for determining model context (e.g., recipe) dependency, methods for model optimization to fab financials, and methods for rejecting run-time disturbances in PdM modeling. As a result of these and other innovations, the landscape of PdM in semiconductor manufacturing has rapidly advanced to the point that, from a technical perspective, solutions are now available for fab-wide PdM realization.


title: A comparative study of classification algorithms for forecasting rainfall

abstract: India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.


PRO-ORAM: Constant Latency Read-Only Oblivious RAM.


title: SPARQL Graph Pattern Processing with Apache Spark

abstract: A common way to achieve scalability for processing SPARQL queries is to choose MapReduce frameworks like Hadoop or Spark. Processing basic graph pattern (BGP) expressions generating large join plans over distributed data partitions is a major challenge in these frameworks. In this article, we study the use of two distributed join algorithms, partitioned join and broadcast join, for the evaluation of BGP expressions on top of Apache Spark. We compare five possible implementation and illustrate the importance of cautiously choosing the physical data storage layer and of the possibility to use both join algorithms to efficiently take account of existing data partitioning schemes. Our experimentations with different SPARQL benchmarks over real-world and synthetic workloads emphasize that hybrid join plans introduce more flexibility and often achieve better performance than single kind join plans.


title: Discriminative multiple sound source localization based on deep neural networks using independent location model

abstract: We propose a training method for multiple sound source localization (SSL) based on deep neural networks (DNNs). Such networks function as posterior probability estimator of sound location in terms of position labels and achieve high localization correctness. Since the previous DNNs' configuration for SSL handles one-sound-source cases, it should be extended to multiple-sound-source cases to apply it to real environments. However, a naive design causes 1) an increase in the number of labels and training data patterns and 2) a lack of label consistency across different numbers of sound sources, such as one and two-or-more-sound cases. These two problems were solved using our proposed method, which involves an independent location model for the former and an block-wise consistent labeling with ordering for the latter. Our experiments indicated that the SSL based on DNNs trained by our proposed training method out-performed a conventional SSL method by a maximum of 18 points in terms of block-level correctness.


title: Performance Analysis of Routing Protocol for Low Power and Lossy Networks (RPL) in Large Scale Networks

abstract: With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks, to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the routing protocol for low power and lossy networks (RPL) to facilitate the multihop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases.


title: DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

abstract: The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an efficient deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating clustered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional steps to generate contours based on low-level cues for post-separating. Our method won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams, surpassing all the other methods by a significant margin.


title: SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

abstract: Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment. ::: In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network, we use convolutional layers not only to extract feature maps but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fully-convolutional, which leads to a small model size and better energy efficiency. While achieving the same accuracy as previous baselines, our model is 30.4x smaller, 19.7x faster, and consumes 35.2x lower energy. The code is open-sourced at \url{this https URL}.


title: Planar millimeter-wave antenna on low-cost flexible PET substrate for 5G applications

abstract: This paper introduces a millimeter-wave (MMW) high bandwidth antenna on low-cost flexible PET substrate for future 5G wireless networks. The antenna design consists of a rectangular patch structure having symmetrical arrangements of slots on both sides of patch. The antenna consists of a coplanar waveguide (CPW) feed with a rectangular aperture in the ground plane. The proposed antenna shows a high bandwidth of 22GHz–40GHz, which covers the Ka band (26.5–40GHz) of electromagnetic (EM) spectrum. The peak gain of antenna is 8.2dBi at 38.75GHz and the gain is above 4dBi in the complete range of operation. Antenna is well suited for future 5G wireless networks with the flexibility to incorporate on planar surfaces like windows, doors etc. or can be made a part of non-planar surfaces like uniform or casual clothing.


title: A Fuzzy Logic Approach to Target Tracking

abstract: This paper discusses a target tracking problem in which no dynamic mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy If-then rules is developed. A comparison with a Kalman filter is made, and empirical results show that the performance of the fuzzy filter is better. Intensive simulations suggest that theoretical justification of the empirical results is possible.


title: Simulation and performance evaluation of DAG construction with RPL

abstract: In this paper, we simulate and analyze the performance of the network formation process with the RPL (IPv6 Routing Protocol for Low Power and Lossy Networks), which is a routing protocol specifically designed for Low power and Lossy Networks (LLN) compliant with 6LoWPAN (IPv6 Low power Wireless Personal Area Networks). One motivation behind this work is that RPL is the first prospective candidate routing protocol for low-power and lossy networks, which are a main component in the next generation Internet-of-Things. RPL is still under development, although having gained maturity, and is open to improvements. Indeed, there is a need to understand well its behavior, and investigate its relevance. Our analysis is based on the ContikiRPL accurate and realistic simulation model developed under Contiki operating system. The performance of RPL is evaluated and analyzed for different network settings to understand the impact of the protocol attributes on the network formation performance, namely in terms energy, storage overhead, communication overhead, network convergence time and the maximum hop count. We argue through simulation that RPL provides several features that make it suitable to large scale networks.


title: A high-performanance remote computing platform

abstract: The pervasive computing environment and the wide network bandwidth provide users more opportunities to utilize remote computing resources. In this paper, we introduce a high-performance remote computing platform to enable thin clients to access the applications in remote servers. Our system employs a compression-friendly model, where all the data redundancies are exploited in the compression viewport. Within this model, we also propose a unified coding scheme to efficiently compress the vast-spectral screens. Several mechanisms are further proposed to improve interactivity and reduce the bandwidth consumption in the system, including adaptive transmission for heterogeneous display devices. Simulations with real applications demonstrate that our system significantly outperforms previous thin-client systems. Especially for video playback under moderate- and low-bandwidth network conditions, our system can achieve the frame update rate 5 times as high as the previous counterparts.


title: Optimized Frame Packing for Embedded Systems

abstract: During system synthesis (i.e., task allocation) the transmission of messages between tasks is usually addressed in a simplistic way. If a message is exchanged via an external bus, it is assumed each message is packed in an individual frame. This assumption leads to an overestimation of bus bandwidth demand and frame response time. For some systems (i.e., automotive), this pessimism is not acceptable and therefore frame packing is often performed where multiple messages are packed into a single frame. In this paper, an improved frame packing approach is provided.


title: Happiness and health behaviours in Chilean college students: A cross-sectional survey

abstract: BackgroundHappiness has been associated with a range of favourable health outcomes through two pathways: its relationship with favourable biological responses to stress and with healthy lifestyles and prudent health behaviours. There are a substantial number of cross-cultural studies about happiness, but none of them has studied the association of happiness with perceived stress and health behaviours in Latin American samples. Therefore, the aim of this study was to examine the association between general happiness and these variables in a Latin American sample.MethodsWe conducted a survey to examine the status of 3461 students aged between 17 and 24 years old (mean age = 19.89; SD = 1.73) who attended University of Santiago de Chile during 2009. The healthy behaviours indexes assessed were the frequency of daily physical exercise, fruits/vegetables intake, breakfast and lunch intake, smoking, alcohol and other drugs consumption. We also included the assessment of perceived stress and Body Mass Index. All of them were evaluated using a self-report questionnaire.ResultsThe univariate and multivariate binary logistic regression analyses showed that being female and younger was related to a higher happiness, as well as that people self-reporting daily physical activity, having lunch and fruits and vegetables each day had a higher likelihood (OR between 1.33 and 1.40) of being classified as "very happy". Those who informed felt stressed in normal circumstances and during tests situations showed a lower likelihood (0.73 and 0.82, respectively) of being considered "very happy". Regarding drug consumption, taking tranquilizers under prescription was negative related to "subjective happiness" (OR = 0.62), whereas smoking was positive associated (OR = 1.20).ConclusionsThe findings of this study mainly support the relationship between happiness and health outcomes through the two pathways previously mentioned. They also underscore the importance of that some healthy behaviours and person's cognitive appraisal of stress are integrated into their lifestyle for college students. Additionally, highlight the importance of taking into account these variables in the design of strategies to promote health education in university setting.


Immune-related adverse events during anticancer immunotherapy: Pathogenesis and management


Curve Structure Extraction for Cartoon Images


title: An empirical Bayes approach to inferring large-scale gene association networks

abstract: Motivation: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. ::: ::: Methods: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. ::: ::: Results: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes. ::: ::: Availability: The authors have implemented the approach in the R package 'GeneTS' that is freely available from http://www.stat.uni-muenchen.de/~strimmer/genets/, from the R archive (CRAN) and from the Bioconductor website. ::: ::: Contact: korbinian.strimmer@lmu.de


title: Barriers to health care for people with hearing loss: a review of the literature.

abstract: Deaf individuals face many barriers when trying to access health care. The reasons why barriers are encountered, difficulties met as a result of the barriers, and ways that health professionals and others working with deaf people can overcome obstacles are included in this review of the literature. A brief summary of Deaf culture and history gives background information to better understand the problems. Misunderstandings by d/Deaf patients and hearing health professionals are addressed, as well as issues related to medical interpreters. This paper aims to make health professionals more aware of the needs and cultural differences that must be considered when providing care to the d/Deaf population.


title: Symbiosis, Printspeak, and Politics: Topics in the Orality and Literacy Debate.

abstract: Orality and literacy are not antithetical, rather they exist in a complex symbiosis at the individual, family, and community levels. Such a symbiosiL is inevitable and appears in all kinds of institutions, including economic, political, social, cultural, and educatonal institutions. Out of this relationship, "printspeak" has emerged. Printspeak is a form of oral speech that is highly formalized and has the character of being read aloud from a written record. The speaker of printspeak structures his argument as if for writing, and uses syntax and semantics peculiar to the written text. The logical is favored over the intuitive, and characteristics peculiar to oral language are greatly reduced. Because literacy is a social process, politics is part of the relationship between literacy and orality. Generally, the oral culture is disadvantaged relative to the literate culture. While literacy is not necessarily good in and of itself, acquisition of literacy does tend to engender creative discontent necessary for social change to occur. Therefore, literacy is a good in all circumstances, in all places, at all times. Literacy must be made universal. A nine-item bibliography is included. (MSE) (Adjunct ERIC Clearinghouse on Literacy Education) ******************************************M0************************* Reproductions supplied by EDRS are the best that can be made from the original document. ***********************************************************************


title: A simple music/voice separation method based on the extraction of the repeating musical structure

abstract: Repetition is a core principle in music. This is especially true for popular songs, generally marked by a noticeable repeating musical structure, over which the singer performs varying lyrics. On this basis, we propose a simple method for separating music and voice, by extraction of the repeating musical structure. First, the period of the repeating structure is found. Then, the spectrogram is segmented at period boundaries and the segments are averaged to create a repeating segment model. Finally, each time-frequency bin in a segment is compared to the model, and the mixture is partitioned using binary time-frequency masking by labeling bins similar to the model as the repeating background. Evaluation on a dataset of 1,000 song clips showed that this method can improve on the performance of an existing music/voice separation method without requiring particular features or complex frameworks.


title: IOS Press Intelligent virtual humans with autonomy and personality: State-of-the-art

abstract: Intelligent virtual characters has been subject to exponential growth in the last decades and they are utilized in many application areas such as education, training, human-computer interfaces and entertainment. In this paper, we present a state-of-the-art of virtual human mentioning about the use of intelligent decision technologies in order to build virtual human architectures. We consider various aspects such as autonomy, interaction and personification. Each of these aspects comes to prominence in different applications. This survey provides a novel insight to the current state of designing and modeling virtual humans using different decision technologies and can be used as a basis for several future directions.


title: Dermoscopy in the diagnosis of hair and scalp disorders.

abstract: Dermoscopy is a non-invasive, in vivo technique that has been recently utilized for the diagnosis and management of hair and scalp disorders. In the last few years many studies have been published in this field; this review will describe the dermoscopic patterns observed in the most common hair and scalp disorders and discuss their diagnostic relevance.


title: Detection Algorithms for Communication Systems Using Deep Learning

abstract: The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, it is not possible to accurately model this relationship. In these scenarios, because of the lack of mathematical channel models, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by borrowing tools from deep learning, it is possible to train detectors that perform well, without any knowledge of the underlying channel models. We evaluate these algorithms using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a simple detector that was used in previous works, which also did not assume any knowledge of the channel.


title: A half-mode substrate integrated waveguide based leaky-wave antenna with open-stopband suppression

abstract: This paper presents a leaky-wave antenna (LWA) based on half-mode substrate integrated waveguide (HMSIW). The proposed LWA consists of transverse and longitudinal slots in one unit cell, by properly adjusting the structure parameters of the slots, open-stopband can be suppressed and hence the proposed LWA features continuous beam scanning from backward, through broadside, and to forward. Additional transverse slots are also implemented to suppress the cross polarization level. Numerical simulations are conducted and the results verify the theory very well.


title: Quadrature Channel Modulation

abstract: In this letter, we propose the concept of quadrature channel modulation (QCM) by combining quadrature spatial modulation (QSM) and media-based modulation (MBM) transmission principles. The proposed QCM schemes exploit not only the in-phase and quadrature components of complex data symbols for antenna indexing but also channel states for the transmission of additional information bits through index modulation by employing a single radio frequency (RF) chain. It is shown via numerical studies that the proposed QCM schemes can outperform the existing emerging schemes such as QSM, plain MBM, and spatial modulation-based MBM, which also employ a single RF chain at their transmitters.


title: Understanding, Avoiding, and Managing Dermal Filler Complications

abstract: BACKGROUND Dermal fillers are increasingly being utilized for multiple cosmetic dermatology indications. The appeal of these products can be partly attributed to their strong safety profiles. Nevertheless, complications can sometimes occur. ::: ::: OBJECTIVE To summarize the complications associated with each available dermal filling agent, strategies to avoid them, and management options if they do arise. ::: ::: METHODS AND MATERIALS Complications with dermal fillers reported in peer-reviewed publications, prescribing information, and recent presentations at professional meetings were reviewed. Recommendations for avoiding and managing complications are provided, based on the literature review and the author's experience. ::: ::: RESULTS Inappropriate placement or superficial placement is one of the most frequent reasons for patient dissatisfaction. Due to the reversibility of hyaluronic acid, complications from these fillers can be easily corrected. Sensitivity to any of the currently approved FDA products is quite rare and can usually be managed with anti-inflammatory agents. Infection is quite uncommon as well and can usually be managed with either antibiotics or antivirals depending on the clinical features. The most concerning complication is cutaneous necrosis, and a protocol to treat the full spectrum of this process is reviewed. ::: ::: CONCLUSIONS Complications with dermal fillers are infrequent, and strategies to minimize their incidence and impact are easily deployed. Familiarity with each family of soft-tissue augmentation products, potential complications, and their management will optimize the use of these agents.


title: A Semantic Middleware Architecture Focused on Data and Heterogeneity Management within the Smart Grid

abstract: There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests.


title: Quaternion Fourier transform based alpha-rooting method for color image measurement and enhancement

abstract: The aim of this paper is to present a transform-based framework for color image enhancement which processes all three color components (R,G,B) simultaneously. In this framework, the image enhancement procedure is supported by a two-dimensional discrete quaternion Fourier transform and the new multi-frequency band α-rooting concept. Moreover, we introduce a simple measure to evaluate the quality of enhanced color images. Additionally, we demonstrate the performance of the proposed algorithms against recently proposed methods which separately process color channels. Preliminary results show that the presented algorithm out-performs other enhancement methods such as the traditional Fourier transform-based alpha-rooting and the multi-scale Retinex algorithms. The proposed algorithms are fast and simple in implementation and design which makes them very practical for image enhancement. Graphical abstractDisplay Omitted HighlightsWe propose a quaternion Fourier transform-based alpha-rooting color image enhancement.We introduce a measure to evaluate quality of enhanced color images.We examine the proposed method in RGB and XYZ color models.The presented algorithm out-performs other transform-based enhancement methods.


title: Spacecraft Attitude Control System Simulator

abstract: Spacecraft attitude control system simulator helps to create an environment utilized for simulating attitude control system of satellites. A set of three inertial wheels is used as primary actuators for the attitude control. A flywheel is a disk with significant moment of inertia which is used as a storage device for rotational energy, i.e. momentum can be imparted on it or taken away from it. The hardware simulator provides a real-time freedom of motion in the axis of rotation. The spacecraft simulators are widely used to develop and verify spacecraft control techniques required by modern spacecraft applications. The simulator find many applications in various ongoing research flywheel energy management, non linear compensation of an under actuated system, the problems of attitude and orbit control and development of inertial wheels. The simulator provides the flexibility to experimentally implement many types of control techniques. The simulator is employed to develop, improve and carry out the objective tests of the actuators and the algorithms developed for the attitude control of the spacecraft.


title: Learning organization principles & project management

abstract: Technology projects are getting more numerous and more complex for nearly everyone. At the same time, our colleagues are becoming less accepting of older management models. This combination can be a formula for disaster or can be used as a springboard to initiate management changes and simultaneously do better in managing both single projects and entire slates of projects. At the University of Memphis under the leadership of Dr. James I. Penrod who was at that time the CIO, we began a concerted effort to create a cultural transformation of the information technology organization by using Peter Senge's learning organization principles. This article shows how these principles were applied to improving the way our division handled the management of technology projects on behalf of the University.


title: Frobenius lifts and elliptic curves with complex multiplication

abstract: We give a new characterisation of elliptic curves of Shimura type in terms commuting families of Frobenius lifts. We also strengthen an old principal ideal theorem for ray class fields which combined. These combined allow us to prove the existence of global minimal models for such curves, generalising a result of Gross. Along the way we also prove a handful of small but new results regarding elliptic curves with complex multiplication.


title: ISO/IEC 27000, 27001 and 27002 for Information Security Management

abstract: With the increasing significance of information technology, there is ::: an urgent need for adequate measures of information security. ::: Systematic information security management is one of most important initiatives ::: for IT management. At least since reports about privacy and security breaches, ::: fraudulent accounting practices, and attacks on IT systems appeared ::: in public, organizations have recognized their responsibilities to safeguard ::: physical and information assets. Security standards can be used as guideline or ::: framework to develop and maintain an adequate information security management ::: system (ISMS). The standards ISO/IEC 27000, 27001 and 27002 are international ::: standards that are receiving growing recognition and adoption. They are ::: referred to as “common language of organizations around the world” for ::: information security [1]. With ISO/IEC 27001 companies can have their ISMS ::: certified by a third-party organization and thus show their customers evidence ::: of their security measures.


title: Network Hypothesis Testing Using Mixed Kronecker Product Graph Models

abstract: The recent interest in networks-social, physical, communication, information, etc.-has fueled a great deal of research on the analysis and modeling of graphs. However, many of the analyses have focused on a single large network (e.g., a sub network sampled from Facebook). Although several studies have compared networks from different domains or samples, they largely focus on empirical exploration of network similarities rather than explicit tests of hypotheses. This is in part due to a lack of statistical methods to determine whether two large networks are likely to have been drawn from the same underlying graph distribution. Research on across-network hypothesis testing methods has been limited by (i) difficulties associated with obtaining a set of networks to reason about the underlying graph distribution, and (ii) limitations of current statistical models of graphs that make it difficult to represent variations across networks. In this paper, we exploit the recent development of mixed-Kronecker Product Graph Models, which accurately capture the natural variation in real world graphs, to develop a model-based approach for hypothesis testing in networks.


title: An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels

abstract: Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to represent the label assignments in a low-dimensional space. However, by doing so, these methods actually neglect the tail labels - labels that are infrequently assigned to instances. We propose an embedding-based method that non-linearly embeds the label vectors using an stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method have excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. With the best of our knowledge, our proposed method is the first multi-label classifier that simultaneously addresses all of the mentioned challenges. Experiments on real-world datasets show that our method outperforms stateof-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time.


title: On the Viability of Conspiratorial Beliefs

abstract: Conspiratorial ideation is the tendency of individuals to believe that events and power relations are secretly manipulated by certain clandestine groups and organisations. Many of these ostensibly explanatory conjectures are non-falsifiable, lacking in evidence or demonstrably false, yet public acceptance remains high. Efforts to convince the general public of the validity of medical and scientific findings can be hampered by such narratives, which can create the impression of doubt or disagreement in areas where the science is well established. Conversely, historical examples of exposed conspiracies do exist and it may be difficult for people to differentiate between reasonable and dubious assertions. In this work, we establish a simple mathematical model for conspiracies involving multiple actors with time, which yields failure probability for any given conspiracy. Parameters for the model are estimated from literature examples of known scandals, and the factors influencing conspiracy success and failure are explored. The model is also used to estimate the likelihood of claims from some commonly-held conspiratorial beliefs; these are namely that the moon-landings were faked, climate-change is a hoax, vaccination is dangerous and that a cure for cancer is being suppressed by vested interests. Simulations of these claims predict that intrinsic failure would be imminent even with the most generous estimates for the secret-keeping ability of active participants—the results of this model suggest that large conspiracies (≥1000 agents) quickly become untenable and prone to failure. The theory presented here might be useful in counteracting the potentially deleterious consequences of bogus and anti-science narratives, and examining the hypothetical conditions under which sustainable conspiracy might be possible.


title: UWB Low-Profile Tightly Coupled Dipole Array With Integrated Balun and Edge Terminations

abstract: Tightly coupled phased arrays (TCPAs) provide UWB performance due to their strong inter-element coupling. However, in finite tightly coupled phased array realizations, mutual coupling is reduced near the array edges, causing the edge elements to become narrowband. To address this issue, a nonuniform array excitation scheme, referred to as “characteristic mode (CM) excitation,” was recently proposed. A more practical alternative for designing UWB tightly coupled phased arrays is proposed here. In this paper, we present a strategy that employs uniform excitation of the central array elements and short-circuits the periphery elements. We report that at least for medium size arrays, this approach provides up to 3 dB more gain and 50% higher efficiency than typical resistive termination. This concept is demonstrated using a 7 × 7 linearly polarized dipole array, 60.96 cm × 60.96 cm (2' × 2') in size, for operation from 200 MHz-600 MHz. Feeding of the active elements is challenging due to several constraints on the feed design, including balanced to unbalanced transitions, impedance transformations, common mode suppression, compact size, low cost, etc. To address these issues we propose a novel array feed using a compact, ultrawideband balun (with 10:1 bandwidth for VSWR ). Simulated and measured data are provided for broadside and 30 scan in the H-plane.


title: Genome size variation in the Western Carpathian Sesleria (Poaceae) species

abstract: The genus Sesleria represents a taxonomically intricate polyploid complex of morphologically highly varied and weakly differentiated taxa. In the wider area of the Western Carpathians, the genus is represented by three tetraploid (S. caerulea, S. heufleriana, and S. uliginosa) and three octoploid (S. hungarica, S. sadleriana, and S. tatrae) species. Although these taxa play a dominant role in a number of plant communities, their identification is often problematic. We examined relative (RGS; 637 individuals, 49 populations) and absolute (AGS; 64 individuals, 19 populations) genome size variation of all six taxa and revealed that genome size could be an effective marker for their identification. Genome size allowed not only separation of the two ploidy levels (4x vs. 8x), but helped to identify at least four of the six studied species. Among tetraploids, RGS appeared to be diagnostic for S. heufleriana (even at individual level), but failed to differentiate S. caerulea from S. uliginosa. RGS appeared to be diagnostic for octoploid species at population level, although the identity of populations at Vrsatec (Slovakia) and Naszaly (Hungary) require further studies. We found that octoploid plants from Slavec (Slovakia), formerly identified as S. heufleriana, actually represent S. hungarica, which is the first reliable record of this species from Slovakia. In the geographically isolated or peripheral populations, an increased or decreased genome size has been detected.


The Gamification of Learning and Instruction: Game-Based Methods and Strategies for Training and Education


title: Modeling the Progress and Retention of International Students Using Markov Chains

abstract: International students are a small and diverse student population present in any sizable American university. One of the greatest obstacles in their path is the acquisition of the English language. English for Academic Purposes (EAP) programs, such as the English Language Institute (ELI) at the University of Akron, attempt to address this problem. By studying how this student population progresses in their academic studies, EAP programs and their associated universities can make well-informed decisions on how best to serve their English Language Learners. One way to study International students is through the use of a Markov model based on university data. The statistically calibrated model is helpful in that it can reveal at what stage of their studies students are struggling the most or succeeding the most. Our results show that ELI students are well prepared, since international students who are first time freshman seem to have a higher progression rate to the sophomore level than the standard STEM population of students. We also find that students who repeat their freshman year are more likely to repeat again than progress to the sophomore level.


title: Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011–2014

abstract: The CROHME competitions have helped organize the field of handwritten mathematical expression recognition. This paper presents the evolution of the competition over its first 4 years, and its contributions to handwritten math recognition, and more generally structural pattern recognition research. The competition protocol, evaluation metrics and datasets are presented in detail. Participating systems are analyzed and compared in terms of the central mathematical expression recognition tasks: (1) symbol segmentation, (2) classification of individual symbols, (3) symbol relationships and (4) structural analysis (parsing). The competition led to the development of label graphs, which allow recognition results with conflicting segmentations to be directly compared and quantified using Hamming distances. We introduce structure confusion histograms that provide frequencies for incorrect subgraphs corresponding to ground-truth label subgraphs of a given size and present structure confusion histograms for symbol bigrams (two symbols with a relationship) for CROHME 2014 systems. We provide a novel analysis combining results from competing systems at the level of individual strokes and stroke pairs; this virtual merging of system outputs allows us to more closely examine limitations for current state-of-the-art systems. Datasets along with evaluation and visualization tools produced for the competition are publicly available.


title: Functional Design of a Powered Elbow Orthosis Toward its Clinical Employment

abstract: This paper presents the design and preliminary evaluation of a novel version of the robotic elbow exoskeleton NEUROExos, designed for the in-clinic treatment of stroke survivors in acute and subacute phases. The robotic design implements a novel series elastic actuation system, a 4-degree-of-freedom (DoFs) passive mechanism for the anatomical axis alignment, and one active DoF with remote cable-driven actuation. The low-level control system allows two working modalities: a torque control and a joint position control. The high-level control system employs a finite-state machine that allows the setting and execution of these modalities during rehabilitation exercises. Preliminary pilot tests based on passive exercises, with three chronic post-stroke patients, demonstrated the effectiveness of the proposed approach in assessing joint rigidity and its usability within a rehabilitation clinic.


title: Software defined IoT: Issues and challenges

abstract: Internet of Things (IoT) is going to be an integral part of connected living and industrialization. Already several billion devices are connected in the IoT framework and this number is growing very fast. Rigidity of current network infrastructure limits the enormous possibilities which IoT can deliver. Software defined networking (SDN) with its agility and elasticity appears promising networking technique for the realization of robust IoT. SDN is quite suitable for the control and management of IoT. However, there are several issues and challenges pertaining to the implementation of SDN based IoT ecosystem. In this article, we present the issues and challenges faced by the network designers while deploying SDN over the IoT.


title: Wireless Monitoring of Automobile Tires for Intelligent Tires

abstract: This review discusses key technologies of intelligent tires focusing on sensors and wireless data transmission. Intelligent automobile tires, which monitor their pressure, deformation, wheel loading, friction, or tread wear, are expected to improve the reliability of tires and tire control systems. However, in installing sensors in a tire, many problems have to be considered, such as compatibility of the sensors with tire rubber, wireless transmission, and battery installments. As regards sensing, this review discusses indirect methods using existing sensors, such as that for wheel speed, and direct methods, such as surface acoustic wave sensors and piezoelectric sensors. For wireless transmission, passive wireless methods and energy harvesting are also discussed.


title: A survey of shadow algorithms

abstract: The various types of shadows are characterized. Most existing shadow algorithms are described, and their complexities, advantages, and shortcomings are discussed. Hard shadows, soft shadows, shadows of transparent objects, and shadows for complex modeling primitives are considered. For each type, shadow algorithms within various rendering techniques are examined. The aim is to provide readers with enough background and insight on the various methods to allow them to choose the algorithm best suited to their needs and to help identify the areas that need more research and point to possible solutions. >


title: A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming

abstract: Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process.


title: Drug Addiction and Its Underlying Neurobiological Basis: Neuroimaging Evidence for the Involvement of the Frontal Cortex

abstract: OBJECTIVE: Studies of the neurobiological processes underlying drug addiction primarily have focused on limbic subcortical structures. Here the authors evaluated the role of frontal cortical structures in drug addiction. METHOD: An integrated model of drug addiction that encompasses intoxication, bingeing, withdrawal, and craving is proposed. This model and findings from neuroimaging studies on the behavioral, cognitive, and emotional processes that are at the core of drug addiction were used to analyze the involvement of frontal structures in drug addiction. RESULTS: The orbitofrontal cortex and the anterior cingulate gyrus, which are regions neuroanatomically connected with limbic structures, are the frontal cortical areas most frequently implicated in drug addiction. They are activated in addicted subjects during intoxication, craving, and bingeing, and they are deactivated during withdrawal. These regions are also involved in higher-order cognitive and motivational functions, such as the ability to tr...


title: A review of stator fault monitoring techniques of induction motors

abstract: Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30-40% of faults of induction motors are stator faults. This work presents a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology. It is aimed at providing a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors. A list of 183 research publications on the subject is appended for quick reference.


title: Non-Uniform Sinusoidally Modulated Half-Mode Leaky-Wave Lines for Near-Field Focusing Pattern Synthesis

abstract: A novel non-uniform sinusoidally modulated half-mode microstrip structure with application to near-field focused leaky-wave radiation in the backward Fresnel zone is proposed. First, it is presented a dispersion analysis of the constituent backward leaky wave in the sinusoidally modulated unit cell in half-width microstrip technology. This information is then used to design a finite non-uniform line that focuses the radiated fields at the desired point. Finally, eight similar line sources are arranged in a radial array to generate a three-dimensional focused spot located at the desired focal length over the simple central coaxial feeding. Simulated and experimental results are presented to validate the proposed simple approach.


title: Grounded Theory Research: Procedures, Canons and Evaluative Criteria

abstract: Using grounded theory as an example, this paper examines three methodological questions that are generally applicable to all qualitative methods. How should the usual scientific canons be reinterpreted for qualitative research? How should researchers report the procedures and canons used in their research? What evaluative criteria should be used in judging the research products? We propose that the criteria should be adapted to fit the procedures of the method. We demonstrate how this can be done for grounded theory and suggest criteria for evaluating studies following this approach. We argue that other qualitative researchers might be similarly specific about their procedures and evaluative criteria.


title: Channel redistribution with direct selling

abstract: Abstract It seems nowadays that a company can be considered either a “pure-direct” such as Amazon.com and Dell Computers, or an “adopted-direct”, like Barnes and Noble for example. An adopted-direct company’s decision to directly sell “on-line” has become a fashionable choice. But the results of such choice differ drastically. Glorious success mixed with pathetic failure. One of the pending issues has been the channel redistribution strategy, as whether to entirely switch to direct sell or to maintain a combination of both. In this paper, we develop a dynamic channel diffusion model and analyze how the sales will redistribute between the two channels in a given time-variant marketplace. This paper provides two major contributions: First, we obtain the existence of a steady-state redistribution between the two channels and the necessary conditions for this existence; Second, we establish measures of industry-specific suitability for on-line business operations. In short, our results concur with the theory: IT changes the way of doing business, but does not change the business itself.


title: Learning a Foreign Language in a Mixed-Reality Environment

abstract: This article describes a mixed-reality experience for learning Spanish as a foreign language, which takes place in a virtual world that mirrors a boulevard in Madrid. To this end, the authors have extended the capabilities of an open source multiuser 3D virtual world platform to orchestrate learning activities and use augmented reality and augmented virtuality. The results of their evaluation show positive effects on student motivation and improvement in learning outcomes.


title: Kd-Jump: a Path-Preserving Stackless Traversal for Faster Isosurface Raytracing on GPUs

abstract: Stackless traversal techniques are often used to circumvent memory bottlenecks by avoiding a stack and replacing return traversal with extra computation. This paper addresses whether the stackless traversal approaches are useful on newer hardware and technology (such as CUDA). To this end, we present a novel stackless approach for implicit kd-trees, which exploits the benefits of index-based node traversal, without incurring extra node visitation. This approach, which we term Kd-Jump, enables the traversal to immediately return to the next valid node, like a stack, without incurring extra node visitation (kd-restart). Also, Kd-Jump does not require global memory (stack) at all and only requires a small matrix in fast constant-memory. We report that Kd-Jump outperforms a stack by 10 to 20% and kd-restar t by 100%. We also present a Hybrid Kd-Jump, which utilizes a volume stepper for leaf testing and a run-time depth threshold to define where kd-tree traversal stops and volume-stepping occurs. By using both methods, we gain the benefits of empty space removal, fast texture-caching and realtime ability to determine the best threshold for current isosurface and view direction.


title: Sequenceserver: a modern graphical user interface for custom BLAST databases

abstract: The dramatic drop in DNA sequencing costs has created many opportunities for novel biological research. These opportunities largely rest upon the ability to effectively compare newly obtained and previously known sequences. This is commonly done with BLAST, yet using BLAST directly on new datasets requires substantial technical skills or helpful colleagues. Furthermore, graphical interfaces for BLAST are challenging to install and largely mimic underlying computational processes rather than work patterns of researchers. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver (http://sequenceserver.com), a modern graphical user interface for BLAST. Sequenceserver substantially increases the efficiency of researchers working with sequence data. This is due first to innovations at three levels. First, our software can be installed and used on custom datasets extremely rapidly for personal and shared applications. Second, based on analysis of user input and simple algorithms, Sequenceserver reduces the amount of decisions the user must make, provides interactive visual feedback, and prevents common potential errors that would otherwise cause erroneous results. Finally, Sequenceserver provides multiple highly visual and text-based output options that mirror the requirements and work patterns of researchers. Together, these features greatly facilitate BLAST analysis and interpretation and thus substantially enhance researcher productivity.


title: A Right to Access Implies a Right to Know: An Open Online Platform for Research on the Readability of Law

abstract: The widespread availability of legal materials online has opened the law to a new and greatly expanded readership. These new readers need the law to be readable by them when they encounter it. However, the available empirical research supports a conclusion that legislation is difficult to read if not incomprehensible to most citizens. We review approaches that have been used to measure the readability of text including readability metrics, cloze testing and application of machine learning. We report the creation and testing of an open online platform for readability research. This platform is made available to researchers interested in undertaking research on the readability of legal materials. To demonstrate the capabilities of the platform, we report its initial application to a corpus of legislation. Linguistic characteristics are extracted using the platform and then used as input features for machine learning using the Weka package. Wide divergences are found between sentences in a corpus of legislation and those in a corpus of graded reading material or in the Brown corpus (a balanced corpus of English written genres). Readability metrics are found to be of little value in classifying sentences by grade reading level (noting that such metrics were not designed to be used with isolated sentences).


title: LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA.

abstract: Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc.97 (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].


title: Planning Adaptive Mobile Experiences When Wireframing

abstract: Machine learning improves mobile user experience. Interestingly, envisioning apps with adaptive interfaces that reduce navigation and selection effort is not standard UX practice. When implementing an adaptive UI for our mobile transit app, we encountered a number of problems. Our original design did not log necessary information nor did it induce users to provide good labels. On reflection, we realized UX designers should identify and refine UI adaptions when sketching wireframes. To advance on this insight, we reviewed the interfaces of popular apps and extracted six design patterns where UI adaptation can improve in-app navigation. Next, we designed an exemplar set of wireframes, illustrating how UX designers might annotate their interaction flows to communicate planned adaptation and note the information (logs and labels) needed to make the desired inferences.


title: Two-handed interaction on a tablet display

abstract: A touchscreen can be overlaid on a tablet computer to support asymmetric two-handed interaction in which the preferred hand uses a stylus and the non-preferred hand operates the touchscreen. The result is a portable device that allows both hands to interact directly with the display, easily constructed from commonly available hardware. The method for tracking the independent motions of both hands is described. A wide variety of existing two-handed interaction techniques can be used on this platform, as well as some new ones that exploit the reconfigurability of touchscreen interfaces. Informal tests show that, when the non-preferred hand performs simple actions, users find direct manipulation on the display with both hands to be comfortable, natural, and efficient.


title: HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

abstract: The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life- or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs).


title: Person-Specific Face Antispoofing With Subject Domain Adaptation

abstract: Face antispoofing is important to practical face recognition systems. In previous works, a generic antispoofing classifier is trained to detect spoofing attacks on all subjects. However, due to the individual differences among subjects, the generic classifier cannot generalize well to all subjects. In this paper, we propose a person-specific face antispoofing approach. It recognizes spoofing attacks using a classifier specifically trained for each subject, which dismisses the interferences among subjects. Moreover, considering the scarce or void fake samples for training, we propose a subject domain adaptation method to synthesize virtual features, which makes it tractable to train well-performed individual face antispoofing classifiers. The extensive experiments on two challenging data sets: 1) CASIA and 2) REPLAY-ATTACK demonstrate the prospect of the proposed approach.


title: Coordination of voltage and frequency feedback in load-frequency control capability of wind turbine

abstract: In close future, with high Wind Power (WP) penetration in the power system, the burden of Load-Frequency Control (LFC) is gradually shifted to Variable Speed Wind Turbines (VSWTs). In order to equip the VSWT with LFC capability to support the grid during sudden load-generation imbalance, sufficient reserve capacity should be procured. This paper addresses the LFC scheme offered by VSWT. Feedback loop of locally measured voltage and frequency data is employed to improve transient and permanent response to achieve faster and more efficient LFC action and voltage regulation. The proposed scheme demonstrates remarkable improvement in transient state of both voltage and frequency profiles in comparison with conventional LFC designs provided by Central Power Plants (CPP) or Wind Power Plants (WPP). Numerical simulations carried out in DigSilent PowerFactory confirm the superiority of proposed methodology used in Permanent Magnet Synchronous Machine (PMSG) connected to 9-bus IEEE standard test system.


title: Airborne Infrared and Visible Image Fusion Combined with Region Segmentation

abstract: This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result. For high-frequency components, the region weights need to be assigned by the information richness of region details to conduct fusion based on both weights and adaptive phases, and then introducing a shrinkage function to suppress noise; Finally, the fused low-frequency and high-frequency components are reconstructed to obtain the fusion image. The experimental results show that the proposed method can fully extract complementary information from the source images to obtain a fusion image with good target indication and rich information on scene details. They also give a fusion result superior to existing popular fusion methods, based on eithers subjective or objective evaluation. With good stability and high fusion accuracy, this method can meet the fusion requirements of IR-visible image fusion systems.


title: Joint 3D Proposal Generation and Object Detection from View Aggregation

abstract: We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: this https URL


title: Automated exam question generator using genetic algorithm

abstract: Manual preparation of exam questions is a challenging task for educators especially within a short time frame. It requires a lot of time and efforts in order to meet the standard quality of exam questions. This research introduces an automated exam question generator to resolve this issue in preparation of multiple choice exam questions. The generator can auto generate new exam questions set using Genetic Algorithm and covers six levels of Bloom's Taxonomy to produce high quality exam questions that can evaluate different level of learners based on Bloom's cognitive domains and the selection of chapters made by educators. The prototype with 500 sample questions has been run 50 times with different number of chapters selected for each test case. It manages to achieve 90% for the highest exam questions weightage while the average value of exam questions weightage percentage generated is 70%. The lowest exam questions weightage percentage generated is 40%. The result is affected by the smaller number of questions for each Bloom's taxonomy level in questions bank. The automated exam generator can extends to be used for any type of exam questions and it can be used for preparation of quiz or test questions.


title: InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

abstract: The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.


title: Differences in the Effects of EGCG on Chromosomal Stability and Cell Growth between Normal and Colon Cancer Cells

abstract: The tea catechin epigallocatechin-3-gallate (EGCG) proved to be the most potent physiologically active tea compound in vitro. It possesses antioxidant as well as pro-oxidant properties. EGCG has the effect of inducing apoptosis of tumor cells and inhibiting cell proliferation. Whether this effect is associated with the antioxidant or pro-oxidative effects of EGCG affecting the genome stability of normal and cancer cells has not been confirmed. Here, we selected Human normal colon epithelial cells NCM460 and colon adenocarcinoma cells COLO205 to investigate the effects of EGCG (0–40 μg/mL) on the genome stability and cell growth status. Chromosomal instability (CIN), nuclear division index (NDI), and apoptosis was measured by cytokinesis-block micronucleus assay (CBMN), and the expression of core genes in mismatch repair (hMLMLH1 and hMSH2) was examined by RT-qPCR. We found that EGCG significantly reduced CIN and apoptosis rate of NCM460 at all concentrations (5–40 μg/mL) and treatment time, EGCG at 5 μg/mL promoted cell division; EGCG could significantly induce chromosome instability in COLO205 cells and trigger apoptosis and inhibition of cell division. These results suggest that EGCG exhibits different genetic and cytological effects in normal and colon cancer cells.


title: Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

abstract: We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (at a rate of up to 50 characters per second). We apply our iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.