title: Paediatric burn injuries in Cape Town, South Africa : Context, circumstances, and prevention barriers

abstract: Despite concerns about the impact of childhood burns, there remains a paucity of systematic descriptions of its epidemiology, contexts of occurrence, and preventability. This information is essential for the development of prevention interventions in Cape Town, South Africa. The thesis sought to identify the individual patterns and circumstances of the burn injury, neighbourhood determinants of risk, caregiver accounts of these injury events and their mechanisms, and the barriers and enablers to prevention. It comprises four studies, two of which use currently available register data, one of these in combination with South African census data, while two utilise interviews as a basis for qualitative studies into the burn event and counter-measure barriers and enablers. The first study describes the epidemiology and patterns of moderate to severe childhood burn injuries in the surrounding Western Cape province. Burn injury incidence is particularly high for toddlers and infants, for boys, and for African children. There are four burn injury patterns of occurrence: “infant scalding”, “toddler scalding”, “injuries among older children with an over-representation of flame-related burns” and “other causes of burns sustained to the head and neck region”. The second study is an ecological investigation into the impact of local circumstances of living in Cape Town on childhood burn injury levels. The main dimensions of contextual exposure were defined as housing conditions, socio-economic barriers and child dependency. All have associations, and the first two even graded relationships with injury level. The third study reports on the testimonies of caregivers whose children have recently been hospitalised with burn injuries. They highlighted the multi-factorial nature of the injury event; contexts of extensive exposure to adverse environmental, domestic and personal conditions; and environmental, spatial and supervision strategies for future prevention. The fourth study identifies caregiver and stakeholder representations of scalding countermeasures, and barriers and enablers to its implementation, in response to depictions of typical scalding scenarios. Counter-measures included: changes to home practices, safety enhancements to the home environment, and improvements of individual competence. Barriers to these focused on limitations in the physical environment, product limitations, executive hinderers, and hardship, most of which are not easily modifiable. The proposed enablers involved the reversal or alleviation of the identified barriers. In conclusion, the thesis highlights the need for burn injury prevention efforts that address the physical aspects of the home environment, such as spatial layout, storage and working facilities, and heating appliances that incorporate safety technology. This may require concerted efforts and advocacy given the various needs of impoverished and marginalized communities.


title: ‘The Curse of the Co-ops’: Co-operation, the Mass Press and the Market in Interwar Britain

abstract: Though it continues to be neglected by historians, the British Co-operative movement had 6.5 million members in the early 1930s. The movement’s phenomenal success inevitably brought it into conflict with both private capitalists and government, who sought to contain co-operators’ ambitions. The movement’s ideologues proclaimed that the great lesson of the Depression was that markets ought to be regulated by the democratic will of the people and that co-operative success proved that this was both practicable and possible. This article assesses the threat that co-operation posed to economic and business elites in this period, highlighting particularly co-operators’ critique of the myth of the ‘free’ market and their fears concerning the rapid post-war growth of combines and syndicates. It explores co-operators’ thwarted attempt to regulate markets in the interest of working-class shoppers by means of the Consumers’ Council Bill after 1929 and then goes on to discuss the ‘penal’ taxation imposed on the Co-operative movement as a kind of disciplinary measure by the National Government in 1933. The role of the mass-circulation press provides a vital thread in the story throughout, for the anti-co-operative ‘crusade’ was fervently taken up by the press barons, Lord Beaverbrook and Lord Rothermere, who used all the powers at their disposal to try to extinguish the largest democratic movement of consumers Britain had ever had.


Research Trends in Security and DDoS in SDN


title: Experiment on Triangular Copper-Strip as Impedance Matching for 2.4-GHz Helical Antenna

abstract: Performance of a helical antenna designed for 2.4-GHz WLAN is experimentally investigated in terms of impedance matching. A section of the helical wire near the antenna feed point is replaced by a simple triangular copper strip. By deploying such technique, impedance matching can be achieved. Overall return loss is below −14 dB across entire WLAN frequency range. Moreover, overall return loss does not strongly depend on strip length around its optimum, allowing 15 mm of strip-length variation around the optimum of 50 mm. Adding the triangular impedance-matching strip causes the characteristic of the helical antenna to be similar to a parallel resonant RLC circuit, resulting in frequency dependence of return loss for a given length of triangular impedance-matching strip.


title: Going Further with Point Pair Features

abstract: Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.


title: Software as a Service: Implications for Investment in Software Development

abstract: Software as a service (SaaS) is a rapidly growing model of software licensing. In contrast to traditional software where users buy a perpetual-use license, SaaS users buy a subscription from the publisher. Whereas traditional software publishers typically release new product features as part of new versions of software once in a few years, publishers using SaaS have an incentive to release new features as soon as they are completed. We show that this property of the SaaS licensing model leads to greater investment in product development under most conditions. This increased investment leads to higher software quality in equilibrium under SaaS compared to perpetual licensing. The software publisher earns greater profits under SaaS while social welfare is also higher


title: Summarizing Dialogic Arguments from Social Media

abstract: Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.


title: Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings

abstract: The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar con-texts, such spaces - called "word embeddings" - can be learned from patterns of lexical co-occurrences in natural language. Despite their popularity, a fundamental concern about word embeddings is that they appear to be semantically "rigid": inter-word proximity captures only overall similarity, yet human judgments about object similarities are highly context-dependent and involve multiple, distinct semantic features. For example, dolphins and alligators appear similar in size, but differ in intelligence and aggressiveness. Could such context-dependent relationships be recovered from word embeddings? To address this issue, we introduce a powerful, domain-general solution: "semantic projection" of word-vectors onto lines that represent various object features, like size (the line extending from the word "small" to "big"), intelligence (from "dumb" to "smart"), or danger (from "safe" to "dangerous"). This method, which is intuitively analogous to placing objects "on a mental scale" between two extremes, recovers human judgments across a range of object categories and properties. We thus show that word embeddings inherit a wealth of common knowledge from word co-occurrence statistics and can be flexibly manipulated to express context-dependent meanings.


title: Blockchain-Assisted Crowdsourced Energy Systems

abstract: Crowdsourcing relies on people's contributions to meet product- or system-level objectives. Crowdsourcing-based methods have been implemented in various cyber-physical systems and realtime markets. This paper explores a framework for Crowdsourced Energy Systems (CES), where small-scale energy generation or energy trading is crowdsourced from distributed energy resources, electric vehicles, and shapable loads. The merits/pillars of energy crowdsourcing are discussed. Then, an operational model for CESs in distribution networks with different types of crowdsourcees is proposed. The model yields a market equilibrium depicting traditional and distributed generator and load setpoints. Given these setpoints, crowdsourcing incentives are designed to steer crowdsourcees to the equilibrium. As the number of crowdsourcees and energy trading transactions scales up, a secure energy trading platform is required. To that end, the presented framework is integrated with a lightweight Blockchain implementation and smart contracts. Numerical tests are provided to showcase the overall implementation.


title: Comprehensive grant-free random access for massive & low latency communication

abstract: In this paper, we introduce a comprehensive grant-free random access scheme for machine-type communication which is characterized by massive connectivity and low latency. The scheme presented in here is comprehensive in a sense that, synchronization, channel estimation, and users identification & data detection (multi-user detection) are performed all in a single shot. The scheme employs compressive sensing by exploiting two sparse phenomena: sparsity in users activity and sparsity in multi-path channel. Furthermore, the scheme is designed in such a way that channel estimation and multi-user detection have a bi-directional mutual relationship, enabling one to reinforce the other for accurate detection and estimation. Moreover, the iterative order recursive least square (IORLS) estimation algorithm is modified & employed in such a way that it exploits the joint structure in multi-path channel and multi-user signal sparsity.


title: Gray Wolf Optimizer for hyperspectral band selection

abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new approach for feature selection in hyperspectral image classification.The problem of band selection is reformulated as a combinatorial problem.We design a new objective function which takes into account two term, the classification error rate and the class separability distance.To optimize the objective function, we propose to use a new meta-heuristic called Gray Wolf Optimizer.The experiments have been conducted in three widely used hyperspectral images and compared with other approaches. In this paper, we propose a new optimization-based framework to reduce the dimensionality of hyperspectral images. One of the most problems in hyperspectral image classification is the Hughes phenomenon caused by the irrelevant spectral bands and the high correlation between the adjacent bands. The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate. We propose to reformulate the problem of band selection as a combinatorial problem by modeling an objective function based on class separability measures and the accuracy rate. We use the Gray Wolf Optimizer, which is a new meta-heuristic algorithm more efficient than Practical Swarm Optimization, Gravitational Search Algorithm, Differential Evolution, Evolutionary Programming and Evolution Strategy. The experimentations are performed on three widely used benchmark hyperspectral datasets. Comparisons with the state-of-the-art approaches are also conducted. The analysis of the results proves that the proposed approach can effectively investigate the spectral band selection problem and provides a high classification accuracy rate by using a few samples for training.


title: Rational Extremism : Understanding Terrorism in the Twenty-first Century

abstract: In the last decade, the field of international relations has undergone a revolution in conflict studies. Where earlier approaches attempted to identify the attributes of individuals, states, and systems that produced conflict, the “rationalist approach to war” now explains violence as the product of private information with incentives to misrepresent, problems of credible commitment, and issue indivisibilities. 1 In this new approach, war is understood as a bargaining failure that leaves both sides worse off than had they been able to negotiate an efficient solution. This rationalist framework has proven remarkably general—being applied to civil wars, ethnic conflicts, and interstate wars—and fruitful in understanding not only the causes of war but also war termination and conflict management. 2 Interstate war is no longer seen as sui generis, but as a particular form within a single, integrated theory of conflict. This rationalist approach to war may at first appear to be mute in the face of the terrorist attacks of September 11, 2001. Civilian targets were attacked “out of the blue.” The terrorists did not issue prior demands. A theory premised on bargaining, therefore, would seem ill-suited to explaining such violence. Yet, as I hope to show, extremist terrorism can be rational and strategic. 3 A rationalist approach also yields insights into the nature and strategy of terrorism and offers some general guidelines that targets should consider in response, including the importance of a multilateral coalition as a means of committing the target to a moderate military strategy. Analytically, and more centrally for this essay, extremist terrorism reveals a silence at the heart of the current rationalist approach to war even as it suggests a potentially fruitful way of extending the basic model. In extant models, the distribution of capabilities and, thus, the range of acceptable bargains are exogenous,


title: An improved PV system based on dual axis solar tracking and MPPT

abstract: This paper presents the design and construction of a photovoltaic system (PV system) using dual axis solar tracking and maximum power point tracking (MPPT) controller to improve the efficiency of PV cell output power. The dual axis solar tracking technique is designed to track the sun both the azimuth angle and altitude angle to enable the solar panel perpendicular to the illumination of the sun. The MPPT algorithm is designed to keep the PV system operating at maximum power point, a modified variable step size incremental conductance algorithm has been proposed in this paper to meet the requirements of response speed and dynamic stability. Both the solar tracking and MPPT algorithms are implemented into TMD320F2812 controller. Simulations and practical experiments have been made through Matlab/Simulink and prototype device respectively. The results show that both the solar tracking and MPPT algorithms could improve the efficiency of PV array output power.


title: Cognitive radio networks: realistic or not?

abstract: A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important aspects of commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. While on the one hand it is necessary to detect the existence of spectrum holes, on the other hand, simply sensing (cooperatively or not) the energy emitted from a primary transmitter cannot enable correct dynamic spectrum access. For example, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing solutions to the DSA problem no robust, implementable scheme has emerged. The set of assumptions that these schemes are built upon do not always hold in realistic, wireless environments. Specific settings are assumed, which differ significantly from how existing telecommunication networks work. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, namely TV and cellular, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments. The proposed study extends beyond cognitive radio networks, and further highlights the often existing gap between research and commercialization, paving the way to new thinking about how to accelerate commercialization and adoption of new networking technologies and services.


title: Learning to Filter Object Detections

abstract: Most object detection systems consist of three stages. First, a set of individual hypotheses for object locations is generated using a proposal generating algorithm. Second, a classifier scores every generated hypothesis independently to obtain a multi-class prediction. Finally, all scored hypotheses are filtered via a non-differentiable and decoupled non-maximum suppression (NMS) post-processing step. In this paper, we propose a filtering network (FNet), a method which replaces NMS with a differentiable neural network that allows joint reasoning and re-scoring of the generated set of hypotheses per image. This formulation enables end-to-end training of the full object detection pipeline. First, we demonstrate that FNet, a feed-forward network architecture, is able to mimic NMS decisions, despite the sequential nature of NMS. We further analyze NMS failures and propose a loss formulation that is better aligned with the mean average precision (mAP) evaluation metric. We evaluate FNet on several standard detection datasets. Results surpass standard NMS on highly occluded settings of a synthetic overlapping MNIST dataset and show competitive behavior on PascalVOC2007 and KITTI detection benchmarks.


title: Inductive Sensor for Temperature Measurement in Induction Heating Applications

abstract: A new inductive sensor for temperature measurement in domestic induction hobs and results of its applications are presented. It consists of a coil and a resonant electronic circuit which measures the impedance variation of ferromagnetic pots when their temperature changes. This sensor has promising applicability in induction hobs because it has instantaneous response, it is cheap, and contactless. The performance of the sensor is tested in the cooking range from 20°C to 220° C with pots of different materials, revealing a measurement error lower than 6°C. This error is much lower than that obtained from measurements with current sensing systems installed in induction hobs, namely a thermistor measuring the temperature of the glass in the cooking zone. The proposed sensor will allow fire prevention in cooking processes with a cost comparable to that of conventional measurement systems.


title: Anytime Exploitation of Stragglers in Synchronous Stochastic Gradient Descent

abstract: In this paper we propose an approach to parallelizing synchronous stochastic gradient descent (SGD) that we term “Anytime-Gradients”. The Anytime-Gradients is designed to exploit the work completed by slow compute nodes or “stragglers”. In many approaches work completed by these nodes, while only partial, is discarded completely. To maintain synchronization in our approach, each computational epoch is of fixed duration, and at the end of each epoch, workers send updated parameter vectors to a master mode for combination. The master weights each update by the amount of work done. The Anytime-Gradients scheme is robust to both persistent and non-persistent stragglers and requires no prior knowledge about processor abilities. We show that the scheme effectively exploits stragglers and outperforms existing methods.


title: Cannabis use in adolescence and risk for adult psychosis: longitudinal prospective study

abstract: Papers pp 1195, 1199 ::: ::: The strongest evidence that cannabis use may be a risk factor for later psychosis comes from a Swedish cohort study which found that heavy cannabis use at age 18 increased the risk of later schizophrenia sixfold. 1 2 This study could not establish whether adolescent cannabis use was a consequence of pre-existing psychotic symptoms rather than a cause. We present the first prospective longitudinal study of adolescent cannabis use as a risk factor for adult schizophreniform disorder, taking into account childhood psychotic symptoms3 antedating cannabis use. ::: ::: View this table: ::: ::: Association between cannabis use in adolescence and schizophrenia and depressive symptoms and disorders at age 26 (n=759), controlling for childhood psychotic symptoms and use of other drugs in adolescence ::: ::: ::: ::: The Dunedin multidisciplinary health and development study (a study of a general population birth cohort of 1037 individuals born in Dunedin, New Zealand, in 1972-3)4 has a 96% follow up rate at age 26. It obtained information on psychotic symptoms at age 11 and drug use at ages 15 and 18 from self reports and assessed …


title: Network Random KeysA Tree Representation Scheme for Genetic and Evolutionary Algorithms

abstract: When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors.This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.


title: Ultra-Resolving Face Images by Discriminative Generative Networks

abstract: Conventional face super-resolution methods, also known as face hallucination, are limited up to \(2 \! \sim \! 4\times \) scaling factors where \(4 \sim 16\) additional pixels are estimated for each given pixel. Besides, they become very fragile when the input low-resolution image size is too small that only little information is available in the input image. To address these shortcomings, we present a discriminative generative network that can ultra-resolve a very low resolution face image of size \(16 \times 16\) pixels to its \(8\times \) larger version by reconstructing 64 pixels from a single pixel. We introduce a pixel-wise \(\ell _2\) regularization term to the generative model and exploit the feedback of the discriminative network to make the upsampled face images more similar to real ones. In our framework, the discriminative network learns the essential constituent parts of the faces and the generative network blends these parts in the most accurate fashion to the input image. Since only frontal and ordinary aligned images are used in training, our method can ultra-resolve a wide range of very low-resolution images directly regardless of pose and facial expression variations. Our extensive experimental evaluations demonstrate that the presented ultra-resolution by discriminative generative networks (UR-DGN) achieves more appealing results than the state-of-the-art.


Beyond incremental processing : Tracking concept drift


title: An exact algorithm based on MaxSAT reasoning for the maximum weight clique problem

abstract: Recently, MaxSAT reasoning is shown very effective in computing a tight upper bound for a Maximum Clique (MC) of a (unweighted) graph. In this paper, we apply MaxSAT reasoning to compute a tight upper bound for a Maximum Weight Clique (MWC) of a wighted graph. We first study three usual encodings of MWC into weighted partial MaxSAT dealing with hard clauses, which must be satisfied in all solutions, and soft clauses, which are weighted and can be falsified. The drawbacks of these encodings motivate us to propose an encoding of MWC into a special weighted partial MaxSAT formalism, called LW (Literal-Weighted) encoding and dedicated for upper bounding an MWC, in which both soft clauses and literals in soft clauses are weighted. An optimal solution of the LW MaxSAT instance gives an upper bound for an MWC, instead of an optimal solution for MWC. We then introduce two notions called the Top-k literal failed clause and the Top-k empty clause to extend classical MaxSAT reasoning techniques, as well as two sound transformation rules to transform an LW MaxSAT instance. Successive transformations of an LW MaxSAT instance driven by MaxSAT reasoning give a tight upper bound for the encoded MWC. The approach is implemented in a branch-and-bound algorithm called MWCLQ. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark, random graphs and the benchmark from the winner determination problem show that our approach allows MWCLQ to reduce the search space significantly and to solve MWC instances effectively. Consequently, MWCLQ outperforms state-of-the-art exact algorithms on the vast majority of instances. Moreover, it is surprisingly effective in solving hard and dense instances.


title: Multilayer 180$^{\circ}$ Coupled Line Hybrid Coupler

abstract: This paper presents a theoretical analysis of a 180deg hybrid coupler based on coupled lines. The analysis is applicable for the case where the non-TEM nature of coupled lines must be considered. The method used to analyze the coupler is based on an analytical T matrix approach, which provides suitable expressions for the design of the coupler. The design procedure is demonstrated by the fabrication of a 24-GHz coupler using low-temperature co-fired ceramic. The realized coupler has non-interspersed inputs and outputs, reduced substrate area and robustness to layer misalignment. The measured characteristics are in good agreement with simulations and demonstrate good performance of the fabricated coupler. An application example of the hybrid coupler in a compact-sized phasing network for an in-phase/quadrature mixer is also described.


title: Bird brains and songs: neural mechanisms of birdsong perception and memory

abstract: The males of songbirds and parrots learn their songs from a tutor. Until recently it was thought that in songbirds, brain nuclei in the so-called 'song system' were involved in song learning, in addition to their role in song perception and production. Experiments involving measurement of the expression of immediate early genes (IEG) showed that exposure to song leads to activation of cells in brain regions outside the song system, notably the caudomedial neostriatum (NCM) and the caudomedial hyperstriatum ventrale (CMHV), suggesting that these regions are involved in auditory perception. In addition, neuronal activation in the NCM correlates with the number of song elements that a male has learned from its tutor, suggesting that NCM may be (part of) the neural substrate for stored tutor song. Songbird females do not usually sing, but nevertheless they can learn the characteristics of tutor song, and they can develop a perceptual preference for tutor song over novel song. When exposed to male song, female songbirds and parrots show increased IEG expression in NCM and CMHV that is related to song complexity. In addition, the IEG response in the NCM (and perhaps in the CMHV) of female songbirds may also be related to song learning.


title: Analyzing communities and their evolutions in dynamic social networks

abstract: We discover communities from social network data and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. This novel framework will discover communities and capture their evolution with temporal smoothness given by historic community structures. Our approach relies on formulating the problem in terms of maximum a posteriori (MAP) estimation, where the community structure is estimated both by the observed networked data and by the prior distribution given by historic community structures. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.


title: Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing

abstract: We present a novel stereo-based obstacle avoidance system on a vision-guided micro air vehicle (MAV) that is capable of fully autonomous maneuvers in unknown and dynamic environments. All algorithms run exclusively on the vehicle's on-board computer, and at high frequencies that allow the MAV to react quickly to obstacles appearing in its flight trajectory. Our MAV platform is a quadrotor aircraft equipped with an inertial measurement unit and two stereo rigs. An obstacle mapping algorithm processes stereo images, producing a 3D map representation of the environment; at the same time, a dynamic anytime path planner plans a collision-free path to a goal point.


title: Comparative Analysis of Cryptographic Algorithms

abstract: Cyber security ensures a secure information exchange and enables communication through the Internet. The data needs to be protected from unauthorized access and transmitted to the intended receiver with confidentiality and integrity. Cryptography enhances security by encrypting and decrypting raw data in a secured network. Many cryptographic algorithms are available, and they fall under either symmetric or asymmetric techniques. To choose an algorithm for secure data communication, the candidate algorithm should provide higher accuracy, security and efficiency. This paper presents the implementation limitations of existing cryptographic algorithms such as DES, 3DES, CAST-128, BLOWFISH, IDEA, AES, and RC6 of symmetric techniques and RSA of asymmetric techniques. This paper also analyzes parameters like key exchange, flexibility and security issues of the algorithms which determines the efficiency of the cryptosystem.


title: Drivable Road Detection Based on Dilated FPN with Feature Aggregation

abstract: In the field of ADAS and self-driving car, lane and drivable road detection play an essential role in reliably accomplishing other tasks, such as objects detection. For monocular vision based semantic segmentation of lane and road, we propose a dilated feature pyramid network (FPN) with feature aggregation, called DFFA, where feature aggregation is employed to combine multi-level features enhanced with dilated convolution operations and FPN under the framework of ResNet. Experimental results validate effectiveness and efficiency of the proposed deep learning model for semantic segmentation of lane and drivable road. Our DFFA achieves the best performance both on Lane Estimation Evaluation and Behavior Evaluation tasks in KITTI-ROAD and take the second place on UU ROAD task.


title: A single-model approach for Arabic segmentation, POS tagging, and named entity recognition

abstract: This paper presents an entirely new, one-million-word annotated corpus for a comprehensive, machine-learning-based preprocessing of text in Modern Standard Arabic. Contrary to the conventional pipeline architecture, we solve the NLP tasks of word segmentation, POS tagging and named entity recognition as a single sequence labeling task. This single-component configuration results in a faster operation and is able to provide state-of-the-art precision and recall according to our evaluations. The fine-grained output tag set output by our annotator greatly simplifies downstream tasks such as lemmatization. Provided as a trained OpenNLP component, the annotator is free for research purposes.


title: Building customer value in the hospitality industry: towards the definition of a customer-centric information system.

abstract: Increasing occupancy rates and revenue by improving customer experience is the aim of modern hospitality organizations. To achieve these results, hotel managers need to have a deep knowledge of customers’ needs, behavior, and preferences and be aware of the ways in which the services delivered create value for the customers and then stimulate their retention and loyalty. In this article a methodological framework to analyze the guest–hotel relationship and to profile hotel guests is discussed, focusing on the process of designing a customer information system and particularly the guest information matrix on which the system database will be built.


Shelftorchlight: Augmenting a shelf using a camera projector unit


title: Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

abstract: With the proliferation of social media, information generated and disseminated from these outlets has become an important part of our everyday lives. For example, this type of information has great potential for effectively distributing political messages, hazard alerts, or messages of other social functions. In this work, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management and urban sustainability.


title: From Ads to Interventions: Contextual Bandits in Mobile Health.

abstract: The first paper on contextual bandits was written by Michael Woodroofe in 1979 (Journal of the American Statistical Association, 74(368), 799–806, 1979) but the term “contextual bandits” was invented only recently in 2008 by Langford and Zhang (Advances in neural information processing systems, pages 817–824, 2008). Woodroofe’s motivating application was clinical trials whereas modern interest in this problem was driven to a great extent by problems on the internet, such as online ad and online news article placement. We have now come full circle because contextual bandits provide a natural framework for sequential decision making in mobile health. We will survey the contextual bandits literature with a focus on modifications needed to adapt existing approaches to the mobile health setting. We discuss specific challenges in this direction such as: good initialization of the learning algorithm, finding interpretable policies, assessing usefulness of tailoring variables, computational considerations, robustness to failure of assumptions, and dealing with variables that are costly to acquire and missing.


title: Image-guided spine surgery: state of the art and future directions

abstract: Navigation technology is a widely available tool in spine surgery and has become a part of clinical routine in many centers. The issue of where and when navigation technology should be used is still an issue of debate. It is the aim of this study to give an overview on the current knowledge concerning the technical capabilities of image-guided approaches and to discuss possible future directions of research and implementation of this technique. Based on a Medline search total of 1,462 publications published until October 2008 were retrieved. The abstracts were scanned manually for relevance to the topics of navigated spine surgery in the cervical spine, the thoracic spine, the lumbar spine, as well as ventral spine surgery, radiation exposure, tumor surgery and cost-effectivity in navigated spine surgery. Papers not contributing to these subjects were deleted resulting in 276 papers that were included in the analysis. Image-guided approaches have been investigated and partially implemented into clinical routine in virtually any field of spine surgery. However, the data available is mostly limited to small clinical series, case reports or retrospective studies. Only two RCTs and one metaanalysis have been retrieved. Concerning the most popular application of image-guided approaches, pedicle screw insertion, the evidence of clinical benefit in the most critical areas, e.g. the thoracic spine, is still lacking. In many other areas of spine surgery, e.g. ventral spine surgery or tumor surgery, image-guided approaches are still in an experimental stage. The technical development of image-guided techniques has reached a high level as the accuracies that can be achieved technically meet the anatomical demands. However, there is evidence that the interaction between the surgeon ('human factor') and the navigation system is a source of inaccuracy. It is concluded that more effort needs to be spend to understand this interaction.


title: Kernel regression in mixed feature spaces for spatio-temporal saliency detection

abstract: A spatio-temporal saliency model for salient region detection in video is proposed.Kernel regression in mixed feature spaces and its three entity-models are proposed.Based on kernel regression, a hybrid fusion strategy is proposed.The hybrid fusion strategy performs better than the independent fusion strategy.The proposed spatio-temporal saliency model outperforms existing approaches. Spatio-temporal saliency detection has attracted lots of research interests due to its competitive performance on wide multimedia applications. For spatio-temporal saliency detection, existing bottom-up algorithms often over-simplify the fusion strategy, which results in the inferior performance than the human vision system. In this paper, a novel bottom-up spatio-temporal saliency model is proposed to improve the accuracy of attentional region estimation in videos through fully exploiting the merit of fusion. In order to represent the space constructed by several types of features such as location, appearance and temporal cues extracted from video, kernel regression in mixed feature spaces (KR-MFS) including three approximation entity-models is proposed. Using KR-MFS, a hybrid fusion strategy which considers the combination of spatial and temporal saliency of each individual unit and incorporates the impacts from the neighboring units is presented and embedded into the spatio-temporal saliency model. The proposed model has been evaluated on the publicly available dataset. Experimental results show that the proposed spatio-temporal saliency model can achieve better performance than the state-of-the-art approaches.


title: Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]

abstract: The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field


title: Unveiling patterns of international communities in a global city using mobile phone data

abstract: We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city’s hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city.


title: An Ultra-Wideband 80 GHz FMCW Radar System Using a SiGe Bipolar Transceiver Chip Stabilized by a Fractional-N PLL Synthesizer

abstract: A radar system with an ultra-wide FMCW ramp bandwidth of 25.6 GHz (≈32%) around a center frequency of 80 GHz is presented. The system is based on a monostatic fully integrated SiGe transceiver chip, which is stabilized using conventional fractional-N PLL chips at a reference frequency of 100 MHz. The achieved in-loop phase noise is ≈ -88 dBc/Hz (10 kHz offset frequency) for the center frequency and below ≈-80 dBc/Hz in the wide frequency band of 25.6 GHz for all offset frequencies >;1 kHz. The ultra-wide PLL-stabilization was achieved using a reverse frequency position mixer in the PLL (offset-PLL) resulting in a compensation of the variation of the oscillators tuning sensitivity with the variation of the N-divider in the PLL. The output power of the transceiver chip, as well as of the mm-wave module (containing a waveguide transition), is sufficiently flat versus the output frequency (variation <;3 dB). In radar measurements using the full bandwidth an ultra-high spatial resolution of 7.12 mm was achieved. The standard deviation between repeated measurements of the same target is 0.36 μm.


title: Generalized Dantzig Selector: Application to the k-support norm

abstract: We propose a Generalized Dantzig Selector (GDS) for linear models, in which any norm encoding the parameter structure can be leveraged for estimation. We investigate both computational and statistical aspects of the GDS. Based on conjugate proximal operator, a flexible inexact ADMM framework is designed for solving GDS, and non-asymptotic high-probability bounds are established on the estimation error, which rely on Gaussian width of unit norm ball and suitable set encompassing estimation error. Further, we consider a non-trivial example of the GDS using $k$-support norm. We derive an efficient method to compute the proximal operator for $k$-support norm since existing methods are inapplicable in this setting. For statistical analysis, we provide upper bounds for the Gaussian widths needed in the GDS analysis, yielding the first statistical recovery guarantee for estimation with the $k$-support norm. The experimental results confirm our theoretical analysis.


title: GPU-Based Speculative Query Processing for Database Operations

abstract: With an increasing amount of data and user demands for fast query processing, the optimization of database operations continues to be a challenging task. A common optimization method is to leverage parallel hardware architectures. With the introduction of general-purpose GPU computing, massively parallel hardware has become available within commodity hardware. To efficiently exploit this technology, we introduce the method of speculative query processing. This speculative query processing works on, but is not limited to, a prefix tree structure to efficiently support heavily used database index operations. Fundamentally, our developed approach traverse a prefix tree structure in a speculative, parallel way instead of a step-by-step traversing. To show the benefits and opportunities of our novel approach, we present an exhaustive evaluation on a graphical processing unit.


title: Accurate 3D measurement using a Structured Light System

abstract: This paper discusses a method for obtaining accurate 3D measurements using a temporally encoded structured light system. An objective of the work was to have a balance in the accuracy of all components in the system. This was achieved by including lens distortion in the models for both the camera and projector which comprise the structured light system. In addition, substripe estimation was used to estimate projector stripe values as a complement to subpixel estimators used for locating image features. Experimental evaluation shows that it is important to use substripe estimation and incorporate lens distortion in the projector model.


title: 3DCityDB - a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML

abstract: Over the last decade, more and more cities and even countries worldwide are creating semantic 3D city models of their physical environment based on the international CityGML standard issued by the Open Geospatial Consortium (OGC). CityGML is an open data model and XML-based data exchange format describing the most relevant urban and landscape objects along with their spatial and non-spatial attributes, relations, and their complex hierarchical structures in five levels of detail. 3D city models, which are structured according to CityGML, are often used for various complex GIS simulation and analysis tasks, which go far beyond pure 3D visualization. Due to the large size and complexity of the sometimes country-wide 3D geospatial data, the GIS software vendors and service providers face many challenges when building 3D spatial data infrastructures for realizing the efficient storage, analysis, management, interaction, and visualization of the 3D city models based on the CityGML standard. Hence, there has been strong demand for an open and comprehensive software solution that can provide full support of the aforementioned functionalities. The ‘3D City Database’ (3DCityDB) is a free 3D geo-database solution for CityGML-based 3D city models. 3DCityDB has been developed as an Open Source and platform-independent software suite to facilitate the development and deployment of 3D city model applications. The 3DCityDB software package consists of a database schema for spatially enhanced relational database management systems (ORACLE Spatial or PostgreSQL/PostGIS) with a set of database procedures and software tools allowing to import, manage, analyze, visualize, and export virtual 3D city models according to the CityGML standard. Within this paper, the software suite is illustrated and explained in detail with respect to the related technical implementations and the underlying conceptual software design. Moreover, the utilization of 3DCityDB in different projects and practical application fields are also presented in this paper.


title: Full Length Research Paper A survey of job recommender systems

abstract: The Internet-based recruiting platforms become a primary recruitment channel in most companies. While such platforms decrease the recruitment time and advertisement cost, they suffer from an inappropriateness of traditional information retrieval techniques like the Boolean search methods. Consequently, a vast amount of candidates missed the opportunity of recruiting. The recommender system technology aims to help users in finding items that match their personnel interests; it has a successful usage in e-commerce applications to deal with problems related to information overload efficiently. In order to improve the e-recruiting functionality, many recommender system approaches have been proposed. This article will present a survey of e-recruiting process and existing recommendation approaches for building personalized recommender systems for candidates/job matching. ::: ::: ::: ::: Key words: Recommender systems, collaborative filtering, content-based filtering, hybrid approach, machine learning, e-recruiting, similarity measure.


title: Understanding the determinants of business intelligence system adoption stages: an empirical study of SMEs

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to provide a better understanding of the determinants of business intelligence system (BIS) adoption stages. It develops and empirically tests a conceptual model for assessing the determinants of BIS diffusion on the evaluation, adoption, and use stages in the context of small and medium enterprises (SMEs). ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Drawing on data from 181 SMEs the influence of technological, organizational, and environmental factors on BIS adoption stages were analyzed using the PLS-SEM method. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The paper provides empirical insights about how technological, organizational, and environmental factors affect individual BIS adoption stages. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The paper includes implications for managers and solution providers to understand the influence of various determinants to more effectively conclude the adoption process. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study represents important progress in the theoretical understanding of the role of technological, organizational, and environmental factors across the different BIS adoption stages.


title: A Survey of Memristive Threshold Logic Circuits

abstract: In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore’s law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing, control systems, programmable logic, image processing, reconfigurable computing, and pattern recognition are identified as some of the potential applications of MTL systems. The physical realization of nanoscale devices with memristive behavior from materials, such as TiO2, ferroelectrics, silicon, and polymers, has accelerated research effort in these application areas, inspiring the scientific community to pursue the design of high-speed, low-cost, low-power, and high-density neuromorphic architectures.


Adaptive VM Handoff Across Cloudlets


title: Linggle: a Web-scale Linguistic Search Engine for Words in Context

abstract: In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.


title: Real-time pen-and-ink illustration of landscapes

abstract: Non-photorealistic rendering has been proven to be particularly efficient in conveying and transmitting selected visual information. Our paper presents a NPR rendering pipeline that supports pen-and-ink illustration for, but not limited to, complex landscape scenes in real time. This encompasses a simplification framework using clustering which enables new approaches to efficient and coherent rendering of stylized silhouettes, hatching and abstract shading. Silhouette stylization is performed in image-space. This avoids explicit computation of connected lines. Further, coherent hatching of the tree foliage is performed using an approximate view-dependent parameterization computed on-the-fly within the same simplification framework. All NPR algorithms are integrated with photorealistic rendering, allowing seamless transition and combination between a variety of photorealistic and non-photorealistic drawing styles.


title: An Efficient Synchronization Mechanism for Mirrored Game Architectures

abstract: Existing online multiplayer games typically use a client-server model, which introduces added latency as well as a single bottleneck and single point of failure to the game. Distributed multiplayer games minimize latency and remove the bottleneck, but require special synchronization mechanisms to provide a consistent game for all players. Current synchronization methods have been borrowed from distributed military simulations and are not optimized for the requirements of fast-paced multiplayer games. In this paper we present a new synchronization mechanism, trailing state synchronization (TSS), which is designed around the requirements of distributed first-person shooter games. ::: ::: We look at TSS in the environment of a mirrored game architecture, which is a hybrid between traditional centralized architectures and the more scalable peer-to-peer architectures. Mirrored architectures allow for improved performance compared to client-server architectures while at the same time allowing for a greater degree of centralized administration than peer-to-peer architectures. ::: ::: We evaluate the performance of TSS and other synchronization methods through simulation and examine heuristics for selecting the synchronization delays needed for TSS.


title: Interactive schematic summaries for exploration of surveillance video

abstract: We present a new and scalable technique to explore surveillance videos by scatter/gather browsing of trajectories of moving objects. The proposed approach facilitates interactive clustering of trajectories by an effective way of cluster visualization that we term schematic summaries. This novel visualization illustrates cluster summaries in a schematic, non-photorealistic style. To reduce visual clutter, we introduce the trajectory bundling technique. The fusion of schematic summaries and user interaction leads to efficient hierarchical exploration of video data. Examples of different browsing scenarios demonstrate the effectiveness of the proposed method.


title: Review on image sonification: A non-visual scene representation

abstract: With the advent of image and video representation of visual scenes in digital computer, subsequent necessity of vision-substitution representation of a given image is felt. The medium for non-visual representation of an image is chosen to be sound due to well developed auditory sensing ability of human beings and wide availability of cheap audio hardware. Visionary information of an image can be conveyed to blind and partially sighted persons through auditory representation of the image within some of the known limitations of human hearing system. The research regarding image sonification has mostly evolved through last three decades. The paper also discusses in brief about the reverse mapping, termed as sound visualization. This survey approaches to summarize the methodologies and issues of the implemented and unimplemented experimental systems developed for subjective sonification of image scenes and let researchers accumulate knowledge about the previous direction of researches in this domain.


Genuine, Independent, Holistic, and Appropriate: Four Considerations for the Optimization of Positive Interventions


Ph.D. Research Proposal Information Extraction for Legal Documents


title: Two-dimensional packing for irregular shaped objects

abstract: Packing problems arise in a wide variety of application areas. The basic problem is that of determining an efficient arrangement of different objects in a region without any overlap. The simplest packing problem is rectangular packing, where both the objects and the region are rectangular. Many research works have been done on two and three dimensional rectangular packing. However, there are many situations when either objects or the containing region is irregular in shape. In the project, we concentrate on two-dimensional packing problems involving irregular shaped objects (both convex and concave). We have applied several approaches to solve such problems.


title: Relationships among strain energies of mono- and poly-cyclic cyclohexanoid molecules and strain of their component rings

abstract: The standard molar enthalpies of vaporizationΔlgHmo of N -(hydroxyethyl)-piperazine, bis-piperidino-methane, 1,3,5-tri-methyl-hexahydro-s-triazine, and also the enthalpies of sublimation ΔcrgHmoofN,N′ -di(hydroxyethyl)-piperazine, and hexamethylenetetramine were obtained from the temperature dependence of the vapor pressure measured in a flow system. The standard molar enthalpies of formationΔfHmo (l) at the temperatureT = 298.15 K were measured by means combustion calorimetry for bis-piperidino-methane and 1,3,5-tri-methyl-hexahydro-s-triazine. Strain enthalpies (ring-correction terms) of these cyclohexanoid molecules were derived from the experimental enthalpies of formation of the gaseous compounds. These experimental results, together with data available from the literature, provided a quantitative understanding of the interrelations of structure and energetics of a mono- and poly-cyclic cyclohexanoid molecules and strain of their component rings.


title: Attend Before you Act: Leveraging human visual attention for continual learning

abstract: When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining it to build a representation from the sensory data. In this work, we explore leveraging where humans look in an image as an implicit indication of what is salient for decision making. We build on top of the UNREAL architecture in DeepMind Lab's 3D navigation maze environment. We train the agent both with original images and foveated images, which were generated by overlaying the original images with saliency maps generated using a real-time spectral residual technique. We investigate the effectiveness of this approach in transfer learning by measuring performance in the context of noise in the environment.


title: Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

abstract: Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.


title: Stock market prediction using an improved training algorithm of neural network

abstract: Predicting closing stock price accurately is an challenging task. Computer aided systems have been proved to be helpful tool for stock prediction such as Artificial Neural Net-work(ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) etc. Latest research works prove that Adaptive Neuro Fuzzy Inference System shows better results than Neural Network for stock prediction. In this paper, an improved Levenberg Marquardt(LM) training algorithm of artificial neural network has been proposed. Improved Levenberg Marquardt algorithm of neural network can predict the possible day-end closing stock price with less memory and time needed, provided previous historical stock market data of Dhaka Stock Exchange such as opening price, highest price, lowest price, total share traded. Morever, improved LM algorithm can predict day-end stock price with 53% less error than ANFIS and traditional LM algorithm. It also requires 30% less time, 54% less memory than traditional LM and 47% less time, 59% less memory than ANFIS.


title: Learning to Prioritize Test Programs for Compiler Testing

abstract: Compiler testing is a crucial way of guaranteeing the reliability of compilers (and software systems in general). Many techniques have been proposed to facilitate automated compiler testing. These techniques rely on a large number of test programs (which are test inputs of compilers) generated by some test-generation tools (e.g., CSmith). However, these compiler testing techniques have serious efficiency problems as they usually take a long period of time to find compiler bugs. To accelerate compiler testing, it is desirable to prioritize the generated test programs so that the test programs that are more likely to trigger compiler bugs are executed earlier. In this paper, we propose the idea of learning to test, which learns the characteristics of bug-revealing test programs from previous test programs that triggered bugs. Based on the idea of learning to test, we propose LET, an approach to prioritizing test programs for compiler testing acceleration. LET consists of a learning process and a scheduling process. In the learning process, LET identifies a set of features of test programs, trains a capability model to predict the probability of a new test program for triggering compiler bugs and a time model to predict the execution time of a test program. In the scheduling process, LET prioritizes new test programs according to their bug-revealing probabilities in unit time, which is calculated based on the two trained models. Our extensive experiments show that LET significantly accelerates compiler testing. In particular, LET reduces more than 50% of the testing time in 24.64% of the cases, and reduces between 25% and 50% of the testing time in 36.23% of the cases.


title: Automatic license plate recognition in difficult conditions — Technical report

abstract: By a car license plate recognition, we mean a software system processing images and providing an alphanumeric transcription of car plates included in an image. We divide the task into four sub-tasks: license plate localization, license plate extraction, characters segmentation and characters recognition. All four sub-tasks are discussed in the context of standard approaches and own solution based on a chain of standard and soft computing image processing algorithms is presented. In this chain, the F-transform approximate pattern matching algorithm plays the crucial role. For the solution, we presented recognition ability for a dataset which includes 500 images with difficult conditions.


Research on Cultural Diversity and Intercultural Communication under the New Media Environment


title: Low Cost Arduino/Android-Based Energy-Efficient Home Automation System with Smart Task Scheduling

abstract: In this paper, we make use of Home Automation techniques to design and implement a remotely controlled, energy-efficient and highly scalable Smart Home with basic features that safeguard the residents' comfort and security. Our system consists of a house network (sensors and appliance actuators to respectively get information from and control the house environment). As a central controller, we used an Arduino microcontroller that communicates with an Android application, our user interface. Our house network brings together both wireless Zigbee and wired X10 technologies, thus making it a cost-efficient hybrid system. Events can be programmed to be triggered under specific conditions, and this can have a great role in reducing the total energy consumed by some appliances. On the other hand, the system can suggest smart task scheduling. The scheduling algorithm we present is a heuristic for the Resource-constrained-scheduling problem (RCPSP) with hybrid objective function merging both resource-leveling and weighted completion time considerations.


title: GaN-on-Si Power Technology: Devices and Applications

abstract: In this paper, we present a comprehensive reviewand discussion of the state-of-the-art device technology and application development of GaN-on-Si power electronics. Several device technologies for realizing normally off operation that is highly desirable for power switching applications are presented. In addition, the examples of circuit applications that can greatly benefit from the superior performance of GaN power devices are demonstrated. Comparisonwith other competingpower device technology, such as Si superjunction-MOSFET and SiC MOSFET, is also presented and analyzed. Critical issues for commercialization of GaN-on-Si power devices are discussed with regard to cost, reliability, and ease of use.


SOCIAL NETWORK DATA ANALYTICS SOCIAL NETWORK DATA ANALYTICS


title: Interventions Improve Gait Regularity in Patients with Peripheral Neuropathy While Walking on an Irregular Surface Under Low Light

abstract: OBJECTIVES ::: To determine which, if any, of three inexpensive interventions improve gait regularity in patients with peripheral neuropathy (PN) while walking on an irregular surface under low-light conditions. ::: ::: ::: DESIGN ::: Observational. ::: ::: ::: SETTING ::: University of Michigan Biomechanics Research Laboratory. ::: ::: ::: PARTICIPANTS ::: Forty-two patients with PN (20 women), mean age+/-standard deviation=64.5+/-9.7. ::: ::: ::: INTERVENTIONS ::: A straight cane, touch of a vertical surface, or semirigid ankle orthoses. ::: ::: ::: MEASUREMENTS ::: Step-width variability and range, step-time variability, and speed. ::: ::: ::: RESULTS ::: Subjects demonstrated significantly less step-width variability (mean=41.0+/-1.5, 36.9+/-1.6, 37.2+/-1.3, and 35.9+/-1.5 mm for baseline, cane, orthoses, and vertical surface, respectively; P<.0001) and range (182.7+/-7.4, 163.7+/-8.3, 164.3+/-7.4, 154.3+/-6.9 mm for baseline, cane, orthoses and vertical surface, respectively; P=.0006) with each of the interventions than under baseline conditions. Step-time variability significantly decreased with use of the orthoses and vertical surface but not the cane (P=.0001). Use of a cane, but not orthoses or vertical surface, was associated with decreased speed (0.79+/-0.03, 0.73+/-0.03, 0.79+/-0.03, 0.80+/-0.03 m/s for baseline, cane, orthoses, and vertical surface, respectively; P=.0001). ::: ::: ::: CONCLUSION ::: Older patients with PN demonstrate improved spatial and temporal measures of gait regularity with the use of a cane, ankle orthoses, or touch of a vertical surface while walking under challenging conditions. The decreased speed and stigma associated with the cane and uncertain availability of a vertical surface suggest that the ankle orthoses may be the most practical intervention.


title: Classifying Options for Deep Reinforcement Learning

abstract: In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different"option heads"on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.


title: Predicting User Engagement on Twitter with Real-World Events

abstract: People invest time, attention, and emotion while engaging in various activities in the real-world, for either purposes of awareness or participation. Social media platforms such as Twitter offer tremendous opportunities for people to become engaged in such real-world events through information sharing and communicating about these events. However, little is understood about the factors that affect people’s Twitter engagement in such real-world events. In this paper, we address this question by first operationalizing a person’s Twitter engagement in real-world events such as posting, retweeting, or replying to tweets about such events. Next, we construct statistical models that examine multiple predictive factors associated with four different perspectives of users’ Twitter engagement, and quantify their potential influence on predicting the (i) presence; and (ii) degree ‐ of the user’s engagement with 643 real-world events. We also consider the effect of these factors with respect to a finer granularization of the different categories of events. We find that the measures of people’s prior Twitter activities, topical interests, geolocation, and social network structures are all variously correlated to their engagement with real-world events.


title: Semantic Instance Segmentation with a Discriminative Loss Function

abstract: Semantic instance segmentation remains a challenging task. In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. The loss function encourages the network to map each pixel to a point in feature space so that pixels belonging to the same instance lie close together while different instances are separated by a wide margin. Our approach of combining an off-the-shelf network with a principled loss function inspired by a metric learning objective is conceptually simple and distinct from recent efforts in instance segmentation. In contrast to previous works, our method does not rely on object proposals or recurrent mechanisms. A key contribution of our work is to demonstrate that such a simple setup without bells and whistles is effective and can perform on par with more complex methods. Moreover, we show that it does not suffer from some of the limitations of the popular detect-and-segment approaches. We achieve competitive performance on the Cityscapes and CVPPP leaf segmentation benchmarks.


title: Use of transcranial magnetic stimulation to influence behavior

abstract: Transcranial magnetic stimulation (TMS) is a noninvasive technique that allows researchers to induce electrical currents in focal areas of the cerebral cortex. These currents can transiently activate or inhibit focal cortical areas and thus influence behavior. In the 20 years since its introduction, TMS has been applied to the study and treatment of a wide variety of neurologic and psychiatric conditions. This review focuses particular attention on the use of TMS as a research tool for understanding brain-behavior relationships, an adjunct to traditional neurorehabilitation strategies, and a potential treatment option for depression and other psychiatric disorders.


title: High-Bandwidth Memory (HBM) Test Challenges and Solutions

abstract: TSV-based 3-D stacking enables large-capacity, power-efficient DRAMs with high bandwidth, such as specified by JEDEC's HBM standard. This article is a written version of Jun's very interesting presentation at 3D-TEST 2015 on how such DRAM stacks are tested at SK hynix.


title: Two-level Message Clustering for Topic Detection in Twitter

abstract: This paper presents our approach to the topic detection challenge organized by the 2014 SNOW workshop. The applied approach utilizes a document-pivot algorithm for topic detection, i.e. it clusters documents and treats each cluster as a topic. We modify a previous version of a common document-pivot algorithm by considering specific features of tweets that are strong indicators that particular sets of tweets belong to the same cluster. Additionally, we recognize that the granularity of topics is an important factor to consider when performing topic detection and we also take advantage of this when ranking topics.


title: Retargeting Semantically-Rich Photos

abstract: Semantically-rich photos contain a rich variety of semantic objects (e.g., pedestrians and bicycles). Retargeting these photos is a challenging task since each semantic object has fixed geometric characteristics. Shrinking these objects simultaneously during retargeting is prone to distortion. In this paper, we propose to retarget semantically-rich photos by detecting photo semantics from image tags, which are predicted by a multi-label SVM. The key technique is a generative model termed latent stability discovery (LSD). It can robustly localize various semantic objects in a photo by making use of the predicted noisy image tags. Based on LSD, a feature fusion algorithm is proposed to detect salient regions at both the low-level and high-level. These salient regions are linked into a path sequentially to simulate human visual perception . Finally, we learn the prior distribution of such paths from aesthetically pleasing training photos. The prior enforces the path of a retargeted photo to be maximally similar to those from the training photos. In the experiment, we collect 217 $1600 \times 1200$ photos, each containing over seven salient objects. Comprehensive user studies demonstrate the competitiveness of our method.


title: New insight into the Nox4 subcellular localization in HEK293 cells: first monoclonal antibodies against Nox4.

abstract: Nox4, a member of Nox family of NADPH oxidase expressed in nonphagocytic cells, is a major source of reactive oxygen species in many cell types. But understanding of the role of Nox4 in the production of ROS and of regulation mechanism of oxidase activity is largely unknown. This study reports for the first time the generation and characterization of 5 mAbs against a recombinant Nox4 protein (AA: 206-578). Among 5 novel mAbs, 3 mAbs (8E9, 5F9, 6B11) specifically recognized Nox4 protein in HEK293 transfected cells or human kidney cortex by western blot analysis; mAb 8E9 reacted with intact tet-induced T-REx™ Nox4 cells in FACS studies. The other 2 mAbs 10B4 and 7C9 were shown to have a very weak reactivity after purification. Immunofluorescence confocal microscopy showed that Nox4 localized not only in the perinuclear and endoplasmic reticulum regions but also at the plasma membrane of the cells which was further confirmed by TIRF-microscopy. Epitope determination showed that mAb 8E9 recognizes a region on the last extracellular loop of Nox4, while mAbs 6B11 and 5F9 are directed to its cytosolic tail. Contrary to mAb 6B11, mAb 5F9 failed to detect Nox4 at the plasma membrane. Cell-free oxidase assays demonstrated a moderate but significant inhibition of constitutive Nox4 activity by mAbs 5F9 and 6B11. In conclusion, 5 mAbs raised against Nox4 were generated for the first time. 3 of them will provide powerful tools for a structure/function relationship of Nox4 and for physiopathological investigations in humans.


title: Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

abstract: Mobile-Edge computing (MEC) is an emerging technology currently recognized as a key enabler for 5G networks. Compatible with current 4G networks, MEC will address many key uses of the 5G system, motivated by the massive diffusion of the Internet of Things (IoT). This article aims to provide a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the European Telecommunications Standards Institute (ETSI) MEC Industry Specification Group (ISG) standardization organization. We provide some examples of MEC deployment, with special reference to IoT cases, since IoT is recognized as a main driver for 5G. Finally, we discuss the main benefits and challenges for MEC moving toward 5G.


title: Analytic Provenance for Sensemaking: A Research Agenda

abstract: Sensemaking is a process of finding meaning from information that often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved. This history, commonly known as provenance information, can be a resource for "reflection-in-action"' during analysis, supporting collaboration between analysts, and can help trace data quality and uncertainty through the analysis process. Currently, there is limited work on utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. This article presents and extends the research challenges discussed in a IEEE VIS 2014 workshop on this topic to provide an agenda for sensemaking analytic provenance.


title: Low-Cost and Green Fabrication of Polymer Electronic Devices by Push-Coating of the Polymer Active Layers

abstract: Because of both its easy processability and compatibility with roll-to-roll processes, polymer electronics is considered to be the most promising technology for the future generation of low-cost electronic devices such as light-emitting diodes and solar cells. However, the state-of-the-art deposition technique for polymer electronics (spin-coating) generates a high volume of chlorinated solution wastes during the active layer fabrication. Here, we demonstrate that devices with similar or higher performances can be manufactured using the push-coating technique in which a poly(dimethylsiloxane) (PDMS) layer is simply laid over a very small amount of solution (less than 1μL/covered cm2), which is then left for drying. Using mm thick PDMS provides a means to control the solvent diffusion kinetics (sorption/retention) and removes the necessity for additional applied pressure to generate the desired active layer thickness. Unlike spin-coating, push-coating is a slow drying process that induces a higher degree o...


title: Xen on ARM: System Virtualization Using Xen Hypervisor for ARM-Based Secure Mobile Phones

abstract: Mobile phones security is becoming an important issue because they are being connected to an Internet through wireless modem technologies. System virtualization technology provides trusted computing capability by running isolated multiple virtual machines under hypervisor. In this paper, we propose a design of system virtualization for ARM CPU architecture and describe implementation of prototype called Xen on ARM using Xen hypervisor. Secure and nonsecure guest Linux virtual machines are executing under Xen on ARM isolated with each other and virtualization overhead is shown to be moderate compared to native Linux running on bare metal H/W.


title: Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey

abstract: The problem of tagging in natural language processing is to find a way to tag every word in a text as a particular part of speech, e.g., proper pronoun. POS tagging is a very important preprocessing task for language processing activities. This paper reports about the Part of Speech (POS) taggers proposed for various Indian Languages like Hindi, Punjabi, Malayalam, Bengali and Telugu. Various part of speech tagging approaches like Hidden Markov Model (HMM), Support Vector Model (SVM), Rule based approaches, Maximum Entropy (ME) and Conditional Random Field (CRF) have been used for POS tagging. Accuracy is the prime factor in evaluating any POS tagger so the accuracy of every proposed tagger is also discussed in this paper.


title: ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks

abstract: One of the most prominent and comprehensive ways of data collection in sensor networks is to periodically extract raw sensor readings. This way of data collection enables complex analysis of data, which may not be possible with in-network aggregation or query processing. However, this flexibility in data analysis comes at the cost of power consumption. In this paper, we develop ASAP, which is an adaptive sampling approach to energy-efficient periodic data collection in sensor networks. The main idea behind ASAP is to use a dynamically changing subset of the nodes as samplers such that the sensor readings of the sampler nodes are directly collected, whereas the values of the nonsampler nodes are predicted through the use of probabilistic models that are locally and periodically constructed. ASAP can be effectively used to increase the network lifetime while keeping the quality of the collected data high in scenarios where either the spatial density of the network deployment is superfluous, which is relative to the required spatial resolution for data analysis, or certain amount of data quality can be traded off in order to decrease the power consumption of the network. The ASAP approach consists of three main mechanisms: First, sensing-driven cluster construction is used to create clusters within the network such that nodes with close sensor readings are assigned to the same clusters. Second, correlation-based sampler selection and model derivation are used to determine the sampler nodes and to calculate the parameters of the probabilistic models that capture the spatial and temporal correlations among the sensor readings. Last, adaptive data collection and model-based prediction are used to minimize the number of messages used to extract data from the network. A unique feature of ASAP is the use of in-network schemes, as opposed to the protocols requiring centralized control, to select and dynamically refine the subset of the sensor nodes serving as samplers and to adjust the value prediction models used for nonsampler nodes. Such runtime adaptations create a data collection schedule, which is self-optimizing in response to the changes in the energy levels of the nodes and environmental dynamics. We present simulation-based experimental results and study the effectiveness of ASAP under different system settings.


title: A Generative Programming Approach for Game Development

abstract: Nowadays, due to the great distance between design and implementation worlds, different skills are necessary to create a game system. To solve this problem, a lot of strategies for game development, trying to increase the abstraction level necessary for the game production, were proposed. In this way, a lot of game engines, game frameworks and others, in most cases without any compatibility or reuse criteria between them, were developed. This paper presents a new generative programming approach, able to increase the production of a digital game by the integration of different game development artifacts, following a system family strategy focused on variable and common aspects of a computer game. As result, high level abstractions of games, based on a common language, can be used to configure met programming transformations during the game production, providing a great compatibility level between game domain and game implementation artifacts.


title: Human simulations of vocabulary learning

abstract: Abstract The work reported here experimentally investigates a striking generalization about vocabulary acquisition: Noun learning is superior to verb learning in the earliest moments of child language development. The dominant explanation of this phenomenon in the literature invokes differing conceptual requirements for items in these lexical categories: Verbs are cognitively more complex than nouns and so their acquisition must await certain mental developments in the infant. In the present work, we investigate an alternative hypothesis; namely, that it is the information requirements of verb learning, not the conceptual requirements, that crucially determine the acquisition order. Efficient verb learning requires access to structural features of the exposure language and thus cannot take place until a scaffolding of noun knowledge enables the acquisition of clause-level syntax. More generally, we experimentally investigate the hypothesis that vocabulary acquisition takes place via an incremental constraint-satisfaction procedure that bootstraps itself into successively more sophisticated linguistic representations which, in turn, enable new kinds of vocabulary learning. If the experimental subjects were young children, it would be difficult to distinguish between this information-centered hypothesis and the conceptual change hypothesis. Therefore the experimental “learners” are adults. The items to be “acquired” in the experiments were the 24 most frequent nouns and 24 most frequent verbs from a sample of maternal speech to 18–24-month-old infants. The various experiments ask about the kinds of information that will support identification of these words as they occur in mother-to-child discourse. Both the proportion correctly identified and the type of word that is identifiable changes significantly as a function of information type. We discuss these results as consistent with the incremental construction of a highly lexicalized grammar by cognitively and pragmatically sophisticated human infants, but inconsistent with a procedure in which lexical acquisition is independent of and antecedent to syntax acquisition.


title: Modeling and control of hysteresis in magnetostrictive actuators

abstract: A novel dynamic model is proposed for the hysteresis in magnetostrictive actuators by coupling a Preisach operator to an ordinary differential equation, and a parameter identification method is described. An efficient inversion algorithm for a class of Preisach operators with piecewise uniform density functions is then introduced, based upon which an inverse control scheme for the dynamic hysteresis model is presented. Finally the inversion error is quantified and l"1 control theory is applied to improve the robustness of inverse compensation. Simulation and experimental results based on a Terfenol-D actuator are provided.


title: Exudate detection in color retinal images for mass screening of diabetic retinopathy

abstract: The automatic detection of exudates in colour eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to auto-matically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also de-tect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods.


title: A millimeter-wave patch-fed slot antenna with air cavity

abstract: In this paper, a patch-fed slot antenna with air cavity is proposed in millimeter-wave band. The antenna is manufactured by using the silicon micromachining technology which is utilized to designed air cavity structure and mitigate dielectric loss in the millimeter-wave band. However, this technique is based on silicon processing, the large permittivity difference between the air (e r = 1) and silicon (e r = 11.9) would contribute to the mismatch issue. In this paper, a patch structure is utilized to excite the air cavity and broaden impedance width. The numerical results of the proposed antenna show that the impedance bandwidth is from 55.5 GHz to 62.6 GHz, and the simulated gain is 7.55 dBi at 60 GHz.


title: Volume/weight/cost comparison of a 1MVA 10 kV/400 V solid-state against a conventional low-frequency distribution transformer

abstract: Solid-State Transformers (SSTs) are an emergent topic in the context of the Smart Grid paradigm, where SSTs could replace conventional passive transformers to add flexibility and controllability, such as power routing capabilities or reactive power compensation, to the grid. This paper presents a com- parison of a 1000kVA three-phase, low-frequency distribution transformer (LFT) and an equally rated SST, with respect to volume, weight, losses, and material costs, where the correspond- ing data of the SST is partly based on a full-scale prototype design. It is found that the SST's costs are at least five times and its losses about three times higher, its weight similar but its volume reduced to less than 80%. In addition, an AC/DC application is also considered, where the comparison turns out in favor of the SST-based concept, since its losses are only about half compared to the LFT-based system, and the volume and the weight are reduced to about one third, whereas the material costs advantage of the LFT is much less pronounced. I. INTRODUCTION


title: Physiological Changes Associated with the Pre-Event Taper in Athletes

abstract: Some of the physiological changes associated with the taper and their relationship with athletic performance are now known. Since the 1980s a number of studies have examined various physiological responses associated with the cardiorespiratory, metabolic, hormonal, neuromuscular and immunological systems during the pre-event taper across a number of sports. Changes in the cardiorespiratory system may include an increase in maximal oxygen uptake, but this is not a necessary prerequisite for taper-induced gains in performance. Oxygen uptake at a given submaximal exercise intensity can decrease during the taper, but this response is more likely to occur in less-skilled athletes. Resting, maximal and submaximal heart rates do not change, unless athletes show clear signs of overreaching before the taper. Blood pressure, cardiac dimensions and ventilatory function are generally stable, but submaximal ventilation may decrease. Possible haematological changes include increased blood and red cell volume, haemoglobin, haematocrit, reticulocytes and haptoglobin, and decreased red cell distribution width. These changes in the taper suggest a positive balance between haemolysis and erythropoiesis, likely to contribute to performance gains.


title: A systematic survey on the design of self-adaptive software systems using control engineering approaches

abstract: Control engineering approaches have been identified as a promising tool to integrate self-adaptive capabilities into software systems. Introduction of the feedback loop and controller into the management system potentially enables the software systems to achieve the runtime performance objectives and maintain the integrity of the system when they are operating in unpredictable and dynamic environments. There is a large body of literature that has proposed control engineering solutions for different application domains, handling different performance variables and control objectives. However, the relevant literature is scattered over different conference proceedings, journals and research communities. Consequently, conducting a survey to analyze and classify the existing literature is a useful, yet a challenging task. This paper presents the results of a systematic survey that includes classification and analysis of 161 papers in the existing literature. In order to capture the characteristics of the control solutions proposed in these papers we introduce a taxonomy as a basis for classification of all articles. Finally, survey results are presented, including quantitative, cross and trend analysis.


title: Automatic hand gesture segmentation for recognition of Vietnamese sign language

abstract: In this paper, we propose a new solution to identify hand gestures corresponding to alphabetic characters of Vietnamese Sign Language (VSL) via a sequence of images (video) collected from the depth sensor in a Microsoft Kinect. First, a preprocessing is performed to localize and separate the hand from each image and then remove possible noise. In the next stage, the object is extracted to select key frames, which support to represent a segment of the video. Each determined key frame is then converted to a binary image and estimate some biological information such as the hand boundary, finger positions and the palm center. The position of palm centre and fingertips are also localized in 3D space. The process of recognition is performed using Support Vector Machine (SVM) method. The experiments show that the proposed approach is promising since the recognition accuracy is about 91%.