title: A dynamic programming offloading algorithm for mobile cloud computing

abstract: Computational offloading is an effective method to address the limited battery power of a mobile device, by executing some components of a mobile application in the cloud. In this paper, a novel offloading algorithm called ‘Dynamic Programming with Hamming Distance Termination’ (denoted DPH) is presented. Our algorithm uses randomization and a hamming distance termination criterion to find a nearly optimal offloading solution quickly. The algorithm will offload as many tasks as possible to the cloud when the network transmission bandwidth is high, thereby improving the total execution time of all tasks and minimizing the energy use of the mobile device. The algorithm can find very good solutions with low computational overhead. A novel and innovative approach to fill the dynamic programming table is used to avoid unnecessary computations, resulting in lower computation times compared to other schemes. Furthermore, the algorithm is extensible to handle larger offloading problems without a loss of computational efficiency. Performance evaluation shows that the proposed DPH algorithm can achieve near minimal energy while meeting an application's execution time constraints, and it can find a nearly optimal offloading decision within a few iterations.


title: uRank: Exploring Document Recommendations through an Interactive User-Driven Approach

abstract: Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.


title: Semi-supervised Word Sense Disambiguation with Neural Models

abstract: Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.


title: DATA REDUCTION TECHNIQUES TO ANALYZE NSL-KDD DATASET

abstract: In information field we have huge amount of data available that need to be turned into useful information. So we used Data reduction and its techniques. A process in which amount of data is minimized and that minimized data are stored in a data storage environment is known as data reduction. By this process of reducing data various advantages have been achieved in computer networks such as increasing storage efficiency and reduced computational costs. In this paper we have applied data reduction algorithms on NSL-KDD dataset. The output of each data reduction algorithm is given as an input to two classification algorithms i.e. J48 and Naive Bayes. Our main is to find out which data reduction technique proves to be useful in enhancing the performance of the classification algorithm. Results are compared on the bases of accuracy, specificity and sensitivity.


title: Human-like walking with knee stretched, heel-contact and toe-off motion by a humanoid robot

abstract: A humanoidrobot, WABIAN-2R,capable of human-like walkwithstretched kneesandheel-contact andtoe- offmotions isproposed inthis paper. WABIAN-2Rhastwo1- DOF passive joints initsfeet toenable ittobendits toesinsteady walking. Further, ithastwo6-DOFlegs, a2-DOFpelvis, a 2- DOF trunk, two7-DOFarmswith3-DOFhands, anda3-DOF neck.In addition, a new algorithm forgenerating walking patterns withstretched kneesand heel-contact and toe-off motions basedontheZMP criterion isdescribed. Inthis pattern generation, someparameters ofthefoottrajectories ofabiped robotareoptimized byusinga genetic algorithm inorderto generate a continuous and smoothlegmotion.Software simulations andwalking experiments areconducted, andthe effectiveness ofthepatterngeneration and mechanismof WABIAN-2R, whichhavetheability torealize morehuman-like walking styles inahumanoid robot, areconfirmed. IndexTerms-RobotDesign, HumanoidRobot, BipedRobot, Stretch Walking, Heel-contact andtoe-off motions


Constructing Curriculum Ontology and Dynamic Learning Path Based on Resource Description Framework


title: Elevated skin autofluorescence is strongly associated with foot ulcers in patients with diabetes: a cross-sectional, observational study of Chinese subjects

abstract: OBJECTIVE ::: This study was designed to evaluate the association between skin autofluorescence (AF), an indicator of advanced glycation end-products (AGEs), and foot ulcers in subjects with diabetes. ::: ::: ::: METHODS ::: In this study, 195 Chinese diabetic subjects were examined. Their feet were examined regardless of whether an ulcer was present or not. Skin AF was measured with an AGE reader. Demographic characteristics and blood data were recorded. ::: ::: ::: RESULTS ::: The mean values of skin AF were 2.29 ± 0.47 for subjects without foot ulcers, and 2.80 ± 0.69 for those with foot ulcers, a significant difference (P<0.05). Skin AF was strongly correlated with age and duration of diabetes. After adjusting for these factors, multivariate logistic regression showed that skin AF was independently associated with foot ulcerations. ::: ::: ::: CONCLUSIONS ::: Skin AF is independently associated with diabetic foot ulcerations. It might be a useful screening method for foot ulceration risk of diabetic patients.


title: Online Advertising: Analysis of Privacy Threats and Protection Approaches

abstract: Online advertising, the pillar of the free content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.


title: Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning

abstract: In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.


title: Fingerprint Classification by Directional Image Partitioning

abstract: In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into "homogeneous" connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness.


title: Lifetime Portfolio Selection by Dynamic Stochastic Programming

abstract: The following sections are included:IntroductionBasic AssumptionsSolution of the ProblemBernoulli and Isoelastic CasesConclusionREFERENCES


title: DDoS: threats and mitigation

abstract: It's likely that 2011 will be remembered by many as the year of Distributed Denial of Service (DDoS) attacks. There's nothing new about this kind of threat, but its use has increased and it has even achieved a kind of mainstream notoriety thanks to the antics of self-publicising groups like Anonymous. But how is the threat evolving? And what can you do about it? Both the size of the attacks and the frequency are increasing. Cybercrime remains the main motivation for attacks, but some are now ideologically driven – in the case of hacktivism – and we're seeing an increasing number of state actors. Mitigation can be difficult, especially if you attempt it at your organisation's perimeter. More and more companies are now offering mitigation services and, typically, these are hosted in the cloud – as far upstream as possible. Steve Mansfield-Devine explores the issues and the solutions.


title: Online Communities: Design, Theory, and Practice

abstract: This special thematic section of the Journal of Computer-Mediated Communication brings together nine articles that provide a rich composite of the current research in online communities. The articles cover a range of topics, methodologies, theories and practices. Indirectly they all speak to design since they aim to extend our understanding of the field. The variety shown in these articles illustrates how broad the definition is of this rapidly growing field known as ‘online communities.’


Isaac Wilson Comparing Approaches to Connect6: Temporal difference vs. Minimax


title: Learning by Teaching Human Pupils and Teachable Agents: The Importance of Recursive Feedback

abstract: Feedback is important for learning. However, there are different types of feedback, and not all feedback is effective. Here we introduce recursive feedback (RF), which occurs when tutors observe their pupils use what they have been taught. Two experiments examined the value of RF during learning by teaching. In the first study adults taught another adult face to face about human biology. Those participants who observed their pupil interact with an examiner exhibited superior learning relative to individuals in several control conditions that included elements of learning by teaching but not RF. The second study examined whether RF benefits extend to teaching computerized teachable agents in regular classrooms. High school students played games in which they induced logical rules. Students taught their agent the governing rules. They received RF when they observed their teachable agent play a prediction game against a second competitor agent. On a posttest, these students exhibited greater abilities to use...


title: Data mining in brain imaging

abstract: Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task- activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.


title: Time Delay and Spatial Signature Estimation Using Known Asynchronous Signals

abstract: This paper considers the problem of estimating the parameters of known signals received asynchronously by an array of antennas. The parameters of primary interest are the time delays of the signals and their spatial signatures at the array. Estimates of the signal directions of arrival are also considered but are of secondary importance in this work. Maximum likelihood algorithms and more computationally efficient approximations are developed for both the case where all received signals are identical (the channel estimation/overlapping echo problem) and where they are all distinct. Conditions are also derived under which the standard matched filter approach yields consistent and statistically efficient parameter estimates. The issue of solution uniqueness is addressed, and in particular, an upper bound on the number of signals whose parameters may be uniquely identified is derived for a number of different cases. Typically, the bound is far greater than the number of sensors and is limited only by the number of data samples collected. Some representative simulation examples are also included to illustrate the algorithms' performance relative to the Cramer-Rao bound.


Design and Governance of Collaborative Business Processes in Industry 4.0


title: Deep learning for robust feature generation in audiovisual emotion recognition

abstract: Automatic emotion recognition systems predict high-level affective content from low-level human-centered signal cues. These systems have seen great improvements in classification accuracy, due in part to advances in feature selection methods. However, many of these feature selection methods capture only linear relationships between features or alternatively require the use of labeled data. In this paper we focus on deep learning techniques, which can overcome these limitations by explicitly capturing complex non-linear feature interactions in multimodal data. We propose and evaluate a suite of Deep Belief Network models, and demonstrate that these models show improvement in emotion classification performance over baselines that do not employ deep learning. This suggests that the learned high-order non-linear relationships are effective for emotion recognition.


The Neuroscience Literacy of Trainee Teachers


title: Diversifying query suggestion results

abstract: In order to improve the user search experience, Query Suggestion, a technique for generating alternative queries to Web users, has become an indispensable feature for commercial search engines. However, previous work mainly focuses on suggesting relevant queries to the original query while ignoring the diversity in the suggestions, which will potentially dissatisfy Web users' information needs. In this paper, we present a novel unified method to suggest both semantically relevant and diverse queries to Web users. The proposed approach is based on Markov random walk and hitting time analysis on the query-URL bipartite graph. It can effectively prevent semantically redundant queries from receiving a high rank, hence encouraging diversities in the results. We evaluate our method on a large commercial clickthrough dataset in terms of relevance measurement and diversity measurement. The experimental results show that our method is very effective in generating both relevant and diverse query suggestions.


title: Examining the Bilingual Advantage on Conflict Resolution Tasks: A Meta-Analysis.

abstract: A great deal of research has compared monolingual and bilinguals on conflict resolution tasks, with inconsistent findings: Some studies reveal a bilingual advantage on global RTs, some reveal a bilingual advantage on interference cost, and some show no advantage. We report a meta-analysis of 73 comparisons (N = 5538), with estimates of global RTs and interference cost for each study. Results revealed a moderately significant effect size that was not moderated by type of cost (global RT or interference cost) or task. Age interacted with type of cost, showing a pattern difficult to reconcile with theories of bilingualism and executive control. Additionally there was a significant main effect of lab, which might be due to sociolinguistic differences in samples, data treatment and methodology, or Hawthorne effects.


title: cuDNN: Efficient Primitives for Deep Learning

abstract: We present a library that provides optimized implementations for deep learning primitives. Deep learning workloads are computationally intensive, and optimizing the kernels of deep learning workloads is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized for new processors, which makes maintaining codebases difficult over time. Similar issues have long been addressed in the HPC community by libraries such as the Basic Linear Algebra Subroutines (BLAS) [2]. However, there is no analogous library for deep learning. Without such a library, researchers implementing deep learning workloads on parallel processors must create and optimize their own implementations of the main computational kernels, and this work must be repeated as new parallel processors emerge. To address this problem, we have created a library similar in intent to BLAS, with optimized routines for deep learning workloads. Our implementation contains routines for GPUs, and similarly to the BLAS library, could be implemented for other platforms. The library is easy to integrate into existing frameworks, and provides optimized performance and memory usage. For example, integrating cuDNN into Caffe, a popular framework for convolutional networks, improves performance by 36% on a standard model while also reducing memory consumption.


title: Wood Veneer Species Recognition Using Markovian Textural Features

abstract: A mobile Android application that can automatically recognize wood species from a low quality mobile phone photo under varying illumination conditions is presented. The wood recognition is based on the Markovian, spectral, and illumination invariant textural features. The method performance was verified on a wood database, which contains veneers from sixty-six varied European and exotic wood species. The Markovian features improvement of the correct wood recognition rate is about 40% compared to the best alternative - the Local Binary Patterns features.


title: Complexity of Manipulation with Partial Information in Voting

abstract: The Coalitional Manipulation problem has been studied extensively in the literature for many voting rules. However, most studies have focused on the complete information setting, wherein the manipulators know the votes of the non-manipulators. While this assumption is reasonable for purposes of showing intractability, it is unrealistic for algorithmic considerations. In most real-world scenarios, it is impractical for the manipulators to have accurate knowledge of all the other votes. In this paper, we investigate manipulation with incomplete information. In our framework, the manipulators know a partial order for each voter that is consistent with the true preference of that voter. In this setting, we formulate three natural computational notions of manipulation, namely weak, opportunistic, and strong manipulation. We say that an extension of a partial order is if there exists a manipulative vote for that extension. ::: 1. Weak Manipulation (WM): the manipulators seek to vote in a way that makes their preferred candidate win in at least one extension of the partial votes of the non-manipulators. ::: 2. Opportunistic Manipulation (OM): the manipulators seek to vote in a way that makes their preferred candidate win in every viable extension of the partial votes of the non-manipulators. ::: 3. Strong Manipulation (SM): the manipulators seek to vote in a way that makes their preferred candidate win in every extension of the partial votes of the non-manipulators. ::: We consider several scenarios for which the traditional manipulation problems are easy (for instance, Borda with a single manipulator). For many of them, the corresponding manipulative questions that we propose turn out to be computationally intractable. Our hardness results often hold even when very little information is missing, or in other words, even when the instances are quite close to the complete information setting.


title: Mobile Robot Navigation and Obstacles Avoidance based on Planning and Re-Planning Algorithm

abstract: This paper deals with a multi-mode control architecture for robot navigation and obstacle avoidance. It presents an adaptive and flexible algorithm of control which guarantees the stability and the smoothness of mobile robot navigation dealing with unexpected events. Moreover, the proposed Planning and Re-Planning (PRP) algorithm combine the two schools of thought, the one based on the path planning to avoid obstacles and reach the target, described as cognitive, and the second using the reactive algorithms. In fact the mix of these two approaches allows us to develop a very reliable algorithm. It provides us a scalable mobile robot navigation and obstacle avoidance, with less processing. It is accomplished by making an initial path planning, then to resolve the problem of unexpected static or dynamic obstacles while tracking the trajectory. A system of hierarchical action selection allows us to switch to a reactive avoidance, then to re-plan a new and safe trajectory to reach the target. A large number of simulations in different environments are performed to show the efficiency of the proposed PRP algorithm.


MIST : Multiple Instance Spatial Transformer Networks


title: SafeDrive: Detecting Distracted Driving Behaviors Using Wrist-Worn Devices

abstract: Distracted driving causes a large number of fatalities every year and is now becoming an important issue in the traffic safety study. In this paper, we present SafeDrive, a driving safety system that leverages wearable wrist sensing techniques to detect and analyze driver distracted behaviors. Existing wrist-worn sensing approaches, however, do not address challenges under real driving environments, such as less distinguishable gesture patterns due to in-vehicle physical constraints, various gesture hallmarks produced by different drivers and significant noise introduced by various driving conditions. In response, SafeDrive adopts a semi-supervised machine learning model for in-vehicle distracting activity detection. To improve the detection accuracy, we provide online updated classifiers by collecting real-time gesture data, while at the same time utilize smartphone sensing to generate soft hints filtering out anomalies and non-distracted hand movements. In the evaluation, we conduct extensive real-road experiments involving 20 participants (10 males and 10 females) and 5 vehicles (a sedan, a minivan and three SUVs). Our approach can achieve an average classification accuracy of over 90% with a error rate of a few percent, which demonstrate that SafeDrive is robust to real driving environments, and has great potential to help drivers shape safe driving habits.


Mapping the impact of climate change on international law


title: Profit-Based DG Planning Considering Environmental and Operational Issues: A Multiobjective Approach

abstract: In this paper, a methodology is presented to find the optimal size, location and technology of distributed generation (DG) units considering economic, technical, and environmental issues simultaneously. This technique can be implemented to both renewable and nonrenewable DGs. From economic perspective, minimizing cost and maximizing profit are investigated separately and compared with each other. In addition, from technical and environmental viewpoints, a complete set of indices are defined. In order to find the optimal solution, multiobjective particle swarm optimization, and fuzzy decision-making techniques are utilized. Furthermore, to evaluate the proposed method more accurately, two scenarios are defined. In fact, the first scenario, which is only based on cost minimization and technical objective functions, plays an introductory role for better comprehension of the second scenario. In the second scenario, which includes the presentation of the methodologies of this paper, the effect of selecting either profit maximization or cost minimization as an economic objective function along with the minimization of technical and environmental objective functions are precisely investigated. By carrying out the simulations on the IEEE 33-bus distribution test system, it is concluded that the cost-based framework is not the most profitable one, whereas the profit-based one may well lead to a more appropriate result from economic viewpoints.


title: A Water Dense Dielectric Patch Antenna

abstract: A novel water dense dielectric patch antenna (DDPA) fed by an L-shaped probe is proposed and investigated. In contrast to the water antennas in the literature, including the water monopole and the water dielectric resonator antenna, the operation mechanism of the proposed water DDPA is similar to the conventional metallic patch antenna. The antenna is excited in a mode like the TM $_{{\textrm {10}}}$ mode of the rectangular patch antenna. An L-shaped probe, which is widely used for the conventional patch antenna, is used to excite the water DDPA. A study on the bandwidth performance of the proposed design reveals that wide bandwidth can be achieved for the antenna by choosing a thick supporting substrate between the water patch and the ground plane. A prototype is fabricated to confirm the correctness of the design. An impedance bandwidth of 8%, maximum gain of 7.3 dBi, radiation efficiency up to 70%, and symmetrically unidirectional patterns with low backlobe and low cross polarization levels are obtained. Furthermore, owing to the transparency of the water patch, the proposed water DDPA can be conveniently integrated with the solar cells to realize a dual-function design. Measurements on the prototype demonstrate that the existence of the solar cells does not significantly affect the performance of the antenna and vice versa.


title: Fine-Grained Sentiment Analysis with Structural Features

abstract: Sentiment analysis is the problem of determining the polarity of a text with respect to a particular topic. For most applications, however, it is not only necessary to derive the polarity of a text as a whole but also to extract negative and positive utterances on a more finegrained level. Sentiment analysis systems working on the (sub-)sentence level, however, are difficult to develop since shorter textual segments rarely carry enough information to determine their polarity out of context. In this paper, therefore, we present a fully automatic framework for fine-grained sentiment analysis on the subsentence level combining multiple sentiment lexicons and neighborhood as well as discourse relations to overcome this problem. We use Markov logic to integrate polarity scores from different sentiment lexicons with information about relations between neighboring segments, and evaluate the approach on product reviews. The experiments show that the use of structural features improves the accuracy of polarity predictions achieving accuracy scores of up to 69%.


title: Channel model and analysis for wireless underground sensor networks in soil medium

abstract: Wireless underground sensor networks (WUSNs) constitute one of the promising application areas of the recently developed wireless sensor networking techniques. The main difference between WUSNs and the terrestrial wireless sensor networks is the communication medium. The propagation characteristics of electromagnetic (EM) waves in soil and the significant differences between propagation in air prevent a straightforward characterization of the underground wireless channel. To this end, in this paper, advanced channel models are derived to characterize the underground wireless channel and the foundational issues for efficient communication through soil are discussed. In particular, the underground communication channel is modeled considering not only the propagation of EM waves in soil, but also other effects such as multipath, soil composition, soil moisture, and burial depth. The propagation characteristics are investigated through simulation results of path loss between two underground sensors. Moreover, based on the proposed channel model, the resulting bit error rate is analyzed for different network and soil parameters. Furthermore, the effects of variations in soil moisture are investigated through field measurement results. The theoretical analysis and the simulation results prove the feasibility of wireless communication in underground environment and highlight several important aspects in this field. This work will lead to the provision of a generic framework for underground wireless communication and the realization of WUSNs.


Relationship between “a body shape index (ABSI)” and body composition in obese patients with type 2 diabetes


title: Evaluating the impact of DevOps practice in Sri Lankan software development organizations

abstract: The purpose of this study is to conduct an analysis on practicing DevOps in software development companies in Sri Lanka. DevOps is extended from agile with a mix of patterns intended to improve collaboration between development and operations. The main objective of this research is to identify whether there is a relationship between quality, responsiveness to business needs and agility with implementation of DevOps. Other objectives of this research are to identify barriers Sri Lankan software companies have faced and provide recommendations to overcome those issues. This study was carried out using a deductive approach. Data have been collected from IT professionals in Sri Lanka who had prior experience or knowledge about DevOps. Recommendations are given based on interview feedback, hypotheses output and descriptive statistical analysis. According to results software professionals in Sri Lanka are in a perception that implementation of DevOps has a positive impact on Quality, Responsiveness to Business Needs and Agility to New Technologies. Culture, Automation, Measurement and Sharing are important factors to be considered when implement DevOps in Sri Lanka. As a result of this study it is recommended to implement and practice DevOps in Sri Lankan Software companies.


title: Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting

abstract: With the growing prevalence of smart grid technology, short-term load forecasting (STLF) becomes particularly important in power system operations. There is a large collection of methods developed for STLF, but selecting a suitable method under varying conditions is still challenging. This paper develops a novel reinforcement learning based dynamic model selection (DMS) method for STLF. A forecasting model pool is first built, including ten state-of-the-art machine learning based forecasting models. Then a Q-learning agent learns the optimal policy of selecting the best forecasting model for the next time step, based on the model performance. The optimal DMS policy is applied to select the best model at each time step with a moving window. Numerical simulations on two-year load and weather data show that the Q-learning algorithm converges fast, resulting in effective and efficient DMS. The developed STLF model with Q-learning based DMS improves the forecasting accuracy by approximately 50%, compared to the state-of-the-art machine learning based STLF models.


title: The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid

abstract: Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular. The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed: quadratic and dynamic programming.


title: A Novel Locally Linear KNN Method With Applications to Visual Recognition

abstract: A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients’ truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.


title: Resource Efficient Mobile Computing Using Cloudlet Infrastructure

abstract: Mobile Cloud Computing (MCC) has been introduced as a viable solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power, and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This will reduce the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. In this paper, we introduce a Cloudlet based MCC system aiming to reduce the power consumption and the network delay while using MCC. We merged the MCC concepts with the proposed Cloudlet framework and propose a new framework for the MCC model. Our practical experimental results showed that using the proposed model reduces the power consumption from the mobile device, besides reducing the communication latency when the mobile device requests a job to take place remotely while keeping high quality of service stander.


title: Hidden Markov map matching through noise and sparseness

abstract: The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.


title: Feature Diagrams and Logics: There and Back Again

abstract: Feature modeling is a notation and an approach for modeling commonality and variability in product families. In their basic form, feature models contain mandatory/optional features, feature groups, and implies and excludes relationships. It is known that such feature models can be translated into propositional formulas, which enables the analysis and configuration using existing logic- based tools. In this paper, we consider the opposite translation problem, that is, the extraction of feature models from propositional formulas. We give an automatic and efficient procedure for computing a feature model from a formula. As a side effect we characterize a class of logical formulas equivalent to feature models and identify logical structures corresponding to their syntactic elements. While many different feature models can be extracted from a single formula, the computed model strives to expose graphically the maximum of the original logical structure while minimizing redundancies in the representation. The presented work furthers our understanding of the semantics of feature modeling and its relation to logics, opening avenues for new applications in reverse engineering and refactoring of feature models.


title: Genre pedagogy: Language, literacy and L2 writing instruction

abstract: Abstract For teacher educators, genre-based pedagogies offer a valuable resource for assisting both pre- and in-service writing instructors to assist their students to produce effective and relevant texts. Instead of focusing on the process of composition, the content of texts, or the abstract prescriptions of disembodied grammars, genre pedagogies enable teachers to ground their courses in the texts that students will have to write in their target contexts, thereby supporting learners to participate effectively in the world outside the ESL classroom. Genre theory and research thus give teacher educators a more central role in preparing individuals to teach second language writing and to confidently advise them on the development of curriculum materials and activities for writing classes. In this paper, I will briefly introduce the principles of genre-based language instruction and sketch some broad classroom models, looking at ESP and SFL approaches. I then explore what it means to implement genre teaching in more practical terms, setting out some key ways in which teachers can plan, sequence, support, and assess learning.


500OCONCUR: A RANDOMIZED, PLACEBO-CONTROLLED PHASE 3 STUDY OF REGORAFENIB (REG) MONOTHERAPY IN ASIAN PATIENTS WITH PREVIOUSLY TREATED METASTATIC COLORECTAL CANCER (MCRC)


title: Online participation in democratic processes: the case of the Green Party, Germany

abstract: Much of the research in the online participation area focuses on citizen participation or social movements, while the internal processes of political parties have so far been neglected. We will address this gap, focusing on the introduction of online participation methods for internal democratic decision-making processes in the Green Party Germany. Use of technology in a democratic process makes higher demands off organisation, since exclusion can delegitimize the outcome. Specifically, we investigate the launch of two new processes: a regular grass-roots survey that feeds into the policy development process, and an online verification process for members’ submission and support of proposals to the party assembly. These processes both support and challenge the strong grass-roots mentality of the party: access is maximised, while participation costs for certain groups may increase. We will discuss the expectations and conflicts related to the introduction of these new online process. In particular we will address the expected risks and opportunities of facilitating online participation, including the potential for changing or reinforcing the current experience of participation, and the risk of perpetuating biases relating to online access. The analysis is based on preliminary data, in particular observations and interviews with selected members.


title: Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods

abstract: Educational data mining has received considerable attention in the last few years. Many data mining techniques are proposed to extract the hidden knowledge from educational data. The extracted knowledge helps the institutions to improve their teaching methods and learning process. All these improvements lead to enhance the performance of the students and the overall educational outputs. In this paper, we propose a new student’s performance prediction model based on data mining techniques with new data attributes/features, which are called student’s behavioral features. These type of features are related to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is evaluated by set of classifiers, namely; Artificial Neural Network, Naive Bayesian and Decision tree. In addition, we applied ensemble methods to improve the performance of these classifiers. We used Bagging, Boosting and Random Forest (RF), which are the common ensemble methods used in the literature. The obtained results reveal that there is a strong relationship between learner’s behaviors and their academic achievement. The accuracy of the proposed model using behavioral features achieved up to 22.1% improvement comparing to the results when removing such features and it achieved up to 25.8% accuracy improvement using ensemble methods. By testing the model using newcomer students, the achieved accuracy is more than 80%. This result proves the reliability of the proposed model.


title: Reverse Engineering Digital Circuits Using Structural and Functional Analyses

abstract: Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of ${>}{45\%}$ and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.


title: An Efficient Passage Ranking Technique For a QA System

abstract: Question answering (QA) systems provide an intuitive way of requesting concise information from a given data source. An important stage of such a system is the passage ranking stage, which ranks the possible answers based on their relevance to the question.There has been a lot of previous work onpassage ranking, employing lexical, semantic or syntactic methods, but to our knowledge there has been no method that comprehensively combines all 3 features. In this paper, we present a passage ranking technique that leverages lexical, semantic and syntactic features to gether to rank the answers efficiently and effectively. This paper highlights the differences and improvements of the proposed technique over existing state-ofthe-art techniques like SSTK and IBM Model. The passage ranking technique has been evaluated with TREC QA dataset and is observed to give a significant 26.5% improvement in MRR over the existing stateof-the-art SSTK technique.


title: Factorized Hidden Layer Adaptation for Deep Neural Network Based Acoustic Modeling

abstract: In this paper, we propose the factorized hidden layer (FHL) approach to adapt the deep neural network (DNN) acoustic models for automatic speech recognition (ASR). FHL aims at modeling speaker dependent (SD) hidden layers by representing an SD affine transformation as a linear combination of bases. The combination weights are low-dimensional speaker parameters that can be initialized using speaker representations like i-vectors and then reliably refined in an unsupervised adaptation fashion. Therefore, our method provides an efficient way to perform both adaptive training and (test-time) adaptation. Experimental results have shown that the FHL adaptation improves the ASR performance significantly, compared to the standard DNN models, as well as other state-of-the-art DNN adaptation approaches, such as training with the speaker-normalized CMLLR features, speaker-aware training using i-vector and learning hidden unit contributions (LHUC). For Aurora 4, FHL achieves 3.8% and 2.3% absolute improvements over the standard DNNs trained on the LDA + STC and CMLLR features, respectively. It also achieves 1.7% absolute performance improvement over a system that combines the i-vector adaptive training with LHUC adaptation. For the AMI dataset, FHL achieved 1.4% and 1.9% absolute improvements over the sequence-trained CMLLR baseline systems, for the IHM and SDM tasks, respectively.


title: Ego networks in Twitter: An experimental analysis

abstract: Online Social Networks are amongst the most important platforms for maintaining social relationships online, supporting content generation and exchange between users. They are therefore natural candidate to be the basis of future humancentric networks and data exchange systems, in addition to novel forms of Internet services exploiting the properties of human social relationships. Understanding the structural properties of OSN and how they are influenced by human behaviour is thus fundamental to design such human-centred systems. In this paper we analyse a real Twitter data set to investigate whether well known structures of human social networks identified in "offline" environments can also be identified in the social networks maintained by users on Twitter. According to the well known model proposed by Dunbar, offline social networks are formed of circles of relationships having different social characteristics (e.g., intimacy, contact frequency and size). These circles can be directly ascribed to cognitive constraints of human brain, that impose limits on the number of social relationships maintainable at different levels of emotional closeness. Our results indicate that a similar structure can also be found in the Twitter users' social networks. This suggests that the structure of social networks also in online environments are controlled by the same cognitive properties of human brain that operate offline.


title: A 13.5-MHz relaxation oscillator with ±0.5% temperature stability for RFID application

abstract: This paper presents a 13.5-MHz low-power, RC on-chip relaxation oscillator with split-capacitor technique for RFID application. This oscillator implements only one comparator and one reference voltage to minimize power consumption and silicon area. A loop delay variation cancellation technique that employs an integrator loop and a split-capacitor architecture helps attained the temperature stability in the proposed oscillator. The proposed design is fabricated in 0.18-μm CMOS process. The relaxation oscillator consumes 48.8 μW at 1.8-V power supply. The measurement results show that the circuit can generate a stable frequency of 13.5 MHz. The output frequency variation is less than ±0.5% of temperature range from −30° C to 120° C, and the supply voltage variation coefficient is 0.5%/V across 1.5 V to 2.1V supply voltage.


title: Neuromarketing: Marketing through Science

abstract: Neuro marketing is still less than a decade since it emerged. Although Magnetic Resonance Imaging (fMRI) has already applied to the practice by marketing practicers, it is controversial in the academic field. The authors reviewed neuromarketing academic papers so far from the aspect of product, service marketing, customer's buying decision, brand, price and perceived quality. The strength and shortage of fMRI is discussed lately. In the end of the paper, the authors put forward future directions of neuromarketing.


title: Suicidal strangulation by double ligature: A case report

abstract: This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.


title: Iris movement based wheel chair control using raspberry Pi — A state of art

abstract: Paralysis is considered as a major curse in this world. The number of persons who are paralyzed and therefore dependent on others due to loss of self-mobility is growing with the population. Quadriplegia is a form of Paralysis in which you can only move your eyes. Much work has been done to help disabled persons to live independently. Various methods are used for the same and this paper enlists some of the already existing methods along with some add-ons to improve the existing system. Add-ons include a system, which will be designed using Raspberry Pi and IR Camera Module. OpenCV will be used for image processing and Python is used for programming the Raspberry Pi.


title: Pavlovian to Instrumental Transfer of Control in a Human Learning Task

abstract: Pavlovian learning tasks have been widely used as tools to understand basic cognitive and emotional processes in humans. The present studies investigated one particular task, Pavlovian-to-instrumental transfer (PIT), with human participants in an effort to examine potential cognitive and emotional effects of Pavlovian cues upon instrumentally-trained performance. In two experiments subjects first learned two separate instrumental response-outcome relationships (R1-O1, R2-O2) and then were exposed to various stimulus-outcome relationships (S1-O1, S2-O2, S3-O3, S4-) before the effects of the Pavlovian stimuli on instrumental responding were assessed during a nonreinforced test. In Experiment 1 instrumental responding was established using a positive reinforcement procedure whereas in Experiment 2 a quasi-avoidance learning task was used. In both cases the Pavlovian stimuli exerted selective control over instrumental responding, whereby S1 & S2 selectively elevated the instrumental response with which it shared an outcome. In addition, in Experiment 2, S3 exerted a nonselective transfer of control effect, whereby both responses were elevated over baseline levels. These data identify two ways, one specific and one general, in which Pavlovian processes can exert control over instrumental responding in human learning paradigms, and suggest that this method may serve as a useful tool in the study of basic cognitive and emotional processes in human learning.


title: Pain physiology: basic science

abstract: LTHOUGH there is considerable informa-tion about the mechanisms through whichinjury stimuli produce acute pain, recentstudies have established that there are signif-icant long-term consequences of persistent injury. Painis exacerbated, in part, because of a reorganization ofspinal cord circuitry in the setting of persistent injury. Inthis brief review I will address some of the mechanismsthat have been identified, focusing on issues of interestto our own laboratory, and indicate how they are likelyto operate in the production of the intense pain associ-ated with tissue or nerve injury. A major interest of our laboratory is in the contri-bution of the primary afferent neurotransmitter, sub-stance P (SP), to these changes. By followinginternalization of the SP receptor in spinal cord dorsalhorn neurons, we have identified the stimuli thatevoke SP release and the neurons that respond tothese stimuli. Importantly, based on the intensities ofstimuli required to evoke internalization, we concludethat SP, in the normal animal, is only released underconditions in which severe pain would be produced.


title: All-Metal Antenna Array Based on Microstrip Line Structure

abstract: In this communication, an all-metal standing wave antenna array is proposed based on the microstrip transmission line. By alternatively blocking the microstrip line every half wavelength, the symmetrical equivalent magnetic currents are broken for effectively radiating. By properly tuning the blocking positions along the microstrip line, a broadside pattern is achieved with high gain. The proposed antenna is scalable along one direction to a certain level to acquire the directivity as high as about 15 dBi. More importantly, the proposed antenna array has the potential to be built only by metal without any dielectrics. The all-metal structure has the merits of being low-cost, light weight, easily fabricated, and having no dielectric losses. Based on this idea, a five-segment linear array with a center feed is designed and tested. The measured bandwidth is from 7.83 to 8.81 GHz (980 MHz, 11.78%), and the measured gain is ranging from 9.1 to 12.47 dBi.


title: Uneven Pattern of Dopamine Loss in the Striatum of Patients with Idiopathic Parkinson's Disease

abstract: Autografting of dopamine-producing adrenal medullary tissue to the striatal region of the brain is now being attempted in patients with Parkinson's disease. Since the success of this neurosurgical approach to dopamine-replacement therapy may depend on the selection of the most appropriate subregion of the striatum for implantation, we examined the pattern and degree of dopamine loss in striatum obtained at autopsy from eight patients with idiopathic Parkinson's disease. We found that in the putamen there was a nearly complete depletion of dopamine in all subdivisions, with the greatest reduction in the caudal portions (less than 1 percent of the dopamine remaining). In the caudate nucleus, the only subdivision with severe dopamine reduction was the most dorsal rostral part (4 percent of the dopamine remaining); the other subdivisions still had substantial levels of dopamine (up to approximately 40 percent of control levels). We propose that the motor deficits that are a constant and characteristic feature of idiopathic Parkinson's disease are for the most part a consequence of dopamine loss in the putamen, and that the dopamine-related caudate deficits (in "higher" cognitive functions) are, if present, less marked or restricted to discrete functions only. We conclude that the putamen--particularly its caudal portions--may be the most appropriate site for intrastriatal application of dopamine-producing autografts in patients with idiopathic Parkinson's disease.


title: Preserving privacy by de-identifying face images

abstract: In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers' license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by deidentifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to deidentifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This work presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize deidentified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.


title: A Tutorial on Visual Servo Control

abstract: This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, position-based and image-based systems, are then discussed in detail. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking. We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.


title: A prospective, double-blind, split-subject study on local skin reactions after administration of human menopausal gonadotrophin preparations to healthy female volunteers

abstract: This study was designed to investigate local reactions after the intracutaneous (i.c.) administration of two human menopausal gonadotrophin preparations. For this purpose, 20 healthy female volunteers received six i.c. injections simultaneously, viz. three different batches of both Humegon (Organon, Oss, The Netherlands) and Pergonal (Serono, Geneva, Switzerland) at six different sites on their bodies. Local pain, induration and erythema were registered at 2, 4 and 24 h after administration. No pain was observed. At 4 h after administration, Pergonal-treated sites showed more induration (P = 0.008) and greater surfaces of erythema (P < 0.001) than Humegon-treated sites. Batches of Pergonal showed variation in the surface of erythema induced (P < 0.001), indicating heterogeneity of the batches tested.


Time to Burn the TOAST


title: ArrayBridge: Interweaving declarative array processing with high-performance computing

abstract: Author(s): Xing, Haoyuan; Floratos, Sofoklis; Blanas, Spyros; Byna, Suren; Prabhat; Wu, Kesheng; Brown, Paul | Abstract: Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.


title: Online Real-Time Multiple Spatiotemporal Action Localisation and Prediction

abstract: We present a deep-learning framework for real-time multiple spatio-temporal (S/T) action localisation, classification and early prediction. Current state-of-the-art approaches work offline and are too slow to be useful in real- world settings. To overcome their limitations we introduce two major developments. Firstly, we adopt real-time SSD (Single Shot MultiBox Detector) convolutional neural networks to regress and classify detection boxes in each video frame potentially containing an action of interest. Secondly, we design an original and efficient online algorithm to incrementally construct and label `action tubes' from the SSD frame level detections. As a result, our system is not only capable of performing S/T detection in real time, but can also perform early action prediction in an online fashion. We achieve new state-of-the-art results in both S/T action localisation and early action prediction on the challenging UCF101-24 and J-HMDB-21 benchmarks, even when compared to the top offline competitors. To the best of our knowledge, ours is the first real-time (up to 40fps) system able to perform online S/T action localisation and early action prediction on the untrimmed videos of UCF101-24.


title: An efficient RANSAC for 3D object recognition in noisy and occluded scenes

abstract: In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robust geometric descriptor, a hashing technique and an efficient RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method recognizes multiple model instances and estimates their position and orientation in the scene. The algorithm scales well with the number of models and its main procedure runs in linear time in the number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible.


title: A Framework for Cyber Security in Africa

abstract: This paper deals with at least four major cyber safety concerns in Africa discussed in recent literature. These cyber concerns include aspects such as policies, procedure, awareness, research and the provision of technical security measures. Each concern is examined, the main focus areas are highlighted and a solution is proposed. This paper concludes by combining all relevant solutions into a proposed cyber security framework to assist Africa in decreasing its cybercrime rate especially among home users with no or limited cyber safety knowledge.


title: Position estimation and control of compact BLDC motors based on analog linear Hall effect sensors

abstract: Bio-mimetic robotic systems, among many other engineered systems, require motors which possess high torque, little or no torque ripple, compact size, and precise position control. It is frequently challenging to include a motor with a design which fits all of these requirements. Several companies have recently released compact pancake brushless DC motors which possess excellent characteristics. However, ripple-free precision control of brushless motors requires precision position measurement. It can be difficult to mount encoders directly to the shafts of these motors, and for precision position control, a sensorless configuration may be inappropriate. We propose a novel method of measuring rotor position using analog Hall effect sensors to measure the magnetic field of the rotor magnets to create an absolute position measurement of the electrical cycle, which can be used efficiently to create a computationally simple motor control scheme. These Hall effect sensors are mounted directly to the motor and are highly compact, have high bandwidth, are extremely low cost, and have high precision, providing position measurements with insignificant increases to motor size.


title: Friend-space: Cluster-based users similar post friend recommendation technique in social networks

abstract: Online social networking is a way of access and share the information with user friends. All the social networking sites like Facebook, Twitter are provided the services. Parameters like life-style, interest, education, similarity or common things, mutual friends are considered for friend recommendation in social networks. In this paper we propose the best clusters based friend recommendation technique name as Friend-Space. For development of Friend-Space application four algorithms are implement and use in system. K-Means, Apriori, Ranking and Recommendation algorithms are developed for Friend-Space Application. Friend-space Cluster based application gives better performance in case of execution time for K-Means algorithm. In this paper we achieve the parameters like efficiency, effectiveness and execution time for number of executions. Recommendation algorithm implements for display the final output.


title: Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes.

abstract: Abstract People vary in their tendencies to compare themselves to others, an individual difference variable called social comparison orientation (SCO). Social networking sites provide information about others that can be used for social comparison. The goal of the present set of studies was to explore the relationship between SCO, Facebook use, and negative psychological outcomes. Studies 1a and 1b used correlational approaches and showed that participants high (vs. low) in SCO exhibited heavier Facebook use. Study 2 used an experimental approach and revealed that participants high in SCO had poorer self-perceptions, lower self-esteem, and more negative affect balance than their low-SCO counterparts after engaging in brief social comparisons on Facebook. SCO did not have as strong or consistent effects for participants engaging in control tasks. Results are discussed in the context of extant literature and the impact of social media use on well-being.


Phone Synchronous Decoding with CTC Lattice.


title: Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)

abstract: The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.


title: Survey on liver CT image segmentation methods

abstract: The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images, recent methods presented in the literature to obtain liver segmentation are viewed. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative study for liver segmentation methods, pros and cons of methods will be accentuated carefully. In this paper, we concluded that automatic liver segmentation using CT images is still an open problem since various weaknesses and drawbacks of the proposed methods can still be addressed.


A Comprehensive Phylogeny of Tettigoniidae (Orthoptera: Ensifera) Reveals Extensive Ecomorph Convergence and Widespread Taxonomic Incongruence


title: Multilevel direct PWM AC-AC converters

abstract: Due to an increase in power demand, different direct AC-AC conversion structures were developed. Classical direct converters present the disadvantage of a high harmonic content and the consumption of reactive power. For these reasons, different classes of PWM AC choppers were developed during the last decade. Of these solutions, two-level and four-level PWM AC choppers are presented. Multilevel converters using the flying capacitor (FC) concept present the advantages of a lower voltage stress in the semiconductor devices, increased apparent frequency and lower output filters being useful for medium voltage static compensation (STATCOM) applications.


title: Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

abstract: Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.


title: TimeMachine: Timeline Generation for Knowledge-Base Entities

abstract: We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.


title: Art-directed muscle simulation for high-end facial animation

abstract: We propose a new framework for the simulation of facial muscle and flesh that so significantly improves the technique that it allows for immediate mainstream use of anatomically and biomechanically accurate muscle models as a bread and butter technique in a high-end production quality pipeline. The key idea is to create a blendshape system for the muscles that gives the precise directability and controllability required in a high-end production environment. The blendshape muscles are used to drive the underlying anatomically and biomechanically motivated simulation in a way that is unbound by the typical restrictions of a simulation system while still retaining the desirable degree of freedom richness that leads to high quality results. We show that we are able to target production quality facial shapes, whether from scans or an animation system, and illustrate that the resulting nonlinear simulation in-betweens are of higher quality than those obtained from traditional linear blendshapes. We also demonstrate the ability to selectively improve areas on a given blendshape using the results of a simulation, as well as the ability to edit muscle shapes and paths in order to produce directability for animator control. Then, we show how these techniques can be used to transition from one blendshape to another or even track and selectively modify an entire performance. The efficacy of our system is further demonstrated by using it to retarget animation onto new creature models given only a single static rest pose as input.


title: Workload Patterns for Quality-Driven Dynamic Cloud Service Configuration and Auto-Scaling

abstract: Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.


title: Depth Map Super-Resolution Considering View Synthesis Quality

abstract: Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions.


title: Liveboard: a large interactive display supporting group meetings, presentations, and remote collaboration

abstract: This paper describes the Liveboard, a large interactive display system. With nearly one million pixels and an accurate, multi-state, cordless pen, the Liveboard provides a basis for research on user interfaces for group meetings, presentations and remote collaboration. We describe the underlying hardware and software of the Liveboard, along with several software applications that have been developed. In describing the system, we point out the design rationale that was used to make various choices. We present the results of an informal survey of Liveboard users, and describe some of the improvements that have been made in response to user feedback. We conclude with several general observations about the use of large public interactive displays.


title: Linear Reranking Model for Chinese Pinyin-to-Character Conversion

abstract: Pinyin-to-character conversion is an important task for Chinese natural language processing tasks. Previous work mainly focused on n-gram language models and machine learning approaches, or with additional hand-crafted or automatic rule-based post-processing. There are two problems unable to solve for word n-gram language model: out-of-vocabulary word recognition and long-distance grammatical constraints. In this study, we proposed a linear reranking model trying to solve these problems. Our model uses minimum error learning method to combine different sub models, which includes word and character n-gram LMs, part-of-speech tagging model and dependency model. Impact of different sub models on the conversion are fully experimented and analyzed. Results on the Lancaster Corpus of Mandarin Chinese show that our new model outperforms word n-gram language model.


title: Biochemical and Kinetic Characterization of Xylulose 5-Phosphate/Fructose 6-Phosphate Phosphoketolase 2 (Xfp2) from Cryptococcus neoformans

abstract: Xylulose 5-phosphate/fructose 6-phosphate phosphoketolase (Xfp), previously thought to be present only in bacteria but recently found in fungi, catalyzes the formation of acetyl phosphate from xylulose 5-phosphate or fructose 6-phosphate. Here, we describe the first biochemical and kinetic characterization of a eukaryotic Xfp, from the opportunistic fungal pathogen Cryptococcus neoformans, which has two XFP genes (designated XFP1 and XFP2). Our kinetic characterization of C. neoformans Xfp2 indicated the existence of both substrate cooperativity for all three substrates and allosteric regulation through the binding of effector molecules at sites separate from the active site. Prior to this study, Xfp enzymes from two bacterial genera had been characterized and were determined to follow Michaelis-Menten kinetics. C. neoformans Xfp2 is inhibited by ATP, phosphoenolpyruvate (PEP), and oxaloacetic acid (OAA) and activated by AMP. ATP is the strongest inhibitor, with a half-maximal inhibitory concentration (IC50) of 0.6 mM. PEP and OAA were found to share the same or have overlapping allosteric binding sites, while ATP binds at a separate site. AMP acts as a very potent activator; as little as 20 μM AMP is capable of increasing Xfp2 activity by 24.8% ± 1.0% (mean ± standard error of the mean), while 50 μM prevented inhibition caused by 0.6 mM ATP. AMP and PEP/OAA operated independently, with AMP activating Xfp2 and PEP/OAA inhibiting the activated enzyme. This study provides valuable insight into the metabolic role of Xfp within fungi, specifically the fungal pathogen Cryptococcus neoformans, and suggests that at least some Xfps display substrate cooperative binding and allosteric regulation.


title: Identifying hardware failures systematically

abstract: Critical control systems can only be used after approval of certification authorities due to safety reasons, among other aspects. Undetected failures in such systems can be catastrophic, including the loss of human lives or huge amounts of money. The safety assessment process aims to minimize such problems. But actually it still is largely dependent on human support (engineer's experience). To decrease this human dependency, we propose a systematic hardware-based failure identification strategy. Following common practices in industry, which use Simulink diagrams to design (critical) control systems, the starting point of our proposed strategy is Simulink diagrams. The systematic identification is performed by the model checker FDR [11]. Therefore, we translate Simulink diagrams into CSPM specifications [30]. With our strategy, engineers only need to label certain Simulink elements as hardware and choose specific failure names for the generic ones our strategy provides. We illustrate our work on a simple but real case study supplied by our industrial partner EMBRAER.


title: Bounded Uncertainty Roadmaps for Path Planning

abstract: Motion planning under uncertainty is an important problem in robotics. Although probabilistic sampling is highly successful for motion planning of robots with many degrees of freedom, sampling-based algorithms typically ignore uncertainty during planning. We introduce the notion of a bounded uncertainty roadmap (BURM) and use it to extend sampling-based algorithms for planning under uncertainty in environment maps. The key idea of our approach is to evaluate uncertainty, represented by collision probability bounds, at multiple resolutions in different regions of the configuration space, depending on their relevance for finding a best path. Preliminary experimental results show that our approach is highly effective: our BURM algorithm is at least 40 times faster than an algorithm that tries to evaluate collision probabilities exactly, and it is not much slower than classic probabilistic roadmap planning algorithms, which ignore uncertainty in environment maps.


title: Trust in e-commerce

abstract: Web-based businesses succeed by cultivating consumers' trust, starting with their beliefs, attitudes, intentions, and willingness to perform transactions at Web sites and with the organizations behind them.


title: Temporal Frequency Channels Are Linked across Audition and Touch

abstract: Temporal frequency is a fundamental sensory dimension in audition and touch. In audition, analysis of temporal frequency is necessary for speech and music perception; in touch, the spectral analysis of vibratory signals has been implicated in texture perception and in sensing the environment through tools. Environmental oscillations impinging upon the ear are generally thought to be processed independently of oscillations impinging upon the skin. Here, we show that frequency channels are perceptually linked across audition and touch. In a series of psychophysical experiments, we demonstrate that auditory stimuli interfere with tactile frequency perception in a systematic manner. Specifically, performance on a tactile-frequency-discrimination task is impaired when an auditory distractor is presented with the tactile stimuli, but only if the frequencies of the auditory and tactile stimuli are similar. The frequency-dependent interference effect is observed whether the distractors are pure tones or band-pass noise, so an auditory percept of pitch is not required for the effect to be produced. Importantly, distractors that strongly impair frequency discrimination do not interfere with judgments of tactile intensity. This surprisingly specific crosstalk between different modalities reflects the importance of supramodal representations of fundamental sensory dimensions.


Blockchain Consensus


title: A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides

abstract: Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each and every ROI available. Our research is motivated toward computer-assisted classification of digitized slides. The objective in this study is to develop a classifier to optimize classification accuracy at the slide level. Traditional supervised training techniques which are trained to optimize classifier performance at the ROI level yield suboptimal performance in this problem. We propose a multiple instance learning approach based on the implementation of the large margin principle with different loss functions defined for positive and negative samples. We consider the classification of intraductal breast lesions as a case study, and perform experimental studies comparing our approach against the state-of-the-art.


title: SALSA: systematic logic synthesis of approximate circuits

abstract: Approximate computing has emerged as a new design paradigm that exploits the inherent error resilience of a wide range of application domains by allowing hardware implementations to forsake exact Boolean equivalence with algorithmic specifications. A slew of manual design techniques for approximate computing have been proposed in recent years, but very little effort has been devoted to design automation. We propose SALSA, a Systematic methodology for Automatic Logic Synthesis of Approximate circuits. Given a golden RTL specification of a circuit and a quality constraint that defines the amount of error that may be introduced in the implementation, SALSA synthesizes an approximate version of the circuit that adheres to the pre-specified quality bounds. We make two key contributions: (i) the rigorous formulation of the problem of approximate logic synthesis, enabling the generation of circuits that are correct by construction, and (ii) mapping the problem of approximate synthesis into an equivalent traditional logic synthesis problem, thereby allowing the capabilities of existing synthesis tools to be fully utilized for approximate logic synthesis. In order to achieve these benefits, SALSA encodes the quality constraints using logic functions called Q-functions, and captures the flexibility that they engender as Approximation Don't Cares (ADCs), which are used for circuit simplification using traditional don't care based optimization techniques. We have implemented SALSA using two off-the-shelf logic synthesis tools - SIS and Synopsys Design Compiler. We automatically synthesize approximate circuits ranging from arithmetic building blocks (adders, multipliers, MAC) to entire datapaths (DCT, FIR, IIR, SAD, FFT Butterfly, Euclidean distance), demonstrating scalability and significant improvements in area (1.1X to 1.85X for tight error constraints, and 1.2X to 4.75X for relaxed error constraints) and power (1.15X to 1.75X for tight error constraints, and 1.3X to 5.25X for relaxed error constraints).


title: Mechanisms Involved in Gender Differences in Alzheimer's Disease: The Role of Leuteinizing and Follicle Stimulating Hormones

abstract: Abstract Alzheimer's disease, the leading cause of senile dementia, is clinically characterized by progressive memory loss; impairments in behavior, language, and visual-spatial skills; and, ultimately, death. Unfortunately, at present, therapeutic management of the disease is primarily targeted toward palliative treatment of symptoms rather than forestalling the progression of the disease. As such, even with state of the art pharmaceutical intervention, continued and progressive cognitive decline in patients is inevitable. The major obstacle in managing the disease and designing rational therapeutic targets is our incomplete understanding of the pathogenesis of the disease. In fact to date, although we know that age, gender, genetics, and environment all play a role in the disease, there has been no single mechanistic hypothesis, aside from that discussed herein, that explains all of the known features and risk factors for the disease. One of the most intriguing aspects concerning the epidemiology of Alzheimer's disease is that the prevalence rate in women is roughly twice that in men and this skewed sex ratio is specific for Alzheimer's disease but not other dementias. The goal of this review is to delve further into these gender differences and to examine potential mechanistic hypotheses that underlie such differences. Specifically, we focus on how hormones of the hypothalamic-pituitary-gonadal axis, in particular the gonadotropins, play a central and determining role in disease susceptibility and progression such that therapeutic interventions targeted at gonadotropins could both prevent disease in those patients currently asymptomatic or halt, and even reverse, disease in those currently afflicted.


The Minimum Conductance Dissimilarity Cut ( MCDC ) Algorithm to Increase Novelty and Diversity of Recommendations


title: The Role of Home Literacy Practices in Preschool Children's Language and Emergent Literacy Skills

abstract: This study examined how 4 specific measures of home literacy practices (i.e., shared book reading frequency, maternal book reading strategies, child's enjoyment of reading, and maternal sensitivity...


title: Simpler Context-Dependent Logical Forms via Model Projections

abstract: We consider the task of learning a context-dependent mapping from utterances to denotations. With only denotations at training time, we must search over a combinatorially large space of logical forms, which is even larger with context-dependent utterances. To cope with this challenge, we perform successive projections of the full model onto simpler models that operate over equivalence classes of logical forms. Though less expressive, we find that these simpler models are much faster and can be surprisingly effective. Moreover, they can be used to bootstrap the full model. Finally, we collected three new context-dependent semantic parsing datasets, and develop a new left-to-right parser.


title: First demonstration of drop-test reliability of ultra-thin glass BGA packages directly assembled on boards for smartphone applications

abstract: This paper reports the first demonstration of the drop-test reliability performance of large, ultra-thin glass BGA packages that are directly mounted onto the system board, unlike the current approach of flip-chip assembly of interposers, involving additional organic packages which are then SMT assembled onto boards. The packages, 18.4mm × 18.4mm in size made of 100µm-thick glass, were also successfully assembled, for the first time, in a SMT line. The effect on drop reliability of the glass BGAs with circumferential polymer collars was studied extensively. While the glass BGA packages met the reliability requirements, both with and without polymer collars, the polymer collars were found to further enhance the drop performance, as well as the fatigue life of solders. Finite element modeling was used to understand strain-relief mechanisms and provide design guidelines for reliability. The glass substrates fabrication process along with the formation of polymer collars by spin coating is detailed. The glass package-to-PCB assemblies were formed using SMT-compatible processes with standard equipment, followed by reliability testing through thermal cycling and drop tests. The compiled failure data from drop testing was fitted into a Weibull distribution plot. Comprehensive failure analysis was performed to assess the structural integrity of the glass substrates and identify the predominant failure mechanisms in drop test.


title: Magnetic Resonance for Wireless Power Transfer [A Look Back]

abstract: Magnetic resonance has been a cornerstone of nonradiative wireless power transfer (WPT) since the late 19th century. However, some researchers have the misconception that magnetic resonance for WPT was developed recently. This article traces some early work of Tesla and other researchers related to the use of magnetic resonance in WPT. Included are some examples of magnetic resonance-based WPT projects conducted by researchers in the biomedical and power electronics communities over the last few decades. Two principles used in WPT are reiterated in this article, and their advantages and disadvantages are addressed. Some issues that may affect future trends of short- and midrange applications are discussed.


REMOVING SHADOWS FROM HIGH-RESOLUTION URBAN AERIAL IMAGES BASED ON COLOR CONSTANCY


title: The epidemiology of UV induced skin cancer

abstract: Abstract There is persuasive evidence that each of the three main types of skin cancer, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma, is caused by sun exposure. The incidence rate of each is higher in fairer skinned, sun-sensitive rather than darker skinned, less sun-sensitive people; risk increases with increasing ambient solar radiation; the highest densities are on the most sun exposed parts of the body and the lowest on the least exposed; and they are associated in individuals with total (mainly SCC), occupational (mainly SCC) and non-occupational or recreational sun exposure (mainly melanoma and BCC) and a history of sunburn and presence of benign sun damage in the skin. That UV radiation specifically causes these skin cancers depends on indirect inferences from the action spectrum of solar radiation for skin cancer from studies in animals and the action spectrum for dipyrimidine dimers and evidence that presumed causative mutations for skin cancer arise most commonly at dipyrimidine sites. Sun protection is essential if skin cancer incidence is to be reduced. The epidemiological data suggest that in implementing sun protection an increase in intermittency of exposure should be avoided, that sun protection will have the greatest impact if achieved as early as possible in life and that it will probably have an impact later in life, especially in those who had high childhood exposure to solar radiation.


title: Detection of parked vehicles from a radar based occupancy grid

abstract: For autonomous parking applications to become possible, knowledge about the parking environment is required. Therefore, a real-time algorithm for detecting parked vehicles from radar data is presented. These data are first accumulated in an occupancy grid from which objects are detected by applying techniques borrowed from the computer vision field. Two random forest classifiers are trained to recognize two categories of objects: parallel-parked vehicles and cross-parked vehicles. Performances of the classifiers are evaluated as well as the capacity of the complete system to detect parked vehicles in real world scenarios.


title: The Euclidean travelling salesman problem is NP-complete

abstract: Abstract The Travelling Salesman Problem is shown to be NP-Complete even if its instances are restricted to be realizable by sets of points on the Euclidean plane.