title: Landmark based radar SLAM using graph optimization

abstract: On the way to achieving higher degrees of autonomy for vehicles in complicated, ever changing scenarios, the localization problem poses a very important role. Especially the Simultaneous Localization and Mapping (SLAM) problem has been studied greatly in the past. For an autonomous system in the real world, we present a very cost-efficient, robust and very precise localization approach based on GraphSLAM and graph optimization using radar sensors. We are able to prove on a dynamically changing parking lot layout that both mapping and localization accuracy are very high. To evaluate the performance of the mapping algorithm, a highly accurate ground truth map generated from a total station was used. Localization results are compared to a high precision DGPS/INS system. Utilizing these methods, we can show the strong performance of our algorithm.


title: Data analytics in smart distribution networks: Applications and challenges

abstract: The large volumes of data that will be produced by ubiquitous sensors and meters in future smart distribution networks represent an opportunity for the use of data analytics to extract valuable knowledge and, thus, improve Distribution Network Operator (DNO) planning and operation tasks. Indeed, applications ranging from outage management to detection of non-technical losses to asset management can potentially benefit from data analytics. However, despite all the benefits, each application presents DNOs with diverse data requirements and the need to define an adequate approach. Consequently, it is critical to understand the different interactions among applications, monitoring infrastructure and approaches involved in the use of data analytics in distribution networks. To assist DNOs in the decision making process, this work presents some of the potential applications where data analytics are likely to improve distribution network performance and the corresponding challenges involved in its implementation.


title: Graph Coloring for class scheduling

abstract: The class scheduling problem can be modeled by a graph where the vertices and edges represent the courses and the common students, respectively. The problem is to assign the courses a given number of time slots (colors), where each time slot can be used for a given number of class rooms. The Vertex Coloring (VC) algorithm is a polynomial time algorithm which produces a conflict free solution using the least number of colors [9]. However, the VC solution may not be implementable because it uses a number of time slots that exceed the available ones with unbalanced use of class rooms. We propose a heuristic approach VC* to (1) promote uniform distribution of courses over the colors and to (2) balance course load for each time slot over the available class rooms. The performance function represents the percentage of students in all courses that could not be mapped to time slots or to class rooms. A randomized simulation of registration of four departments with up to 1200 students is used to evaluate the performance of proposed heuristic.


title: Application of data mining methods for link prediction in social networks

abstract: Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.


title: A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes

abstract: Hurricanes constitute major natural disasters that lead to destruction and loss of lives. Therefore, to reduce economic loss and to save human lives, an accurate forecast of hurricane occurrences is crucial. Despite the availability of data and advanced forecasting techniques, there is a need for effective methods with higher accuracy of prediction. We propose a sparse Recurrent Neural Network (RNN) with flexible topology for trajectory prediction of the Atlantic hurricanes. Topology of the RNN along with the strength of the connections are evolved by a customized Genetic Algorithm. The network is particularly suitable for modeling of hurricanes which have complex systems with unknown dynamics. For prediction of the future trajectories of a target hurricane, the Dynamic Time Warping (DTW) distances between direction of the target hurricane over time, and other hurricanes in the dataset are determined and compared. The most similar hurricanes to the target hurricane are then used for training of the network. Comparisons between the actual tracks of the hurricanes DEAN, SANDY, ISSAC and HUMBERTO, and the generated predictions by the sparse RNN for one and two steps ahead of time show that our approach is quite promising for this aim.


title: Kinect V2 as a tool for stroke recovery: Pilot study of motion scale monitoring

abstract: This paper investigates Kinect device application during rehabilitation of people with an ischemic stroke. There are many similar application using Kinect as a tool during rehabilitation. This paper is focused on measurement of Kinect's spatial accuracy and proposition of body states and exercises according to the Motor assessment scale for stroke (MAS). The system observes the whole rehabilitation process and objectively compares ranges of movement during each exercise. Angles between limbs are computed in the skeletal body joints projection to three anatomical planes, which enables a better insight to subject performance. The system is easily implemented with a consumer-grade computer and a low-cost Kinect device. Selected exercises are presented together with the angles evolution, body states recognition and the MAS Scale after the stroke classification.


title: Kiting in RTS Games Using Influence Maps

abstract: Influence Maps have been successfully used in controlling the navigation of multiple units. In this paper, we apply the idea to the problem of simulating a kiting behavior (also known as “attack and flee”) in the context of real-time strategy (RTS) games. We present our approach and evaluate it in the popular RTS game StarCraft, where we analyze the benefits that our approach brings to a StarCraft playing bot.


title: Research Commentary: Setting a Definition, Context, and Theory-Based Research Agenda for the Gamification of Non-Gaming Applications

abstract: As a nascent area of study, gamification has attracted the interest of researchers in several fields, but little attention has been focused on creating a theoretical foundation for gamification research. The basic idea of gamification is to use game-like features in non-game contexts to motivate users and improve performance outcomes. As a boundary-spanning subject by nature, gamification has drawn the interest of scholars from diverse communities, including information systems, education, marketing, computer science, and business administration. To establish a theoretical foundation, it is important to clearly define and explain gamification in comparison with similar concepts and areas of research. Likewise, it is important to define the scope of the domain and develop a research agenda that explicitly takes into account the important role of theory. In this review article, we have set forth the pre-theoretical structures necessary for theory-building in this area. Accordingly, we engaged an interdisciplinary group of discussants to evaluate and select the most relevant theories for gamification. Moreover, we developed exemplary research questions to help create a research agenda for gamification. We conclude that using a multi-theoretical perspective in the creation of a research agenda should help and encourage IS researchers to take a lead role in this promising and emerging area.


title: Moving beyond the exchange value in the nonmarket valuation of ecosystem services

abstract: There has been much discussion across the ecosystem services literature as to the role of economic valuation in identifying ecosystem service values and shaping policy. This article demonstrates a non-typical use of a nonmarket valuation technique known as the stated choice experiment (CE) for understanding a range of public preferences for stream-related ecosystem services in Macon County, NC. The experiment was carried out as part of the National Science Foundation funded Coweeta Long Term Ecological Research initiative, and it reflects an interdisciplinary attempt to produce knowledge regarding ecosystem service values that is of relevance to policy makers. The CE uses a split-sample design to test for the impact of mechanism of program implementation on respondent preferences and demonstrate a range of public willingness to pay (WTP) for stream health improvements. Responses are analyzed with a latent class logit and the results show that altering the mechanism of program implementation changes the latent class composition. Results also demonstrate consistent preferences for certain attributes of stream health, but WTP for ecosystem service provisioning varies widely with proposed program implementation. The use of the CE in this research demonstrates the flexibility of the tool for combining with interdisciplinary knowledge, as well as the usefulness of information provided by nonmarket valuation techniques for informing policy design.


title: SpeechPy - A Library for Speech Processing and Recognition

abstract: SpeechPy is an open source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification.


title: PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks

abstract: Similarity search is a primitive operation in database and Web search engines. With the advent of large-scale heterogeneous information networks that consist of multi-typed, interconnected objects, such as the bibliographic networks and social media networks, it is important to study similarity search in such networks. Intuitively, two objects are similar if they are linked by many paths in the network. However, most existing similarity measures are defined for homogeneous networks. Different semantic meanings behind paths are not taken into consideration. Thus they cannot be directly applied to heterogeneous networks. In this paper, we study similarity search that is defined among the same type of objects in heterogeneous networks. Moreover, by considering different linkage paths in a network, one could derive various similarity semantics. Therefore, we introduce the concept of meta path-based similarity, where a meta path is a path consisting of asequence of relations defined between different object types (i.e., structural paths at the meta level). No matter whether a user would like to explicitly specify a path combination given sufficient domain knowledge, or choose the best path by experimental trials, or simply provide training examples to learn it, meta path forms a common base for a network-based similarity search engine. In particular, under the meta path framework we define a novel similarity measure called PathSim that is able to find peer objects in the network (e.g., find authors in the similar field and with similar reputation), which turns out to be more meaningful in many scenarios compared with random-walk based similarity measures. In order to support fast online query processing for PathSim queries, we develop an efficient solution that partially materializes short meta paths and then concatenates them online to compute top-k results. Experiments on real data sets demonstrate the effectiveness and efficiency of our proposed paradigm.


title: Simple Models for Word Formation in English Slang

abstract: We propose generative models for three types of extra-grammatical word formation phenomena abounding in English slang: Blends, Clippings, and Reduplicatives. Adopting a data-driven approach coupled with linguistic knowledge, we propose simple models with state of the art performance on human annotated gold standard datasets. Overall, our models reveal insights into the generative processes of word formation in slang -- insights which are increasingly relevant in the context of the rising prevalence of slang and non-standard varieties on the Internet.


Multi-View 3 D Face Reconstruction with Deep Recurrent Neural Networks


title: Analysis of How Underlying Topics in Financial News Affect Stock Prices Using Latent Dirichlet Allocation

abstract: Companies listed on the stock markets are typically obliged to publicly disclose any information that might have a significant influence on their stock prices. This transparency regulation is intended to ensure that all market participants have access to the same information. The corresponding press releases are one of the most reliable news sources concerning a company's operations. Interestingly, even though researcher have investigated the timing of releases, research has invested little effort into examining the underlying news topics. In this paper, we analyze the effects of topics found in such corporate press releases on stock market returns in the German market. We determine the topic of ad hoc announcements by using Latent Dirichlet Allocation. Effectively, we succeed in extracting 40 topics. As hypothesized, the effect of these topic groups differ greatly from each other. Some topics have no resulting effect on abnormal returns of stocks, whereas other topics, such as drug testing, exhibit a large effect.


title: Empowering Low-Latency Applications Through a Serverless Edge Computing Architecture

abstract: The exponential increase of the data generated by pervasive and mobile devices requires disrupting approaches for the realization of emerging mobile and IoT applications. Although cloud computing provides virtually unlimited computational resources, low-latency applications cannot afford the high latencies introduced by sending and retrieving data from/to the cloud. In this scenario, edge computing appears as a promising solution by bringing computation and data near to users and devices. However, the resource-finite nature of edge servers constrains the possibility of deploying full applications on them. To cope with these problems, we propose a serverless architecture at the edge, bringing a highly scalable, intelligent and cost-effective use of edge infrastructure’s resources with minimal configuration and operation efforts. The feasibility of our approach is shown through an augmented reality use case for mobile devices, in which we offload computation and data intensive tasks from the devices to serverless functions at the edge, outperforming the cloud alternative up to 80% in terms of throughput and latency.


title: Sketch Interpretation and Refinement Using Statistical Models

abstract: We present a system for generating 2D illustrations from hand drawn outlines consisting of only curve strokes. A user can draw a coarse sketch and the system would automatically augment the shape, thickness, color and surrounding texture of the curves making up the sketch. The styles for these refinements are learned from examples whose semantics have been pre-classified. There can be several styles applicable on a curve and the system automatically identifies which one to use and how to use it based on a curve's shape and its context in the illustration. Our approach is based on a Hierarchical Hidden Markov Model. We present a two level hierarchy in which the refinement process is applied at: the curve level and the scene level.


title: The go-go interaction technique: non-linear mapping for direct manipulation in VR

abstract: The Go-Go immersive interaction technique uses the metaphor of interactively growing the user’s arm and non-linear mapping for reaching and manipulating distant objects. Unlike others, our technique allows for seamless direct manipulation of both nearby objects and those at a distance.


title: Structural Representations for Learning Relations between Pairs of Texts

abstract: This paper studies the use of structural representations for learning relations between pairs of short texts (e.g., sentences or paragraphs) of the kind: the second text answers to, or conveys exactly the same information of, or is implied by, the first text. Engineering effective features that can capture syntactic and semantic relations between the constituents composing the target text pairs is rather complex. Thus, we define syntactic and semantic structures representing the text pairs and then apply graph and tree kernels to them for automatically engineering features in Support Vector Machines. We carry out an extensive comparative analysis of stateof-the-art models for this type of relational learning. Our findings allow for achieving the highest accuracy in two different and important related tasks, i.e., Paraphrasing Identification and Textual Entailment Recognition.


title: Survey of Research for Performance Measurement of Mobile Manipulators

abstract: T his survey provides the basis for developing research in the area of mobile manipulator performance measurement, an area that has relatively few research articles when compared to other mobile manipulator research areas. The survey provides a literature review of mobile manipulator research with examples of experimental applications. The survey also provides an extensive list of planning and control references as this has been the major research focus for mobile manipulators which factors into performance measurement of the system. The survey then reviews performance metrics considered for mobile robots, robot arms, and mobile manipulators and the systems that measure their performance, including machine tool measurement systems through dynamic motion tracking systems. Lastly, the survey includes a section on research that has occurred for performance measurement of robots, mobile robots, and mobile manipulators beginning with calibration, standards, and mobile manipulator artifacts that are being considered for evaluation of mobile manipulator performance.


title: An efficient algorithm for the Knight's tour problem

abstract: A knight’s tour is a series of moves made by a knight visiting every square of an n x n chessboard exactly once. The knight’s tour problem is the problem of constructing such a tour, given n. A knight’s tour is called closed if the last square visited is also reachable from the first square by a knight’s move, and open otherwise. Define the knight’s graph for an n x n chessboard to be the graph G = (V,E), where V={(i,j) ( 1 d i,j Gn}, and E={((i,j),(k,Q) ( {Ii-kl,Ijt[} = {1,2}}. That is, there is a vertex for every square of the board and an edge between two vertices exactly when there is a knight’s move from one to the other. Then, more formally, an open knight’s tour is defined to be a Hamiltonian path, and a closed knight’s tour is defined to be a Hamiltonian cycle on a knight’s graph. A knight’s graph has n2 vertices and 4n2 12n + 8 edges. The formal study of the knight’s tour problem is said to have begun with Euler [lo] in 1759, who considered the standard 8 x 8 chessboard. Rouse Ball and Coxeter [l] give an interesting bibliography of the history of the problem from this point. Dudeney [8, 91 contains a description of exactly which rectangular chessboards have knight’s tours; in particular, an n x n chessboard has a closed knight’s tour iff n Z 6 is even, ’ and an open knight’s tour iff n 3 5. It is not clear who first proved this fact, but it appears to be part of the folklore of the subject (see, for example, [2]). There exist several independently conceived linear time (i.e. O(n2)) algorithms for constructing knight’s tours (see, for example, [5, 191). Takemji and Lee [22, 231 recently proposed a neural network solution to the knight’s tour problem, although it appears to be of little use in practice (see [ 17, 181). We will describe in this paper a new, simple, and fast algorithm for constructing knight’s tours on square boards.


title: Vacuum-Assisted Thermal Annealing of CH3NH3PbI3 for Highly Stable and Efficient Perovskite Solar Cells

abstract: Solar cells incorporating lead halide-based perovskite absorbers can exhibit impressive power conversion efficiencies (PCEs), recently surpassing 15%. Despite rapid developments, achieving precise control over the morphologies of the perovskite films (minimizing pore formation) and enhanced stability and reproducibility of the devices remain challenging, both of which are necessary for further advancements. Here we demonstrate vacuum-assisted thermal annealing as an effective means for controlling the composition and morphology of the CH3NH3PbI3 films formed from the precursors of PbCl2 and CH3NH3I. We identify the critical role played by the byproduct of CH3NH3Cl on the formation and the photovoltaic performance of the perovskite film. By completely removing the byproduct through our vacuum-assisted thermal annealing approach, we are able to produce pure, pore-free planar CH3NH3PbI3 films with high PCE reaching 14.5% in solar cell device. Importantly, the removal of CH3NH3Cl significantly improves the de...


title: Comparative Study and Enhancement of Camera Tampering Detection Algorithms

abstract: Recently the use of video surveillance systems is widely increasing. Different places are equipped by camera surveillances such as hospitals, schools, airports, museums and military places in order to ensure the safety and security of the persons and their property. Therefore it becomes significant to guarantee the proper working of these systems. Intelligent video surveillance systems equipped by sophisticated digital camera can analyze video information s and automatically detect doubtful actions. The camera tampering detection algorithms may indicate that accidental or suspicious activities have occurred and that causes the abnormality works of the video surveillance. ::: Camera Tampering Detection uses several techniques based on image processing and computer vision. In this paper, comparative study of performance of three algorithms that can detect abnormal disturbance for video surveillance is presented.


Revising the Newman-Girvan Algorithm


title: Informing Loads: Enabling Software To Observe And React To Memory Behavior

abstract: Memory latency is an important bottleneck in system performance that cannot be adequately solved by hardware alone. Several promising software techniques have been shown to address this problem successfully in specific situations. However, the generality of these software approaches has been limited because current architectures do not provide a fine-grained, low-overhead mechanism to observe memory behavior directly. To fill this need, we propose a new set of memory operations called informing memory operations, and in particular, we describe the design and functionality of an informing load instruction. This instruction serves as a primitive that allows the software to observe cache misses and to act upon this information inexpensively (i.e. under the miss, when the processor would typically be idle) within the current software context. Informing loads enable new solutions to several important software problems. We demonstrate this through examples that show their usefulness in (i) the collection of fine-grained memory profiles with high precision and low overhead and (ii) the automatic improvement of memory system performance through compiler techniques that take advantage of cache-miss information. Overall, we find that the apparent benefit of an informing load instruction is quite high, while the hardware cost of this functionality is quite modest. In fact, the bulk of the required hardware support is already present in today''s high-performance processors.


title: An efficient and simple model for multiple criteria supplier selection problem

abstract: Abstract Simply looking for vendors offering the lowest prices is not “efficient sourcing” any more. Selection of suppliers is a multiple criteria decision. We propose a weighted linear program for the multi-criteria supplier selection problem. In addition to mathematical formulation, this paper studies a transformation technique which enables our proposed model to be solved without an optimizer. The model for multi-criteria supplier selection problem can be easily implemented with a spreadsheet package. The model can be widely applied to practical situations and does not require the user with any optimization background.


Goal setting and goal orientation: an integration of two different yet related literatures. Academy of management journal


title: Application of transfer learning in RGB-D object recognition

abstract: In this work, we apply Transfer Learning for a Multimodal Deep learning network for fast and robust object recognition using RGB-D dataset. The ability for a network to train quickly and recognize objects robustly is very important in the field of Robotics. The Multimodal deep learning network avoids time-consuming hand-crafted features and makes use of a RGB-D architecture for robust object recognition. Our architecture has two important features. First, it makes use of both RGB and Depth information of an image to recognize it. To achieve this, our architecture has two CNN processing streams, one for RGB modality and the other for the depth modality. This enables the network to achieve higher accuracy than normal single stream RGB network. We encoded the depth image into colour image before passing it into the CNN stream. The other important feature is the speed of training and improving the accuracy further. To achieve this, we made use of Transfer learning. Firstly we trained a CNN network with 10 classes of different objects and then we transfer the parameters to RGB and depth CNN network. This enables the network to train faster and also achieve higher accuracy for a given number of epochs.


Fingerprint Classification


title: Learning controls for blend shape based realistic facial animation

abstract: Blend shape animation is the method of choice for keyframe facial animation: a set of blend shapes (key facial expressions) are used to define a linear space of facial expressions. However, in order to capture a significant range of complexity of human expressions, blend shapes need to be segmented into smaller regions where key idiosyncracies of the face being animated are present. Performing this segmentation by hand requires skill and a lot of time. In this paper, we propose an automatic, physically-motivated segmentation that learns the controls and parameters directly from the set of blend shapes. We show the usefulness and efficiency of this technique for both, motion-capture animation and keyframing. We also provide a rendering algorithm to enhance the visual realism of a blend shape model.


title: Characterizing the usability of interactive applications through query log analysis

abstract: People routinely rely on Internet search engines to support their use of interactive systems: they issue queries to learn how to accomplish tasks, troubleshoot problems, and otherwise educate themselves on products. Given this common behavior, we argue that search query logs can usefully augment traditional usability methods by revealing the primary tasks and needs of a product's user population. We term this use of search query logs CUTS - characterizing usability through search. In this paper, we introduce CUTS and describe an automated process for harvesting, ordering, labeling, filtering, and grouping search queries related to a given product. Importantly, this data set can be assembled in minutes, is timely, has a high degree of ecological validity, and is arguably less prone to self-selection bias than data gathered via traditional usability methods. We demonstrate the utility of this approach by applying it to a number of popular software and hardware systems.


title: Crime prediction using Twitter sentiment and weather

abstract: Social networking services have the hidden potential to reveal valuable insights when statistical analysis is applied to their unstructured data. As shown by previous research, GPS-tagged Twitter data enables the prediction of future crimes in a major city, Chicago, Illinois, of the United States. However, existing crime prediction models that incorporate data from Twitter have limitations in describing criminal incidents due to the absence of sentiment polarity and weather factors. The addition of sentiment analysis and weather predictors to such models would deliver significant insight about how crime. Our aim is to predict the time and location in which a specific type of crime will occur. Our approach is based on sentiment analysis by applying lexicon-based methods and understanding of categorized weather data, combined with kernel density estimation based on historical crime incidents and prediction via linear modeling. By testing our model’s ability to predict future crime on each area of the city, we observed that the model surpassed the benchmark model, which predicts crime incidents using kernel density estimation.


title: An efficient reconstruction approach for improving Bluetree scatternet formation in personal area networks

abstract: Bluetooth is a communication technology for personal area networks (PANs). To support communication with more than eight bluetooth-enabled devices, a scatternet must be formed in the PAN. Bluetree is one commonly used topology for scatternet formation. To reduce traffic load of the Bluetree scatternet, we use the piconet transfer concept to move piconets on a well-formed Bluetree scatternet. The piconet movements are performed based on a distributed manner using two well-known tree traversal procedures: post-order traversal and level-order traversal. These two procedures do not take much computation time, where time complexities are O(n"p) and n"p is the number of piconets on a Bluetree scatternet (not the number of nodes on a Bluetree). Compared with previous approaches, the proposed approach can greatly reduce the traffic load and computational costs. Finally, simulation experiments show the effectiveness of the proposed approach in improving the formation of Bluetree scatternet.


title: International Anthropometric Study of Facial Morphology in Various Ethnic Groups/Races

abstract: When anthropometric methods were introduced into clinical practice to quantify changes in the craniofacial framework, features distinguishing various races/ethnic groups were discovered. To treat congenital or post-traumatic facial disfigurements in members of these groups successfully, surgeons require access to craniofacial databases based on accurate anthropometric measurements. Normative data of facial measurements are indispensable to precise determination of the degree of deviations from the normal. The set of anthropometric measurements of the face in the population studied was gathered by an international team of scientists. Investigators in the country of the given ethnic group, experienced and/or specially trained in anthropometric methods, carried out the measurements. The normal range in each resultant database was then established, providing valuable information about major facial characteristics. Comparison of the ethnic groups' databases with the established norms of the North America whites (NAW) offered the most suitable way to select a method for successful treatment. The study group consisted of 1470 healthy subjects (18 to 30 years), 750 males and 720 females. The largest group (780 subjects, 53.1%) came from Europe, all of them Caucasians. Three were drawn from the Middle-East (180 subjects, 12.2%), five from Asia (300 subjects, 20.4%) and four from peoples of African origin (210 subjects, 14.3%). Their morphological characteristics were determined by 14 anthropometric measurements, 10 of them used already by classic facial artists, Leonardo da Vinci and Albrecht Durer, complemented by four measurements from the nasal, labio-oral and ear regions. In the regions with single measurements, identical values to NAW in forehead height, mouth width, and ear height were found in 99.7% in both sexes, while in those with multiple measurements, vertical measurements revealed a higher frequency of identical values than horizontal ones. The orbital regions exhibited the greatest variations in identical and contrasting measurements in comparison to NAW. Nose heights and widths contrasted sharply: in relation to NAW the nose was very or extremely significantly wide in both sexes of Asian and Black ethnic groups. Among Caucasians, nose height significantly differed from NAW in three ethnic groups, with one shorter and two greater. In the Middle Eastern groups nose width was identical to those of NAW but the height was significantly greater. The present study, conducted by investigators working separately across the world and with small samples of the population, is clearly preliminary in nature and extent. Yet it may fulfill its mission if medical and anthropological investigators continue the work of establishing normative data of the face. These data are urgently needed by medical professionals but have been lacking up till now in western and northern Europe, Asia, and Africa.


title: An Efficient Hardware Implementation of Canny Edge Detection Algorithm

abstract: The edge detection is one of the key techniques in most image processing applications. The canny edge detection is proven to be able to significantly outperform existing edge detection techniques due to its superior performance. Unfortunately, the implementation of the systems in real-time is computationally complex, high hardware cost with increased latency. The proposed canny edge detection algorithm uses approximation methods to replace the complex operations, the pipelining is employed to reduce the latency. Finally, this algorithm is implemented on Xilinx Virtex-5 FPGA. When compared with the previous hardware architecture for canny edge detection, the proposed architecture requires fewer hardware costs and takes 1ms to detect the edges of 512x512 image.


title: Integration of Microstrip Patch Antenna With Polycrystalline Silicon Solar Cell

abstract: The implementation of a polycrystalline silicon solar cell as a microwave groundplane in a low-profile, reduced-footprint microstrip patch antenna design for autonomous communication applications is reported. The effects on the antenna/solar performances due to the integration, different electrical conductivities in the silicon layer and variation in incident light intensity are investigated. The antenna sensitivity to the orientation of the anisotropic solar cell geometry is discussed.


title: Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers

abstract: Flexible organic electronics could eventually be used to create electronic skin. Films of a pressure-sensitive microstructured elastomer are now used as the dielectric layer in organic field-effect transistors to create highly sensitive devices. The elastomer is also used in a matrix pressure sensor that can detect loads in numerous positions.


title: Automatic Identification of Human Parasite Eggs Based on Multitexton Histogram Retrieving the Relationships between Textons

abstract: In order to identify the parasitic diseases, this paper propose the automatic identification of Human Parasite Eggs to eight different species: Ascaris, Uncinarias, Trichuris, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepaticaand Enterobius-Vermicularis from their microscopic imagesbased on Multitexton Histogram - MTH using new structuresof textons. This proposed system includes two stages. In firststage, a feature extraction mechanism that is based on MTHdescriptor retrieving the relationships between textons. In second stage, an CBIR system has been implemented in orden to detect their correct species of helminths. Finally, simulation results show overall success rates of 94,78% in the detection.


title: Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

abstract: Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.


title: Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models

abstract: Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.


title: Web: www.harzing.comGoogle Scholar: the democratization of citation analysis?

abstract: Traditionally, the most commonly used source of bibliometric data is Thomson ISI Web of Knowledge, in particular the (Social) Science Citation Index and the Journal Citation Reports (JCR), which provide the yearly Journal Impact Factors (JIF). This paper presents an alternative source of data (Google Scholar, GS) as well as three alternatives to the JIF to assess journal impact (the h-index, g-index and the number of citations per paper). Because of its broader range of data sources, the use of GS generally results in more comprehensive citation coverage in the area of Management and International Business. The use of GS particularly benefits academics publishing in sources that are not (well) covered in ISI. Among these are: books, conference papers, non-US journals, and in general journals in the field of Strategy and International Business. The three alternative GS-based metrics showed strong correlations with the traditional JIF. As such, they provide academics and universities committed to JIFs with a good alternative for journals that are not ISI-indexed. However, we argue that these metrics provide additional advantages over the JIF and that the free availability of GS allows for a democratization of citation analysis as it provides every academic access to citation data regardless of their institution’s financial means.


title: Immobilization of enzymes: a literature survey.

abstract: The term immobilized enzymes refers to "enzymes physically confined or localized in a certain defined region of space with retention of their catalytic activities, and which can be used repeatedly and continuously." Immobilized enzymes are currently the subject of considerable interest because of their advantages over soluble enzymes. In addition to their use in industrial processes, the immobilization techniques are the basis for making a number of biotechnology products with application in diagnostics, bioaffinity chromatography, and biosensors. At the beginning, only immobilized single enzymes were used, after 1970s more complex systems including two-enzyme reactions with cofactor regeneration and living cells were developed. The enzymes can be attached to the support by interactions ranging from reversible physical adsorption and ionic linkages to stable covalent bonds. Although the choice of the most appropriate immobilization technique depends on the nature of the enzyme and the carrier, in the last years the immobilization technology has increasingly become a matter of rational design. As a consequence of enzyme immobilization, some properties such as catalytic activity or thermal stability become altered. These effects have been demonstrated and exploited. The concept of stabilization has been an important driving force for immobilizing enzymes. Moreover, true stabilization at the molecular level has been demonstrated, e.g., proteins immobilized through multipoint covalent binding.


title: Notes on the ecology and behaviour ofPseudamphithoides incurvaria(Just) (Crustacea, Amphipoda, Ampithoidae)

abstract: Summary The algal-dwelling habit of the herbivorous amphipod Pseudamphithoides incurvaria is a unique instance of a portable domicile constructed from a primary food plant. The range of P. incurvaria is extended to include the western Caribbean and the Bahamas. The occurrence of these amphipods on two new brown algal hosts in the Dictyotaceae, Dictyota bartayresii and D. ciliolata, is reported. Pseudamphithoides incurvaria occurs on D. bartayresii from 1·5–27 m depths in the vicinity of Carrie Bow Cay, Belize. Habitat and local distribution are described for P. incurvaria population in a shallow water back-reef location. Observations of feeding behaviour and the sequence of pod construction are reported.


title: Impulsivity, Compulsivity, and Top-Down Cognitive Control

abstract: Impulsivity is the tendency to act prematurely without foresight. Behavioral and neurobiological analysis of this construct, with evidence from both animal and human studies, defines several dissociable forms depending on distinct cortico-striatal substrates. One form of impulsivity depends on the temporal discounting of reward, another on motor or response disinhibition. Impulsivity is commonly associated with addiction to drugs from different pharmacological classes, but its causal role in human addiction is unclear. We characterize in neurobehavioral and neurochemical terms a rodent model of impulsivity based on premature responding in an attentional task. Evidence is surveyed that high impulsivity on this task precedes the escalation subsequently of cocaine self-administration behavior, and also a tendency toward compulsive cocaine-seeking and to relapse. These results indicate that the vulnerability to stimulant addiction may depend on an impulsivity endophenotype. Implications of these findings for the etiology, development, and treatment of drug addiction are considered.


title: A 0.35μm CMOS sub-1V low-quiescent-current low-dropout regulator

abstract: A sub-1 V CMOS low-dropout (LDO) voltage regulator with 103 nA low-quiescent current is presented in this paper. The proposed LDO uses a digital error amplifier that can make the quiescent current lower than other LDOs with the traditional error amplifier. Besides, the LDO can be stable even without the output capacitor. With a 0.9 V power supply, the output voltage is designed as 0.5 V. The maximum output current of the LDO is 50 mA at an output of 0.5 V. The prototype of the LDO is fabricated with TSMC 0.35 mum CMOS processes. The active area without pads is only 240 mum times 400 mum.


Comparison of MFCC and PLP parameterizations in the speaker independent continuous speech recognition task.


title: Automotive Three-Dimensional Vision Through a Single-Photon Counting SPAD Camera

abstract: We present an optical 3-D ranging camera for automotive applications that is able to provide a centimeter depth resolution over a $\mbox{40}^{\circ} \times \mbox{20}^{\circ}$ field of view up to 45 m with just 1.5 W of active illumination at 808 nm. The enabling technology we developed is based on a CMOS imager chip of 64 $\times$ 32 pixels, each with a single-photon avalanche diode (SPAD) and three 9-bit digital counters, able to perform lock-in time-of-flight calculation of individual photons emitted by a laser illuminator, reflected by the objects in the scene, and eventually detected by the camera. Due to the SPAD single-photon sensitivity and the smart in-pixel processing, the camera provides state-of-the-art performance at both high frame rates and very low light levels without the need for scanning and with global shutter benefits. Furthermore, the CMOS process is automotive certified.


title: Segment routing for effective recovery and multi-domain traffic engineering

abstract: Segment routing is an emerging traffic engineering technique relying on Multi-protocol Label-Switched (MPLS) label stacking to steer traffic using the source-routing paradigm. Traffic flows are enforced through a given path by applying a specifically designed stack of labels (i.e., the segment list). Each packet is then forwarded along the shortest path toward the network element represented by the top label. Unlike traditional MPLS networks, segment routing maintains a per-flow state only at the ingress node; no signaling protocol is required to establish new flows or change the routing of active flows. Thus, control plane scalability is greatly improved. Several segment routing use cases have recently been proposed. As an example, it can be effectively used to dynamically steer traffic flows on paths characterized by low latency values. However, this may suffer from some potential issues. Indeed, deployed MPLS equipment typically supports a limited number of stacked labels. Therefore, it is important to define the proper procedures to minimize the required segment list depth. This work is focused on two relevant segment routing use cases: dynamic traffic recovery and traffic engineering in multi-domain networks. Indeed, in both use cases, the utilization of segment routing can significantly simplify the network operation with respect to traditional Internet Protocol (IP)/MPLS procedures. Thus, two original procedures based on segment routing are proposed for the aforementioned use cases. Both procedures are evaluated including a simulative analysis of the segment list depth. Moreover, an experimental demonstration is performed in a multi-layer test bed exploiting a software-defined-networking-based implementation of segment routing.


title: Two practical man-in-the-middle attacks on Bluetooth secure simple pairing and countermeasures

abstract: We propose two new Man-In-The-Middle (MITM) attacks on Bluetooth Secure Simple Pairing (SSP). The attacks are based on the falsification of information sent during the input/output capabilities exchange and also the fact that the security of the protocol is likely to be limited by the capabilities of the least powerful or the least secure device type. In addition, we devise countermeasures that render the attacks impractical, as well as improvements to the existing Bluetooth SSP in order to make it more secure. Moreover, we provide a comparative analysis of the existing MITM attacks on Bluetooth. Index Terms—Bluetooth, man-in-the-middle attack, out-of- band channel, secure simple pairing, wireless security.


title: Selective Classification for Deep Neural Networks

abstract: Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage.


title: 3D Shape Processing by Convolutional Denoising Autoencoders on Local Patches

abstract: We propose a system for surface completion and inpainting of 3D shapes using denoising autoencoders with convolutional layers, learnt on local patches. Our method uses height map based local patches parameterized using 3D mesh quadrangulation of the low resolution input shape. This provides us sufficient amount of local 3D patch dataset to learn deep generative Convolutional Neural Networks (CNNs) for the task of repairing moderate sized holes. We design generative networks specifically suited for the 3D encoding following ideas from the recent progress in 2D inpainting, and show our results to be better than the previous methods of surface inpainting that use linear dictionary. We validate our method on both synthetic shapes and real world scans.


title: MPCA: Multilinear Principal Component Analysis of Tensor Objects

abstract: This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2D/3D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2D variant (2D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.


Using Content-Based Filtering for Recommendation


title: Learning Belief Networks in the Presence of Missing Values and Hidden Variables

abstract: In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both network structure and parameters from complete data, and learning parameters for a fixed network from incomplete data—that is, in the presence of missing values or hidden variables. However, no method has yet been demonstrated to effectively learn network structure from incomplete data. In this paper, we propose a new method for learning network structure from incomplete data. This method is based on an extension of the Expectation-Maximization (EM) algorithm for model selection problems that performs search for the best structure inside the EM procedure. We prove the convergence of this algorithm, and adapt it for learning belief networks. We then describe how to learn networks in two scenarios: when the data contains missing values, and in the presence of hidden variables. We provide experimental results that show the effectiveness of our procedure in both scenarios.


title: Visual odometry for ground vehicle applications

abstract: We present a system that estimates the motion of a stereo head, or a single moving camera, based on video input. The system operates in real time with low delay, and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates motion estimates from visual input alone. No prior knowledge of the scene or the motion is necessary. The visual estimates can also be used in conjunction with information from other sources, such as a global positioning system, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive, and handheld platforms. We focus on results obtained with a stereo head mounted on an autonomous ground vehicle. We give examples of camera trajectories estimated in real time purely from images over previously unseen distances (600 m) and periods of time. © 2006 Wiley Periodicals, Inc.


title: Cloud computing in higher education: A snapshot of software as a service

abstract: Cloud Computing increases the flexibility and access of educational users to a wide range of educational resources. This includes access to infrastructure, software, hardware, and platform at any time in any place provided there is internet access. The users of cloud computing in higher educational institutions include students, lecturers, administrative staff, faculty staff, developers, programmers and researchers. Among the different cloud service models, software as a service (SaaS) is the most common service model used by higher educational institutions. As a result, the effect of SaaS on education can be serious and can impact education either positively or negatively. In this paper, we explore the use of SaaS in South African higher educational institutions with the intention to determine whether SaaS is a viable option for higher educational institutions. This paper also aims to identify the benefits and limitations of SaaS in higher educational institutions.


title: End-to-end Optimized Image Compression

abstract: We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM.


title: Multiresolution genetic clustering algorithm for texture segmentation

abstract: Abstract This work plans to approach the texture segmentation problem by incorporating genetic algorithm and K-means clustering method within a multiresolution structure. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class–position uncertainty and to improve the segmentation accuracy. The procedure is described as follows. In the first step, a quad-tree structure of multiple resolutions is constructed. Sampling windows of different sizes are utilized to partition the underlying image into blocks at different resolution levels and texture features are extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. While the select and mutate operators of the traditional genetic algorithm are adopted in this work, the crossover operator is replaced with K-means clustering method. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.


title: Investigating the role of computer-supported annotation in problem-solving-based teaching: An empirical study of a Scratch programming pedagogy

abstract: For more than 2 years, Scratch programming has been taught in Taiwanese elementary schools. However, past studies have shown that it is difficult to find appropriate learning methods or tools to boost students' Scratch programming performance. This inability to readily identify tutoring tools has become one of the primary challenges addressed in Scratch programming studies. To cope with this problem, we propose an innovative approach, which combines an Annotation-based Scratch Programming ( ASP) tool with the problem-solving-based teaching approach in Scratch programming pedagogy. The ASP tool was developed to enable students to create, review and share Scratch programming and homework annotations. In a quasi-experimental study, we have evaluated Scratch programming pedagogy at a North Taiwanese elementary school to investigate the effects of instructional-tools-supported programming instructional modes on Scratch programming performance. The experimental results show that students who received ASP tool support in conjunction with a problem-solving-based teaching approach performed significantly better than the other groups. Based on our findings, the innovative approach was believed to play an important role in improving the learning patterns of younger pupils. Therefore, we suggest that teachers consider incorporating the innovative method into their teaching environments in order to boost students' learning achievements in the area of Scratch programming and the subsequent learning process. [ABSTRACT FROM AUTHOR]


title: RF MEMS Actuated Reconfigurable Reflectarray Patch-Slot Element

abstract: This paper describes the design of a reconfigurable reflectarray element using commercially available radio frequency micro-electromechanical system (RF MEMS) switches. The element consists of a microstrip patch on the top surface and a slot with an actuated variable length in the ground plane. RF MEMS switches are mounted on the slot to electronically vary the slot length by actuating the switches and thus obtaining the desired phase response. Waveguide measurements and high frequency structure simulator (HFSS) simulations are used to characterize the reflectarray element. The four MEMS switches element gives 10 independent states with a phase swing of 150 deg and a loss variation from 0.4 dB to 1.5 dB at 2 GHz (more switches can provide larger phase shift). The loss is mainly attributed to the dielectric loss and the conductor loss, which occur due to the relatively strong electric fields in the substrate region below the patch and the large currents on the top surface of the patch, respectively, close to the patch resonance. Detailed analysis is performed to characterize the effect of the switches by taking into consideration the switch model and wire bonding effects.


title: Social Interactions Across Media Interpersonal Communication on the Internet, Telephone and Face-to-Face

abstract: Two studies compared college students’ interpersonal interaction online, face-to-face, and on the telephone. A communication diary assessed the relative amount of social interactions college students conducted online compared to face-to-face conversation and telephone calls. Results indicated that while the internet was integrated into college students’ social lives, face-to-face communication remained the dominant mode of interaction. Participants reported using the internet as often as the telephone. A survey compared reported use of the internet within local and long distance social circles to the use of other media within those circles, and examined participants’ most recent significant social interactions conducted across media in terms of purposes, contexts, and quality. Internet interaction was perceived as high in quality, but slightly lower than other media. Results were compared to previous conceptualizations of the roles of internet in one’s social life.


Improved drug therapy: triangulating phenomics with genomics and metabolomics


Big Data Quality - Towards an Explanation Model in a Smart CityContext


title: Assessment of disease named entity recognition on a corpus of annotated sentences

abstract: BackgroundIn recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions.ResultsAs part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance.ConclusionsThe annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseasesand can serve as a benchmark to other systems. In addition, we found that dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature. MetaMap generates precise results at the expense of insufficient recall while our statistical method obtains better recall at a lower precision rate. Even better results in terms of precision are achieved by combining at least two of the three methods leading, but this approach again lowers recall. Altogether, our analysis gives a better understanding of the complexity of disease annotations in the literature. MetaMap and the dictionary based approach are available through the Whatizit web service infrastructure (Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: Calling Whatizit. Bioinformatics 2008, 24:296-298).


title: Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

abstract: Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.


title: Rapid assessment of disaster damage using social media activity

abstract: Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and an increasing intensity of natural disasters resulting from climate change. During such events, citizens turn to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. In addition, the spatiotemporal distribution of disaster-related messages helps with the real-time monitoring and assessment of the disaster itself. We present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandy’s path and hurricane-related social media activity. We show that real and perceived threats, together with physical disaster effects, are directly observable through the intensity and composition of Twitter’s message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. We verify our findings for a wide range of disasters and suggest that massive online social networks can be used for rapid assessment of damage caused by a large-scale disaster.


title: On Similarity Preserving Feature Selection

abstract: In the literature of feature selection, different criteria have been proposed to evaluate the goodness of features. In our investigation, we notice that a number of existing selection criteria implicitly select features that preserve sample similarity, and can be unified under a common framework. We further point out that any feature selection criteria covered by this framework cannot handle redundant features, a common drawback of these criteria. Motivated by these observations, we propose a new "Similarity Preserving Feature Selection” framework in an explicit and rigorous way. We show, through theoretical analysis, that the proposed framework not only encompasses many widely used feature selection criteria, but also naturally overcomes their common weakness in handling feature redundancy. In developing this new framework, we begin with a conventional combinatorial optimization formulation for similarity preserving feature selection, then extend it with a sparse multiple-output regression formulation to improve its efficiency and effectiveness. A set of three algorithms are devised to efficiently solve the proposed formulations, each of which has its own advantages in terms of computational complexity and selection performance. As exhibited by our extensive experimental study, the proposed framework achieves superior feature selection performance and attractive properties.


title: Visual Madlibs: Fill in the blank Image Generation and Question Answering

abstract: In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset, the Visual Madlibs dataset, is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context. We provide several analyses of the Visual Madlibs dataset and demonstrate its applicability to two new description generation tasks: focused description generation, and multiple-choice question-answering for images. Experiments using joint-embedding and deep learning methods show promising results on these tasks.


title: Continuous Deep Q-Learning with Model-based Acceleration

abstract: Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However, the sample complexity of modelfree algorithms, particularly when using high-dimensional function approximators, tends to limit their applicability to physical systems. In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks. We propose two complementary techniques for improving the efficiency of such algorithms. First, we derive a continuous variant of the Q-learning algorithm, which we call normalized advantage functions (NAF), as an alternative to the more commonly used policy gradient and actor-critic methods. NAF representation allows us to apply Q-learning with experience replay to continuous tasks, and substantially improves performance on a set of simulated robotic control tasks. To further improve the efficiency of our approach, we explore the use of learned models for accelerating model-free reinforcement learning. We show that iteratively refitted local linear models are especially effective for this, and demonstrate substantially faster learning on domains where such models are applicable.


title: SmartDEMAP: A Smart Contract Deployment and Management Platform

abstract: Smart contracts on a blockchain behave exactly as specified by their code. To be sure that a smart contract behaves as expected, the end-user has to either analyze its code or trust a potentially anonymous developer or auditor to do so. This approach proposes a smart contract deployment and management platform that can execute development tools and code quality tools in a trusted way and uses this to reduce the trust required into the smart contract developer or auditor. Additionally, such a platform can provide new capabilities for developers aiding them in the creation of smart contracts.


title: Adaptation of OFDM under visible light communications and illumination constraints

abstract: Orthogonal frequency division multiplexing (OFDM) is increasingly studied and adopted as a modulation technique for RF and optical communication systems. In this paper, we investigate challenges to the adoption of OFDM for use in lighting systems that support both intensity control and data communication based on the visible light communication (VLC) technology. In particular, we survey the requirements for energy efficiency, intensity control (dimming) and light-emitting diode (LED) driver integration in lighting systems. These requirements are mapped to contemporary and novel OFDM adaptations to show how both the lighting and communications needs can be met in dual-use scenarios while preserving both missions with reasonable performance.


title: Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

abstract: In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).


title: SoftBound: highly compatible and complete spatial memory safety for c

abstract: The serious bugs and security vulnerabilities facilitated by C/C++'s lack of bounds checking are well known, yet C and C++ remain in widespread use. Unfortunately, C's arbitrary pointer arithmetic, conflation of pointers and arrays, and programmer-visible memory layout make retrofitting C/C++ with spatial safety guarantees extremely challenging. Existing approaches suffer from incompleteness, have high runtime overhead, or require non-trivial changes to the C source code. Thus far, these deficiencies have prevented widespread adoption of such techniques. This paper proposes SoftBound, a compile-time transformation for enforcing spatial safety of C. Inspired by HardBound, a previously proposed hardware-assisted approach, SoftBound similarly records base and bound information for every pointer as disjoint metadata. This decoupling enables SoftBound to provide spatial safety without requiring changes to C source code. Unlike HardBound, SoftBound is a software-only approach and performs metadata manipulation only when loading or storing pointer values. A formal proof shows that this is sufficient to provide spatial safety even in the presence of arbitrary casts. SoftBound's full checking mode provides complete spatial violation detection with 67% runtime overhead on average. To further reduce overheads, SoftBound has a store-only checking mode that successfully detects all the security vulnerabilities in a test suite at the cost of only 22% runtime overhead on average.


title: Changes in fat-free mass during significant weight loss: a systematic review

abstract: To identify the proportion of weight lost as fat-free mass (FFM) by various weight loss interventions. Medline and Embase were systematically searched for reliable measurements of FFM before and after weight loss of >10 kg and eligible data were pooled. In a fixed effect model of % FFM loss/weight loss (%FFML), linear regression analysis was used to determine the influence of degree of caloric restriction, exercise, magnitude of weight loss, initial body mass index (BMI) and type of surgery. Data were included from 26 cohorts treated with dietary and behavioral interventions and 29 cohorts of bariatric surgery patients. The degree of caloric restriction was positively associated with %FFML (r2=0.31, P=0.006) and in three randomized controlled trials exercise was shown to decrease %FFML. Compared with laparoscopic adjustable gastric banding (LAGB), biliopancreatic diversion (BPD) and roux en Y gastric bypass (RYGB) caused greater loge (natural log) %FFML (r2=0.453, P<0.001). Differences in loge %FFML between surgical procedures were independent of initial BMI and magnitude of weight loss. The degree of caloric restriction, exercise and rate of weight loss influence the proportion of weight lost as FFM after non-surgical interventions. For surgical interventions, BPD and RYGB result in greater %FFML than LAGB.


Developing health service delivery in a poor and marginalised community in North West Pakistan


title: Vision in autism spectrum disorders

abstract: Autism spectrum disorders (ASDs) are developmental disorders which are thought primarily to affect social functioning. However, there is now a growing body of evidence that unusual sensory processing is at least a concomitant and possibly the cause of many of the behavioural signs and symptoms of ASD. A comprehensive and critical review of the phenomenological, empirical, neuroscientific and theoretical literature pertaining to visual processing in ASD is presented, along with a brief justification of a new theory which may help to explain some of the data, and link it with other current hypotheses about the genetic and neural aetiologies of this enigmatic condition.


title: Data integration with uncertainty

abstract: This paper reports our first set of results on managing uncertainty in data integration. We posit that data-integration systems need to handle uncertainty at three levels, and do so in a principled fashion. First, the semantic mappings between the data sources and the mediated schema may be approximate because there may be too many of them to be created and maintained or because in some domains (e.g., bioinformatics) it is not clear what the mappings should be. Second, queries to the system may be posed with keywords rather than in a structured form. Third, the data from the sources may be extracted using information extraction techniques and so may yield imprecise data. ::: ::: As a first step to building such a system, we introduce the concept of probabilistic schema mappings and analyze their formal foundations. We show that there are two possible semantics for such mappings: by-table semantics assumes that there exists a correct mapping but we don't know what it is; by-tuple semantics assumes that the correct mapping may depend on the particular tuple in the source data. We present the query complexity and algorithms for answering queries in the presence of approximate schema mappings, and we describe an algorithm for efficiently computing the top-k answers to queries in such a setting.


title: Knowledge Graph Representation via Similarity-Based Embedding

abstract: Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.


title: Management of Information Security: Challenges and Research Directions

abstract: Over the past decade management of information systems security has emerged to be a challenging task. Given the increased dependence of businesses on computer-based systems and networks, vulnerabilities of systems abound. Clearly, exclusive reliance on either the technical or the managerial controls is inadequate. Rather, a multifaceted approach is needed. In this paper, based on a panel presented at the 2007 Americas Conference on Information Systems held in Keystone, Colorado, we provide examples of failures in information security, identify challenges for the management of information systems security, and make a case that these challenges require new theory development via examining reference disciplines. We identify these disciplines, recognize applicable research methodologies, and discuss desirable properties of applicable theories.


title: Centroid-based analysis of quadruped-robot walking balance

abstract: Multi-legged robots are very useful to do tasks in rough environment. A challenging task in their applications is to manage its walking balance. To analyze such a walking balance, we consider a simple model of quadruped robotic walking and try to identify the centroid of foot polygons formed in every step. Also, we propose a performance index to estimate the walking balance. Simulation studies show that an indispensable waddling motion of the quadruped walking can be estimated by the robot's centroid trajectory and the proposed balance index. The walking balance of the robot depends on the walking pattern employed. Finally, we discuss on a strategy to maintain the best walking balance and to facilitate an excessive dynamic walking motion. A useful walking style for the quadruped robot is also addressed in the bio-mimetic aspect.


title: Attribute-Based Encryption for Circuits from Multilinear Maps

abstract: In this work, we provide the first construction of Attribute- Based Encryption (ABE) for general circuits. Our construction is based on the existence of multilinear maps. We prove selective security of our scheme in the standard model under the natural multilinear generalization of the BDDH assumption. Our scheme achieves both Key-Policy and Ciphertext-Policy variants of ABE. Our scheme and its proof of security directly translate to the recent multilinear map framework of Garg, Gentry, and Halevi.


title: Online heterogeneous mixture modeling with marginal and copula selection

abstract: This paper proposes an online mixture modeling method- ology in which individual components can have different marginal distributions and dependency structures. Mixture models have been widely studied and applied to various ap- plication areas, including density estimation, fraud/failure detection, image segmentation, etc. Previous research has been almost exclusively focused on mixture models hav- ing components of a single type (e.g., a Gaussian mixture model.) However, recent growing needs for complicated data modeling necessitate the use of more flexible mixture mod- els (e.g., a mixture of a lognormal distribution for medical costs and a Gaussian distribution for blood pressure, for medical analytics.) Our key ideas include: 1) separating marginal distributions and their dependencies using copulas and 2) online extension of a recently-developed "expecta- tion minimization of description length," which enable us to efficiently learn types of both marginal distributions and copulas as well as their parameters. The proposed method provides not only good performance in applications, but also scalable, automatic model selection, which greatly reduces the intensive modeling costs in data mining processes. We show that the proposed method outperforms state-of-the-art methods in application to density estimation and to anomaly detection.


title: Can Good Life Be Measured? The Dimensions and Measurability of a Life Worth Living

abstract: While well-being and happiness have become focal topics of psychological research, questions of good life have been mainly left to philosophers. This is an untenable state of affairs, as it leads to an overemphasis on one dimension of good life while failing to acknowledge that there are centrally important sources of value beyond one’s own happiness that people deeply care about. Therefore, we need more understanding and research on the other potential dimensions of good life. Accordingly, in this chapter I first argue that any proposed dimension of good life should be something that is intrinsically valuable, generally used when evaluating a life, and not derivative of other proposed dimensions. Based on these criteria, I suggest that at least well-being , moral praiseworthiness, meaning in life, and authenticity should be counted as independent dimensions of a good life. Accordingly, I examine these four dimensions in more detail, paying special attention to the question whether they can be measured using standard quantitative evaluation methods. It is concluded that most aspects of good life can in principle be measured, but certain important caveats apply to interpreting these results. All in all, this essay aims not to provide firm conclusions about the dimensions of good life, but rather invite other researchers into a serious discussion about the dimensions of good life and how psychology as a science can start to properly examine them.


title: EA-GPSR, a routing protocol for energy harvesting wireless sensor networks

abstract: Traditional wireless sensor networks (WSNs) are energy-limited. The power supply restricts the lifetime of sensor nodes. Energy-harvesting technology has been studied to solve the problem. The unlimited power supply makes it possible to develop novel protocol which has better performance of other indicators, such as throughput, data failure rate. In this paper, we focus on the improvement of large scale WSNs. We propose Energy Ability algorithm to evaluate the node which has energy supply and optimize Greedy perimeter stateless routing for wireless networks (GPSR) by introduce it. Numerical results have validated the proposed algorithm and show a better performance of throughput and packet loss rate compared with the traditional schemes.


title: Noncontact Power Meter

abstract: Energy metering is increasingly important in today's power grid. With real-time power meters, utilities can efficiently incorporate renewables and consumers can tailor their demand accordingly. Several high-profile attempts at delivering realtime energy analytics to users, including Google Power Meter and Microsoft Hohm, have essentially failed because of a lack of sufficient richness and access to data at adequate bandwidth for reasonable cost. High performance meters can provide adequate data, but require custom installation at prohibitive expense, e.g., requiring an electrician for installation. This paper presents hardware and signal processing algorithms that enable high bandwidth measurements of voltage, current, harmonics, and power on an aggregate electrical service (such as a residential powerline) for nonintrusive analysis with hardware that requires no special skill or safety considerations for installation.


title: 5G Privacy: Scenarios and Solutions

abstract: The next mobile generation, 5G, is expected to bring an enormous amount of new services and increased user experience. However adequate protection mechanisms for data and user privacy are required as this new technology will play a crucial role in society by connecting vertical industries, such as smart-grids, e-health, finance, transport and manufacturing. In this paper, we identify the most important privacy issues caused by the new technologies planned to use in 5G. Then, we discuss the objectives for privacy protection in 5G and correlate the identified issues with these objectives. Finally, we highlight how these objectives can be met by both a regulatory and technological approach. To this end, several privacy preserving technological solutions are presented for 5G networks.


title: Does it matter with whom you slay? The effects of competition, cooperation and relationship type among video game players

abstract: Cooperation and competition have emerged as factors that may affect video game players. Competition consistently has been found to elicit increased aggression whilst cooperation has been found to mitigate aggression and increase cooperative behaviors after game play. Of interest is the effect of the relationship between players (friend vs. stranger) in cooperative and competitive multiplayer contexts. In this study, we considered how game goal structure - competition or cooperation - and relationships between players - friend or stranger - affect aggression and cooperative behaviors. Compared with competition, cooperative play resulted in significantly more cooperative behaviors in a modified Prisoner's Dilemma task. However, neither competitive nor cooperative goal structures significantly increased state hostility, suggesting that altering players' gaming goals (e.g. competition or cooperation) may not be enough to elicit strong affective aggression. Additionally, cooperative game play was found to predict increased cooperative behaviors and trust in their partner. Implications of the findings are discussed.


title: A Demonstration of FPGA-Based You Only Look Once Version2 (YOLOv2)

abstract: We implement the YOLO (You only look once) object detector on an FPGA, which is faster and has higher accuracy. It is based on the convolutional deep neural network (CNN), and it is a dominant part of both the performance and the area. It is widely used in the embedded systems, such as robotics, autonomous driving, security, and drones, all of which require high-performance and low-power consumption. A frame object detection problem consists of two problems: one is a regression problem to spatially separated bounding boxes, the second is the associated classification of the objects within realtime frame rate. We used the binary (1 bit) precision CNN for feature extraction and the half-precision (16 bit) precision CNN for both classification and localization. We implement a pipelined based architecture for the mixed-precision YOLOv2 on the Xilinx Inc. zcu102 board, which has the Xilinx Inc. Zynq Ultrascale+ MPSoC. The implemented object detector archived 35.71 frames per second (FPS), which is faster than the standard video speed (29.9 FPS). Compared with a CPU and a GPU, an FPGA based accelerator was superior in power performance efficiency. Our method is suitable for the frame object detector for an embedded vision system.


title: A Heuristics-based Static Analysis Approach for Detecting Packed PE Binaries

abstract: Malware authors evade the signature based detection by packing the original malware using custom packers. In this paper, we present a static heuristics based approach for the detection of packed executables. We present 1) the PE heuristics considered for analysis and taxonomy of heuristics; 2) a method for computing the score using power distance based on weights and risks assigned to the defined heuristics; and 3) classification of packed executable based on the threshold obtained with the training data set, and the results achieved with the test data set. The experimental results show that our approach has a high detection rate of 99.82% with a low false positive rate of 2.22%. We also bring out difficulties in detecting packed DLL, CLR and Debug mode executables via header analysis.


title: School as both a social and educational path for incarcerated adolescents

abstract: The purpose of this paper is to verify how educators applying social/ educative measures, working with adolescents who have broken the law, evaluate the performance of the school in a community context of social/educative care regarding prevention and reduction of recidivism in breaking the law. Data was collected by means of semi-structured interviews, conducted within the institution and analyzed making use of the discourse of collective subject. The results, after interviews were analyzed, highlight four major themes: the similarities and differences between that specific school and the other public schools; the role of the school while enforcing the social/educative measure of confinement (SEMC) and its preventative actions; the recommendation of preventative actions; and the causes of recidivism. In the collective discourse of educators, one may note that school plays a key role in preventing and reducing recidivism of offenses. However, there is a lack of connections with the social work network outside the institution. Security issues are the major difference, identified by research participants, in relation to schools external to the institution. Family, socioeconomic and personal factors are, according to educators, risk factors for adolescents, which make it difficult to discontinue the legal offenses. The importance of adolescents being able to build their own life project and the inability to follow up with the ex-inmates are emphasized in the narrative of these professionals, which demonstrates the urgency of articulated public policies for a better service to young people when they leave the (Brazilian) social/educative system.


Generalized local and global Sebastiani-Thom type theorems


Battery Power Sensitive Video Processing in Wireless Networks


title: Computational design of twisty joints and puzzles

abstract: We present the first computational method that allows ordinary users to create complex twisty joints and puzzles inspired by the Rubik's Cube mechanism. Given a user-supplied 3D model and a small subset of rotation axes, our method automatically adjusts those rotation axes and adds others to construct a "non-blocking" twisty joint in the shape of the 3D model. Our method outputs the shapes of pieces which can be directly 3D printed and assembled into an interlocking puzzle. We develop a group-theoretic approach to representing a wide class of twisty puzzles by establishing a connection between non-blocking twisty joints and the finite subgroups of the rotation group SO(3). The theoretical foundation enables us to build an efficient system for automatically completing the set of rotation axes and fast collision detection between pieces. We also generalize the Rubik's Cube mechanism to a large family of twisty puzzles.


title: Wideband Segmented Loop Antenna With Dual-Polarized Omnidirectional Patterns for Mobile Platforms

abstract: A wideband planar double-segmented loop antenna that can excite different modes with perpendicular polarized omnidirectional radiation patterns is designed for mobile platforms. The proposed antenna combines these resonant modes with adjacent resonant frequencies for wideband application. One of the modes is induced by the loop element itself and generates horizontally polarized radiation pattern. Meanwhile, the other mode generates vertically polarized radiation pattern when the ground plane is excited by the loop element and operates as a radiator. The measured results confirm that the antenna is capable of achieving wide bandwidth (10-dB return loss) from 2.1 to 2.9 GHz (the corresponding fractional bandwidth is 32%). The average total realized gain of the antenna is 2.75 dBi over the entire operating bandwidth.


title: Selecting Receptive Fields in Deep Networks

abstract: Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer. Unfortunately, for such large architectures the number of parameters can grow quadratically in the width of the network, thus necessitating hand-coded "local receptive fields" that limit the number of connections from lower level features to higher ones (e.g., based on spatial locality). In this paper we propose a fast method to choose these connections that may be incorporated into a wide variety of unsupervised training methods. Specifically, we choose local receptive fields that group together those low-level features that are most similar to each other according to a pairwise similarity metric. This approach allows us to harness the advantages of local receptive fields (such as improved scalability, and reduced data requirements) when we do not know how to specify such receptive fields by hand or where our unsupervised training algorithm has no obvious generalization to a topographic setting. We produce results showing how this method allows us to use even simple unsupervised training algorithms to train successful multi-layered networks that achieve state-of-the-art results on CIFAR and STL datasets: 82.0% and 60.1% accuracy, respectively.


title: Subpixel eye gaze tracking

abstract: This paper addresses the accuracy problem of an eye gaze tracking system. We first analyze the technical barrier for a gaze tracking system to achieve a desired accuracy, and then propose a subpixel tracking method to break this barrier. We present new algorithms for detecting the inner eye corner and the center of an iris at subpixel accuracy, and we apply these new methods in developing a real-time gaze tracking system. Experimental results indicate that the new methods achieve an average accuracy within 1.4/spl deg/ using normal eye image resolutions.